Multi-agent microgrid energy management based on deep learning forecaster

• Published in: Energy (Oxford) 2019-11, Vol.186, p.115873, Article 115873
• Main Authors: Afrasiabi, Mousa, Mohammadi, Mohammad, Rastegar, Mohammad, Kargarian, Amin
• Format: Article
• Language: English
• Subjects: Algorithms; Alternating direction method of multipliers; Artificial neural networks; Convolutional neural networks; Deep learning; Distributed generation; Economic forecasting; Electric power grids; Energy conservation; Energy dissipation; Energy loss; Energy management; Energy storage; Forecasting; Gated recurrent unit; Historical account; Machine learning; Microgrid energy management system; Multiagent systems; Neural networks; Photovoltaic cells; Photovoltaics; Pricing; Scheduling; Short-term forecasting; Storage systems; Turbines; Wind power; Wind turbines
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.115873

This paper presents a multi-agent day-ahead microgrid energy management framework. The objective is to minimize energy loss and operation cost of agents, including conventional distributed generators, wind turbines, photovoltaics, demands, battery storage systems, and microgrids aggregator agent. To forecast market prices, wind generation, solar generation, and load demand, a deep learning-based approach is designed based on a combination of convolutional neural networks and gated recurrent unit. Each agent utilizes the designed learning approach and its own historical data to forecast its required parameters/data for scheduling purposes. To preserve the information privacy of agents, the alternating direction method of multipliers (ADMM) is utilized to find the optimal operating point of microgrid distributedly. To enhance the convergence performance of the distributed algorithm, an accelerated ADMM is presented based on the concept of over-relaxation. In the proposed framework, the agents do not need to share with other parties either their historical data for forecasting purposes or commercially sensitive information for scheduling purposes. The proposed framework is tested on a realistic test system. The forecast values obtained by the proposed forecasting method are compared with several other methods and the accelerated distributed algorithm is compared with the standard ADMM and analytical target cascading. •Designing an end-to-end deep learning structure to forecast time series.•Proposing accelerated alternating direction method of multipliers method.•Presenting a multi-agent framework for day-ahead scheduling of micro-grids.•Integrating the forecasting process with distributed energy management.