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A capacity renting framework for shared energy storage considering peer-to-peer energy trading of prosumers with privacy protection

Yingcong Sun, Laijun Chen, Yue Chen, Mingrui Tang, Shengwei Mei

TL;DR

The paper tackles spatial energy imbalances from high DG penetration by integrating peer-to-peer trading into a capacity renting framework for shared energy storage. It models prosumer interactions as a generalized Nash game and proves that the variational equilibrium corresponds to the solution of a centralized quadratic program, enabling a distributed solution. To protect privacy, the authors develop an ADMM-based algorithm that employs Paillier homomorphic encryption, allowing encrypted exchanges among prosumers, the shared ESS operator, and the P2P center while preserving convergence. A case study demonstrates that P2P trading, in conjunction with shared ESS, significantly improves social welfare and reduces total costs, with the privacy-preserving algorithm solving the day-ahead problem in about two minutes. The work provides a scalable, privacy-protecting approach to co-optimizing energy and capacity trades in distribution networks under high renewable penetration.

Abstract

Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers. However, as DG penetration rates rise, spatial energy imbalances become increasingly significant, necessitating the integration of peer-to-peer (P2P) energy trading within the shared ESS framework. Two key challenges emerge in this context: the absence of effective mechanisms and the greater difficulty for privacy protection due to increased data communication. This research proposes a capacity renting framework for shared ESS considering P2P energy trading of prosumers. In the proposed framework, prosumers can participate in P2P energy trading and rent capacities from shared ESS. A generalized Nash game is formulated to model the trading process and the competitive interactions among prosumers, and the variational equilibrium of the game is proved to be equivalent to the optimal solution of a quadratic programming (QP) problem. To address the privacy protection concern, the problem is solved using the alternating direction method of multipliers (ADMM) with the Paillier cryptosystem. Finally, numerical simulations demonstrate the impact of P2P energy trading on the shared ESS framework and validate the effectiveness of the proposed privacy-preserving algorithm.

A capacity renting framework for shared energy storage considering peer-to-peer energy trading of prosumers with privacy protection

TL;DR

The paper tackles spatial energy imbalances from high DG penetration by integrating peer-to-peer trading into a capacity renting framework for shared energy storage. It models prosumer interactions as a generalized Nash game and proves that the variational equilibrium corresponds to the solution of a centralized quadratic program, enabling a distributed solution. To protect privacy, the authors develop an ADMM-based algorithm that employs Paillier homomorphic encryption, allowing encrypted exchanges among prosumers, the shared ESS operator, and the P2P center while preserving convergence. A case study demonstrates that P2P trading, in conjunction with shared ESS, significantly improves social welfare and reduces total costs, with the privacy-preserving algorithm solving the day-ahead problem in about two minutes. The work provides a scalable, privacy-protecting approach to co-optimizing energy and capacity trades in distribution networks under high renewable penetration.

Abstract

Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers. However, as DG penetration rates rise, spatial energy imbalances become increasingly significant, necessitating the integration of peer-to-peer (P2P) energy trading within the shared ESS framework. Two key challenges emerge in this context: the absence of effective mechanisms and the greater difficulty for privacy protection due to increased data communication. This research proposes a capacity renting framework for shared ESS considering P2P energy trading of prosumers. In the proposed framework, prosumers can participate in P2P energy trading and rent capacities from shared ESS. A generalized Nash game is formulated to model the trading process and the competitive interactions among prosumers, and the variational equilibrium of the game is proved to be equivalent to the optimal solution of a quadratic programming (QP) problem. To address the privacy protection concern, the problem is solved using the alternating direction method of multipliers (ADMM) with the Paillier cryptosystem. Finally, numerical simulations demonstrate the impact of P2P energy trading on the shared ESS framework and validate the effectiveness of the proposed privacy-preserving algorithm.

Paper Structure

This paper contains 17 sections, 54 equations, 6 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. System modeling and problem formulation
  3. System description
  4. Models for prosumers' equipment
  5. Capacity renting of shared ESS
  6. Transactions in wholesale market and P2P market
  7. Generalized Nash Game Model
  8. Solution algorithm for Generalized Nash equilibrium
  9. Equivalent transformation to a 2-block QP problem
  10. Algorithm of ADMM with Paillier cryptosystem
  11. Case study
  12. Test system configuration
  13. Benefit of the mechanism
  14. Capacity renting of shared ESS in different cases
  15. Operation results of prosumers
  16. ...and 2 more sections

Figures (6)

  • Figure 1: Profiles of energy and capacity transactions. The solid line means energy and capacity transactions, and the dashed line means data communication about energy and capacity transactions.
  • Figure 2: (a) PV/WT generation of prosumers. Only prosumer 1 is equipped with WTs, and others are equipped with PVs. (b) Loads of prosumers (c) The electricity price
  • Figure 3: Energy stored in the shared ESS of each prosumer (a) case 4: with P2P (b) case 3: without P2P
  • Figure 4: Profiles of energy supply and consumption of (a) prosumer 1 (with WTs) (b) prosumer 9 (with PVs)
  • Figure 5: The value of plaintext and ciphertext in each iteration. The dashed line is the variational equilibrium obtained by centralized solution.
  • ...and 1 more figures