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ERROR: type should be string, got "https://doi.org/10.1051/e3sconf/201911301008 https://doi.org/10.1051/e3sconf/201911301008 E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons \nAttribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). Integration of heat pump and gas turbine \ncombined cycle: market and climatic conditions \nfor power plant flexibility enhancement \nAndrea Giugno1, Alessandro Sorce1, Alessandra Cuneo2* and Stefano Barberis2 \n1 Thermochemical Power Group, University of Genoa, Via Montallegro 1, 16145 Genoa, Italy \n2 RINA Consulting, Via San Nazaro 19, 16145, Genoa, Italy Abstract. The increasing share of electricity produced from renewable \nenergy sources (RES), with the consequent strong penetration in the current \nenergy network, is causing a growing need of balancing power to \ncompensate power supply from such fluctuating sources. For these reasons, \nnowadays the power plants are requested to improve their operational \nflexibility, together with their global efficiency in part-load operation, for \nancillary services and to sustain the grid operability. A possible solution for \nflexibility enhancement is characterized by a highly efficient heat pump \nintegrated in a conventional natural gas combined cycle (CC). Such concept \ncan be applied both to power oriented combined cycle (POCC), to modify \nthe compressor intake temperature with a consequent increase or decrease \nof the power production, and both to cogeneration CC in association with \nDistrict Heating Network (DHN). In this work, a statistical analysis of \nclimatic data and their correlations with energy market condition will be \nperformed considering Italian context, to understand which the more \nsuitable conditions for such integrated system are. The analysis will be \nperformed on seasonal and daily basis. The final aim of this work is to \nidentify how such integrated system can be operated at its best in the \ndifferent Italian markets and climatic frames. E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 https://doi.org/10.1051/e3sconf/201911301008 E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 An innovative concept based on the coupling of a fast-cycling highly efficient Heat Pump \n(HP) with the CCGTs is here proposed [4], within the framework of an H2020 EU project \ncalled PUMP-HEAT [5]. This possible solution for flexibility enhancement is characterized \nby a highly efficient heat pump integrated in a CCGT, featuring cold/warm thermal storage \nand advanced control. Such concept can be applied both to power oriented combined cycle \n(POCC), to modify the compressor intake temperature with a consequent increase or decrease \nof the power production, and both to cogeneration CCGT in association with District Heating \nNetwork (DHN). In this work, a statistical analysis of climatic data and their correlations \nwith energy market condition will be performed considering the Italian market scenario, to \nunderstand which is the more suitable conditions for such integrated systems. The final aim is to identify how PUMP HEAT concept can be operated and the influences \nthat different Italian price zones and climatic conditions can have on its profitability. The \nPower Oriented (PO) combined cycle configuration is taken into consideration during this \nanalysis, with a particular attention on the continuous cooling mode (Figure 1). Such \noperational mode allows to modify the compressor intake temperature thanks to the cold \nenergy produced by the heat pump. The cooling of the inlet temperature increases the \nmaximum power output of the CC (about 10%) during peak hours, with also an increase of \nthe yearly efficiency and production. Figure 1. Power oriented HP-CC concept scheme working in continuous cooling Figure 1. Power oriented HP-CC concept scheme working in continuous cooling The heat pump produces cold energy using ambient air through a dedicate heat exchanger \n(ambient HX in Figure 1) as energy source. The performances of the heat pump in terms of \nCOP and the