Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition

Abstract: Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided. Over two years (2019–2020), power generation and weather-related data were collected at 15-minute intervals. The dataset was used in the Renewable Energy Generation Forecasting Competition hosted by the Chinese State Grid in 2021. The process of data collection, data processing, and potential applications are described. The use of this dataset is promising for the development of data-driven forecasting models for renewable energy generation and the optimization of electricity demand response (DR) programs for the power grid. Measurement(s) renewable energy generation Technology Type(s) supervisory control and data acquisition system Sample Characteristic - Location China


Comparative techno-economic analysis of various stand-alone and grid connected (solar/wind/fuel cell) renewable energy systems

International journal of hydrogen energy

International Journal of Hydrogen Energy


Energy Trading Interface for Grid-Tied Wind-Solar Power Generation System

Abstract: Surging clean energy demand clashes with grid stability concerns, calling for creative solutions. This work explores the synergy between peer-to-peer (P2P) energy trading and grid-connected wind-solar systems. It is apparent that, in the contemporary grid scenario, renewable power injection involving integrated wind-solar power generation is proliferating, and hence grid stability and excess energy utilization become critical challenges. P2P trading emerges as a promising remedy, allowing prosumers (consumers who also produce) to directly exchange surplus energy, fostering a more decentralized and efficient energy system. Leveraging MATLAB simulations, the project assesses the technical feasibility of such systems. It then designs and implements a P2P energy transfer system with a user-friendly dashboard, empowering prosumers to actively participate and optimize their energy usage. By analyzing benefits for both prosumers and the grid, the project showcases the significant potential of P2P trading in paving the way for a sustainable and equitable energy future.

2024 Control Instrumentation System Conference (CISCON)


Advances and development of wind–solar hybrid renewable energy technologies for energy transition and sustainable future in India

Abstract: While solar power projects are built on a continuous ground, wind power projects require scattered land, raising transmission costs and increasing the risk of land-related complications. Wind–solar hybrid (WSH) projects have been proposed to address these issues and accelerate installation. WSH power projects will create a well-defined area with sufficient infrastructure, including evacuation facilities, where the project’s risks can be reduced. The extensive coastline of India is endowed with high wind flow speed and plentiful solar power resources, creating an ideal environment for WSH projects to prosper while simultaneously improving grid stability and reliability. WSH plants guarantee higher transmission efficiency and cost-effectiveness than their stand-alone counterparts. As of 30.11.2021, 3.75 GW of WSH projects have been granted, with 0.148 GW of operational capacity and 1.7 GW of WSH projects in various bidding phases. In this paper, we discussed state-wise WSH potential, the key players in the WSH project, the National WSH, and the State WSH policy and amendments. Also, the WSH project’s physical progress and commercial details are covered. A feasibility study of the WSH plant is performed, and the primary design strategy for deploying WSH power facilities in India is discussed. It covers every step of this process, from design technique to choosing and evaluating potential locations for such hybrid projects, optimally placing wind turbines and solar panels, overall capacity mix for hybrid plants, and ultimately power evacuation optimization. Additionally, a brief study of the savings from these hybrid plants and the environmental, social, and governance standards which are necessary to implement these projects are provided. The potential challenges connected with WSH technologies are examined in depth, and potential solutions and mitigations for the challenges are provided. Designing a WSH for small-scale irrigation is provided along with the size and choice of wind and solar systems. Degradation of PV systems and carbon savings are included, along with some policy measures to boost the proportion of WSH in the entire power mix. In India, the development of large-scale WSH projects is still in its early stages, and more research is required to explore technical, commercial, and policy elements that influence project design. The policy suggestions for improvement of the WSH project are provided. The WSH project developers, potential investors, stakeholders, innovators, policymakers, manufacturers, designers, and researchers will benefit from the recommendations based on the review’s findings.


Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage

Abstract: This article evaluates the present global condition of solar and wind energy adoption and explores their benefits and limitations in meeting energy needs. It examines the historical and evolutionary growth of solar and wind energy, global trends in the usage of renewable energy, and upcoming technologies, including floating solar and vertical‐axis wind turbines. The importance of smart grid technology and energy storage alternatives for enhancing the effectiveness and dependability of renewable energy is explored. In addition, the role of Electric Vehicles (EVs) in a modern smart grid has been assessed. Furthermore, the economic benefits, and most recent technological developments of solar and wind energy and their environmental and social ramifications. The potential of solar and wind energy to meet the increasing global energy demand and the problems and opportunities facing the renewable energy industry have shown excellent promise. Machine learning applications for solar and wind energy generation are vital for sustainable energy production. Machine learning can help in design, optimization, cost reduction, and, most importantly, in improving the efficacy of solar and wind energy, including advancing energy storage. This assessment is a crucial resource for policymakers, industry leaders, and researchers who aim to make the world cleaner and more sustainable. Ultimately, this review has shown the great potential of solar and wind energy in meeting global energy demands and sustainable goals.


Mitigating Uncertainty Problems of Renewable Energy Resources Through Efficient Integration of Hybrid Solar PV/Wind Systems Into Power Networks

Abstract: In recent years, there has been a proliferation of studies focusing on sustainable energy sources aimed at delivering reliable power with reduced greenhouse gas emissions. Among these endeavors, electrification of urban areas through incorporation of renewable energy sources (RESs) stands out as a pivotal application of RES. Increasingly, the integration of RESs like PV and wind into the power grid is being pursued as an alternative to traditional centralized power stations, offering a means to ensure continuous power supply during peak demand periods while simultaneously managing unit costs, thereby providing a systematic and readily accessible power supply to consumers. This paper presents a comprehensive examination of hybrid wind and PV with focus on achieving consistent DC bus-bar voltage through integration with microcontroller system via buck-boost converter. Additionally, hybrid system’s output voltage is tailored to accommodate necessary battery connections. However, given influence of environmental variables like fluctuations in solar irradiation and wind velocity on power output of PV and wind seamless integration of these sources becomes paramount. To address this challenge MATLAB model is developed to simulate grid integration of hybrid wind and PV, enabling a detailed analysis of integrated grid’s performance. Simulation results are then proposed with those obtained from conventional fossil fuel-based power generation methods. In this research underscores importance of integrating RES into existing power grids and highlights efficacy of hybrid wind and PV in meeting this objective. Findings presented herein contribute to advancing discourse on sustainable energy solutions, offering valuable insights for policymakers, industry stakeholders, and researchers.


Research on key technologies of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization

Abstract: Due to the uncertainty and randomness of large-scale wind and light, the output power of the power grid has great fluctuations. If it is directly connected to the grid, it will affect the main grid. In addition, when the grid switches between on-grid/off-grid operation modes, there will be power shortages, shocks and oscillations. The scientific and reasonable configuration of energy storage system capacity big data can reduce the load power shortage rate, improve the utilization rate of renewable energy, and ensure the reliable operation of the power grid. For this reason, the key technology of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization is studied. A large-scale wind-solar hybrid grid energy storage structure is proposed, and the working characteristics of photovoltaic power generation and wind power generation are analyzed, and the probability model of photovoltaic power generation, wind power generation and load, as well as the charging and discharging model of battery and super capacitor are established accordingly. On this basis, the optimization objective function is set, the constraints are determined, and the large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization model is constructed. And the PSO algorithm is used to solve the model to realize the big data configuration optimization of large-scale wind-solar hybrid grid energy storage capacity. The research results show that the proposed method of large-scale wind-solar hybrid grid energy storage system has good power supply reliability and economy, and can effectively improve the utilization rate of renewable energy.

