Energy management for multi-home installation of solar PhotoVoltaics (solar PVs) combined with Electric Vehicles’ (EVs) charging scheduling has a rich complexity due to the uncertainties of solar PV generation and EV usage. Changing clients from multi-consumers to multi-prosumers with real-time energy trading supervised by the aggregator is an efficient way to solve undesired demand problems due to disorderly EV scheduling. Therefore, this paper proposes real-time multi-home energy management with EV charging scheduling using multi-agent deep reinforcement learning optimization. The aggregator and prosumers are developed as smart agents to interact with each other to find the best decision. This paper aims to reduce the electricity expense of prosumers through EV battery scheduling. The aggregator calculates the revenue from energy trading with multi-prosumers by using a real-time pricing concept which can facilitate the proper behavior of prosumers. Simulation results show that the proposed method can reduce mean power consumption by 9.04% and 39.57% compared with consumption using the system without EV usage and the system that applies the conventional energy price, respectively. Also, it can decrease the costs of the prosumer by between 1.67% and 24.57%, and the aggregator can generate revenue by 0.065 USD per day, which is higher than that generated when employing conventional energy prices.

Increasing of electric vehicle demand and uncertain electricity generation from solar photovoltaics lead to poor reliability in the Distribution Network (DN). MicroGrids (MG) connecting to the DN with suitable energy trading is an effective way to solve this issue. However, suitable energy trading considering grid constraints with a low computational burden is a challenge for solving Optimal Energy Management (OEM). Therefore, this paper proposes an OEM using a surrogate which is modeled based on a Deep Neural Network to assist the Deep Reinforcement Learning Optimization for reducing the computational burden. The proposed method is applied to a bi-level OEM for multi-MGs connected in the DN with real-time pricing consideration, represented as the proposed strategy. The surrogate is modeled to predict the power system parameters of the MG based on probabilistic power flow, whereas a deterministic power flow is applied to evaluate the parameters of DN. To validate and demonstrate the proposed method and strategy, the modified IEEE-33 test feeder and a residential distribution system which are defined as the DN and MG, respectively are used to test. Simulation results show that the proposed method can reduce computational burden by 89.23% compared with the Differential Evolution algorithm. Moreover, the proposed strategy provides the optimal purchased energy price of DN offering to each MG which can reduce the cost of energy purchased from the main grid resulting in lower operating cost of DN.

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