temperature that can be reached at the compressor inlet are dependent on the \nambient temperature. However, to consistently evaluate the HP performance, both ambient \ntemperature and humidity were considered. In addition, in this work, it was always \nconsidered to work at the maximum load of the heat pump, not considering off-design \nconditions. In fact, from the optimization analysis performed during the PUMP-HEAT \nproject, it resulted that this would be the condition that maximizes the economic benefit. However, a minimum threshold value of 5°C was set at the compressor inlet to avoid the risk \nof ice formation. E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 The CCGT model was validated in previous work with respect to the effect \nof ambient temperature. the effect of GT intake humidity on the CCGT overall performance \nwas neglected as conservative assumption as explained in [4]. 1 Introduction In the last years, a consistent evolution of combined cycle performance was shown, mainly \ncommanded by the market. The intermittent renewable capacity has increased dramatically \nin the last 10 years, due also to the EU target [1], having a major impact on markets. The \ngrowth of renewable energy sources is, of course, highly beneficial to the environment but \nintroduces intermittency into grid management. This rapid growth in variable generation is \ndriving the need for a more flexible power system and for a research and development \nstrategy to help achieve that. In such new energy scenario, the combined cycle gas turbines (CCGT) have to change \nthe way they operate: from a baseload to backup [2]. CCGT must now respond rapidly to \nvariations due to renewable supply and demand, requiring a more flexible cyclic pattern of \nuse [3]. Making conventional power plants more flexible can be a key strategy to integrate \nlarge shares of renewables more effectively in power systems [1,2]. 2 Italian electricity scenario The volatility of minimum and maximum prices \nof this region is much higher than in the rest of Italy and the other macro regions [8] compared to 2016), for 12.8% by hydroelectric production and for the remaining 16.3% by \ngeothermal, wind and photovoltaic sources (with an increase of 10.3% respect to the previous \nyear) [7]. The Italian electricity market operates in three different periods: (i) daily market \n(MGP), which trades most of the electricity purchase and sale transactions (ii) intra-day \nmarket (MI) consisting of seven sessions which allow market participants to modify the \nprograms defined in the daily market by sending additional sales or purchase offers and (iii) \nthe market for continuous trading of daily products (MPEG). The electricity system is divided \ninto zones classified into geographical zones, national virtual zones and foreign virtual zones. The geographical zones represent a part of the national network; there are currently six active \nzones: Northern Italy (NORD), Central-Northern Italy (CNOR), Central-Southern Italy \n(CSUD), Southern Italy (SUD), Sicily (SICI) and Sardinia (SARD). The MGP market is a \nmarginal market in which the price and volume of each hour are established from the point \nof equilibrium between supply and demand. Matched purchase offers referring to units of \nconsumption belonging to Italian geographical areas are valued at the single national price \n(PUN). The PUN price is equal to the average of the prices of the zones, by zonal \nconsumption and represents the purchase price for end customers. In the last years, due to \nthe increase of RES and to a reduction of the natural gas price, the PUN in all the zones has \ndecreased consistently (Figure 2). The Sicily zone, however, seems to be the most interesting \none where to study continuous cooling concept, with an average zonal price always higher \nrespect to the national PUN (+13% in 2017). The volatility of minimum and maximum prices \nof this region is much higher than in the rest of Italy and the other macro regions [8] Figure 2. Annual average zonal price on MGP (left) and monthly average zonal price in 2017 (right) \n[9] Figure 2. Annual average zonal price on MGP (left) and monthly average zonal price in 2017 (right) \n[9] Figure 2. 