Wind Engineering : The International Journal of Wind Power


Optimizing wind/solar combinations at finer scales to mitigate renewable energy variability in China

Abstract: Abstract China has set ambitious goals to cap its carbon emissions and increase low-carbon energy sources to 20% by 2030 or earlier. However, wind and solar energy production can be highly variable: the stability of single wind/solar and hybrid wind-solar energy and the effects of wind/solar ratio and spatial aggregation on energy stability remain largely unknown in China, especially at the grid cell scale. To address these issues, we analyzed the newly 2007–2014 hourly wind and solar data, which have higher resolution and quality than those used in previous research. The stability of single wind/solar energy production clearly increased as the wind/solar energy capacity factor increased, and there were significant functional relationships between single wind/solar energy stability and the wind/solar energy capacity factor. Highly stable wind energy was concentrated in eastern Inner Mongolia, northeastern China, and northern China while highly stable solar energy was concentrated in the Tibetan Plateau, Inner Mongolia, and northwestern China. Different wind/solar ratios affected the stability of hybrid wind-solar energy through a unimodal relationship, allowing us to produce a map of optimal wind/solar ratios throughout China in order to minimize the variability of hybrid wind-solar energy production. At the optimal wind/solar ratio, the most stable hybrid wind-solar energy was concentrated in eastern Inner Mongolia, northeastern China, and northern China. The variability of single and hybrid wind/solar energy decreased as the aggregated area size increased, especially for wind-dominated energy systems. These results have important practical applications: (a) using the optimal wind/solar ratio to install simple hybrid wind-solar energy systems locally; (b) prioritizing the deployment of large-scale wind farms or centralized solar photovoltaic stations in regions with high hybrid energy stability; and (c) strongly promoting regional cooperation, such as breaking inter-provincial power grid barriers, to reduce the variability of hybrid wind-solar energy production and thus operational costs.


Optimization of hybrid grid-tie wind solar power system for large-scale energy supply in Cameroon

International Journal of Energy and Environmental Engineering


Optimized Grid-Connected Hybrid Renewable Energy Power Generation: A Comprehensive Analysis of Photovoltaic, Wind, and Fuel Cell Systems

Abstract: This paper provides a comprehensive analysis of a grid-connected hybrid microgrid system that seamlessly integrates renewable energy sources, encompassing wind generators, solar arrays, and Fuel Cells (FCs). Emphasis is placed on the pivotal role of power electronic converters in optimizing control and energy management strategies for these diverse sources. The wind and solar subsystems employ Perturb and Observe (P&O) controllers to achieve Maximum Power Point Tracking (MPPT). Additionally, the study delves into the analysis and control design of the grid-connected hybrid system inverter, employing a Proportional-Integral (PI) control technique in the synchronous d-q frame to maximize the output voltage response and active power. In managing renewable grid energy based on artificial neural networks (ANNs), the main goal is to address grid availability concerns by prioritizing renewable sources. The hybrid system acts as a backup during grid unavailability and simultaneously produces hydrogen via electrolysis. The excess energy is seamlessly supplied to the grid upon filling the hydrogen tank. The proposed solution shows great promise for use in renewable energy management systems that combine hybrid technologies.

Engineering, Technology & Applied Science Research


Advancements in hybrid energy storage systems for enhancing renewable energy-to-grid integration

Abstract: The global energy sector is currently undergoing a transformative shift mainly driven by the ongoing and increasing demand for clean, sustainable, and reliable energy solutions. However, integrating renewable energy sources (RES), such as wind, solar, and hydropower, introduces major challenges due to the intermittent and variable nature of RES, affecting grid stability and reliability. Hybrid energy storage systems (HESS), which combine multiple energy storage devices (ESDs), present a promising solution by leveraging the complementary strengths of each technology involved. This comprehensive review examines recent advancements in grid-connected HESS, focusing on their components, design considerations, control strategies, and applications. It provides a detailed analysis of technological progress in various ESDs and the critical role of power conversion, control, energy management, and cooling systems in optimizing HESS performance. Highlighting case studies of some notable and successful HESS implementations across the globe, we illustrate practical applications and identify the benefits and challenges encountered. By addressing these challenges, HESS can significantly enhance the efficiency and reliability of RES, supporting the shift towards a sustainable and resilient energy infrastructure. The paper concludes by identifying future research directions, highlighting the development of intelligent control systems, sustainable materials, and efficient recycling processes to ensure the widespread adoption and long-term viability of HESS.


Sustainable CO2 Refrigeration System for Fish Cold Storage Facility Using a Renewable Integrated System with Solar, Wind and Tidal Energy for Cape Verde—Analyzing Scenarios

Abstract: This study compares four feasible alternative solutions for an integrated cold storage system in the city of Tarrafal, Santiago, Cape Verde. Integrated systems using grid electricity are compared with autonomous systems generating electrical energy from renewable sources, alongside various types of refrigeration facility systems. Its objective is to assess the energy efficiency, financial feasibility, and environmental impact across four scenarios. Scenario 1 utilizes two R134a refrigeration units powered by the public grid. Scenario 2 employs a transcritical R744 (CO2) system using grid electricity. Scenario 3 incorporates R744 and autonomous renewable energy. Scenario 4 employs R744 for refrigeration with seawater heat exchange and autonomous renewable energy sources. The findings favor Scenario 4, emitting 15,882 kg CO2 eq with a 5-year return on investment. Autonomous electricity production in this scenario reduces emissions by 95%. Despite an initial cost of EUR 769,172.00, Scenario 3 demonstrates financial viability, contributing to energy sustainability. This autonomous production reduces emissions by 360,697 kg CO2 compared to conventional systems, highlighting the positive impact of local renewable energy integration.


HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries

Abstract: Abstract. Hybrid renewable power plants consisting of collocated wind, solar photovoltaic (PV), and lithium-ion battery storage connected behind a single grid connection can provide additional value to the owners and society in comparison to individual technology plants, such as those that are only wind or only PV. The hybrid power plants considered in this article are connected to the grid and share electrical infrastructure costs across different generation and storing technologies. In this article, we propose a methodology for sizing hybrid power plants as a nested-optimization problem: with an outer sizing optimization and an internal operation optimization. The outer sizing optimization maximizes the net present values over capital expenditures and compares it with standard designs that minimize the levelized cost of energy. The sizing problem formulation includes turbine selection (in terms of rated power, specific power, and hub height), a wind plant wake loss surrogate, simplified wind and PV degradation models, battery degradation, and operation optimization of an internal energy management system. The problem of outer sizing optimization is solved using a new parallel “efficient global optimization” algorithm. This new algorithm is a surrogate-based optimization method that ensures a minimal number of model evaluations but ensures a global scope in the optimization. The methodology presented in this article is available in an open-source tool called HyDesign. The hybrid sizing algorithm is applied for a peak power plant use case at different locations in India where renewable energy auctions impose a monetary penalty when energy is not supplied at peak hours. We compare the hybrid power plant sizing results when using two different objective functions: the levelized cost of energy (LCoE) or the relative net present value with respect to the total capital expenditure costs (NPV/CH). Battery storage is installed only on NPV/CH-based designs, while the hybrid design, including wind, solar, and battery, only occurs on the site with good wind resources. Wind turbine selection on this site prioritizes cheaper turbines with a lower hub height and lower rated power. The number of batteries replaced changes at the different sites, ranging between two or three units over the lifetime. A significant oversizing of the generation in comparison to the grid connection occurs on all NPV/CH-based designs. As expected LCoE-based designs are a single technology with no batteries.


Advancements and Challenges in Integrating Renewable Energy Sources into Distribution Grid Systems: A Comprehensive Review

Abstract: The issues in integrating renewable energy sources (RES) into distribution grid structures are thoroughly examined in this research. It highlights how important this integration is to updating the energy system and attaining environmental goals. The study explores the specific problems confronted by means of on-grid power structures, along with overall performance metrics and compatibility issues. Additionally, it presents a thorough assessment of the attributes for various RES hybrid systems, together with technology from the fields of solar, wind, batteries, and biomass. to be able to spotlight the significance of innovative solutions inside the dispersed technology environment, the integration of RES with CCHP structures is investigated. This study addresses the numerous problems with RES integration into the grid to better comprehend their intricacies. The viability of RES integration is supported by real-world case studies that provide operational examples of dispersed generation systems. The study concludes by discussing the technical, financial, and grid-related problems associated with distributed generating systems' limits and highlighting the contribution of cutting-edge technology and artificial intelligence to their removal. In conclusion, the report highlights the development toward smarter grids and improved distributed generating capacities as the essential component of a robust and sustainable energy future.


