Abstract
This paper proposes a novel multi-searcher optimization (MSO) algorithm for the optimal energy dispatch (OED) of combined heat and power-thermal-wind-photovoltaic systems. The available power of wind turbine (WT) units and photovoltaic (PV) units is approximated with the probability density functions of wind speed and solar irradiance, respectively. The chaos theory is used to implement a wide global search, which can effectively avoid a low-quality local optimum for OED. Besides, a double-layer searcher is designed to guarantee fast convergence to a high-quality optimal solution. Finally, three benchmark functions and an energy system with 27 units are used for testing the performance of the MSO compared with nine other frequently used heuristic algorithms. The simulation results demonstrate that the proposed technique not only can solve the highly nonlinear, non-smooth, and non-convex OED problem of an energy system, but can also achieve a superior performance for the convergence speed and the optimum quality.
Highlights
As one of the critical operation tasks of power systems, economic dispatch (ED) is usually employed to minimize the total operating cost via optimally calculating the active power outputs of all the generators to balance the active power demand under various operating constraints [1]
If the optimization is non-linear with discontinuous functions and multiple local optimums, this will trap into a low-quality local optimum, especially for the optimal energy dispatch (OED). It can be solved by metaheuristic optimization algorithms, such as genetic algorithms (GA) [11], particle swarm optimization (PSO) [12], differential evolution (DE) algorithms [13] and the grey wolf optimizer (GWO) [14], which are highly independent from the specific mathematical models, and are much easier to apply for OED compared to the first type of method
The multi-searcher optimization (MSO) consists of a two-layer searcher, in which the first-layer searcher is responsible for a wide global search and the second-layer searcher is implemented for a deep local search
Summary
As one of the critical operation tasks of power systems, economic dispatch (ED) is usually employed to minimize the total operating cost via optimally calculating the active power outputs of all the generators to balance the active power demand under various operating constraints [1]. ED via construction of the probability density functions of the wind speed and solar irradiance [7] This new ED is called the optimal energy dispatch (OED) of the combined heat and power-thermal-wind-photovoltaic systems in this paper. It can be solved by metaheuristic optimization algorithms, such as genetic algorithms (GA) [11], particle swarm optimization (PSO) [12], differential evolution (DE) algorithms [13] and the grey wolf optimizer (GWO) [14], which are highly independent from the specific mathematical models, and are much easier to apply for OED compared to the first type of method Motivated by this advantage, this paper proposes a novel algorithm named multi-searcher optimization (MSO) for the OED of combined heat and power-thermal-wind- photovoltaic systems, which has the following novelties:.
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