DOI: 10.14457/tu.the.2018.1692
title: Optimal solution technique for solving dynamic economic dispatch problem of power generating units and planning of public fast charging station on residential power distribution system

description: In order to achieve the optimal efficiency in the operation of a power system, it is required to optimally schedule the power generating units, which is typically known as economic dispatch problem, a highly complex non-linear constrained optimization problem. There have been a lot of research efforts made to date in tackling this prevailing issue. Various numerical optimization techniques have been previously employed to solve this problem algorithmically reported in the literature, but the main problem from which most traditional algorithms suffer to provide good quality solutions is due to their stagnancy problem, i.e. trapping into the local optimum, while searching for optimal solutions. Considering the shortcomings of heuristic algorithms, in this thesis, an improved bees algorithm (IBA) is being proposed for solving dynamic economic dispatch (DED) problem of generating units considering prohibited operating zones for small- and large-scale power system, which can be seen as the first main practical contribution of this thesis. Additional techniques, such as constraint handling technique, adaptive patch size and limited search space, are also integrated into the traditional bees algorithm, thereby resulting in significant improvement in the overall performance of the proposed IBA technique. Here, DED problem is performed over the whole dispatch period (24 Hour) while satisfying the operational constraints such as prohibited operating zones, ramp rate limits, spinning reserve, system load demand and transmission losses. To validate the effectiveness and robustness of the proposed method in comparison with other traditional swarm-based optimization algorithms, simulations were performed to test the proposed method for DED problem considering six-unit, fifteen-unit, and forty-unit system, respectively. Numerical results obtained from the proposed algorithm are compared with the bees algorithm (BA), genetic algorithm (GA) and particle swarm optimization (PSO) affirming the superior performance of the proposed technique. The total generation cost distribution and convergence characteristic graph demonstrate that the proposed IBA approach is capable enough of yielding a good quality solution with much better computational efficiency.With the recent advancement in communication technologies and the rapidly growing transportation sector, there is an emerging need to meet the globally growing charging demand of Electric Vehicles (EVs) on and off road, estimated by International Energy Agency (IEA), to be expected to have 70 million EVs population by 2025. Hence, it is highly important to develop technologies for incorporating EVs within the modern power system for decarbonized future transportation sustainably, along with the other disruptive energy technologies. Hence, public fast charging stations (FCSs) must be prepared to serve this emerging plug-in electric vehicle charging demand. In this thesis, a hybrid swarm-based optimization technique, named HACOBA, blending the seminal characteristics of the ant colony optimization (ACO) with BA, is developed in finding the optimal locations of the FCSs having its locality within the urban area on the residential power distribution grid such that it maximizes the fast charging serviceability while minimizing the total social cost and power line losses subject to power distribution limit and public road traffic constraints, respectively, when considered as a single-objective optimization problem that can be seen as the second main practical contribution of this thesis. To validate the effectiveness and robustness of the proposed method, it has been investigated on the IEEE-69-bus test system. Numerical results demonstrate that the proposed algorithm meets solution quality with high computational efficiency, in terms of the rate of convergence, with significant performance improvement, when compared with other traditional swarm-based optimization algorithms such as ACO, BA and GA, and therefore, it can be concluded that the proposed planning approach is pivotally suitable for the planning of FCSs in the residential zone condition within the urban area.