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26 AEP’s Climate Impact Analysis.
LOAD FORECASTING.
The first step in creating a resource plan is to forecast, or predict, future customer load. Load forecasting plays an important role in power system planning, operation and control. Load forecasts include a series of underlying forecasts that build on one another. An economic forecast provided by Moody’s Analytics is used to develop a customer forecast, which is then used to develop a sales forecast, which is ultimately used to develop a peak load and internal energy requirements forecast. In the case of the 100% Clean Energy and Fast Transition scenarios, the forecasts were adjusted to account for potential changes in load that could result from changes in public policy and/or consumer behavior. The load forecasts were developed using a combination of external data and internal resources.
These forecasts take into account various changes in the economy, households, appliance efficiency, energy use, and consumer behavior over time. The impacts of energy efficiency (EE) and demand-side management (DSM) are also embedded within the forecasts. AEP included more optimistic assumptions about DSM/EE measures in both the Fast Transition and 100% Clean Energy scenarios. We rationalized that the transition to a clean energy economy would foster increased adoption of these measures, either mandated or induced.
Key Takeaways.
The load scenarios produced some unique load changes with different assumptions. First, within the Fast Transition and 100% Clean Energy scenarios, overall load initially declines from the Business As Usual scenario. This can be attributed to assumed reduction in overall fossil fuel demand consistent with the clean energy transition. AEP’s service territory has a high concentration of fossil fuel extractive and processing industries, and, as they reduce their output, electricity consumption also declines. The indirect impacts include reductions in labor force and associated wages, disposable income and other economic activity as these industries reduce operations or shut down completely. The Fast Transition and 100% Clean Energy scenarios include more aggressive assumptions for increased energy efficiency adoption, which also reduces demand.
At the same time, the scenarios show countercurrents that drive increased demand as electrification grows. Presuming carbon emissions mitigation/reduction will accelerate, we assumed that increased adoption, deployment and use of electric vehicles (EVs) would follow. It is expected this would occur through a.
Millions of MWh.
AEP Vertically Integrated Load 112 110 108 106 104 102 100 98 96 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050.
BAU Fast Transition 100% Clean Energy.
Technology Chairman’s Message Introduction and TCFD Framework Transition Analysis Just Transition.
Physical Risks and Opportunities
27 AEP’s Climate Impact Analysis combination of public policy, economics and consumer preference.
In the 100% Clean Energy scenario, 100% of light-duty vehicles are assumed to be replaced by EVs by 2050. However, the speed of the change will be gradual. It is likely to take decades to achieve full conversion due to the long turnover rate of conventional vehicle stock. In addition, AEP’s service territory is more rural in nature with household incomes that are largely below the national average — factors likely to affect the pace of EV adoption.
FUNDAMENTALS FORECAST.
Underlying the resource planning process is the Fundamentals Forecast, which is a long-term, weathernormalized commodity market forecast. AEP’s operating companies use the Fundamentals Forecast for fixed asset impairment accounting, capital improvement analyses, resource planning and strategic planning. These projections cover the electricity market within the Eastern Interconnect, which includes the Southwest Power Pool and PJM, where AEP’s vertically integrated utilities are located. The Fundamentals Forecast includes: • Monthly and annual regional power prices (in both nominal and real dollars) • Prices for various qualities of Central Appalachian (CAPP), Northern Appalachian (NAPP), Illinois Basin (ILB), Powder River Basin (PRB) and Colorado coals • Monthly and annual locational natural gas prices, including the benchmark Henry Hub • Uranium fuel prices • Sulfur dioxide (SO2), nitrogen oxide (NOx) and carbon dioxide (CO2) values • Locational implied heat rates • Electric generation capacity values • Renewable energy subsidies • Inflation.
The primary tool used for the development of the North American long-term energy market pricing forecasts is the Aurora energy market simulation model. The Aurora model is widely used by utilities for integrated resource and transmission planning, power cost analysis and detailed generator evaluation. It iteratively generates zonal, but not company-specific, long-term capacity expansion plans, annual energy dispatch, fuel burns, and emissions totals from inputs that include fuel, load, emissions, and capital costs, among others. Ultimately, Aurora creates a weather-normalized, long-term forecast of the market in which a utility operates. We also have access to extensive energy market research information, including third-party consultants, industry groups, governmental agencies, trade press, investment community, internal expertise, various stakeholders and others.
