Generation Scheduling of Coordinated Power System Using Heuristic Optimization Technique

Abstract

The modern electrical power system focuses on acquiring an economical and environmentally favourable operation with high operating efficiency. The optimum generation scheduling is an essential research study for economic operation of power system. The primary objective of generation scheduling (GS) problem is to allocate the load demand to the generating units with minimal fuel cost while satisfying all the operational constraints. In the existing power system, thermal power generation contributes to the significant amount of total power generation in the world. However, power generation plants using fossil fuels release pollutant emissions in the atmosphere. Thus, reducing the pollutant emissions along with cost-effective power generation is one of the foremost challenges for electric utilities. The combined heat and power (CHP) generation (or cogeneration) has been proven very efficient not only due to the concurrent production of heat and electricity from a single source of fuel but also because of high fuel conversion efficiency, saving the generation cost and reducing the greenhouse gas emissions as compared to thermal plants. Thus, CHP units have been considered as a promising substitute for conventional thermal power units for saving fuel and protecting the environment. Referable to the huge requirement of high temperature for hot water in the household and industrial fields, heat units can be incorporated along with CHP units to fulfil the heat demand. The increasing concern for global warming and environment-friendly energy requirement has prompted the utilization of high-efficiency renewable energy sources like solar, wind and hydro power in the energy sector. Though renewable energy sources provide pollution-free energy but the power generation capacity is comparatively less and these alone may not supply sufficient power to the electric load. Hydro plants are extensively preferred due to their competence to fulfill peak load demand, insignificant pollutant emissions, and fuel costs. However, due to uncertainty and reliability concerns of hydro resources, it is difficult to rely solely on hydro units to provide all the electrical energy demand. Thus, the hydro plants must coordinate with other reliable energy sources like thermal, CHP and heat units to fulfill the power and heat demands at minimum operating cost and pollutant emissions. To achieve the maximum utilization of all the energy sources economically, the use of an interconnected or coordinated power system (CPS) is beneficial to the suppliers and society. Since simultaneously minimizing the fuel cost and pollutant emissions are conflicting in nature, hence, the problem is treated as a multi-objective (MO) generation scheduling problem. Researchers have investigated conventional or mathematical and global search techniques for searching the optimum generation schedule of the CPS for achieving economic and environmental benefits. The conventional optimization techniques undertake a few assumptions to solve the optimization problem and able to search only local optimal solutions and these are not able to find the global best solution. The conventional techniques are effective to solve differentiable and continuous optimization problems and may fail to perform for solving complex, discontinuous, non-differentiable, non-linear, non-convex and large-scale problems, since the search space increases exponentially with the size of the problem. Due to these limitations of conventional techniques, researchers have given more attention to the global search optimization techniques. The performance of global search optimization techniques is superior to conventional optimization techniques in terms of better exploration capability, robustness, derivative-free nature and avoiding local optima. Thus, these can be applied to solve non-differential and dis-continuous optimization problems. Despite the numerous advantages of global search techniques, these techniques still lack in exploitation capability and have high computational complexity when applied to solve complex, multi-dimensional and multi-modal optimization problems. Considering the ‘no free lunch theorem (NFLT)’ which says that no single optimization technique can optimally resolve all the optimization problems, the researchers have proposed and implemented hybrid/modified optimization techniques to solve complex multi-dimensional optimization problems. The hybrid/modified optimization techniques may be more efficient, flexible and effective for attaining better performance for complex optimization problems. Moreover, hybrid optimization techniques maintain a balance between exploration and exploitation abilities. The intent of the thesis is to formulate the single and MO coordinated power system generation scheduling (CPSGS) problem incorporating thermal, CHP, heat and hydro units. The CHP-photo-voltaic (PV) generation scheduling (CHP-PV-GS) problem has also been formulated considering CHP, heat, and PV units. The