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To meet the future power demand, distribution systems (DSs) need to be optimally planned. In this paper, an optimal expansion planning approach of DS is suggested. The capability of the existing DS is evaluated and load forecasting for next ten years is performed. Further, the load flow analysis by using backward-forward sweep algorithm is performed. Results show that existing networks would not be able to meet the future demand and due to this, voltage drop (VD) and power losses might be increased. Therefore, DS expansion planning is carried out considering future demand growth and distributed generation (DG) placement using grid dependent multi-objective harmony search algorithm (GrMHSA). The implementation of GrMHSA optimization technique reduces the total losses as well as VD for the targeted year. A practical Debre Markos (D/M) distribution network of Ethiopia is used for illustrating the superiority of the proposed technique and verifies its effectiveness. [ABSTRACT FROM AUTHOR]

• AGSO algorithm is proposed and implemented on economic cooling-load dispatch of multi-chiller plants. • Energy-saving capability of AGSO based OCL strategy is proved. • Numerical results of AGSO approach are compared with best solutions of recently published algorithms. • AGSO with fast convergence finds better solutions in lower calculation times. Improper operation of a multi-chiller plant would significantly increase associated electric consumption. This paper introduces a new model to determine the optimal loading point of the chillers. To do so, optimal dispatching of multi-chiller plant is expressed as an optimization problem and associated constraints are all together accommodated in the process of optimization. The problem is tackled by a new version of a metaheuristics approach. The proposed approach is entitled as augmented group search optimization (AGSO) algorithm, which is devised to avoid drawbacks of conventional group search optimization algorithm such as trapping in the local minima. The effectiveness and robustness of the proposed approach in comparison with available methods are studied through three well-known test cases. Numerical results demonstrate that AGSO with its strong exploration capability achieves a lower energy consumption than that of recently published methods with higher convergence speed. [ABSTRACT FROM AUTHOR]