Overview of Aurora Modeling Process.
Input.
Iterate.
Output.
REPORT GENERATION: • Zonal Market Prices • Fuels Consumption • Emission Totals.
Fuels Forecast.
Load Forecast.
Emissions Forecast and Retrofits.
Long-term Capacity Expansion.
Hourly Optimization.
Annual Dispatch Capital Costs.
Technology Chairman’s Message Introduction and TCFD Framework Transition Analysis Just Transition.
Physical Risks and Opportunities
Average Power Prices $100 $80 $60 $40 $20 0
2030 2040 2050 2030 2040 2050.
PJM SPP.
Base Fast Transition.
Natural Gas Price — Henry Hub $8.00 $7.00 $6.00 $5.00 $4.00 $3.00 $2.00 $1.00 0
2025 2030 2035 2040 2045 2050 $/MMBtu $/MWh 28 AEP’s Climate Impact Analysis.
For purposes of this analysis, AEP mirrored the assumptions of the Annual Energy Outlook 2020 for most available parameters. The resulting natural gas prices and energy prices are shown in the following charts. AEP dispatches energy into two Regional Transmission Operators (RTOs) — PJM and SPP.
The BAU Fundamentals Forecast employed a CO2 dispatch burden on all existing fossil-fueled generating units that escalates 3.5% per year from $15 per metric ton starting in 2028. The direct effect of a $15 per metric ton allowance price is equivalent to ~$15 per MWh increase in operating costs for a coal unit and $6 per MWh for a natural gas-fired combined cycle unit. The CO2 burden was increased to $30 per metric ton in the Fast Transition scenario. The increase in carbon prices results in an uptick in power prices between the two scenarios.
MODELING 100% CLEAN ENERGY.
We made the decision to defer modeling a 100% Clean Energy Scenario because the tool we use to project power market implications and pricing (Aurora model) became bogged down and was unable to solve the complexity of the scenario despite numerous attempts. The issue was rooted in not having adequate resources available to ramp up and down during the course of a day to meet swings in demand and to smooth out the variability of renewables. While energy storage was an.
Technology Chairman’s Message Introduction and TCFD Framework Transition Analysis Just Transition.
Physical Risks and Opportunities
29 AEP’s Climate Impact Analysis allowed solution within the model, the added complexity of trying to balance demand with intermittent resources severely impacted the model’s ability to solve.
The scenario required zero carbon emissions from generation by 2050, and in some states before 2050, with no opportunity for offsets and insufficient information on the costs and efficiency of anticipated new carbon-free technologies. The results created more questions than answers, producing an unrealistic market dispatch and prices. There also were challenges with using a 100% clean energy constraint without causing unintended consequences, such as negative energy prices. These challenges highlighted the current policy and regulatory framework governing bulk electric supply that will need to be significantly adjusted moving forward.
Following several attempts to adjust the model, and given that our other two scenarios were consistent with a lowemission future, we opted to stop modeling this scenario until we can further refine our tools and assumptions. We intend to pursue future modeling of this scenario as part of our ongoing analysis of climate change impacts to AEP, which will be integrated with our strategic planning and enterprise risk management processes.
RESOURCE PLANNING / GENERATION SCENARIOS.
The results of the Aurora model were used to populate a second model that allows us to evaluate generation supply options available to meet customer load. The Plexos® LP long-term optimization model, also known as “LT Plan®,” enables us to evaluate capacity requirements for each of the three scenarios. The LT Plan® finds the optimal portfolio of future capacity and energy resources by finding a solution that minimizes generation costs over the planning horizon. By minimizing costs, the model will provide optimized portfolios with the lowest and most stable customer rates while adhering to the company’s constraints. Low, stable electricity rates benefit all customers and regional economies by attracting new and retaining/ expanding commercial and industrial customers.
To achieve the best solution, Plexos® is designed to minimize the aggregate of capital and production-related (energy) costs of the resource portfolio. These include: • Fixed costs of capacity additions, i.e., carrying charges on incremental capacity additions (based on a weighted average cost of capital), and fixed Operation & Maintenance (O&M) costs; • Fixed costs of any capacity purchase; • Variable costs associated with AEP’s generating units. This includes fuel, start-up, consumables, market replacement cost of emission allowances, and/or carbon “tax” and variable O&M costs; • Distributed resources, which were valued at the equivalent of a full-retail “net metering” credit to those customers; and • A “netting” of the production revenue earned in the PJM and SPP regional power markets from AEP generation resource sales and the cost of energy — based on unique load shapes from PJM and SPP and purchases necessary to meet AEP’s load obligation.