Technoeconomic Analysis and Optimization of Hybrid Solar-Wind-Hydrodiesel Renewable Energy Systems Using Two Dispatch Strategies

Abstract: Sustainable generation is impacted by the adoption of renewable energy, the growth of energy markets, and economic strategies. This paper offers a sustainable strategy and a technoeconomic analysis of off-grid hybrid energy systems (HES) in remote islands of Iran, including Lavan, Larak, and Failaka, utilizing PV module, wind turbine, and hydrokinetic turbines. Hourly wind speed, solar irradiation, and hydrovelocity have been implemented under load following (LF) and cycle charging (CC) dispatch strategies in order to ascertain the most appropriate systems. Lavan Island achieves the winning HES with a CC dispatch strategy, which consists of 3 hydroelectric turbines, 1 wind turbine, 349 kW of solar power, 150 kW of generator power, 316 kWh of batteries, and 287 kW of the converter. This ideal HES, which generates a consistent generation profile and reasonable net present cost (NPC) and cost of energy (COE) of M0.160$ and $0.013 kWh, respectively, can be practically attained in these areas. LF-controlled optimal solutions use less fuel than CC-based ones, leading to a higher share of renewable energy. Compared to Larak and Lavan, the CC- and LF-controlled options on Failaka Island generate cleaner electricity with emissions that are 57% and 44% lower. Regarding the ability to recoup the project’s initial investment costs, long-term energy production would be more financially viable than short-term. Short-term projects with higher financial uncertainty due to the salvage cost should use the CC method.


RETRACTED ARTICLE: Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

Abstract: This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.


An Innovative Converterless Solar PV Control Strategy for a Grid Connected Hybrid PV/Wind/Fuel-Cell System Coupled With Battery Energy Storage

Abstract: The proposed work addresses the modeling, control, energy management and operation of hybrid grid connected system with wind-PV-Battery Energy Storage System (BESS) integrated with Fuel Cell (FC) and Electrolyzer. A hybrid PV-Wind-FC with electrolyzer consisting of BESS with the least number of control loops and converters has been proposed. The proposed hybrid system presents a cost efficient solution for integrating PV into a hybrid system by eliminating the PV converter. This includes the design of controllers for grid-connected hybrid systems with a renewable distributed generator (Wind and PV) as a primary source, BESS as a secondary source and FC with Electrolyzer as a tertiary source. In addition, the lead compensator along with integrator is used for obtaining enough phase margin and removing steady state error completely. It increases the stability of the controller and adds phase shift $\varphi _{s}$ at a cross gain frequency ( $\omega _{cut}$ ). The Grid Side Controller (GSC) is capable of providing frequency support to the utility grid, when it is linked to the grid. In the proposed configuration, PV power is maximized and injected into grid through GSC. Rotor Side Converter (RSC) and GSC ensure the support for sharing the burden of the grid station. Moreover, the proposed controller of BESS with coordination of FC eliminates the effect of intermittency of power generated from wind and PV. Excess power production by renewable distribution generation is used by Electrolyzer to generate hydrogen. This hydrogen is further used by FC when there is not enough power generation due to unfavorable weather conditions. The energy management has been presented to fulfill the load profile, avoid BESS overcharging and to minimize the intermittency and fluctuation of Wind and PV sources. This method guarantees steady power flow and service continuity. The Simulink model of the proposed system results validate the efficiency of the proposed hybrid system as compared to the conventional hybrid system reported in the literature. The modeling of the proposed system and analysis has been demonstrated using the MATLAB Simulink model. Lastly, the energy management of the system has also been examined and compared with the conventional power system.


Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Abstract: How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh.


Optimal Modeling and Feasibility Analysis of Grid-Interfaced Solar PV/Wind/Pumped Hydro Energy Storage Based Hybrid System

Abstract: Access to inexpensive, clean energy is a key factor in a country’s ability to grow sustainably The production of electricity using fossil fuels contributes significantly to global warming and is becoming less and less profitable nowadays. This work therefore proposes to study the different possible scenarios for the replacement of light fuel oil (LFO) thermal power plants connected to the electrical network in northern Cameroon by renewable energy plants. Several scenarios such as the combination of solar photovoltaic (PV) with a pumped hydro storage system (PHSS), Wind and PHSS and PV-Wind-PHSS have been studied. The selected scenarios are evaluated based on two factors such as the system’s total cost (TC) and the loss of load probability (LOLP). To achieve the results, metaheuristics such the non-dominated sorting whale optimization algorithm (NSWOA) and non-dominated sorting genetic algorithm-II (NSGA-II) have been applied under MATLAB software. The optimal sizing of the components was done using hourly meteorological data and the hourly power generated by the thermal power plants connected to the electrical grid. Both algorithms provided satisfactory results. However, the total cost in the PV-PHSS, Wind-PHSS, and PV-Wind-PHSS scenarios with NSWOA is, respectively, 1%, 6%, and 0.2% lower than with NSGA-II. According to NSWOA results, the total cost for the PV-Wind-PHSS scenario at LOLP 0% is 4.6% and 17% less than the Wind-PHS and PV-PHSS scenarios, respectively. The profitability study of all three scenarios showed that the project is profitable regardless of the scenario considered.


Optimized Sizing of Energy Management System for Off-Grid Hybrid Solar/Wind/Battery/Biogasifier/Diesel Microgrid System

Abstract: Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO2 than traditional power systems, which have significant operational and fuel expenses. In this paper, the proposed hybrid MG adopts renewable energies, including solar photovoltaic (PV), wind turbines (WT), biomass gasifiers (biogasifier), batteries’ storage energies, and a backup diesel generator. The energy management system of the adopted MG resources is intended to satisfy the load demand of Basra, a city in southern Iraq, considering the city’s real climate and demand data. For optimal sizing of the proposed MG components, a meta-heuristic optimization algorithm (Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO)) is applied. The simulation results are compared with those achieved using Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), and Antlion Optimization (ALO) to evaluate the optimal sizing results with minimum costs. Since the adopted GWCSO has the lowest deviation, it is more robust than the other algorithms, and their optimal number of component units, annual cost, and Levelized Cost Of Energy (LCOE) are superior to the other ones. According to the optimal annual analysis, LCOE is 0.1192 and the overall system will cost about USD 2.6918 billion.


Optimizing microgrid integration of renewable energy for sustainable solutions in off/on-grid communities

Abstract: Rising energy costs, climate change impacts, and transmission losses have increased demand for renewable energy sources and decentralized solutions. As more people seek smart living and working environments, integrated smart microgrids powered by hybrid renewable systems have become attractive solutions for off-grid and on-grid communities. This study proposes designing a solar-wind-battery hybrid microgrid supplying a medical load et al.-Ain Al-Sokhna, Egypt. The optimization objectives aim to minimize the loss of power supply probability (LPSP %) and the levelized cost of energy (LCOE, $/kWh). A key consideration when designing and optimizing hybrid microgrids is the energy management strategy, which coordinates different generation sources and fluctuating load demand. Therefore, optimization algorithms were applied to balance energy flows while meeting loads, mitigating weather impacts, and preventing overcharging/deep discharge of battery storage. Models of wind turbines, photovoltaic panels, and battery storage were developed to simulate and analyze proposed microgrid operations. A multi-objective optimization approach evaluated LPSP and LCOE metrics using transit search, grey wolf, and particle swarm algorithms to find optimal system configurations. The optimization algorithms demonstrated varying performances in minimizing the multi-objective functions for the on-grid and off-grid microgrids. The particle-swarm optimization technique is the best solution for the off-grid system, which contains PV, wind, and battery storage, with a minimum LCOE of 0.3435 $/kWh and an LPSP of 4.5334%. Meanwhile, the transit-search optimization algorithm found the optimal solution for the on-grid configuration according to the objective function, yielding an LCOE of 0.116 $/kWh and an LPSP value of 3.0639 × 10−16. Statistical analysis confirmed that the algorithms generally exhibited stable and robust optimization capabilities. Of the methods, transit search was the most effective overall optimization approach.

