Abstract

Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, the challenges of PMG's uncertainties such as stochastic behavior of load data and weather conditions (solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated. In this paper, a new energy management systems (EMS) based on weather and load forecasting is proposed for PMG's optimal scheduling and operation. Developing a novel hybrid machine learning-based method using adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) artificial neural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG's operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.

Highlights

  • Sustainable development and environmental issues are crucial objectives of the energy sector

  • This paper proposes a comparative approach for the energy management systems (EMS) of the Prosumer microgrids (PMGs) to precisely select the best-predicted pattern by different machine learning algorithms for each parameter

  • A new hybrid machine learning-based forecasting method consists of adaptive neuro-fuzzy inference system (ANFIS) model, multilayer perceptron (MLP)-artificial neural network (ANN), and radial basis function (RBF)-ANN has been developed to forecast the weather and load data

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Summary

Introduction

Sustainable development and environmental issues are crucial objectives of the energy sector. After monitoring the overall predicted data (solar irradiance, ambient temperature, wind speed, and load demand), the best-selected daily pattern for each parameter is used for solving the optimization problem of PMG’s operation cost. MATHEMATICAL MODELING In the following subsections, methodologies for weather and load forecasting as well as proposed energy scheduling for the day-ahead operation of the PMG are presented.

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