Abstract
This paper presents the applications of Cuckoo search algorithm (CSA) for solving the problem of optimal power flow for hydrothermal system (OPF-HTS) where IEEE 30-bus test system with both thermal plants and hydropower plants is considered. The problem is first developed in the paper by the combination of optimal power flow (OPF) problem and short-term hydrothermal scheduling (STHTS) problem and it becomes much more complicated than the two sub-problems because it includes all constraints of transmission grid from the comer and all hydraulic constraints from the later in addition to the multi optimal subintervals. In order to validate the performance of the CSA when applied to the problem, another existing meta-heuristic algorithm, Particle swarm optimization has been employed to solve the same problem and compare the obtained results. The analysis on the obtained results has indicated that the CSA is more effective and robust than PSO. Consequently, it can be sated that CSA is a very efficient method for solving the problem.
Highlights
Hydrothermal system is comprised of both thermal plants and hydropower plants supplying electricity to load via transmission lines
On the contrary to hydropower plants, the fuel cost for power generation is the main objective during operation of thermal plants but the set of constraints taken into account is much simple once only limitations on thermal generation is included
Hydrothermal system scheduling (HTSS) aims to minimize electricity generation fuel cost of thermal plants using fossil fuels while all constraints from thermal plants and hydropower plants as well as power balance constraint from power grid must be exactly met
Summary
Optimal power flow (OPF) is a static non-linear programming problem where the main task is to determine steady state operation points of all generators available in the power systems so that the total fuel cost is minimized as much as possible while exactly meeting a set of physical and operational constraints imposed by equipment and network limitations such as active and reactive power of generators, transformer tap, switchable capacitor bank, bus voltage, and transmission line capacity limits [1].Generally, the controllable variables of the OPF problem first need to be determined are active powers of generators, voltage magnitude at generation buses, injected reactive power at capacitor buses, and transformer tap settings and other dependent variables are obtained such as reactive power of the generators, voltage magnitude at other buses, etc. Optimal power flow (OPF) is a static non-linear programming problem where the main task is to determine steady state operation points of all generators available in the power systems so that the total fuel cost is minimized as much as possible while exactly meeting a set of physical and operational constraints imposed by equipment and network limitations such as active and reactive power of generators, transformer tap, switchable capacitor bank, bus voltage, and transmission line capacity limits [1]. Objective of a conventional OPF problem is only to minimize total fuel cost whereas other objective such as power losses in transmission lines, emission released into the air by thermal power plants using fossil fuels as well as voltage stability index are neglected. Hydrothermal system scheduling (HTSS) aims to minimize electricity generation fuel cost of thermal plants using fossil fuels while all constraints from thermal plants and hydropower plants as well as power balance constraint from power grid must be exactly met
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