In addition, Plexos® takes into account the following possible constraints that must be met: • Minimum and maximum reserve margins; • Resource additions (i.e., maximum number of units built); • Age and lifetime of power generation facilities; • Operation constraints such as ramp rates, minimum up/down times, capacity, heat rates, etc.
• Fuel burn minimums and maximums; • Emissions limits on effluents such as SO2 and NOx; and • Purchased power contract parameters, such as energy and capacity.
Note: The Plexos® simulation tool does not develop a full regulatory Cost-of-
Service (COS) or retail rate profile. Rather, it is a tool that considers the relative load and generation changes from a scenario. Fixed “embedded” costs associated with existing generating capacity and demand-side programs and changes to transmission and distribution requirements and costs, while important to customers and regulators, are not included.
Technology Chairman’s Message Introduction and TCFD Framework Transition Analysis Just Transition.
Physical Risks and Opportunities
Coal Retirements*
Unit Fast Transition BAU.
Amos 1 2035 2040.
Amos 2 2035 2040.
Amos 3 2035 2040.
Dolet Hills 2021.
Flint Creek 2033 2038.
Mitchell 1 2035 2040.
Mitchell 2 2035 2040.
Mountaineer 2035 2040.
Northeastern 3 2026.
Pirkey 2023.
Rockport 1 2028.
Rockport 2 **
Turk 2040 2067.
Welsh 1 2028.
Welsh 3 2028 * Retirement occurs by end of listed year and dates prior to 2030 same across both cases ** Lease of unit assumed to be terminated per I&M IRP in 2022 30 AEP’s Climate Impact Analysis.
EXISTING CAPACITY RESOURCES.
Resource planning requires a demonstration of the capacity resource requirements. This “needs” assessment must consider projections of: • Existing capacity resource — current levels and anticipated changes; • Anticipated changes in capability due to efficiency and/or environmental considerations; • Changes resulting from decisions surrounding unit disposition evaluations; • Regional and sub-regional capacity and transmission constraints/limitations; • Load and peak demand; • Current demand response/energy efficiency; and • RRO capacity reserve margin and reliability criteria.
Note: RRO is the Regulated Rate Option, similar to the traditional month-to- month method of paying for electricity.
The following chart shows the current coal resources available in the AEP scenarios. Of particular note, the Fast Transition scenario assumes that additional coalfueled resources are retired prior to the end of their current expected lives. Any decision to retire remaining coal-fueled units prior to the end of their book life would be subject to regulatory approval.
For the purposes of this analysis, in addition to the coalfueled generation retirements, we assumed that natural gas assets are retired at the end of their currently projected useful lives. Most of these assets are used in large part for their capacity attributes and do not run often enough to significantly contribute to overall emissions.
The objective of a resource planning effort is to recommend a system resource expansion plan that balances “least-cost” objectives with planning flexibility, asset mix considerations, adaptability to risk, and conformance with applicable NERC and RTO criteria. In addition, the planning effort must ultimately align with anticipated long-term requirements established by the EPA-driven environmental compliance planning process. Resources selected through the modeling process are not location specific. The three scenarios assume compliance with all environmental regulations that were final and in the Federal Register as of January 1, 2021. The only differences in environmental assumptions between the scenarios are around the constraints put on carbon or clean energy to allow for the evaluation of carbon transition pathways.
NEW CAPACITY RESOURCES.
New generation options available to Plexos® were the same as the Aurora model. The following table highlights the parameters modeled.
For this study, we took a unique approach to addressing less-well-defined energy technologies and opted to model.
Technology Chairman’s Message Introduction and TCFD Framework Transition Analysis Just Transition.
Physical Risks and Opportunities
AEP System New Generation Technologies ( key supply-side resources option assumptions 1, 2, 3, 4 )
Capability (MW) 5 Installed.
Cost 4, 6 Capacity LCOE 7.
Type Std. ISO Summer Winter $/kW) Factor % $/MWh.
Base Load.
Small modular reactor nuclear power plant, 600 MW 600 580 630 7,700 90 135.9.