Optimal Electricity Trading Strategy for a Household Microgrid

• Published in: 2020 IEEE 16th International Conference on Control & Automation (ICCA) pp. 1308 - 1313
• Main Authors: Qin, Zhaoming, Hua, Haochen, Liang, Hong, Herzallah, Randa, Zhou, Yuyang, Cao, Junwei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09-10-2020
• Subjects: Companies; Electricity supply industry; Erbium; Load modeling; Microgrids; Optimal control; Power markets
• ISSN: 1948-3457
• DOI: 10.1109/ICCA51439.2020.9264421

The recent integration of distributed generators (DGs) and renewable energy sources (RESs) into the power system led to the manifestation of a significant number of household microgrid (MG) systems in the electricity market. However, in most of the cases, the DGs in the MG system are passive and are not equipped by their own controllers, thus their integration increases the fluctuation in the power system and brings challenges to its management and control. To address these challenges, this paper proposes a novel electricity trading strategy for a household MG. This is achieved by formulating a nonlinear stochastic control problem that will then be solved such that the profit through electricity trading is maximised. To solve this optimisation problem, a gradient descent method based on compressive sensing is applied. Finally, some numerical examples are given to illustrate the effectiveness of the proposed control method. The results from the simulation experiments indicate that the proposed electricity trading strategy achieves the target and satisfies all constraints by controlling the energy router (ER) with the energy storage component.