Journal of Electrical Systems and Information Technology


A Cost-Effective Energy Management Approach for On-Grid Charging of Plug-in Electric Vehicles Integrated with Hybrid Renewable Energy Sources

Abstract: Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO2-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations.


An Analysis of Hybrid Renewable Energy-Based Hydrogen Production and Power Supply for Off-Grid Systems

Abstract: Utilizing renewable energy sources to produce hydrogen is essential for promoting cleaner production and improving power utilization, especially considering the growing use of fossil fuels and their impact on the environment. Selecting the most efficient method for distributing power and capacity is a critical issue when developing hybrid systems from scratch. The main objective of this study is to determine how a backup system affects the performance of a microgrid system. The study focuses on power and hydrogen production using renewable energy resources, particularly solar and wind. Based on photovoltaics (PVs), wind turbines (WTs), and their combinations, including battery storage systems (BSSs) and hydrogen technologies, two renewable energy systems were examined. The proposed location for this study is the northwestern coast of Saudi Arabia (KSA). To simulate the optimal size of system components and determine their cost-effective configuration, the study utilized the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software (Version 3.16.2). The results showed that, when considering the minimum cost of energy (COE), the integration of WTs, PVs, a battery bank, an electrolyzer, and a hydrogen tank brought the cost of energy to almost 0.60 USD/kWh in the system A. However, without a battery bank, the COE increased to 0.72 USD/kWh in the same location because of the capital cost of system components. In addition, the results showed that the operational life of the fuel cell decreased significantly in system B due to the high hours of operation, which will add additional costs. These results imply that long-term energy storage in off-grid energy systems can be economically benefited by using hydrogen with a backup system.


On–off-Grid Optimal Hybrid Renewable Energy Systems for House Units in Iraq

Abstract: This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement.


Design and Implementation of a Hybrid Solar-Wind-Biomass Renewable Energy System considering Meteorological Conditions with the Power System Performances

Abstract: This paper presents a performance evaluation of an off-grid PV-wind-biomass hybrid energy system for a remote area named Kuakata in Bangladesh considering dispatch strategy-based control, power system response, and reliability analysis-based stability and feasibility study. The simulation and optimization of operations of the system have been done by the HOMER software using the real-time field data of solar radiation, wind speed, and biomass of that particular area for ensuring economical and environmental feasibility offering the least net present cost (NPC), cost of energy (COE), and CO2 emission. The result shows that NPC has been reduced by 88 percent, CO2 emissions have been reduced by 99 percent, the operating cost has been lowered by 99 percent, and COE has been reduced by 92 percent than another available work. Besides, in comparison to traditional power sources, COE has been reduced by 40 percent, NPC has been lowered by 90 percent, and CO2 emissions have been reduced by 99 percent. The proposed system has also been analyzed utilizing DIgSILENT PowerFactory software to find the power system responses, i.e., active, reactive powers, voltage, and frequency responses of the proposed microgrid in a per unit fashion on the occurrence of a three-phase fault. To establish the feasibility of the microgrid, a reliability study considering different reliability indices has also been done. The analyzed hybrid energy system might be applicable to other regions of the world where there are similar climatic conditions.


Challenges and opportunities in optimizing hybrid renewable energy systems for grid stability and socio-economic development in rural Sub-saharan Africa: A narrative review

Abstract: The global shift towards renewable energy systems is critical for combating climate change, enhancing energy security, and achieving sustainability. Hybrid renewable energy systems, which integrate multiple renewable sources such as solar, wind, and hydropower, promise increased reliability and efficiency in energy generation. However, their integration into national grids presents significant technical, economic, and regulatory challenges. This narrative review systematically analyzes the hurdles and opportunities associated with hybrid renewable energy systems, drawing from diverse case studies and technological advancements. It discusses technical challenges, including grid stability and interconnection compatibility, and economic issues related to high initial investments and financing. Furthermore, the review highlights regulatory barriers and the necessity for supportive frameworks. It concludes with insights into the potential of hybrid systems to foster technological innovation and contribute to sustainable development and climate change mitigation.

KIU journal of science engineering and technology


Optimal design and analysis of a grid‐connected hybrid renewable energy system using HOMER Pro: A case study of Tumbatu Island, Zanzibar

Abstract: This study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond.


A Comprehensive Review on Opportunities and Challenges of Grid Integration of Renewable Energy Resources

Abstract: The global shift towards sustainable energy has accelerated the integration of Variable Renewable Energy Resources (VRER), such as solar and wind, into mainstream power generation. While VRER offer immense potential for reducing carbon emissions and advancing energy sustainability, their inherent variability poses unique challenges for seamless integration into existing power grids. This paper comprehensively explores challenges and opportunities associated with the efficient grid integration of VRER. A key focus of the paper is to maintain system stability and reliability while improving the economic benefits of incorporating VRER. By offering a balanced examination of challenges and opportunities, this paper aims to guide stakeholders in developing effective policies, technologies, and strategies for navigating the complex landscape of VRER grid integration. Ultimately, the insights presented contribute to realizing a sustainable and resilient future energy system.

2024 Second International Conference on Smart Technologies for Power and Renewable Energy (SPECon)


Grid-Connected Hybrid Renewable Energy System Under Various Operating Conditions

Abstract: Due to the rapid increase in electricity demand, and the need to tackle climate change; renewable energy systems have been utilized considerably in recent years. The world has recently witnessed a dramatic increase in using solar energy based on photovoltaic (PV) technology and wind energy in different applications due to their sustainability, efficient cost, and mature technology. In addition, one of the many advantages of wind and solar based on PV is the possibility of installing them near the load centers in distribution networks. One of the great challenges of renewable energy based on PV and wind is that they have an intermittent nature. Combing wind and PV in one site increases the reliability of such renewable systems due to the complementary nature of these sources. This paper investigates a grid-connected hybrid renewable system based on wind and PV without storage devices. The performance is evaluated in such hybrid renewable scheme with considering different operating conditions and simulation scenarios. MATLAB/SIMULINK has been used to design and test the hybrid renewable system to evaluate the system's performance.

2024 IEEE 8th Energy Conference (ENERGYCON)


A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems

Abstract: The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future.


Joint Optimal Scheduling of Renewable Energy Regional Power Grid With Energy Storage System and Concentrated Solar Power Plant

Abstract: Under the path of global low-carbon development, increasing the proportion of renewable energy in the power grid will become the main goal in the future. But, it will also aggravate the problem of wind and solar curtailment. A joint optimal scheduling model of a renewable energy regional power grid with an energy storage system and concentrated solar power plant is proposed in this study. The proposed model takes the lowest comprehensive operation cost of the power grid as the optimization goal and considers various constraints of concentrated solar power plants, energy storage systems, thermal power units, wind power, and photovoltaic power generation. Finally, a modified IEEE 9-bus system is used to verify the validity. The results show that the model can effectively improve the system node voltage, promote the accommodation of wind and solar power, and alleviate the peak shaving of thermal power units under the premise of optimal economy pressure.


Robust Control for Optimized Islanded and Grid-Connected Operation of Solar/Wind/Battery Hybrid Energy

Abstract: Wind and solar energy systems are among the most promising renewable energy technologies for electric power generations. Hybrid renewable energy systems (HRES) enable the incorporation of more than one renewable technology, allowing increased reliability and efficiency. Nevertheless, the introduction of variable generation sources in concurrence with the existing system load demand necessitates maintaining the power balance between the components of the HRES. Additionally, the efficiency of the hybrid power supply system is drastically affected by the number of converters interfacing its components. Therefore, to improve the performance of the HRES, this paper proposes a robust sliding mode control strategy for both standalone and grid-connected operation. The control strategy achieves maximum power point tracking for both the renewable energy sources and stabilizes the DC-bus and load voltages irrespective of the disturbances, change in load demand, variations of irradiance level, temperature, and wind speed ensuring an efficient energy management. Furthermore, the solar PV system is directly linked to the DC-bus obviating the need for redundant interfacing boost converters with decreased costs and reduced system losses. Lyapunov candidate function is used to prove the asymptotic stability and the convergence of the entire system. The robustness of the proposed control strategy is tested and validated under various conditions of HRES, demonstrating its efficacy and performance under various conditions of the HRES.


