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Transactive Local Energy Markets Enable Community-Level Resource Coordination Using Individual Rewards

Daniel C. May, Petr Musilek

Abstract

ALEX (Autonomous Local Energy eXchange) is an economy-driven, transactive local energy market where each participating building is represented by a rational agent. Relying solely on building-level information, this agent minimizes its electricity bill by automating distributed energy resource utilization and trading. This study examines ALEX's capabilities to align participant and grid-stakeholder interests and assesses ALEX's impact on short- and long-term intermittence using a set of community net-load metrics, such as ramping rate, load factor, and peak load. The policies for ALEX's rational agents are generated using dynamic programming through value iteration in conjunction with iterative best response. This facilitates comparing ALEX and a benchmark energy management system, which optimizes building-level self-consumption, ramping rate, and peak net load. Simulations are performed using the open-source CityLearn2022 dataset to provide a pathway for benchmarking by future studies. The experiments demonstrate that ALEX enables the coordination of distributed energy resources across the community. Remarkably, this community-level coordination occurs even though the system is populated by agents who only access building-level information and selfishly maximize their own relative profit. Compared to the benchmark energy management system, ALEX improves across all metrics.

Transactive Local Energy Markets Enable Community-Level Resource Coordination Using Individual Rewards

Abstract

ALEX (Autonomous Local Energy eXchange) is an economy-driven, transactive local energy market where each participating building is represented by a rational agent. Relying solely on building-level information, this agent minimizes its electricity bill by automating distributed energy resource utilization and trading. This study examines ALEX's capabilities to align participant and grid-stakeholder interests and assesses ALEX's impact on short- and long-term intermittence using a set of community net-load metrics, such as ramping rate, load factor, and peak load. The policies for ALEX's rational agents are generated using dynamic programming through value iteration in conjunction with iterative best response. This facilitates comparing ALEX and a benchmark energy management system, which optimizes building-level self-consumption, ramping rate, and peak net load. Simulations are performed using the open-source CityLearn2022 dataset to provide a pathway for benchmarking by future studies. The experiments demonstrate that ALEX enables the coordination of distributed energy resources across the community. Remarkably, this community-level coordination occurs even though the system is populated by agents who only access building-level information and selfishly maximize their own relative profit. Compared to the benchmark energy management system, ALEX improves across all metrics.
Paper Structure (13 sections, 28 equations, 5 figures, 1 table, 2 algorithms)

This paper contains 13 sections, 28 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Related Literature
  4. Autonomous Local Energy Exchange (ALEX)
  5. Markov Decision Processes
  6. Methodology
  7. Study Hypotheses
  8. ALEX as a Markov Decision Process
  9. Simulation of Rational Agents for ALEX
  10. Evaluation Methodology
  11. Results and Discussion
  12. Summary and Conclusion
  13. Additional Information on Experiments

Figures (5)

  • Figure 1: Average daily community net loads in kWh at hourly resolution for a full simulation on CityLearn2022 data set CityLearn2022Data for NoDERMS, IndividualDERMS, and ALEX scenarios. The plot displays both the average values and the standard deviation bands.
  • Figure 2: Average daily community $\textit{SoC}$ values are presented at hourly resolutions for a full simulation on the CityLearn 2022 dataset CityLearn2022Data, encompassing NoDERMS, IndividualDERMS, and ALEX scenarios. The figure displays both the average values and the standard deviation bands.
  • Figure 3: Average daily net loads in kWh at hourly resolution for winter, spring, summer, and fall in a full simulation on the CityLearn 2022 data set CityLearn2022Data are presented for NoDERMS, IndividualDERMS, and ALEX scenarios. The figures display average values along with standard deviation bands.
  • Figure 4: Average daily SoCs at hourly resolutions for winter, spring, summer and fall of a full simulation on CityLearn 2022 data set CityLearn2022Data for NoDERMS, IndividualDERMS and ALEX scenarios. Shown are the average values as well as the standard deviation bands.
  • Figure 5: Average cumulative building bill for a full simulation on CityLearn 2022 data set CityLearn2022Data for NoDERMS, IndividualDERMS and ALEX scenarios. In the depicted scenario, ALEX has access to the full profitability gap.