Integration of energy communities in the electricity market: A hybrid agent-based modeling and bilevel optimization approach

Abstract

In recent years, many energy markets have seen the rise of battery storage systems (BSSs). This study focuses on home energy storage (HES) and community energy storage (CES) as two known applications of distributed BSSs in energy communities (ECs). We explore the challenge of efficient market integration of these systems by proposing a hybrid methodology combining agent-based electricity market modeling with bilevel EC optimization. This approach allows for deriving an optimal real-time pricing (ORTP) mechanism for the EC users. We apply our methodology to a case study of Germany in 2030, where a BSS capacity of 1.5 GW is installed within the ECs. Subsequently, we evaluate the impact of static energy-based charges included in the end-consumer's electricity price on BSS operations. Our results reveal that future market price fluctuations, when passed through to end-consumers, increase the incentive for market-aligned BSS operations. The ORTP strategy significantly aligns HES with market dynamics, reducing system costs and facilitating renewable energy integration. The profit-driven CES operation emerges as the most efficient use-case, increasing community welfare by 88 k€/MW-year and concurrently reducing the annual operational system costs by 0.6 %. However, static energy-based charges on power consumption hinder cost-effective BSS operations from both community and system perspectives. Our research contributes to understanding the intertwined dynamics between decentralized and central markets, thus advancing the modeling of complex energy markets.