Summary
This paper presents an efficient algorithm based on imperialist competitive algorithm for optimal energy management of a Virtual Power Plant (VPP) including different distributed generation units and energy storage devices. A collection of Distributed Energy Resources, energy storage devices, and controllable loads which are aggregated and then are managed by an Energy Management System and can operate as a single power plant is called VPP. The proposed approach employs imperialist competitive algorithm to minimize the total operating cost of the VPP, considering energy loss cost in a 24‐hour time interval through 3 different scenarios while satisfying various operating constraints. This method has the benefit of escaping from the local optima while converging fast. In order to see the effectiveness and satisfying performance of the proposed algorithm, a case study including Renewable Energy Resources (RESs), storage battery, and controllable loads is studied as test system.
In recent years, a large number of renewable energy sources (RESs) have been added into modern distribution systems because of their clean and renewable property. Nevertheless, the high penetration of RESs and intermittent nature of some resources such as wind power and photovoltaic (PV) cause the variable generation and uncertainty of power system. In this condition, one idea to solve problems due to the variable output of these resources is to aggregate them together. A collection of distributed generations (DGs) such as wind turbine (WT), PV panel, fuel cell (FC), and any other sources of power, energy storage systems, and controllable loads that are aggregated together and are managed by an energy management system (EMS) are called a virtual power plant (VPP). The objective of the VPP in this paper is to minimize the total operating cost for a 24-h period. To solve the problem, a metaheuristic optimization algorithm, teaching–learning based optimization (TLBO), is proposed to determine optimal management of RESs, storage battery, and load control in a real case study.
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