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Abstract: This paper proposes a novel method for solving the Non-convex Economic Dispatch (NED) problems, by the Fuzzy Adaptive Modified Particle Swarm Optimization (FAMPSO). Practical ED problems have non-smooth cost functions with equality and inequality constraints when generator valve-point loading effects are taken into account. Modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution for ED problems. PSO is one of modern heuristic algorithms, in which particles change place to get close to the best position and find the global minimum point. However, the classic PSO may converge to a local optimum solution and the performance of the PSO highly depends on the internal parameters. To overcome these drawbacks, in this paper, a new mutation is proposed to improve the global searching capability and prevent the convergence to local minima. Also, a fuzzy system is used to tune its parameters such as inertia weight and learning factors. In order to evaluate the performance of the proposed algorithm, it is applied to a system consisting of 13 and 40 thermal units whose fuel cost function is calculated by taking account of the effect of valve-point loading. Simulation results demonstrate the superiority of the proposed algorithm compared to other optimization algorithms presented in literature. [Copyright &y& Elsevier]

Dynamic economic dispatch with valve-point effects (DED_ vpe) is a high-dimensional constrained optimization problem with non-convex and non-smooth characteristics. Hybrid methods are one of the most advanced methods to solve the problem. However, most of these methods improve the solution accuracy at the expense of algorithm robustness. This paper proposes an adaptive hybrid backtracking search optimization algorithm (AHBSA) for solving the DED_ vpe. The core idea of AHBSA lies in designing a suitable coupling structure based on the current best individual (called optimal partial coupling). The structure hybridizes an improved BSA mutation operator and the DE/best/1 operator with equal probability. The improved BSA mutation operator uses the current best individual and the historical population to update individual position, called BSA/best/old. It is also the first research work of extending BSA to the problem. In addition, an adaptive parameter control mechanism is proposed to select an appropriate 'mixrate' value for achieving better coupling. The performance of AHBSA is validated on six DED test cases of three systems. Experimental results demonstrate that, compared with some representative methods, AHBSA not only reduces the fuel cost but also ensures the robustness of the algorithm. • A hybrid method AHBSA is proposed to solve DED problem with valve-point effects. • An optimal partial coupling structure is designed in AHBSA. • BSA is the first attempt at solving DED problem. • Results on six benchmark DED test cases show AHBSA is competitive. [ABSTRACT FROM AUTHOR]

Multi-area economic dispatch (MAED) is an important non-linear optimization problem in power system operation. MAED involves multiple power generation areas, and minimizes the total fuel cost by determining the power generation within areas and the power interchange between areas. This paper proposes an improved competitive swarm optimization (ImCSO) algorithm to solve the MAED problems. The ImCSO algorithm introduces two improvements into competitive swarm optimization for performance enhancement. Firstly, a ranking paired learning strategy is adopted to enhance the learning efficiency of the loser particles; secondly, a differential evolution strategy is used to update and improve the winner particles. Combining a constraint repair technique, the proposed ImCSO algorithm is applied to solve 10-unit, 40-unit and 120-unit multi-area static economic dispatch and 40-unit multi-area dynamic economic dispatch problems. The solved MAED problems integrate comprehensive constraints such as valve point effect, multiple fuels, transmission loss, tie-line constraint, prohibited operating zone and ramp rate limit. Through comparison with other well-established optimization algorithms, it is observed that the ImCSO algorithm has superiority in terms of solution accuracy and reliability in solving the MAED problems. • A novel method ImCSO is proposed for solving multi-area economic dispatch (MAED). • Ranking paired learning and differential evolution strategies are adopted in ImCSO. • Static and dynamic MAED problems integrating comprehensive constraints are studied. • Simulation results show that ImCSO has superiority in solution accuracy and reliability. [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses energy savings in wastewater processing plant pump operations and proposes a pump system scheduling model to generate operational schedules to reduce energy consumption. A neural network algorithm is utilized to model pump energy consumption and fluid flow rate after pumping. The scheduling model is a mixed-integer nonlinear programming problem (MINLP). As solving a data-driven MINLP is challenging, a migrated particle swarm optimization algorithm is proposed. The modeling and optimization results show that the performance of the pump system can be significantly improved based on the computed schedules. [Copyright &y& Elsevier]