Abstract
Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues.
Highlights
Emerged microgrid technology will enhance the share of clean energy power generation, ensuring safe and stable functioning of the entire power system, maximizing the use of scattered power sources, and coordinating and optimizing control [1]
This paper suggests a new enhanced sparrow search algorithm (SSA) (ESSA) based on an elite reverse learning strategy and Firefly algorithm (FA) mutation strategies for optimal operations planning of microgrid schedule cycles with a distributed power source’s optimal outputs to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system
I =1 where C2 is the cost of treating pollutants discharged from a microgrid; K is the serial number of pollutants discharged by each distributed power source; bk is the cost of treatment of class K pollutants, $/kg; and ai,k is the coefficient of class K pollutants discharged by the ith distributed power source, g/KWh
Summary
Emerged microgrid technology will enhance the share of clean energy power generation, ensuring safe and stable functioning of the entire power system, maximizing the use of scattered power sources, and coordinating and optimizing control [1]. The metaheuristic algorithm effectively deals with these issues of traditional optimization approaches for the optimal operation of large power grid systems [11,12]. This paper suggests a new enhanced SSA (ESSA) based on an elite reverse learning strategy and FA mutation strategies for optimal operations planning of microgrid schedule cycles with a distributed power source’s optimal outputs to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. The new proposed ESSA approach is applied to solve the microgrid scheduling cycle with the power source planning’s optimal output and total operation cost.
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