A hybrid population-based metaheuristic, Hybrid Canonical Dierential Evolutionary Particle Swarm Optimization (hC-DEEPSO), is applied to solve Security Constrained Optimal Power Flow (SCOPF) problems. Despite the inherent diculties of tackling these real-world problems, they must be solved several times a day taking into account operation and security conditions. A combination of the C-DEEPSO metaheuristic coupled with a multipoint search operator is proposed to better exploit the search space in the vicinity of the best solution found so far by the current population in the rst stages of the search process. A simple diversity mechanism is also applied to avoid premature convergence and to escape from local optima. A experimental design is devised to ne-tune the parameters of the proposed algorithm for each instance of the SCOPF problem. The eectiveness of the proposed hC-DEEPSO is tested on the IEEE 57-bus, IEEE 118-bus and IEEE 300-bus standard systems. The numerical results obtained by the proposed hC-DEEPSO are compared with other evolutionary methods reported in this literature to prove the potential and capability of the proposed hC-DEEPSO for solving the SCOPF at acceptable economical and technical levels.
The Brazilian population increase and the purchase power growth have resulted in a widespread use of electric home appliances.Consequently, the demand for electricity has been growing steadily in an average of 5% a year. In this country, electric demand is supplied predominantly by hydro power. Many of the power plants installed do not operate efficiently from water consumption point of view. Energy Dispatch is defined as the allocation of operational values to each turbine inside a power plant to meet some criteria defined by the power plant owner. In this context, an optimal scheduling criterion could be the provision of the greatest amount of electricity with the lowest possible water consumption, i.e. maximization of water use efficiency. Some power plant operators rely on "Normal Mode of Operation" (NMO) as Energy Dispatch criterion. This criterion consists in equally dividing power demand between available turbines regardless whether the allocation represents an efficient good operation point for each turbine. This work proposes a multiobjective approach to solve electric dispatch problem in which the objective functions considered are maximization of hydroelectric productivity function and minimization of the distance between NMO and "Optimized Control Mode" (OCM). Two well-known Multiobjective Evolutionary Algorithms are used to solve this problem. Practical results have shown water savings in the order of million m 3 /s. In addition, statistical inference has revealed that NSGA-II algorithm is more robust than SPEA-II algorithm to solve this problem.
The optimal solution provided by metaheuristics can be viewed as a random variable, whose behavior depends on the value of the algorithm's strategic parameters and on the type of penalty function used to enforce the problem's soft constraints. This paper reports the use of parametric and non-parametric statistics to compare three different penalty functions implemented to solve the Security Constrained Optimal Power Flow (SCOPF) problem using the new enhanced metaheuristic Differential Evolutionary Particle Swarm Optimization (DEEPSO). To obtain the best performance for the three types of penalty functions, the strategic parameters of DEEPSO are optimized by using an iterative algorithm based on the two-way analysis of variance (ANOVA). The results show that the modeling of soft constraints significantly influences the best achievable performance of the optimization algorithm.
Nowadays, the population growth and economic development causes the need for electricity power to increase every year. An unit dispatch problem is defined as the attribution of operational values to each generation unit inside a power plant, given some criteria to be obeyed like the total power to be generated, operational bounds of these units etc. In this context, an optimal dispatch programming for hydroelectric units in energy plants provides a bigger production of electricity to be generated with a minimal water amount. This paper presents an optimization solution for hydroelectric generating system of a plant, using Differential Evolution algorithms. The novel mathematical model proposed and validation of the obtained algorithms will be performed with practical simulation experiments. Throughout the text, the equations and models for the system simulation will be fully described, and the experiments and results will be objectively analysed through statistical inference.Simulation results indicate savings of 6.5 million litres of water for each month of operation using the proposed solution.
Hybrid micro grid systems (HMGS) are gaining more attention within the World. The balance between load and electricity generation based on fluctuating renewable energy sources is a main challenge within HMGS operation and design. Battery energy storage systems are seen as a crucial component to integrate a high share of renewable energy into a HMGS. Currently, there are very few studies in the field of mathematical optimization and multi-criteria decision analysis that focus on the evaluation of different battery technologies and their impact on HMGS design. The model proposed in this paper aims at optimizing three different criteria, namely the minimization of the electricity costs, the loss of load probability and the maximization of locally available renewable energy usage. The model is applied in a case-study in the south of Germany. The optimization is carried out using the C-DEEPSO algorithm. Results are used in an AHP-TOPSIS model using expert weights to identify the most recommendable alternative out of five different battery technologies. Lithium batteries are considered to be the best solution based on the given group preferences and optimization results.
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