Details

Autor(en) / Beteiligte

• Ye, Guoqiao
• Li, Gaoxiang
• Wu, Di
• Chen, Xu
• Zhou, Yipeng

Titel

Towards Cost Minimization With Renewable Energy Sharing in Cooperative Residential Communities

Ist Teil von

• IEEE access, 2017, Vol.5, p.11688-11699

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2017

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• The recent increasing evolution of renewable energy technologies makes it possible for common residents to afford the cost of installing renewable energy devices (REDs) and energy storage systems (ESSs) in their own houses. With the prevalence of REDs and ESSs, it is a beneficial and also promising idea for residents in a community to share extra energy with others, especially, when they have different electricity usage patterns. However, considering the unpredictable energy usage patterns, radically intermittent characteristics of renewable energy generation, and dynamic electricity price, it would be difficult for residents in a community to intelligently share their energy with others and thus minimize the overall cost of the whole community. In this paper, we design an online algorithm, which can tackle costaware energy sharing among residents in a cooperative community. We formulate the problem as a stochastic constrained problem and the objective is to minimize the time-average cost in the whole community, which includes the cost of purchasing electricity from the main grid, and the cost of charging and discharging ESSs. By exploiting the dynamics of electricity price, we can determine the charging and discharging behaviors of ESSs. We explore our method based on the Lyapunov optimization theory, which does not need any future statistics and possesses low computational complexity. Through theoretical analysis of our algorithm, we can conclude that our strategy can approximate the optimality with provable bounds. Meanwhile, we design a revenue division algorithm based on the Nash bargaining theory to fairly share the revenue among residents. We also conduct extensive trace-driven simulations and results show that our algorithm can obtain nearly 12% of cost reduction for the community when compared with noncooperative algorithms, and ensure the fairness among residents in the meanwhile.