2 Italian electricity scenario Annual average zonal price on MGP (left) and monthly average zonal price in 2017 (rig\n[9] In this market scenario, the use of a heat pump (HP) to decrease the temperature at the \ncompressor inlet do not depend only on the electricity price but it is also related to the ambient \ntemperature. In the following sections, a statistical analysis to correlate the price and the \nambient temperature is performed, as well as a first order economic analysis of the proposed \nintegrated system (CCGT+HP). 2 Italian electricity scenario As part of the new National Energy Strategy, Italy set itself the goal of the phase out of coal \nby 2025, aiming to expand the share of renewable energy in final energy consumption to 28% \nby 2030 [6]. The Italian wholesale electricity market started to operate as an Exchange in \n2005 with the liberalization of the demand side bidding. In 2017 gross national production \namounting to 295.8 TWh was satisfied for 70.8% by thermoelectric production (+5.0% 2 https://doi.org/10.1051/e3sconf/201911301008 E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 compared to 2016), for 12.8% by hydroelectric production and for the remaining 16.3% by \ngeothermal, wind and photovoltaic sources (with an increase of 10.3% respect to the previous \nyear) [7]. The Italian electricity market operates in three different periods: (i) daily market \n(MGP), which trades most of the electricity purchase and sale transactions (ii) intra-day \nmarket (MI) consisting of seven sessions which allow market participants to modify the \nprograms defined in the daily market by sending additional sales or purchase offers and (iii) \nthe market for continuous trading of daily products (MPEG). The electricity system is divided \ninto zones classified into geographical zones, national virtual zones and foreign virtual zones. The geographical zones represent a part of the national network; there are currently six active \nzones: Northern Italy (NORD), Central-Northern Italy (CNOR), Central-Southern Italy \n(CSUD), Southern Italy (SUD), Sicily (SICI) and Sardinia (SARD). The MGP market is a \nmarginal market in which the price and volume of each hour are established from the point \nof equilibrium between supply and demand. Matched purchase offers referring to units of \nconsumption belonging to Italian geographical areas are valued at the single national price \n(PUN). The PUN price is equal to the average of the prices of the zones, by zonal \nconsumption and represents the purchase price for end customers. In the last years, due to \nthe increase of RES and to a reduction of the natural gas price, the PUN in all the zones has \ndecreased consistently (Figure 2). The Sicily zone, however, seems to be the most interesting \none where to study continuous cooling concept, with an average zonal price always higher \nrespect to the national PUN (+13% in 2017). 3 Statistical Analysis The distribution of the electricity price plays a major role over the profitability of CCGT \npower plants, presenting an average production cost, i.e. the Cost of Electricity, COE, aligned \nwith the average value of the PUN. The overall profitability of the proposed power \naugmentation application, continuous cooling by a HP, can be related to the electricity peak \nabsolute price and to the temperature and price peak coupling. In a broader approach, \nconsidering the use of a Thermal Energy Storage (TES) to accumulate the cold energy during \nthe off-peak periods, also the respective temperature and price minima must be considered. The daily temperature and price variation can increase the benefit related to TES installation, \nin particular when electrical price and temperatures maxima or minima occurs at the same 3 E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 https://doi.org/10.1051/e3sconf/201911301008 time. Also the width of the positive and negative peaks, i.e how many hours can be accounted \nas “high range” and “low range” can be related to the system profitability. Such extreme \nconditions were defined with respect to the daily variation ∆dailyx = max(𝑥) −min⁡(𝑥), as: time. Also the width of the positive and negative peaks, i.e how many hours can be accounted \nas “high range” and “low range” can be related to the system profitability. Such extreme \nconditions were defined with respect to the daily variation ∆dailyx = max(𝑥) −min⁡(𝑥), as: 𝐻𝑖𝑔ℎ⁡𝑟𝑎𝑛𝑔𝑒𝑥= [max(𝑥) −∆dailyx\n20\n, max(𝑥)] \n⁡⁡⁡⁡𝐿𝑜𝑤⁡𝑟𝑎𝑛𝑔𝑒𝑥= [min(𝑥) , min(𝑥) + ∆dailyx\n20\n] \n(1) (1) This definition allows preserving at least 90% of the ∆dailyx between high and low range \nof the variable, as depicted in Figure 3 for the temperature for a representative day. The points \nwith the x markers and ▼ are respectively for high and low range and are counted to \nevaluated the peak extension, in this case respectively 4 and 5 hours. Figure 3. Example of the peak analysis: red line, ambient temperature [°C; x high range value; ▼ low \nrange value \n∆dailyT \nHigh⁡rangeT \nLow⁡rangeT \n90%⁡∆dailyT Low⁡rangeT Figure 3. Example of the peak analysis: red line, ambient temperature [°C; x high range value; ▼ low \nrange value The statistical analysis considers six price zones and six geographical location, as \nreported in Table 1, while price data were acquired by the Italian Gestore del Mercato \nElettrico [9]. Table 1. Meteorological data measurement point [10] Table 1. 3 Statistical Analysis Meteorological data measurement point [10] \nZones \nLocation \nPosition [N lat., E long.] \nAltitude [m] \nNorth \nTurin / Bric della Croce (airport) \n45° 02', 07° 44' \n709 \nCNorth \nAncona / Falconara (airport) \n43° 37', 13° 22' \n12 \nCSouth \nRome / Fiumicino (airport) \n41° 48', 12° 14' \n2 \nSouth \nBari \n41° 8.17', 16° 45.78' \n42 \nSard \nCapo Caccia \n40° 34', 08° 10' \n200 \nSici \nTrapani-Birgi (airport) \n37° 54.7’, 12° 29.6' \n7 Figure 4 represents the average maximum and the minimum value of the electrical price \nwithin the six Italian zones. The size of the marker is proportional to the number of hours in \nthe high and low range (i.e. the peak extension). The distance between the two marker \ncentres, in the same month, is equal to the average ∆dailyx. The maximum price values are \nregistered in the North and in Center North during the first two months of the year, while the \nSouth presents always lower prices. This could be related to the high number of CCGT 4 E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 https://doi.org/10.1051/e3sconf/201911301008 production sites and wind farm installed in a region with a relative low power demand. It can \nbe noticed that the prices in all the zones experience a reduction until April (loosing up to 54 \n€/MWh) and then a recovery. The maximum price of Sicily shows a less pronounced \nreduction in price until April (Pmax=85.6 €/MWh) and then remains above 90 €/MWh. In \nthis zone, also the width of the peak price is different respect to the other zones in Italy. In \nSicily, the average of the high range is up to 6h/day during the hot season and the yearly \naverage is around 4 hours versus the average of 1.66 h/day of the other zones. Looking at the \ndaily price variation, for the first five zones the changes appear to be related to a seasonal \ntrend with a strong reduction during the hot months. production sites and wind farm installed in a region with a relative low power demand. It can \nbe noticed that the prices in all the zones experience a reduction until April (loosing up to 54 \n€/MWh) and then a recovery. The maximum price of Sicily shows a less pronounced \nreduction in price until April (Pmax=85.6 €/MWh) and then remains above 90 €/MWh. 3.1 Peak coupling of the ambient temperature and the electrical price 3.1 Peak coupling of the ambient temperature and the electrical price Under the proposed application perspective, another important effect that increases the \nbenefit of the inlet cooling system, is the location of the price and temperature peak within \nthe daytime. Figure 5. Monthly averaged daily peak positions: