Hybrid Optimization Technique to Solve Unit Commitment of Electric Power System

Abstract

Electricity plays a significant role in community life and the development of various sectors of the economy. The standard of living and growth of any country depends on the per capita electric energy consumption. In the past decades, electricity demand has been increased gradually, due to industrialization, modern transportations demand, domestic appliances, etc. Therefore, the dependency of electricity generation from coal and petroleum-based power plant has been increased in the electric systems. The electric power systems consist of generation, transmission, and distribution systems. In the electric power systems, the large number of thermal power plants is connected to different load centers through the transmission network. Hence, the study of the generation system having multiple types of generating units is a challenging task for utility planners. The thermal units take some time to start-up/shut-down the generation. Moreover, the power generation from thermal units depends on the ramp-up/ramp-down rate of generators. Because of this, utility companies need an optimum unit commitment (UC) decision in advance when to turn ON or OFF the generating units. The main objective of the UC problem is to minimize the total system production cost of thermal units with the satisfaction of power system constraints. The UC problem is subjected to various constraints such as the generation capacities, ramp rate limit, minimum up/down time, and reserve requirement. The day-ahead UC is a mixed-integer nondeterministic, polynomial, and highly dimensional hard problem of electric power systems. In electric power systems, the optimum UC schedule of generating units has played an important role in the saving of fuel cost. In the current scenario, the structure of the power system is transforming from the conventional centralized system to the deregulated environment. In the deregulation environment, the generation companies (GENCOs) act as a service provider, and the main motive of GENCOs is to achieve the maximum profit. Hence, the focus of researchers has been transformed from the UC problem to profit based unit commitment (PBUC) problem. The demand of steam, hot water, and heated households has increased drastically in various countries. The power sector is modernized due to an increase in power and heat demand in the modern world. In order to fulfill the needs of industries and public demand, the combined heat and power (CHP) units have been installed by the western world and developing countries. The cogeneration unit has higher energy conversion efficiency, short ramp limits, and start-up/down periods as compared to thermal units. The CHP unit has additional flexibility, such as heat storage facilities and the variable ratio of heat to power output. Based on the variable heat to power output ratio, two modes of CHP unit, i.e., extraction and backpressure have been defined. In extraction mode, the dual mode combined heat and power (DM-CHP) unit provides the flexible power generation, whereas, in the backpressure mode, it has high heat generation capacity. In the current scenario, still, fossil fuels are predominant for power and heat generation. The generation from fossil fuels has two major drawbacks: the eventual depletion of fossil fuels and the release of pollutants emission. In order to overcome these drawbacks, the focus of the power industry is toward the participation of renewable energy sources (RESs). In the power system operation, to unlock the full potential of CHP with RESs, significant attention has been given to solve the UC problem of a cogeneration system. The combined heat and power unit commitment (CHPUC) problem is a more challenging and complicated task as compared to the thermal unit commitment (THUC) problem, both in a conventional and deregulated environment, due to interdependency of heat and power units during the operation of the cogeneration system. Due to growing environmental concerns, the CHPUC and profit based CHPUC (PBCHPUC) problem have considered the pollutants emission as an important objective and hence, single objective UC problem has been extended as a multiobjective (MO) optimization problem. The UC is a nonlinear, mixed-integer, and highly constrained optimization problem. The various conventional methods such as Lagrange relaxation (LR), dynamic programming (DP), and mixed-integer linear programming (MILP) techniques have been successfully applied to solve the THUC problem. In conventional search techniques, the search quality depends on the initial point of search, and it also requires certain presumptions. However, such techniques do not require any derivative information and can be applied to solve discontinuous, non-linear, and multimodal optimization problems. Further, these search techniques require high computational time for large-size, multimodal optimization problems due to their local search capabilities. In order to overcome the limitations of conventional search techniques, researchers have proposed global optimization techniques. The power system researchers have extensively applied various global search techniques to solve the UC problems. The global search techniques have excellent exploration capability; however, the convergence rate of these techniques is very slow as it approaches the optimal best solution. For the large-scale UC problem, the number of fitness function evaluations required by global search techniques increases drastically, hence faces a high computational burden. In order to achieve a global optimal solution with a minimal computational burden, there is a need of hybrid optimization techniques to solve the UC problem. The hybrid optimization techniques search the optimum solution based on their knowledge regarding the behavior of the function and experience and do not follow a set of the predefined path. The proper balance is maintained between exploration and exploitation in hybrid optimization techniques. The hybrid optimization technique is able to solve the nondeterministic polynomial hard constraint optimization problem. The researchers have explored hybrid search techniques to solve the multiobjective unit commitment (MO-UC) problems. In MO optimization problems, the objective functions are conflicting in nature, and there is no single solution exists for simultaneous optimization of objective functions. Hence, the number of Pareto optimal solutions are present in the MO problems. The solution of one objective function cannot be improved without degrading other objective functions is called a non-dominated solution. Therefore, the MO optimization technique has been applied to find the most satisfying the non-dominated solutions in the presence of trade-offs between two or more conflicting objectives. The intent