Short-term hydrothermal scheduling using grey wolf optimization algorithm

Rajkishore Swain

doi:10.1016/j.epsr.2023.109867

Short-term hydrothermal scheduling using grey wolf optimization algorithm

2023-12-01

7

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AI

DOI：10.1016/j.epsr.2023.109867

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This paper presents Grey Wolf Optimization algorithm (GWO) for solving economic analysis of hydrothermal systems (HTS). The mathematical configuration of hydrothermal systems is considered a highly non-linear and complex problem. The intention of the HTS is to evaluate the optimal power allocation during a certain interval of time so as to minimize the total generation cost. The various constraints like the cascading nature of hydro plants, water transport delay, degree of power, water dispense limits, tank storage limits, hydraulic durability constraints, and starting and ending reservoir container limits are fully incorporated in the present work. The presented approach is then benchmarked on three well-known hydrothermal systems and the results are verified against different meta-heuristic techniques like Turbulent Water Flow Optimization (TWFO), Crisscross Optimization (CSO), Red Fox Optimizer (RFO), Remora Optimization Algorithm (ROA), Harris Hawks Optimization (HHO) Monarch Butterfly Optimization (MBO), Student Psychology-Based Optimization (SPBO) and improved Gravitational Search Algorithm (IGSA). The generation scheduling and minimum fuel cost of the thermal system are the main characteristics of the suggested approach, which gives competitive results with less computational burden. The fuel cost of a thermal plant with four cascaded hydro plants is 910,961.32 $/h, which is less than the fuel cost as reported by other existing evolutionary techniques compared in the simulation results. Further, the computational time of the purposed approach is found to be substantially less compared to existing algorithms. The statistical comparison between the GWO technique and the modified differential evolution (MDE) approach for four hydro plants and a thermal plant show that the MDE method has a higher mean cost than the GWO method. The standard deviation of the GWO technique is 5.4450 e + 03, whereas the standard deviation of the MDE approach is 9.2980 e + 03. The mean cost for case study four hydro system and three thermal systems in the MDE method is 4.5013e+04; however, it is 3.9397e+04 with the GWO approach. So, the GWO method outperforms MDE in terms of better statistical stability and resiliency.

Keyword:

Grey wolf optimization algorithm

Encircling prey

Hydrothermal scheduling

Cascaded reservoirs

Optimization

Short-term hydrothermal scheduling using grey wolf optimization algorithm

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