blue circle Zonal Electricity Price peak (max) value \n(size proportional to price), blue lines price peak extension; red circle ambient temperature peak (max) \nvalue (size proportional to temperature), red lines temperature peak extension Figure 5. Monthly averaged daily peak positions: blue circle Zonal Electricity Price peak (max) value \n(size proportional to price), blue lines price peak extension; red circle ambient temperature peak (max) \nvalue (size proportional to temperature), red lines temperature peak extension It can be noticed that the maximum daily temperature is registered around the first two \nhours after noon, apart from the northern locations in which the peak is delayed up to 5 pm. The duration of the peak is intense in Sicily (a year average of 3.1 h/day) and in Center-North \nlocations (2.89 h/day), with a slightly higher value in the summer months. The price peaks occur later in the day moving from North to South, from 5pm in the North \nzone and 5.30pm in the Center-North, to 6pm in the South. The Sicily zone differs also for \nthis parameter, with an average price peak that occurs at 7.25pm. However, the price enters \nthe high range at about 6pm, with a longer persistence in the summer months up to 11pm. The temperature and price peaks appear to be separated in time of 1-2 hours, except for the \nnorthern regions in which there is a partial overlap. 3 Statistical Analysis In \nthis zone, also the width of the peak price is different respect to the other zones in Italy. In \nSicily, the average of the high range is up to 6h/day during the hot season and the yearly \naverage is around 4 hours versus the average of 1.66 h/day of the other zones. Looking at the \ndaily price variation, for the first five zones the changes appear to be related to a seasonal \ntrend with a strong reduction during the hot months. Figure 4. Monthly averaged max daily peak and min off-peak zonal price, the size of the marker is \nproportional to the peak extension hours. Figure 4. Monthly averaged max daily peak and min off-peak zonal price, the size of the marker is \nproportional to the peak extension hours. The daily ambient temperature variation, which is evaluated as the difference between \nthe maximum and the minimum temperature value in the same month, presented in Table 2, \nshows a trend that increases during summer due to the increase of the solar radiance, except \nfor the South location. This is beneficial to the TES application, as they are usually employed \nduring the hot months. The monthly average maximum and minimum temperatures, not \nreported here, as expected, increase during the summer period. Table 2. Monthly average daily variation of ambient Temperature [K] Table 2. Monthly average daily variation of ambient Temperature [K] \nZones \nJan \nFeb \nMar \nApr \nMay \nJun \nJul \nAug \nSep \nOct \nNov \nDec \nNorth \n4.0 \n3.5 \n5.5 \n6.8 \n7.0 \n7.0 \n6.7 \n6.3 \n5.8 \n4.8 \n3.3 \n3.5 \nCNorth \n6.7 \n7.7 \n10.2 \n10.1 \n9.6 \n10.3 \n10.3 \n10.3 \n8.5 \n9.8 \n8.0 \n8.5 \nCSouth \n9.0 \n8.4 \n10.2 \n10.7 \n10.8 \n9.7 \n10.5 \n11.1 \n8.9 \n10.7 \n8.9 \n9.4 \nSouth \n5.0 \n6.1 \n6.0 \n6.3 \n5.6 \n5.3 \n5.3 \n6.0 \n6.9 \n6.4 \n6.5 \n5.7 \nSard \n4.5 \n4.5 \n4.6 \n4.9 \n5.5 \n5.4 \n6.0 \n6.5 \n4.4 \n4.3 \n3.5 \n3.1 \nSici \n7.1 \n7.7 \n9.0 \n9.2 \n9.0 \n10.5 \n9.2 \n10.0 \n7.4 \n7.6 \n6.8 \n6.7 5 5 E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 https://doi.org/10.1051/e3sconf/201911301008 SUPEHR19 Volume 1 4 Economic analysis The continuous inlet cooling system performance were calculated taking into account five \ndifferent heat-pump (HP) sizes [1, 3, 4, 5 and 7.5 MWe] and the six geographical zones \ndescribed in the previous section. Such locations were used as possible sites for the 6 6 6 E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 https://doi.org/10.1051/e3sconf/201911301008 installation of the CC+HP layout to perform the continuous cooling of the turbine intake to \nenhance the performance of the system and so evaluate the earnings that such system would \nguarantee. The HP are considered to operate