Optimal energy planning of multi-microgrids at stochastic nature of load demand and renewable energy resources using a modified Capuchin Search Algorithm

Abstract

The concept of interconnected multi-microgrids (MMGs) is presented as a promising solution for the improvement in the operation, control, and economic performance of the distribution networks. The energy management of the MMGs is a strenuous and challenging task, especially with the integration of renewable energy resources (RERs) and variation in the loading due to the intermittency of these resources and the stochastic nature of the load demand. In this regard, the energy management of the MMGs is optimized with optimal inclusion of a hybrid system consisting of a photovoltaic (PV) and a wind turbine (WT)-based distributed generation (DGs) under uncertainties of the generated powers and the load variation. A modified Capuchin Search Algorithm (MCapSA) is presented and applied for the energy management of the MMGs. The MCapSA is based on enhancing the searching abilities of the standard Capuchin Search Algorithm (CapSA) using three improvement strategies including the quasi-oppositional-based learning (QOBL), the random movement-based Levy flight distribution, and the exploitation mechanism of the prairie dogs in the prairie dog optimization (PDO). The optimized function is a multi-objective function that comprises of the cost and the voltage deviation reduction along with stability enhancement. The effectiveness of the proposed technique is verified on standard benchmark functions and the obtained results. Then, the proposed method is used for energy management of IEEE 33-bus and 69-bus MMGs at uncertainties conation. The results depict that the energy management with inclusion of WTs and PVs using the proposed technique can reduce the cost and summation of the VD by 46.41% and 62.54%, and the VSI is enhanced by 15.1406% for the first MMG. Likewise, for the second MMG, the cost and summation of the VD are reduced by 44.19% and 39.70%, and the VSI is enhanced by 4.49%.

A Novel Adaptive Manta-Ray Foraging Optimization for Stochastic ORPD Considering Uncertainties of Wind Power and Load Demand

“…It should be highlighted here that for a fair comparison, the number of the populations and the maximum number of iterations were selected to be the same, while the other parameters of the SCSO, GWO, WOA, AHA and DO, were selected similarly to [56][57][58][59][60]. Three types of these standard functions were considered where F1 to F7 were Unimodal functions, F8 to F13 were Multimodal functions, and F14 to F23 were Fixed-dimension multimodal functions [61][62][63]. It should be highlighted here that the dimensions of the first 13 objective functions were 30, while the dimensions of F14, F16, F17 and F18 were 2.…”

Optimal Operation of Distribution Networks Considering Renewable Energy Sources Integration and Demand Side Response

“…References [8,17,18,32,33] explored the issue of OOP, taking into consideration uncertainties in load, radiation, and wind speed. However, these studies omitted uncertainties related to temperatures and prices.…”

“…The price of electricity is a highly important random characteristic in a power system since it is an uncertain value derived from the grid. Equation (33) illustrates how the normal PDF, based on its mean value µ P and standard deviation σ P , can be used to simulate the PDF of the price of electricity [50,51]:…”