CPSGS, MO-CPSGS and CHP-PV-GS scheduling problems are high constrained, complex, non-convex, multi-modal, multi-dimensional optimization problems. Thus, three heuristic optimization techniques have been proposed and implemented to these problems. The effectiveness of the proposed optimization techniques has been validated by using small, medium, and large-sized test systems. The results obtained by the proposed optimization techniques have been compared with the reported results and found satisfactory. The thesis work is organized into seven chapters. The brief discussion regarding each chapter is given a follow: The Chapter-1 reviews the significant contributions of researchers to single-objective and MO hydro-thermal generation scheduling (HTGS), combined heat and power generation scheduling (CHPGS) and CPSGS problems. The computational and theoretical backgrounds of conventional and global search optimization techniques and their implementation to optimization problems have been presented. A brief introduction and implementation of hybrid and MO optimization techniques is also presented. In Chapter-2, the CPSGS problem is formulated. For solving the CPSGS problem, a heuristic optimization technique- I, i.e., grey wolf optimizer (GWO) with mutation strategies (MS) (GWO-MS) is proposed. In proposed optimization technique (POT)-I, the GWO is used for its best exploitation ability and three mutation strategies, i.e., Cauchy, Gaussian and opposition based-mutation have been used to enhance the exploration and exploitation. The POT-I have been tested on four test systems, considering one HTGS and three CPSGS problems and the results have been compared with the other well-established techniques. The results attained by POT-I illustrate that it is better than the state-of-art techniques. To validate the statistical performance of the POT-I, t-test and Mann-Whitney tests have been performed on all the test systems, which shows the superiority of POT-I over traditional GWO technique. In Chapter-3, a heuristic optimization technique-II, i.e., quantum-cuckoo search algorithm (QCSA) with MS (QCSA-MS) has been proposed. The POT-II integrated QCSA technique with three mutation strategies, i.e., Cauchy, Gaussian and opposition-based mutation. The QCSA has been used to explore the search space due to its excellent exploration quality and MS has been used for enhancing the good exploitation of the algorithm, which makes it a well-balanced algorithm for solving the complex optimization problem. The POT-II has been implemented to HTGS and CPSGS problems. The POT-II outperforms the QCSA technique in terms of outcomes, convergence characteristics, and distribution diversity. The POT-II achieved significant economic cost while dealing with complex joint constraints of CPSGS problem as compared to optimal cost obtained by QCSA, GWO and POT-I and other established methods. Further, statistical t-test confirms the robustness of the POT-II. In Chapter-4, a heuristic optimization technique-III namely chaotic tent map (CTM) based QCSA with GWO (CTM-QCSA-GWO) has been proposed for finding an optimal solution to the CPSGS problem. The POT-III fully utilizes the diversification ability of QCSA to explore a broader range of search space and exploitation ability of GWO for searching the optimal result by enhanced convergence rate. Further CTM strategy has been applied to hybrid QCSA-GWO technique to reduce the dependency on algorithm parameters and to improve the solution quality and the convergence speed. Three test systems of generation scheduling problems verify the performance of the POTs and the results are compared with published results. The outcomes of the POT-III are compared with the results obtained by the GWO, QCSA, POT-I and II and found satisfactory and cost-effective. Further, the POT-III is validated by t-test to investigate statistical performance. In Chapter-5, the CHP-PV-GS problem is formulated including thermal, CHP, heat and PV units The CHPGS and CHP-PV-GS problems are solved using POT-II and III. The incorporation of PV units with thermal, CHP and heat units significantly saves the fuel cost. The results obtained by POT-III are better than results obtained of GWO, QCSA, QCSA-GWO and POT-II. In Chapter-6, the MO-CPSGS problem has been formulated considering fuel cost and emission as two objectives to be minimized simultaneously. The POT-II and III have been applied to three test systems, including the MO-HTGS and small and medium-sized MO-CPSGS problems, to demonstrate their applicability and efficacy. The cardinal priority method has been utilized for searching the most satisfying non-dominated solution. All the test systems have been solved for three cases, i.e., economic generation, emission generation scheduling and MO generation scheduling. The POT-II and III outperformed the published findings from recent contemporary techniques. Further, the POT-III outperforms the POT-II. Finally, the Chapter-7 concludes the contributions, and future scope of this research work in the field of generation scheduling and optimization techniques.