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.

Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k‐means, k‐medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k‐medoids algorithm has the best performance in comparison with the k‐means and the DEA‐based clustering under various conditions.

Increasing the usage of distributed generations (DGs) leads the microgrids (MGs) to develop. Considering the existence of alternating current (ac) loads and sources and concerning increasing direct current (dc) loads and sources, hybrid ac/dc MGs have been used to have the advantages of both ac and dc MGs and reduce the drawbacks of them. By integrating the DG units in hybrid ac/dc MGs. Hence, a proper control method is required in such MGs to achieve precise voltage regulation and power-sharing, desired power quality, high efficiency, and high reliability. This article focused on nonlinear exponential control and distributed secondary control schemes to properly control the MG and form an integrated nonlinear hierarchical control and management for hybrid ac/dc MGs. Finally, to evaluate the proposed nonlinear control strategy's performance, offline digital time-domain simulation studies are carried out on a test MG system in MATLAB/Simulink environment. The results are also compared with previously reported methods. The simulation results and the comparisons showed that the proposed methods could properly share the power among subgrids and DGs in both ac and dc subgrids. In contrast, the proposed control scheme can prove its effectiveness and superiority over conventional controllers.