Flexible-reliable operation of green microgrids including sources and energy storage-based active loads considering ANFIS-based data forecasting method

• Published in: Electric power systems research Vol. 210; p. 108107
• Main Authors: Shirmardi, Seyed Arman, Joorabian, Mahmood, Barati, Hassan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2022
• Subjects: Data forecasting method; Energy storage-based active load; Flexible-reliable operation; Green microgrid; Hybrid evolutionary algorithm; Flexible-reliable operation; Green microgrid; Data forecasting method; Energy storage-based active load; Hybrid evolutionary algorithm
• ISSN: 0378-7796
• DOI: 10.1016/j.epsr.2022.108107

•Implementation of a flexible-reliable operation strategy by MG operator (MGO) on a green microgrid participated in the DA energy market.•Simultaneous modeling of operation, reliability, flexibility, economic, and environmental indices in MG power management scheme.•Integration of MG operation and data forecasting problems to achieve reliable network scheduling results in the DA energy market.•Using the hybrid ABC+SCA algorithm to reach the optimal solution with unique response conditions. This paper presents the flexible-reliable operation (FRO) of microgrids (MGs) constrained to supplying clean energy. The scheme minimizes the total expected costs of the MG and sources operation, MG reliability, and MG flexibility and is subject to power flow equations, operation, environmental, and reliability constraints, and formulation of sources and energy storage-based active loads. The problem is mixed-integer nonlinear programming (MINLP). To achieve an optimal reliable solution with unique response conditions, a combination of an artificial bee colony (ABC) and sine-cosine algorithm (SCA) is utilized in this study. In the given scheme, stochastic programming is employed to model the uncertainties of load, energy price, renewable power, and availability of MG equipment. Furthermore, mean and standard deviation values of the above-mentioned first three uncertainty parameters are obtained using the data forecasting method based on the hybrid ABC+SCA method, adaptive network-based fuzzy inference system (ANFIS), and wavelet transform (WT). Finally, by implementing the proposed strategy on a standard MG, the capabilities of the proposed scheme in improving economic, environmental, operation, reliability, and flexibility conditions of the network are validated.