Abstract
This paper presents a day-ahead demand-side management (DSM)-integrated hybrid power management algorithm (PMA) with an objective of combined economic and emission load dispatch (CEED) considering losses. The algorithm was tested on an IEEE 30-bus six-generator system consisting of solar thermal/wind/wave/battery energy storage systems (BESSs) considering real-time data of the Gujarat (19°07′ N, 72°51′ E) coastal region and diverse renewable energy (RES) and storage sources. A maiden attempt of utilizing hybrid firefly particle swarm optimization (HFPSO) to reduce thermal energy consumption and carbon emission was presented. Further, a novel attempt for a versatile renewable power management system was proposed based on a day-ahead pricing scheme to manage load demand and generation effectively. The PMA permits the users to bring down the general load demand and adjust the load curve during the peak time frame. The comparative performance of particle swarm optimization (PSO), firefly algorithm (FA), and HFPSO algorithms in solving the objective was presented. The HFPSO algorithm was found to be the best in terms of a fuel cost of 544.160 (USD/h), emission 20.301 (kg/h), and peak-load reduction of 31.292%, 24.210%, and 51.197% for residential, commercial, and industrial loads, respectively, when contrasted with the other two algorithms PSO and FA.
Highlights
The primary goal of modern electrical utilities is to provide customers with a highquality electricity supply at the lowest possible cost while taking care of the environment.Combined economic and emission load dispatch (ELD) is the basic process of matching the load demand by allocating to the available generating units
The rest of the paper is organized as follows: Section 2, literature review; Section 3, illustrated problem formulation, brief study of combined economic and emission load dispatch (CEED), RESs, demand-side management (DSM) strategy, and power management algorithm (PMA); in Section 4, the meta-heuristic algorithms particle swarm optimization (PSO), firefly algorithm (FA), and hybrid firefly particle swarm optimization (HFPSO) are discussed; the results and main findings of the research work are discussed in Section 5; and Section 6 concludes the work
A combined economic emission dispatch problem was solved for a six-generator bus system by integrating renewable energy sources such as solar thermal and wind and wave by incorporating a power management algorithm and demand-side management strategy with different combinations and for three different types of controllable loads for effective reduction in peak-load demand, fuel cost, and emission of thermal plants
Summary
The primary goal of modern electrical utilities is to provide customers with a highquality electricity supply at the lowest possible cost while taking care of the environment. The comprehensive index of the EED problem concerning fuel cost and pollution to off-peak price mode slot is the significant focus of the DSM and power management emission can be improved by using PSO [13,14,15]. To solve the power management problem, performance metrics fuel cost, electricity cost, peak-to-average ratio (PAR), and emission were investigated. The rest of the paper is organized as follows: Section 2, literature review; Section 3, illustrated problem formulation, brief study of CEED, RESs, DSM strategy, and PMA; in Section 4, the meta-heuristic algorithms PSO, FA, and HFPSO are discussed; the results and main findings of the research work are discussed in Section 5; and Section 6 concludes the work
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