Optimal Power Flow of Hybrid Wind/Solar/Thermal Energy Integrated Power Systems Considering Costs and Emissions via a Novel and Efficient Search Optimization Algorithm

Abstract: The OPF problem has significant importance in a power system’s operation, planning, economic scheduling, and security. Today’s electricity grid is rapidly evolving, with increased penetration of renewable power sources (RPSs). Conventional optimal power flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimization problem. This complex problem escalates further with the integration of renewable energy resource (RES), which are generally intermittent in nature. This study suggests a new and effective improved optimizer via a TFWO algorithm (turbulent flow of water-based optimization), namely the ITFWO algorithm, to solve non-linear and non-convex OPF problems in energy networks with integrated solar photovoltaic (PV) and wind turbine (WT) units (being environmentally friendly and clean in nature). OPF in the energy networks is an optimization problem proposed to discover the optimal settings of an energy network. The OPF modeling contains the forecasted electric energy of WT and PV by considering the voltage value at PV and WT buses as decision parameters. Forecasting the active energy of PV and WT units has been founded on the real-time measurements of solar irradiance and wind speed. Eight scenarios are analyzed on the IEEE 30-bus test system in order to determine a cost-effective schedule for thermal power plants with different objectives that reflect fuel cost minimization, voltage profile improvement, emission gases, power loss reduction, and fuel cost minimization with consideration of the valve point effect of generation units. In addition, a carbon tax is considered in the goal function in the examined cases in order to investigate its effect on generator scheduling. A comparison of the simulation results with other recently published algorithms for solving OPF problems is made to illustrate the effectiveness and validity of the proposed ITFWO algorithm. Simulation results show that the improved turbulent flow of water-based optimization algorithm provides an effective and robust high-quality solution of the various optimal power-flow problems. Moreover, results obtained using the proposed ITFWO algorithm are either better than, or comparable to, those obtained using other techniques reported in the literature. The utility of solar and wind energy in scheduling problems has been proposed in this work.


Mitigating Impact of High Power Ramp Rates in Utility Grid Integrated Wind–Solar System Using an RLMAT Adaptive Control Strategy

Abstract: With the unremitting progress in renewable energy based systems into a sizeable contributor in global energy mix, the need to address challenges associated with their inherent power variability has also risen. The high power-ramp rates experienced during sudden wind gusts and cloud passing are known to impact the security of the power grid, especially in remotely-located wind-solar systems. These challenges are further intensified with sudden connection/disconnection of large local loads in the system, which demand power at a high ramp rate. This work deals with the use of a coordinated control strategy for power converters, to limit the grid power-ramp rate during the aforementioned high power-ramp events (HPREs). The deficit power during HPREs is absorbed by the energy storage system (ESS). The reference currents for ramp rate limiting control are computed using a robust least mean absolute third (RLMAT) adaptive filter. The RLMAT filter further assists in improving the grid currents power quality in presence of nonlinear, unbalanced and poor power factor local loads in the system. The effectiveness of the control strategy is evaluated through both simulation analysis and test results obtained from a laboratory-scale prototype.


Assessing Economic Complementarity in Wind–Solar Hybrid Power Plants Connected to the Brazilian Grid

Abstract: The share of electricity generation from Variable Renewable Energy Sources (VRES) has increased over the last 20 years. Despite promoting the decarbonization of the energy mix, these sources bring negative characteristics to the energy mix, such as power ramps, load mismatch, unpredictability, and fluctuation. One of the ways to mitigate these characteristics is the hybridization of power plants. This paper evaluates the benefits of hybridizing a plant using an AI-based methodology for optimizing the wind–solar ratio based on the Brazilian regulatory system. For this study, the hybrid plant was modeled using data collected over a period of 10 months. The measurements were obtained using two wind profilers (LIDAR and SODAR) and a sun tracker (Solys 2) as part of the EOSOLAR R&D project conducted in the state of Maranhão, Brazil. After the power plant modeling, a Genetic Algorithm (GA) was used to determine the optimal wind–solar ratio, considering costs with transmission systems. The algorithm achieved a monthly profit increase of more than 39% with an energy curtailment inferior to 1%, which indicates economic complementarity. Later, the same methodology was also applied to verify the wind–solar ratio’s sensitivity to solar energy pricing. The results show that a price increase of 15% would change the power plant’s optimal configuration.


Economic and environmental assessment of solar-wind-biomass hybrid renewable energy system supplying rural settlement load

Abstract: Abstract Hybrid renewable energy systems are becoming a popular option for rural electrification due to environmental impacts caused by fossil fuels like coal and oil. Iran is a developing country with many renewable energy sources, but most Iranian power plants use non-renewable resources to generate electricity. This study aims to present a hybrid renewable energy system consisting of photovoltaic panels, wind turbines, and biogas generator for rural electrification in Fars province, Iran. The utilization of this off-grid system prevents the development of fossil fuel power plants and CO2 emissions. The optimization problem is solved using the HOMER Pro software, and sensitivity analyses obtain optimal system configurations on input biomass rate, biomass price, and inflation rate parameters. The inflation rate and biomass price are directly related to the cost of electricity (COE). The results show that the COE in optimal systems ranges from 0.128 to $0.223/kW h. Increasing the biomass price from 20 to $60/ton reduces the biogas generator's power generation by at least 86%. The most optimal economic system involves a biogas generator (150 kW), photovoltaic panels (80.7 kW), batteries, and converter. The environmental assessment indicates that CO2 emissions from proposed hybrid renewable energy systems are negligible compared to a coal-based power plant and the grid. Reducing CO2 in the presented approach can save more than $8000 a year.

Sustainable Energy Technologies and Assessments


Synergizing Wind and Solar Power: An Advanced Control System for Grid Stability

Abstract: In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov’s theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system’s adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system’s robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system’s ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies.


A Strategy for Multi-Objective Energy Optimization in Smart Grid Considering Renewable Energy and Batteries Energy Storage System

Abstract: Multi-objective energy optimization is pivotal for reliable and secure power system operation. However, multi-objective energy optimization is challenging due to interdependent and conflicting objectives. Thus, a multi-objective optimization model is needed to cater to conflicting objectives. On this note, a multi-objective optimization model is developed, where a non-dominated genetic sorting algorithm is employed to optimize objectives pollution emission, operation cost, and loss of load expectation (LOLE) considering renewable energy sources (RES). RES, like wind and solar, are intermittent and uncertain, which are modelled using a beta probability density function (PDF). The developed method’s effectiveness and applicability are analyzed by implementing it on the 30-bus system, and the results are compared for two cases. Findings reveal that the developed multi-objective optimization model minimizes operation cost, pollution emission, and LOLE by 59%, 7%, and 2.67%, respectively, compared to existing models.


Techno-Economic Comparative Analysis of Grid-Connected and Islanded Hybrid Renewable Energy Systems in 7 Climate Regions, Turkey

Abstract: The aim of this study is to evaluate the economic, technical, and environmental performances of grid-tied and stand-alone hybrid renewable energy systems (HRESs) in 21 provinces in seven regions of Turkey, considering different regional solar radiation and wind speed diversity. HRES were designed and modeled using the Hybrid Optimization of Multiple Energy Resources software (HOMER PRO) to meet the daily load of 13.26 kWh/day of a household. The analysis results for each province were compared considering the cost of energy, net present cost (NPC), greenhouse gas emissions, renewable fraction (RF), and optimum system configuration. The findings demonstrated that the optimal system configurations are Grid/PV/WT and PV/WT/DG/BESS for grid-tied and stand-alone HRES, respectively. The value of NPC ranges from <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>2,540.00 to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>8,951.00 for grid-tied HRES, while it varies from <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>23,372.00 to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>40,858.00 for stand-alone HRES. The provinces of Çanakkale in the Marmara Region and Artvin in the Black Sea Coast Region have the lowest and highest NPC values, respectively, for all systems. The PV capital cost, WT capital cost, BESS capital cost, solar radiation, and wind speed are considered as sensitivity input parameters that might affect the economic output of the HRES in this study. According to the sensitivity analysis, the NPC value as an economic indicator input decreased for both on-grid and off-grid HRES as the wind speed and solar radiation increased. It was also found that when the capital cost of PV panels and WT were changed, the NPC of the stand-alone HRES was in the range of <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>21,402.27-<inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>29,978.89 for the province of Çanakkale, while it was in the range of <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>37,518.11-<inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>51,939.00 for the province of Artvin. Moreover, when solar radiation and wind speed were increased, the results showed that NPC and CO2 emissions decreased by 9.30% and 9.23%, respectively, for Çanakkale, and by 25.58% and 66.95%, respectively, for Artvin. Finally, the results indicated that the optimal system configuration changes depending on the PV and WT capital cost variations for the grid-tied HRES. This research can be useful for planning grid-tied and stand-alone HRES from different aspects in Turkey, as well as other countries around the world. It contributes to the literature by comparing grid-tied and stand-alone HRES to determine the optimum system configuration and to find the best optimization results in seven regions of Turkey under different climate conditions. In addition, most of the studies related to HRES for residential areas in the literature are reviewed in this research, which intends to serve as a guide for engineers and researchers.


