Intelligent decision making for energy management in microgrids with air pollution reduction policy

Abstract

Fuzzy Logic-based decision-making framework is implemented for energy management in microgrid systems in order to meet targets such as providing local consumers with required energy demand and making good revenue for the microgrid owner under a time-varying electricity cost policy while helping reduce negative environmental effects due to air polluting sources of electrical energy such as coal fire plants which operate in the main grid in order to provide local microgrid loads. Typically, a microgrid system has two modes of operation. It either works synchronously with the main grid or operates independently from the utility grid in an isolated mode. Distributed renewable energy generators including solar, wind in association with batteries and main grid supply power to the consumer in the microgrid network. One day period is divided to a finite number of time slots. The Fuzzy intelligent approach implemented in this article determines the rate at which power has to be delivered to/taken from the storage unit during the next time slot depending on the electricity price per kWh of energy, local load demand, electricity generation rate through renewable resources, and air pollution factor which are sampled at predetermined rates. Cost function is defined as the sum of balance/revenue due to electricity trade between microgrid and the main grid, which includes the power provided to local load and distribution losses. Five different scenarios are considered for local load and microgrid assembly operation. Measures of balance/revenue will be extracted to represent benefits of using Fuzzy logic for energy management in microgrids with air pollution reduction policy.