A Network-Constrained Demand Response Game for Procuring Energy Balancing Services

TL;DR

The paper addresses securing and improving the efficiency of energy balancing in distribution networks under privacy constraints by formulating a network-constrained demand response problem as a Generalized Nash Game ($GNG$). It adopts supply-function bidding and a privacy-preserving, decentralized market clearing based on a preconditioned forward-backward splitting method, proving existence and uniqueness of the variational $GNE$ and deriving an analytical bound on efficiency loss (PoA). The approach explicitly enforces AC power-flow constraints via a DSO-enabled bid adjustment mechanism, ensuring network feasibility and voltage/line security, with convergence guarantees to the $v$-GNE and scalable performance demonstrated on IEEE 33-bus and 69-bus systems. Results indicate that the proposed mechanism achieves near-optimal efficiency and preserves privacy, while illustrating the crucial role of incorporating physical network constraints for secure operation and resilience to faults.

Abstract

Securely and efficiently procuring energy balancing services in distribution networks remains challenging, especially within a privacy-preserving environment. This paper proposes a network-constrained demand response game, i.e., a Generalized Nash Game (GNG), to incentivize energy consumers to offer balancing services. Specifically, we adopt a supply function-based bidding method for our demand response problem, where a requisite load adjustment must be met. To ensure the secure operation of distribution networks, we incorporate physical network constraints, including line capacity and bus voltage limits, into the game formulation. In addition, we analytically evaluate the efficiency loss of this game. Previous approaches to steer energy consumers toward the Generalized Nash Equilibrium (GNE) of the game often necessitated sharing some private information, which might not be practically feasible or desired. To overcome this limitation, we propose a decentralized market clearing algorithm with analytical convergence guarantees, which only requires the participants to share limited, non-sensitive information with others. Numerical analyses illustrate that the proposed market mechanism exhibits a low market efficiency loss. Moreover, these analyses highlight the critical role of integrating physical network constraints. Finally, we demonstrate the scalability of our proposed algorithm by conducting simulations on the IEEE 33-bus and 69-bus test systems.

Figures (14)

• Figure 1: The connection among market participants
• Figure 2: An example of physical network
• Figure 3: IEEE 33-bus distribution network
• Figure 4: The LI and PoA in Scenario 1
• Figure 5: The LI and PoA in Scenario 2

Theorems & Definitions (16)

• Remark 1
• Remark 2
• Lemma 1
• Proposition 1
• proof
• Remark 3
• Remark 4
• Lemma 2
• Remark 5
• Proposition 2