Batteries or hydrogen or both for grid electricity storage upon full electrification of 145 countries with wind-water-solar?

Abstract: Summary Grids require electricity storage. Two emerging storage technologies are battery storage (BS) and green hydrogen storage (GHS) (hydrogen produced and compressed with clean-renewable electricity, stored, then returned to electricity with a fuel cell). An important question is whether GHS alone decreases system cost versus BS alone or BS + GHS. Here, energy costs are modeled in 145 countries grouped into 24 regions. Existing conventional hydropower (CH) storage is used along with new BS and/or GHS. A method is developed to treat CH for both baseload and peaking power. In four regions, only CH is needed. In five, CH + BS is the lowest cost. Otherwise, CH + BS + GHS is the lowest cost. CH + GHS is never the lowest cost. A metric helps estimate whether combining GHS with BS reduces cost. In most regions, merging (versus separating) grid and non-grid hydrogen infrastructure reduces cost. In sum, worldwide grid stability may be possible with CH + BS or CH + BS + GHS. Results are subject to uncertainties.


A Metaheuristic Approach Based Feasibility Assessment and Design of Solar, Wind, and Grid Powered Charging of Electric Vehicles

Abstract: This study assesses the integration of renewable energy sources with grid-connected systems to power electric vehicle charging stations (EVCSs) in Riyadh, Kingdom of Saudi Arabia (KSA), emphasizing its significance for sustainable development. An environmentally friendly EVCS model and a novel metaheuristic optimization technique, the Giza Pyramids Construction Algorithm (GPCA) is employed, to determine the optimal sizing of system components. The objective is to ensure dependability, as determined by the lack of power supply probability (LPSP), while minimizing the levelized cost of energy (LCOE) and total net present cost (TNPC). We assess the efficacy of other algorithms in scaling system components by contrasting the performance of the GPCA with those of the Artificial Hummingbird Algorithm (AHA), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). The study also examines the impact of climatic, financial, and technical factors on system costs, providing insights into the challenges, opportunities, and socio-economic factors influencing the deployment of EV infrastructure in KSA. Findings suggest that the optimal EVCS configuration is a solar photovoltaic/wind turbine (SPV/WT) grid-tied system, resulting in an LCOE of ${\$}$ 0.0796/kWh, a TNPC of ${\$}$ 102,042, and an annual CO2 emission reduction of 18,840 kg. The system achieves a 67.9% renewable penetration rate with 30.8% annual grid electricity purchase and 32.4% sale. Sensitivity analysis on renewable parameters is also conducted, demand charges, grid purchase prices, discount rates, and inflation rates to assess their impact on the system’s economic variables. By identifying current constraints and potential strategies, our study aims to inform policy-making and investment decisions for expanding electric vehicle charging infrastructure, with broader implications for developing nations.


Operating characteristics analysis and capacity configuration optimization of wind-solar-hydrogen hybrid multi-energy complementary system

Abstract: Wind and solar energy are the important renewable energy sources, while their inherent natures of random and intermittent also exert negative effect on the electrical grid connection. As one of multiple energy complementary route by adopting the electrolysis technology, the wind-solar-hydrogen hybrid system contributes to improving green power utilization and reducing its fluctuation. Therefore, the moving average method and the hybrid energy storage module are proposed, which can smooth the wind-solar power generation and enhance the system energy management. Moreover, the optimization of system capacity configuration and the sensitive analysis are implemented by the MATLAB program platform. The results indicate that the 10-min grid-connected volatility is reduced by 38.7% based on the smoothing strategy, and the internal investment return rate can reach 13.67% when the electricity price is 0.04 $/kWh. In addition, the annual coordinated power and cycle proportion of the hybrid energy storage module are 80.5% and 90%, respectively. The developed hybrid energy storage module can well meet the annual coordination requirements, and has lower levelized cost of electricity. This method provides reasonable reference for designing and optimizing the wind-solar-hydrogen complementary system.


Renewable energy sources‐based hybrid microgrid system for off‐grid electricity solution for rural communities

Abstract: Rural electrification is a crucial component of meeting sustainable development goals. In Pakistan, load shedding and power outages are frequent. According to the current data, Pakistan is experiencing a shortfall of power between 6000 and 6500 MW. Microgrid technology has the potential to provide a solution to this problem in an efficient and low‐cost manner. This paper proposes the development of a hybrid microgrid system (HMGS) for rural communities. For that purpose, a technological analysis of the HMGS system for rural electrification is performed. Solar photovoltaic (PV) and wind resource‐based renewable energy systems are considered in this work for the electrification of rural areas of Pakistan. A hybrid PV/wind system is designed using MATLAB software. Simulation results show that a 230‐V sinusoidal output voltage has been produced by the proposed model. The advantage of this model is that it minimizes the impact of transients and provides a sinusoidal output waveform.


A Comparative Study of the Optimal Sizing and Management of Off-Grid Solar/Wind/Diesel and Battery Energy Systems for Remote Areas

Abstract: Electrification in rural areas is relatively costly compared to urban areas. Therefore, the aim of this research is to identify the best combination of hybrid renewable energy systems (HRESs) to satisfy the load demand in a sustainable and cost-efficient way. The techno-economic study of stand-alone hybrid photovoltaic–wind turbine–diesel–battery-converter energy systems based on the hybrid optimization model for electric renewable (HOMER) simulation has been analyzed for various locations in the Tamil Nadu state, India. Various combinations of the systems have been compared and analyzed based on the performance of their technical parameters, costs, the electrical power production of each source, and unmet load. The findings indicated that the off-grid solar–wind–diesel–battery configuration is the most economical for all the sites among other system configurations. Comparing with conventional diesel generators among all the locations, a combination of solar/wind/diesel/battery is the economically best design for Thoothukudi, with the least and most reliable solution in terms of net present cost and cost of energy. Also, the impact of intermittent variables becomes significant, so sensitivity analysis for the various parameters has been carried out. The study finds that the least cost of electricity and the net present cost of electricity for Thoothukudi are achieved at 0.266 $/kWh and 138,197 $, respectively. This is economical compared to a stand-alone diesel system where the obtained COE is $1.88 and the NPC is $977523. In the stand-alone diesel operating mode, 41854 kg of CO2 is produced, which is higher than CO2 emissions associated with any other renewable energy systems. According to the results, the Kanyakumari location outperforms in terms of producing environmental pollutants with emission of 1,020 kg/y CO2 at their best. Moreover, the results of the proposed study imply that the proposed renewable energy system in remote sites could be a more economical measure.


