Purdue University Graduate School

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Decentralized Integration of Distributed Energy Resources into Energy Markets with Physical Constraints

posted on 2024-05-29, 18:58 authored by Chen FengChen Feng

With the growing installation of distributed energy resources (DERs) at homes, more residential households are able to reduce the overall energy cost by storing unused energy in the storage battery when there is abundant renewable energy generation, and using the stored energy when there is insufficient renewable energy generation and high demand. It could be even more economical for the household if energy can be traded and shared among neighboring households. Despite the great economic benefit of DERs, they could also make it more challenging to ensure the stability of the grid due to the decentralization of agents' activities.

This thesis presents two approaches that combine market and control mechanisms to address these challenges. In the first work, we focus on the integration of DERs into local energy markets. We introduce a peer-to-peer (P2P) local energy market and propose a consensus multi-agent reinforcement learning (MARL) framework, which allows agents to develop strategies for trading and decentralized voltage control within the P2P market. It is compared to both the fully decentralized and centralized training & decentralized execution (CTDE) framework. Numerical results reveal that under each framework, the system is able to converge to a dynamic balance with the guarantee of system stability as each agent gradually learns the approximately optimal strategy. Theoretical results also prove the convergence of the consensus MARL algorithm under certain conditions.

In the second work, we introduce a mean-field game framework for the integration of DERs into wholesale energy markets. This framework helps DER owners automatically learn optimal decision policies in response to market price fluctuations and their own variable renewable energy outputs. We prove the existence of a mean-field equilibrium (MFE) for the wholesale energy market, and we develop a heuristic decentralized mean-field learning algorithm to converge to an MFE, taking into consideration the demand/supply shock and flexible demand. Our numerical experiments point to convergence to an MFE and show that our framework effectively reduces peak load and price fluctuations, especially during exogenous demand or supply shocks.


Degree Type

  • Doctor of Philosophy


  • Industrial Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Andrew Lu Liu

Additional Committee Member 2

Dr. Vaneet Aggarwal

Additional Committee Member 3

Dr. Dionysios Aliprantis

Additional Committee Member 4

Dr. Harsha Honnappa

Additional Committee Member 5

Dr. Xiaojun Lin

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