Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations

Multi-energy multi-microgrid (MMG) networks are considered as a promising form of energy systems that can integrate various energy resources and improve energy utilization efficiency. Carbon emission limitation, regarded as a significant factor in energy management, has received increasing attention in recent years. By taking into account both economic and environmental factors, MMG networks can offer a great opportunity to reduce operation costs and carbon emissions. In this paper, we propose an optimal energy management strategy for minimizing the operation cost of an MMG network, considering operation constraints and carbon emissions. The energy management strategy is designed to consist of a day-ahead phase and an intra-day phase to overcome the uncertainty effects of renewable energy sources (RESs) generation and load demands. We first present a day-ahead scheduling strategy for the MMG network, in which microgrids operate in a distributed manner and share electricity while preserving their privacy. We then present an intra-day scheduling strategy for each microgrid, in which the operation costs and penalty costs caused by the adjustment of energy devices and energy procurement are minimized sequentially using a rolling horizon method. Simulation results demonstrate the effectiveness of the proposed energy management strategy in lowering operation costs and carbon emissions. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Multi-energy multi-microgrid (MMG) networks are an important form of energy systems that aim to integrate energy resources and improve energy utilization efficiency. This paper proposes an optimal energy management strategy for minimizing the operation cost of an MMG network, considering operation constraints and carbon emissions. The strategy consists of a day-ahead phase and an intra-day phase to address the uncertainty effects of renewable energy sources (RESs) generation and load demands.