Comparative assessment of solar photovoltaic-wind hybrid energy systems: A case for Philippine off-grid islands


Coupled thermo-electrical dispatch strategy with AI forecasting for optimal sizing of grid-connected hybrid renewable energy systems

Abstract: In multi-energy systems the full utilisation of the generated energy is a challenge. Integrating heat and electricity supply at the system level design could provide an opportunity to address this challenge. In this paper we introduce and examine two coupled thermal-electrical dispatch strategies for grid-connected hybrid multi-energy systems supplying electrical and thermal demand loads. The dispatch strategy employs forecasting of energy resources and demand loads to prioritise supplying the thermal load in times of renewable surplus. Four forecasting algorithms, namely, baseline forecast, Facebook Prophet (FBP), Neural Prophet (NP), and Long Short-Term Memory model (LSTM) are implemented and used to generate annual forecast data for solar irradiance, wind speed, and thermal and electrical demand loads. To integrate forecast data within the dispatch strategy, new parameters are proposed to quantify the expected available energy within the forecast time horizon. A building complex for the Department of Education in the UK is used for conducting a system design case study. A genetic algorithm-based multi-objective optimisation with the levelised costs of electricity and heat as two objectives is conducted. The results show that the proposed dispatch algorithm produces systems with reduced levelised costs compared to the base case of using utility gas and electricity. Forecasting is particularly useful in reducing cost of heat, as it can prioritise supplying the thermal load in times of renewable surplus. LSTM proved to be the most accurate forecasting algorithm for this case, where the data has strong seasonality and trends. The main contribution of this work is to propose and demonstrate the effectiveness of tightly coupling thermo-electrical dispatch algorithms of HRES from the design stage, and how to effectively integrate forecast data within such algorithms.


Hybrid renewable energy systems: the value of storage as a function of PV-wind variability

Abstract: As shares of variable renewable energy (VRE) on the electric grid increase, sources of grid flexibility will become increasingly important for maintaining the reliability and affordability of electricity supply. Lithium-ion battery energy storage has been identified as an important and cost-effective source of flexibility, both by itself and when coupled with VRE technologies like solar photovoltaics (PV) and wind. In this study, we explored the current and future value of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Using a price-taker model with simulated hourly energy and capacity prices, we simulated the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. Holding PV capacity and point-of-interconnection capacity constant, we modeled configurations with varying wind-to-PV capacity ratios and battery-to-PV capacity ratios. We found that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60%–80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility.


Hybridized Renewable Energy for Smart Vehicle-to-Grid (V2G) Systems

Abstract: Wind and sunlight are increasingly being exploited as energy supplies that never run out. Additionally, renewable energy resources, including sun, wind, and geothermal heat, are being used for different technologies. It was considered the use of hybridized wind-solar energy resources in smart vehicle technology. A thorough understanding of an integrated framework of the hybridized renewable energy for smart vehicle-to-grid (V2G) systems is essential and required to further identify and perhaps maximize existing opportunities. Aiming to develop a vehicle-to-grid (V2G) system where the smart vehicle runs on stored sunshine and wind energy, and vehicle batteries store energy and release it to the electricity grid in peak demand periods. To achieve this aim, mathematical models for solar and wind systems were created and entire 24-h simulations were run for case studies of three smart vehicles, which were assessed for different scenarios and circumstances, using the MATLAB/SIMULINK environment. The estimated values obtained were home load 10 MW, power factor 0.15 MVA, industrial load 0.16 MVA, and smart car-to-grid, solar panel farm, and wind farm power of 4 MW, 8 MW, and 4.5 MW, respectively. Therefore, the hybridized wind-solar energy sources were applicable for all three smart vehicles considered.


An Optimized Framework of the Integrated Renewable Energy and Power Quality Model for the Smart Grid

Abstract: The smart grid’s structure is distinctive because it incorporates numerous cutting-edge communication and sensor technologies. It is challenging to manage smart grids using conventional power grids’ unified optimum delivery strategy effectively. This work offers a smart grid power production and maintenance collaborative optimization framework with renewable energy based on power generation and maintenance analysis. The operating costs of conventional units, the price of generating solar or wind power, and the cost of maintaining units are the problems of objective functions; the constraints taken into account are primarily system constraints. The genetic algorithm (GA) is used in this research to examine the optimization solution strategy. Depending on the properties of the system, various loads, power sources, and constraints are considered. The difficulty of adjusting load scheduling is transformed into a control problem by developing an efficient objective function. When uncertain system components are considered, the real-time control system can dynamically alter load scheduling to meet the needs of the actual system. In this study, the real-time algorithm put forward in this research is based on a strategy that satisfies the load optimization requirement and achieves dynamic compensation for unpredictable changes in renewable energy power generation. Moreover, the simulation compares the proposed algorithm capabilities with the existing algorithms/study, demonstrating the effectiveness of the suggested approach.

International Transactions on Electrical Energy Systems


Ancillary services from wind and solar energy in modern power grids: A comprehensive review and simulation study

Abstract: Renewable energy sources like wind and solar have increased demand for surplus power capacity. The demand is primarily fueled by the growing impact of forecasting errors associated with these intermittent energy sources. Implementing advanced control methods for automatic generation control (AGC) is essential to integrate wind and solar power with conventional generation sources to balance the power system and reduce reliance on traditional reserves. Therefore, this paper comprehensively overviews solar and wind energy integration in the AGC framework to provide optimal grid ancillary services. Initially, the paper presents an overview of the basic equations used to integrate reserve power from the photovoltaic (PV) system by employing the de-loading strategy. Subsequently, a comprehensive review is conducted on integrating the PV system in AGC strategies to provide grid ancillary services. The study also analyzes the contribution of wind power in AGC services using relevant equations and past practices. The paper presents a real-time dynamic control strategy to optimize the dispatch of the AGC unit by integrating the operating reserves from wind energy systems in conjunction with thermal power systems. The study simulates an 8-bus, 5-machine model using the Dig-SILENT Power Factory. The findings reveal that utilizing operating reserves from wind power can significantly reduce large-scale forecasting errors in massively renewable energy resources (RES) integrated power systems, thereby ensuring the necessary system operational security and reducing the reliance on traditional generating units.

Journal of Renewable and Sustainable Energy


Dietary sodium intake and mortality: NHANES. The Faculty 31st International Society and Federation of Cardiology 10-day Teaching Seminar in Cardiovascular Disease, Epidemiology and Prevention. National Health and Nutrition Examination Survey.


Respiratory and cardiovascular responses to walking down a traffic-polluted road compared with walking in a traffic-free area in participants aged 60 years and older with chronic lung or heart disease and age-matched healthy controls: a randomised, crossover study

Abstract: Summary Background Long-term exposure to pollution can lead to an increase in the rate of decline of lung function, especially in older individuals and in those with chronic obstructive pulmonary disease (COPD), whereas shorter-term exposure at higher pollution levels has been implicated in causing excess deaths from ischaemic heart disease and exacerbations of COPD. We aimed to assess the effects on respiratory and cardiovascular responses of walking down a busy street with high levels of pollution compared with walking in a traffic-free area with lower pollution levels in older adults. Methods In this randomised, crossover study, we recruited men and women aged 60 years and older with angiographically proven stable ischaemic heart disease or stage 2 Global initiative for Obstructive Lung Disease (GOLD) COPD who had been clinically stable for 6 months, and age-matched healthy volunteers. Individuals with ischaemic heart disease or COPD were recruited from existing databases or outpatient respiratory and cardiology clinics at the Royal Brompton & Harefield NHS Foundation Trust and age-matched healthy volunteers using advertising and existing databases. All participants had abstained from smoking for at least 12 months and medications were taken as recommended by participants' doctors during the study. Participants were randomly assigned by drawing numbered disks at random from a bag to do a 2 h walk either along a commercial street in London (Oxford Street) or in an urban park (Hyde Park). Baseline measurements of participants were taken before the walk in the hospital laboratory. During each walk session, black carbon, particulate matter (PM) concentrations, ultrafine particles, and nitrogen dioxide (NO2) concentrations were measured. Findings Between October, 2012, and June, 2014, we screened 135 participants, of whom 40 healthy volunteers, 40 individuals with COPD, and 39 with ischaemic heart disease were recruited. Concentrations of black carbon, NO2, PM10, PM2.5, and ultrafine particles were higher on Oxford Street than in Hyde Park. Participants with COPD reported more cough (odds ratio [OR] 1·95, 95% CI 0·96–3·95; p<0·1), sputum (3·15, 1·39–7·13; p<0·05), shortness of breath (1·86, 0·97–3·57; p<0·1), and wheeze (4·00, 1·52–10·50; p<0·05) after walking down Oxford Street compared with Hyde Park. In all participants, irrespective of their disease status, walking in Hyde Park led to an increase in lung function (forced expiratory volume in the first second [FEV1] and forced vital capacity [FVC]) and a decrease in pulse wave velocity (PWV) and augmentation index up to 26 h after the walk. By contrast, these beneficial responses were attenuated after walking on Oxford Street. In participants with COPD, a reduction in FEV1 and FVC, and an increase in R5–20 were associated with an increase in during-walk exposure to NO2, ultrafine particles and PM2.5, and an increase in PWV and augmentation index with NO2 and ultrafine particles. In healthy volunteers, PWV and augmentation index were associated both with black carbon and ultrafine particles. Interpretation Short-term exposure to traffic pollution prevents the beneficial cardiopulmonary effects of walking in people with COPD, ischaemic heart disease, and those free from chronic cardiopulmonary diseases. Medication use might reduce the adverse effects of air pollution in individuals with ischaemic heart disease. Policies should aim to control ambient levels of air pollution along busy streets in view of these negative health effects. Funding British Heart Foundation.


