Policy, Markets, and Analytics

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    Aggregator-Enabled Prosumers' Impact on Strategic Hydro-Thermal Operations
    ( 2023-01-03) Hassanzadeh Moghimi, Farzad ; Chen, Yihsu ; Siddiqui, Afzal
    Climate packages envisage decarbonization of the power system and electrification of the wider economy via variable renewable energy (VRE). These trends facilitate the rise of aggregator-enabled prosumers and engender demand for flexibility. By exploiting conducive geography, e.g., in the Nordic region, hydro reservoirs can mitigate VRE's intermittency. Nevertheless, hydro producers may leverage this increased need for flexibility to exert market power through temporal arbitrage. Using a Nash-Cournot model, we examine how aggregator-enabled prosumers with endogenous loads and VRE capacity interact with other agents to affect market outcomes. Based on Nordic data, we find that hydro producers enhance their market power by shifting their production away from periods in which prosumers are net buyers and "dumping" their output during periods in which prosumers are net sellers. Hence, jurisdictions that rely upon (hydro) storage to integrate VRE from prosumers will need to be wary of incumbent firms' incentives to manipulate prices.
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    The Pace of Decarbonization: Can the Power System Transition Meet Climate Policy Goals?
    ( 2023-01-03) Metz, Lucy ; Cardell, Judith
    To reach net zero greenhouse gas emissions by 2050, the United States will need to simultaneously expand and decarbonize its electricity supply. Aggressive clean energy policies are necessary for the pace of the transition to meet this goal. Policymakers rely on computer modeling to inform decarbonization policies, even though the models were not developed for this purpose. This paper investigates the role of electricity modeling in climate policy design through a case study of Massachusetts. The analysis compares modeling results with recent energy projects in order to highlight the strengths and weaknesses of power sector modeling as a tool to inform policy making. The results show that modeling is useful for identifying technically feasible options and for comparing them based on quantifiable indicators. Models are incapable of identifying socially optimal solutions and estimating achievable pace of decarbonization, because they omit social factors that affect decarbonization goals.
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    Valuing Technological Flexibility in Low Carbon Electricity Portfolios
    ( 2023-01-03) Blumsack, Seth
    With the increased focus on responding to climate change by accelerating a transition to a low carbon energy system, differing views remain on the combination of energy technologies that will best achieve this goal. Identifying technological pathways is complicated by wide uncertainties in economic and technological factors. Analyses that neglect these uncertainties can produce pathways for a low carbon energy future that are highly granular and specific, but which are based on a particular assumption about future conditions and imply a need to make specific technology commitments over a long period of time. We frame the energy transition problem as the identification of one near-term investment strategy that is flexible across a wide range of possible future costs, followed by many alternative subsequent investment plans, each of which responds to realized future costs to achieve an aggressive emissions reduction target. Using an example of planning a low carbon power system under uncertainty, we demonstrate the option value of not ruling out some energy technologies in the near-term.
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    A Wake-Up Call for the Utility Industry: Extreme Weather and Fundamental Lessons from 2021
    ( 2023-01-03) Tabors, Richard
    We have examined the critical extreme weather events of 2021 that resulted in disruptions of normal power system operations, the loss of life, and multibillion dollar losses to the US economy. These impacts occurred due to extreme cold, extreme heat, drought, slower post-landfall dissipation of hurricanes, and more intense large-scale thunderstorm systems. We point to the causes but also argue for the changes in planning and operations required to be prepared for and have responses to these events. Specifically, we focus on recognizing the reality of extreme events and planning for their increasing frequency, intensity, duration, and geographic scope; modifying resource planning and adequacy metrics to incorporate common mode events; enabling the power system to depend on reliable natural gas fuel supplies; redesigning power markets to better compensate resources and flexible demand for reducing the probability of outages; and developing resilient systems.
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    Hierarchical Flexibility Offering Strategy for Integrated Hybrid Resources in Real-time Energy Markets
    ( 2023-01-03) Majidi, Majid ; Hosseini, Mohammad Mehdi ; Parvania, Masood
    This paper proposes a hierarchical model for determining the energy flexibility offering strategy of integrated hybrid resources (IHRs) in power distribution systems to participate in real-time energy markets. The proposed model utilizes the scalability, fast response time, and uncertainty observation of deep reinforcement learning (DRL) to overcome the scalability issue of operating numerous flexible resources and deliverability of energy flexibility to the real-time markets in the presence of the network constraints. To that end, the power distribution system is divided into multiple IHRs, where different types of flexible loads, energy storage systems, and solar plants with controllable inverters are operated through local IHR controllers, trained by deep deterministic policy gradient (DDPG) algorithm. Active power request and reactive power capacity of IHRs are then transmitted to a central flexibility controller, where a quadratic optimization model ensures the deliverability of the energy flexibility to the real-time energy market by satisfying the distribution network constraints. The proposed model is implemented on the 123-bus test power distribution system, demonstrating the capability of DRL-based hierarchical model for scalable operation of IHRs in order to offer deliverable energy flexibility to the real-time energy market.
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    A Comparison of Strategies for Managing Energy Constraints of Energy Storage Providing Frequency Regulation
    ( 2023-01-03) Moring, Hannah ; Mathieu, Johanna
    CAISO and PJM operate the majority of grid-connected batteries in the U.S. The two markets manage the energy constraints of batteries providing frequency regulation differently. PJM, which has a fast (RegD) and a slow (RegA) regulation signal, uses RegA to conditionally maintain energy neutrality of the RegD signal over short durations. CAISO offsets net energy produced/consumed for frequency regulation with energy from the real-time energy market. This paper presents a comparison of these strategies with the goal of assessing the advantages and disadvantages of each approach. Specifically, we compare the approaches in terms of regulation signal-following performance and additional system control effort. Case study results suggest that both strategies can reliably keep a battery away from its state of charge limits but that PJM's strategy requires larger energy deviation from base operations.