of the thesis is to cover the problem formulation of unit commitment (UC) for the thermal units only as well as for the cogeneration system with due consideration of dual mode combined heat and power (DM-CHP) units. Further, the solar photovoltaic and energy storage units are also considered in the combined heat and power unit commitment (CHPUC) problem formulation. In addition to that, the UC problem is solved to minimize operating cost under regulated environment as well as to maximize the profit of GENCOs under a deregulated environment. In the current scenario, utility planners not only optimize operating cost and GENCOs profit objective, however, they are also bound to minimize the pollutants emission. Due to conflicting objectives, the unit commitment problem has been framed as a multiobjective optimization problem. Further, the significant contribution of research work relates to the solution methodology for the unit commitment optimization problem. The nature-inspired binary particle swarm optimization (BPSO) and particle swarm optimization (PSO) (BPSO-PSO) technique has applied to deal with binary and real variables of the UC problem, respectively. In order to achieve a global optimal solution with the least computational burden, the hybrid optimization technique is also proposed. The hybrid optimization technique is based on the integration of conventional and global search techniques that has the advantages of both search techniques. The civilized swarm optimization (CSO) is undertaken as a hybrid optimization technique. The CSO technique is based on PSO with a society civilized algorithm (SCA). The combination of PSO and SCA algorithm maintains the proper balance between exploration and exploitation capability of the algorithm. The binary successive approximation (BSA) technique is applied to deal with binary variables of the UC problem. The BSA technique has less number of function evaluations. In this work, the optimization technique integrates BSA and CSO techniques to achieve a global optimal solution with the least computational burden. The proposed optimization technique is applied to solve various types of unit commitment problems and results are compared with other state-of-art optimization techniques. For the multiobjective optimization problem, the fuzzy membership function is used to club different objectives, and the cardinal priority ranking method is used to select the best non-dominated solution. The effect of extraction and backpressure mode of CHP unit has been analyzed in terms of cost, pollutant emission, and system flexibility. The effect of solar photovoltaic and energy storage units on the commitment status of CHP, thermal, and heat units have been examined. The brief discussion regarding each chapter of the thesis is presented in the following sub-sections. The significant contributions of several researchers related to the UC problems and its various aspects have been briefly reviewed in Chapter-1. The theoretical and computational backgrounds of various local, and global optimization techniques are studied, which are employed in the present study to determine the solution of the optimization UC problem. In Chapter-2, the thermal UC (THUC) problem has been solved by the proposed binary successive approximation and civilized swarm optimization (BSA-CSO) technique. In the proposed technique, the BSA technique is used to deal with the binary variables, and the CSO technique searches the generation schedule of the committed units. In order to satisfy the various operational constraints of the UC problem, the heuristic method is applied. Two small size test systems have been undertaken and results reveal that the proposed ‘BSA-CSO’ technique is superior as compared to other state-of-art techniques. To check the statistical performance of the BSA-CSO technique, Wilcoxon signed-rank test is applied, and the performance of the proposed technique found satisfactory. In Chapter-3, the profit based UC (PBUC) problem is formulated for the restructured power system. In this system, the aim of generation companies (GENCOs) is to maximize profit by generating and distributing electricity. The BSA-CSO optimization technique has been applied on the profit based THUC problem, and the performance is compared with the BPSO-PSO optimization technique. The three test systems have been undertaken, and obtained results have been compared with other published results and are found satisfactory. It has been found that the BSA-CSO optimization technique has attained a global optimum solution with the satisfaction of operational constraints for three PBUC test systems. Further, the quality of the solution is very less sensitive to the parametric variation of the hybrid optimization technique. In Chapter-4, the THUC problem formulation is extended to the dual mode CHPUC problem. The dual mode CHPUC model consists of backpressure and extraction mode of CHP units. The backpressure and extraction mode of the CHP unit has variable heat to power output ratio and diverse feasible operating region. The multiobjective CHPUC (MO-CHPUC) and multiobjective profit based CHPUC (MO-PBCHPUC) problem are addressed, in which operating cost and GENCO’s profit are optimized along with pollutants, respectively. The BSA-CSO technique is applied to search the optimal results, and the BPSO-PSO technique has also been implemented. In the multiobjective framework, a fuzzy membership approach unifies the different objectives. The cardinal priority ranking method decides the best-satisfied non dominated solution among Pareto optimal solutions. The three test systems have been undertaken to examine the impact of dual mode CHP unit on an optimum unit commitment schedule. It has been examined from results that the backpressure and extraction mode of dual mode CHP unit has a significant impact on operating cost, GENCOs profit, and pollutants emission of the combined system. In Chapter-5, the dual mode CHPUC model is integrated with solar photovoltaic and energy storage units. The proposed BSA-CSO technique has been implemented to solve the integrated CHPUC problem. Three integrated CHPUC test system have been considered. The study reveals that the energy storage and DM-CHP units have a significant impact on fuel cost. In this thesis, the validity and effectiveness of the proposed BSA-CSO method have been extensively verified and numerically tested by analyzing small, medium, and large THUC and PBUC problems. The performance of the proposed optimization technique is compared with the reported results in the literature. Further, the CHPUC, PBCHPUC, MO-CHPUC, and integrated CHPUC problems have also been solved with the consideration of the DM-CHP unit. Finally, the main conclusions and contributions of the thesis are presented and suggestions for the future of research in this field are indicated.