Effectiveness and Feasibility of Market Makers for P2P Electricity Trading

Abstract

Motivated by the growing demand for distributed energy resources (DERs), peer-to-peer (P2P) electricity markets have been explored worldwide. However, such P2P markets must be balanced in much smaller regions with a lot fewer participants than centralized wholesale electricity markets; hence, the market has inherent problems of low liquidity and price instability. In this study, we propose applying a market maker system to the P2P electricity market and developing an efficient market strategy to increase liquidity and mitigate extreme price fluctuations. To this end, we construct an artificial market simulator for P2P electricity trading and design a market agent and general agents (photovoltaic (PV) generators, consumers, and prosumers) to perform power bidding and contract processing. Moreover, we introduce market-maker agents in this study who follow the regulations set by a market administrator and simultaneously place both sell and buy orders in the same market. We implement two types of bidding strategies for market makers and examine their effects on liquidity improvement and price stabilization as well as profitability, using solar PV generation and consumption data observed in a past demonstration project. It is confirmed that liquidity and price stability may be improved by introducing a market maker although there is a trade-off relationship between these effects and the market maker’s profitability.

Credit Rating-Based Transactive Energy System With Uncertainties in Energy Behavior

“…Additionally, Frei stated that "market liquidity also significantly impacts transactive energy markets, facilitating ease, efficiency, and successful transactions between participants" [37]. Furthermore, Kuno emphasized that "Higher liquidity brings benefits such as increased market participation, improved price discovery, and enhanced market efficiency" [38]. Prosumer groups with similar generation and demand profiles often have high absolute net demand, leading to the exportation or importation of energy from the grid, as highlighted in previous research [7].…”

“…For instance, Grade A comprises participants with credit scores between 91 and 100. In this scenario, the highest value in the credit score range is employed for calculating power exchange limits.In the case study environment, the limits assigned to buyers are determined using (37), while the limits assigned to sellers are calculated using (38). The scaling factors σ n i and σ n j are both set to one in this calculation.…”

Summary of Demand-side Response and Energy Transaction Strategy of Intelligent Building Clusters Driven by Data

“…It is considered that alliance blockchain or private blockchain is a highly distrusted and decentralized database, in which the calculation process can be transparent, the information cannot be tampered with, the shortcomings of decentralized energy linkage can be made up, various threats to energy supply and demand data of intelligent buildings can be effectively avoided, and the sharing, availability, security traceability and high efficiency of supply and demand data can be realized. However, the related research results mainly focus on power systems, such as decentralized coordinated control among distributed sources in microgrids [27], auditing the integrity and effectiveness of energy flow and financial transactions [28] and decentralized energy interaction and capital flow optimization among multiple microgrids [29]. Few scholars have studied the application of blockchain technology in different energy interaction and linkage including DC, AC and heat.…”

Stackelberg game-based optimal electricity trading method for distribution networks with small-micro industrial parks

“…As the electricity market (EM) continues to expanding, the operators and agents in the DNs gradually participate in EM competition (Jin et al, 2022), and the economic operation of the DNs has been significantly improved (Li et al, 2023a). A market simulator for peer-to-peer electricity trading is proposed in Kuno et al (2022), and a market agent is designed to perform power bidding and contract processing. Guo et al (2022) showed that all agents can freely trade in an asynchronous mode without waiting for idle or inactive neighboring agents, and the sublinear regret upper bound is proved for the asynchronous mode, which can maximize the social welfare in the EM.…”