Promoting Shared Energy Storage Aggregation among High Price-Tolerance Prosumer: An Incentive Deposit and Withdrawal Service

TL;DR

This paper introduces a novel SES aggregation framework that incentivizes high price-tolerance prosumers to participate without extra actions, using deposit and withdrawal services optimized by deep reinforcement learning.

Contribution

It proposes a new SES aggregation model with deposit and withdrawal services and a matching mechanism, enabling prosumers to participate easily and profitably.

Findings

The framework effectively incentivizes prosumer participation.

Dynamic coefficients improve energy management efficiency.

Deep reinforcement learning optimizes trading strategies.

Abstract

Many residential prosumers exhibit a high price-tolerance for household electricity bills and a low response to price incentives. This is because the household electricity bills are not inherently high, and the potential for saving on electricity bills through participation in conventional Shared Energy Storage (SES) is limited, which diminishes their motivation to actively engage in SES. Additionally, existing SES models often require prosumers to take additional actions, such as optimizing rental capacity and bidding prices, which happen to be capabilities that typical household prosumers do not possess. To incentivize these high price-tolerance residential prosumers to participate in SES, a novel SES aggregation framework is proposed, which does not require prosumers to take additional actions and allows them to maintain existing energy storage patterns. Compared to conventional long-term operation of SES, the proposed framework introduces an additional short-term construction step during which the energy service provider (ESP) acquires control of the energy storage systems (ESS) and offers electricity deposit and withdrawal services (DWS) with dynamic coefficients, enabling prosumers to withdraw more electricity than they deposit without additional actions. Additionally, a matching mechanism is proposed to align prosumers' electricity consumption behaviors with ESP's optimization strategies. Finally, the dynamic coefficients in DWS and trading strategies are optimized by an improved deep reinforcement learning (DRL) algorithm. Case studies are conducted to verify the effectiveness of the proposed SES aggregation framework with DWS and the matching mechanism.