Bidding Agents for PV and Electric Vehicle-Owning Users in the Electricity P2P Trading Market

As the world strives to decarbonize, the effective use of renewable energy has become an important issue, and P2P power trading is expected to unlock the value of renewable energy and encourage its adoption by enabling power trading based on user needs and user assets. In this study, we constructed a bidding agent that optimizes bids based on electricity demand and generation forecasts, user preferences for renewable energy (renewable energy-oriented or economically oriented), and owned assets in a P2P electricity trading market, and automatically performs electricity trading. The agent algorithm was used to evaluate the differences in trading content between different asset holdings and preferences by performing power sharing in a real scale environment. The demonstration experiments show that: EV-owning and economy-oriented users can trade more favorably in the market with a lower average execution price than non-EV-owning users; forecasting enables economy-enhancing moves to store nighttime electricity in batteries in advance in anticipation of future power generation and market prices; EV-owning and renewable energy-oriented users can trade more favorably in the market with other users. EV-owning and renewable energy-oriented users can achieve higher RE ratios at a cost of about +1 yen/kWh compared to other users. By actually issuing charging and discharging commands to the EV and controlling the charging and discharging, the agent can control the actual use of electricity according to the user’s preferences.

Automated Fuzzy Bidding Strategy for Continuous Double Auctions Using Trading Agent’s Attitude and Market Competition

This paper designs a novel fuzzy competition and attitude based bidding strategy (FCA-Bid) for continuous double auction in which the best transaction price is calculated on account of the attitude of the agents and the competition for the goods in the market. The estimation of attitude is based on the bidding item’s attribute assessment, which adapts the fuzzy sets technique to handle uncertainty of the bidding process. Additionally, it uses heuristic rules to determine the attitude of bidding agents. The bidding strategy also uses and determines competition in the market (based on the two factors, number of the bidders participating and the total time elapsed for an auction) using Mamdani’s Direct Method. Then the range for the trading price will be determined based on the assessed attitude and the competition in the market using the fuzzy reasoning technique. The final transaction price is calculated after considering the conflicting attitudes of the seller and the bidder toward selecting the transaction price.