to reach a target inlet temperature of the GT \nintake of 5°C, which has been identified as the minimum reachable temperature without the \nice formation risk. installation of the CC+HP layout to perform the continuous cooling of the turbine intake to \nenhance the performance of the system and so evaluate the earnings that such system would \nguarantee. The HP are considered to operate to reach a target inlet temperature of the GT \nintake of 5°C, which has been identified as the minimum reachable temperature without the \nice formation risk. The CC net power and efficiency dependence on the inlet GT temperature was obtained \nfrom the Gate Cycle calculation presented in [4] and so the earnings that such system would \nproduce over the year, taking 2017 as reference, were computed as: 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠⁡ =⁡ ∑𝐸𝑝𝑟𝑜𝑑,𝑖\n8760\n𝑖=1\n⁡∙⁡(𝑃𝑍,𝑖⁡−⁡(𝐶𝑓𝑖𝑥⁡+⁡𝐶𝑔𝑎𝑠\n𝜂𝐶𝐶\n))⁡; \n⁡⁡𝑖𝑓⁡𝑃𝑧> 𝐶𝑓𝑖𝑥⁡+⁡𝐶𝑔𝑎𝑠\n𝜂𝐶𝐶\n⁡⁡[€] \n(2) (2) Where 𝐸𝑝𝑟𝑜𝑑,𝑖 is the energy produced by the CC at the i-th hour as function of the \ntemperature, Pz is the zonal price, Cfix and Cgas represent the fixed costs of the plant and \nthe gas cost based on [9,11], and 𝜂𝐶𝐶 is the CC efficiency as function of the temperature. To \nestimate the cooling potential of a given climatic profile, the concept of Continuous \nEquivalent Cooling Degree Hours (ECDH) is introduced, based on [12], and evaluated as: 𝐸𝐶𝐷𝐻⁡ =⁡ ∑(𝑇𝑎𝑚𝑏,𝑖\n8760\n𝑖=1\n−⁡𝑇𝑟𝑒𝑓)⁡⁡⁡⁡⁡⁡𝑖𝑓⁡⁡𝑇𝑎𝑚𝑏,𝑖>⁡𝑇𝑟𝑒𝑓 \n(3) (3) (3) Where 𝑇𝑟𝑒𝑓 for this study is set to 5 °C for the reasons explained before. From Figure 6b \nit can be observed that the ΔEarnings, calculated as percentage variation from the earnings \nthat the same CC would guarantee in ambient conditions, tend to increase while moving from \nNorth to South of Italy, due to the temperature increase over the year which would allow a \nhigher exploitation of the continuous cooling system proposed. 5 Conclusions The statistical analysis of climatic data and their correlations with energy market condition \nwas performed considering Italian context for six different electricity market zones. The \nanalysis was also carried out on monthly and daily basis introducing a technique to identify \nthe range of extreme values of the daily distribution on the base of the daily price variation. It was highlighted how the daily price variation reduces during the hot months, while the \ntemperature daily variation increases, as expected, due to the increase in solar radiant energy \nduring the summer time. Finally, Sicily market zone, shows a strongly different behaviour, \ncharacterized by high and persistent maximum prices, with a yearly average of 20 €/MWh \nover the other zonal prices. Focusing on the Italian market, the profitability of a continuous inlet cooling system \nbecomes more attractive moving from north to south due to the increase of the average \ntemperatures. On the other side, the South electricity price, the lowest in the peninsula, has a \nlow profitability compared to the meteorological potential. The gain in power of the proposed \nsystem is proportional to the climatic condition summarized by the Continuous Equivalent \nCooling Degree Hours, plus the effect of the zonal price. Under the actual condition the Sicily \nmarket has the largest potential, maximizing both the temperature and electrical price effect. This project has received funding from the European Union’s Horizon 2020 research \nand innovation programme under Grant Agreement No 764706, PUMP-HEAT. (http://www.pumpheat.eu). 