Statins: new American guidelines for prevention of cardiovascular disease.


Nurse-coordinated multidisciplinary, family-based cardiovascular disease prevention programme (EUROACTION) for patients with coronary heart disease and asymptomatic individuals at high risk of cardiovascular disease: a paired, cluster-randomised controlled trial.


Cardiovascular prevention guidelines in daily practice: a comparison of EUROASPIRE I, II, and III surveys in eight European countries.


Effect of n-3 polyunsaturated fatty acids in patients with chronic heart failure (the GISSI-HF trial): a randomised, double-blind, placebo-controlled trial.


Spectrum of heart disease and risk factors in a black urban population in South Africa (the Heart of Soweto Study): a cohort study.


Role of MRI in clinical cardiology.


Integrity and the European Society of Cardiology--reply.


Statins, risk assessment, and the new American prevention guidelines.


Identification of intact peanut lectin in peripheral venous blood.


Updated Zika virus recommendations are needed.

Abstract: 818 www.thelancet.com Vol 392 September 8, 2018 4 Fihn SD, Blankenship JC, Alexander KP, et al. 2014 ACC/AHA/AATS/PCNA/SCAI/STS focused update of the guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, and the American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. Circulation 2014; 130: 1749–67.


Department of Error

Abstract: 532 www.thelancet.com Vol 386 August 8, 2015 3 National Drug Evidence Centre. Facts and fi gures. http://www.population-health. manchester.ac.uk/epidemiology/NDEC/ factsandfi gures/ (accessed May 1, 2015). 4 Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther 2002; 27: 299–309. 5 Hinkley DV. Inference about the change-point in a sequence of random variables. Biometrika 1970; 57: 1–17. 6 Wilson W, Taubert KA, Gewitz M, et al. Prevention of infective endocarditis: guidelines from the American Heart Association: a guideline from the American Heart Association Rheumatic Fever, Endocarditis, and Kawasaki Disease Committee, Council on Cardiovascular Disease in the Young, and the Council on Clinical Cardiology, Council on Cardiovascular Surgery and Anesthesia, and the Quality of Care and Outcomes Research Interdisciplinary Working Group. Circulation 2007; 116: 1736–54. 7 Habib G, Hoen B, Tornos P, et al. Guidelines on the prevention, diagnosis, and treatment of infective endocarditis (new version 2009): the Task Force on the Prevention, Diagnosis, and Treatment of Infective Endocarditis of the European Society of Cardiology (ESC). Endorsed by the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) and the International Society of Chemotherapy (ISC) for Infection and Cancer. Eur Heart J 2009; 30: 2369–413. weight of evidence on which infective endocarditis guidelines are based. The AHA and ACC and European Society of Cardiology guidelines are based on Level C evidence (expert opinion). Our analysis is a cohort study—Level B evidence—the same as a single randomised controlled trial. Although our results might be controversial, we argue that they alter the available weight of evidence (especially in the absence of a randomised controlled trial). We agree with James Wong and colleagues that infective endocarditis coding has improved, especially since secondary coding became important for measurement of quality indicators. However, we restricted our analysis to primary codes that have always been mandatory and are unlikely to have changed. A full assessment of the National Institute for Cardiovascular Outcomes Research dataset could add important information. We hope that our data will provoke further debate about the value of antibiotic prophylaxis for infective endocarditis and lead to further collaborative research eff orts to assess safety and eff ectiveness and to inform international guidelines.


Henry Krum.


Quantifying the social impact of research and medical journals.


Statins: friend or foe?


Lipoprotein(a) and cardiovascular disease.

Abstract: One in five people are at high risk for atherosclerotic cardiovascular disease and aortic valve stenosis due to high lipoprotein(a). Lipoprotein(a) concentrations are lowest in people from east Asia, Europe, and southeast Asia, intermediate in people from south Asia, the Middle East, and Latin America, and highest in people from Africa. Concentrations are more than 90% genetically determined and 17% higher in post-menopausal women than in men. Individuals at a higher cardiovascular risk should have lipoprotein(a) concentrations measured once in their lifetime to inform those with high concentrations to adhere to a healthy lifestyle and receive medication to lower other cardiovascular risk factors. With no approved drugs to lower lipoprotein(a) concentrations, it is promising that at least five drugs in development lower concentrations by 65-98%, with three currently being tested in large cardiovascular endpoint trials. This Review covers historical perspectives, physiology and pathophysiology, genetic evidence of causality, epidemiology, role in familial hypercholesterolaemia and diabetes, management, screening, diagnosis, measurement, prevention, and future lipoprotein(a)-lowering drugs.


Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease (The Oxford Risk Factors And Non-invasive imaging study): A multicentre, longitudinal cohort study

Abstract: SUMMARY Background: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. Methods: Consecutive patients undergoing clinically indicated CCTA in 8 UK hospitals (n=40,091) were followed for Major Adverse Cardiac Events (MACE; myocardial infarction, new onset heart failure, or cardiac death) for a median(IQR) of 2·7(1·4–5·3)years. The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3,393 consecutive patients from the 2 hospitals with the longest follow up [7·7(6·4–9·1)years]. An artificial intelligence-enhanced cardiac risk prediction algorithm (AI-Risk), that integrates FAI Score, coronary plaque metrics and clinical risk factors, was then evaluated in this population. Findings: Patients without obstructive CAD (n=32,533, 81·1%) accounted for 66·3% (n=2,857) of MACE and 63·7% (n=1,118) of cardiac deaths during follow up. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (HR 29∙8, 95% CI 13∙9–63∙0, P<0.001) or MACE (HR 12∙6, 95% CI 8∙5–18∙6, p<0.001) comparing 3 vessels with FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence/extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (HR 6∙75, 95% CI 5∙17–8∙82, p<0∙001, for very high vs low/medium risk) and MACE (HR 4∙68, 95% CI 3∙93–5∙57, p<0∙001 for very high vs low/medium risk). Finally, the AI-Risk model was well calibrated against true events. Interpretation: FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, that could be used as an alternative to traditional risk factors-based risk calculators.


The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030.

Abstract: Cardiovascular disease is the leading cause of death in women. Decades of grassroots campaigns have helped to raise awareness about the impact of cardiovascular disease in women, and positive changes affecting women and their health have gained momentum. Despite these efforts, there has been stagnation in the overall reduction of cardiovascular disease burden for women in the past decade. Cardiovascular disease in women remains understudied, under-recognised, underdiagnosed, and undertreated. This Commission summarises existing evidence and identifies knowledge gaps in research, prevention, treatment, and access to care for women. Recommendations from an international team of experts and leaders in the field have been generated with a clear focus to reduce the global burden of cardiovascular disease in women by 2030. This Commission represents the first effort of its kind to connect stakeholders, to ignite global awareness of sex-related and gender-related disparities in cardiovascular disease, and to provide a springboard for future research.