Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management
Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management
复制标题
住宅和商业多能源系统之间公平的 P2P 能源交易，实现综合需求侧管理
DOI：
10.1016/j.apenergy.2020.114551
复制
发表时间：
2020-03-15
期刊：
影响因子：
11.2
通讯作者：
Chen, Long Xiang
中科院分区：
文献类型：
--
作者：
Jing, Rui;Xie, Mei Na;Chen, Long Xiang
Enabling P2P energy trading among prosumers (both producers and consumers) is a promising paradigm in the decentralized energy era. It is important to design a fair pricing strategy for energy trading; however, it leads to a complicated problem particularly when multi-energy systems participating the energy trading. This study proposes a trading aiding tool, which is based on a Nash-type non-cooperative game model between residential and commercial prosumers with guaranteed trading fairness. The model is generalized considering commonly used energy supply technologies and various demand-side management measures. The energy cost for both residential and commercial prosumers can be minimized with fair pricing strategies for both electricity and heating trading. Compared to previous research, this study presents a more concise solution to determine the fair prices for multi-energy trading. Through the McCormick relaxation, the complex problem is linearized as a Mixed Integer Linear Programming (MILP) model with significant improvement on computational efficiency. A case study is conducted in Shanghai, China, where a community is modeled as the residential prosumer and connected to the commercial prosumer including three commercial buildings. The results indicate that compared to two prosumers standing alone, enabling energy trading with fairness can achieve 4.9% cost saving; the fair-trading prices for electricity and heating are 0.090 $/kWh(e) and 0.015 $/kWh(h), respectively. Overall, this study proposes an efficient tool to provide insights into optimal infrastructure designs, and further quantify the fair pricing and schedule of demand response during P2P energy trading.
