Short-term non-convex economic hydrothermal scheduling using dynamically controlled particle swarm optimization

Paper presented to the 3rd Southern African Solar Energy Conference, South Africa, 11-13 May, 2015.The aim of this paper is to present short-term hydrothermal scheduling (STHS) of power system. This problem is solved in such a way that utilizes available hydro reserves optimally and thus minimizes the fuel cost of thermal plants. A PSO based method is developed which can efficiently deals with hydro constraints like reservoir storage volume limits, water discharge rate limits, water dynamic balance, initial and final reservoir storage volume limits, etc. for a given time horizon. The operators of the PSO are dynamically controlled. Moreover, the cognitive and social behaviors of the swarm are modified for better exploration and exploitation of the search space. The effectiveness of the proposed method has been investigated on a standard test generating system considering several operational constraints pertaining to hydrothermal systems.dc201

Optimal coordinated control of OLTCs using Taguchi method to enhance voltage stability of power systems

On-load tap changer (OLTC) plays a significant role to regulate the voltage of the power system. Although some time, secondary voltage of an OLTC is pulled down when tappings are raised to restore the voltage level. This situation may finally lead to voltage collapse. To address this problem, the behavior of tap setting of OLTCs is investigated, and critical transformer and its allowed range of tap settings is identified in the paper. This paper also proposes a Taguchi based method to find optimal tap settings of OLTCs including critical transformer to improve the voltage stability margin and decrease the real power loss of the system. The proposed method is tested on IEEE 30-bus system to validate its applicability.10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, Chinahttp://www.elsevier.com/locate/procediaam2020Electrical, Electronic and Computer Engineerin

Multiobjective nested optimization framework for simultaneous integration of multiple photovoltaic and battery energy storage systems in distribution networks

The rapid growth of renewables in modern distribution networks results in the spilling of energy due to the limited hosting capacity of these networks, violation of system constraints, reduced network efficiency, and improper utilization of resources. Battery energy storage system (BESS), in spite of its high cost, a shorter life and complex control, offers a flexible solution for the problem. In this paper, a multiobjective nested optimization framework is developed for the simultaneous optimal allocation of multiple solar photovoltaics (SPVs) and BESSs in the distribution networks. The framework involves a two-layered structure; the outer layer provides tentative planning solutions to the inner layer that optimizes the desired objectives of network operations and then returns the functional values back to the outer layer. The purpose of the inner-layer is to satisfy the operational constraints of the networks and ensure the optimal utilization of BESS capacities, suggested by the outer layer, at the time of planning itself. A new BESS operating strategy is proposed for optimum utilization of BESS. The nested multiobjective optimization problem is handled by suggesting a new weighted sum approach in conjunction with a recently developed swarm intelligence-based algorithm, i.e. moth search optimization. Overall, the proposed deterministic model essentially ensures the high penetration of SPVs and the optimal utilization of BESSs to justify their installation. The optimization model is investigated on a benchmark 33-bus test distribution network. The application results highlight enhanced energy efficiency, peak load shaving, high renewable penetration, voltage profile improvement, and mitigation of reverse power flow while effectively absorbing the excess renewable power generation during light load hours