4 Economic analysis However, it can also be \nhighlighted that the South area (SUD) presents high ECDH, but low percentage earnings \ncompared to the other areas considered, due to the lower Pz. (a) \n(b) \nFigure 6 : (a) percentage power production and (b) percentage earnings over ambient conditions \nfor five different HP sizes and six different areas of Italy (a) (b) (b) (a) Figure 6 : (a) percentage power production and (b) percentage earnings over ambient conditions \nfor five different HP sizes and six different areas of Italy This trend becomes more evident increasing the HP size over 1 MW, as ΔEarnings \nbecomes lower than all the other areas apart from the North (NORD) one. This effect is 7 7 E3S Web of Conferences 113, 01008 (2019) \nSUPEHR19 Volume 1 https://doi.org/10.1051/e3sconf/201911301008 mostly related to the PUN of the South being lower than the national mean and other areas \nconsidered, which does not allow a good exploitation of the power produced (Figure 6a). mostly related to the PUN of the South being lower than the national mean and other areas \nconsidered, which does not allow a good exploitation of the power produced (Figure 6a). The high PUN of Sicily allows to benefit more from this layout not only thanks to the \nhigher average temperatures, resulting in a maximum ΔEarnings of about 4% using a 7.5MW \nHP. On the other hand, from Figure 6a it can be observed that the percentage power variation \nconsidering Sicily and a 1MW HP is lower than SUD and SARD. This is due to the fact that \ntemperatures in Sicily are particularly high and then a 1MW HP would not be able to reach \nthe target temperature of 5°C, resulting in a power augmentation lower than the actual \npossibility. Indeed, also the worst result, obtained from using a 1MW HP in Northern Italy, \nwould guarantee an increase of earnings of about 0.8% for the year considered. Moreover, it can be highlighted that the lines corresponding to the HPs of 5 and 7.5MW \nbecome very close in terms of earnings, suggesting that when the investment cost comes in \nplace within the calculations, it would probably be more profitable to buy a 5MW HP which \nwould result in lower capital and installation costs. [ ]\ngo\ne g ewe de,\n7,\ny\n.\n[3] \nPrina, M. G., Garegnani, G., Moser, D., Oberegger, U. F., Vaccaro, R., Sparber, \nW., Gazzani, M., and Manzolini, G., 2015, “Economic and Environmental Impact of \nPhotovoltaic and Wind Energy High Penetration towards the Achievement of the Italian 20-\n20-20 Targets,” 2015 10th International Conference on Ecological Vehicles and Renewable \nEnergies, EVER 2015. This project has received funding from the European Union’s Horizon 2020 research \nand innovation programme under Grant Agreement No 764706, PUMP-HEAT. \n(http://www.pumpheat.eu). [4] \nSorce, A., Giugno, A., Marino, D., Piola, S., and Guedez, R., 2019, “Analysis of a [1] \nEuropean Parliament, 2018, Directive (EU) 2018/2001 of the European \nParliament and of the Council of 11 December 2018 on the Promotion of the Use of Energy \nfrom Renewable Sources. [1] \nEuropean Parliament, 2018, Directive (EU) 2018/2001 of the European \nParliament and of the Council of 11 December 2018 on the Promotion of the Use of Energy \nfrom Renewable Sources. \n[2] \nAgora Energiewende, 2017, Flexibility in Thermal Power Plants. \n[3] \nPrina, M. G., Garegnani, G., Moser, D., Oberegger, U. F., Vaccaro, R., Sparber, \nW., Gazzani, M., and Manzolini, G., 2015, “Economic and Environmental Impact of \nPhotovoltaic and Wind Energy High Penetration towards the Achievement of the Italian 20-\n20-20 Targets,” 2015 10th International Conference on Ecological Vehicles and Renewable \nEnergies, EVER 2015. \n[4] \nSorce, A., Giugno, A., Marino, D., Piola, S., and Guedez, R., 2019, “Analysis of a References [1] \nEuropean Parliament, 2018, Directive (EU) 2018/2001 of the European \nParliament and of the Council of 11 December 2018 on the Promotion of the Use of Energy \nfrom Renewable Sources. [1] \nEuropean Parliament, 2018, Directive (EU) 2018/2001 of the European \nParliament and of the Council of 11 December 2018 on the Promotion of the Use of Energy \nfrom Renewable Sources. [2] \nAgora Energiewende, 2017, Flexibility in Thermal Power Plants. [3] \nPrina, M. G., Garegnani, G., Moser, D., Oberegger, U. F., Vaccaro, R., Sparber, \nW., Gazzani, M., and Manzolini, G., 2015, “Economic and Environmental Impact of \nPhotovoltaic and Wind Energy High Penetration towards the Achievement of the Italian 20-\n20-20 Targets,” 2015 10th International Conference on Ecological Vehicles and Renewable \nEnergies, EVER 2015. 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