Search

The development of local electricity markets (LEMs) and energy communities is accelerating the shift from consumerism to prosumerism. However, there is no concrete understanding of how electricity sharing in LEMs should be organized, a local wholesale market within or centralized sharing? This paper explores trading algorithms that can represent a competitive market and bidding conditions within a LEM. That is, how well trading algorithms can represent the wholesale market of an energy community?; What is a fair LEM reference price to create bidding simulations? How do the system characteristics affect the outcome of the trading algorithms? We address these questions by analyzing a community (residential buildings) in Steinkjer (Norway) and London (UK), including PV systems and wind turbines. We first determine bids and offers based on different bidding simulations and develop a market reference price. Afterward, we applied the trading algorithms Peer-to-Peer (P2P) and Multi-unit-Double-Auction (MUDA) for local electricity trading. We compared the results in selected KPIs such as self-sufficiency, traded energy, and curtailment. We find that P2P provides a more economically efficient trading algorithm than MUDA as it generally enables more trading and thus lowers grid imports. However, there are concerns that P2P brings disadvantages such as unfair trading. • Collaborative (energy community) and competitive (Peer-to-peer and Vickrey-MUDA) local electricity markets. • Understanding how a market algorithm will determine local prices in the UK and Norway. • Local market results based on different bidding simulations that reflect participants' behavior. • Developed a reference price index tailored for Local Electricity Markets. • P2P gives better results in terms of trading efficiency compared to MUDA. [ABSTRACT FROM AUTHOR]