Multi-household energy management in a smart neighborhood in the presence of uncertainties and electric vehicles

Abstract

The pathway toward the reduction of greenhouse gas emissions is dependent upon increasing Renewable Energy Sources (RESs), demand response, and electrification of public and private transportation. Energy management techniques are necessary to coordinate the operation in this complex scenario, and in recent years several works have appeared in the literature on this topic. This paper presents a study on multi-household energy management for Smart Neighborhoods integrating RESs and electric vehicles participating in Vehicle-to-Home (V2H) and Vehicle-to-Neighborhood (V2N) programs. The Smart Neighborhood comprises multiple households, a parking lot with public charging stations, and an aggregator that coordinates energy transactions using a Multi-Household Energy Manager (MH-EM). The MH-EM jointly maximizes the profits of the aggregator and the households by using the augmented ɛ-constraint approach. The generated Pareto optimal solutions allow for different decision policies to balance the aggregator’s and households’ profits, prioritizing one of them or the RES energy usage within the Smart Neighborhood. The experiments have been conducted over an entire year considering uncertainties related to the energy price, electric vehicles usage, energy production of RESs, and energy demand of the households. The results show that the MH-EM optimizes the Smart Neighborhood operation and that the solution that maximizes the RES energy usage provides the greatest benefits also in terms of peak-shaving and valley-filling capability of the energy demand.