Search

The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD). [ABSTRACT FROM AUTHOR]

Combined economic emission dispatch (CEED) problems are among the most crucial problems in electrical power systems. The purpose of the CEED is to plan the outputs of all production units available in the electrical power system in such a way that the cost of fuel and polluted emissions are minimized while respecting the equality and inequality constraints of the system and efficiently responding to the power load required. The rapid depletion of these sources causes limitation and increases the price of fuel. It is therefore very important that scientific research in the last few decades has been oriented toward the integration of renewable energy systems (RES) such as wind and PV as an alternative source. Furthermore, the CEED problem including RES is the most important problem with regard to electrical power field optimization. In this study, a classification of optimization techniques that are widely used, such as traditional methods, non-conventional methods, and hybrid methods, is summarized. Many optimization methods have been presented and each of them has its own advantages and disadvantages for solving this complex CEED problem, including renewable energy. A review of different optimization techniques for solving this CEED problem is explored in this present paper. This review will encourage researchers in the future to gain knowledge of the best approaches applicable to solve CEED problems for practical electrical systems. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient optimization algorithm named chaotic teaching–learning algorithm (CTLA), to solve multimachine power system stabilizers design problem. The original teaching learning algorithm as competitive to other optimization algorithms, used two phases to proceed to the global optimal solution: ‘teacher phase’ and ‘learner phase’. However, during the second phase an adequate interaction between the teacher and the learners in entire search space are not guaranteed and the algorithm may be trapped in local optima. Thus, in the proposed CTLA a new phase named “chaotic phase” is added in order to overcome this drawback. The performance of the CTLA is investigated by using a set of benchmark functions. To demonstrate the effectiveness of the proposed algorithm in power systems, the conventional lead-lag power system stabilizers (PSSs) are tuned for: three machines nine bus system (WSCC) and the ten machine thirty-nine bus New England power systems. The performance of the proposed CTLA-based PSS (CTLAPSSs) under different loading conditions and disturbances is investigated through eigen-value analysis, non-linear time domain-simulations and some performance indices. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the extended version of the conventional DEED to overcome the ramp rate violations when its optimal solutions for one period (normally one day) are implemented repeatedly and periodically over consequent dispatch periods to meet the periodic load demands. This dynamic dispatch problem, which is referred to as EDEED, is a multi-objective optimization problem which simultaneously minimizes both fuel cost and pollutants emission while satisfying a set of constraints. A multi-objective particle swarm optimization (MOPSO) method has been applied in this article for solving EDEED problem. The performance of the proposed method has been evaluated on the 10-unit test system with non-smooth fuel cost and emission level functions in comparison with those methods reported in the literature. [ABSTRACT FROM AUTHOR]

The Flexible AC transmission System (FACTS) devices, such as, thyristor controlled series compensators (TCSC) and Static Var Compensator (SVC) can be used to enhance power system performance. To achieve a better use of FACTS devices and because of their considerable costs, it is important to limit the number of theses controllers and locate them optimally in the power system. Firstly, we have applied optimization technique, namely, NSGA-II, to find out the optimal number of multi-devices of TCSC and SVC in order to improve the system loadabilty and to ensure the steady state security of the network. Secondly, we performed a contingency analysis procedure based on severity index (SIL) to identify and classify the most severe line contingencies. Then, we determined the optimal placement and parameter setting of FACTS devices in power system by using the above optimization approach to alleviate the line overloads. To ensure the robustness and effectiveness of the proposed method, N-1 contingency analysis and the stress of power system are considered. The optimization problem presented in this paper aims at reducing FACTS installation cost and decreasing total real power losses. Simulations are performed on IEEE 30-bus test system. [ABSTRACT FROM AUTHOR]