a two loop hybrid method for optimal placement and scheduling of switched capacitors in distribution networks

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A Two-Loop Hybrid Method for Optimal Placement and Scheduling of Switched Capacitors in Distribution Networks

This paper presents a method to find the optimal size and place of the switched capacitors using a hybrid optimization algorithm. The objective function includes the active and reactive power of power plants, the capital and maintenance costs of capacitor banks, and the cost of active and reactive power losses in distribution lines and transformers. The impact of the load model on the optimal sizing and placement of switched capacitors is studied using three different scenarios: In the first scenario, all loads are voltage-dependent; in the second scenario, only a portion of loads are voltage-dependent; in the third scenario, all loads are voltage-independent. The proposed hybrid algorithm incorporates an outer and two inner optimization layers. The outer layer is executed by a genetic algorithm (GA), while the inner layer is performed by a GA, an exchange market algorithm (EMA), or a particle swarm optimization (PSO). The performance of GA-GA, GA-EMA, and GA-PSO hybrid schemes are compared on an IEEE 33-bus test system. Moreover, IEEE 33-bus and 69-bus networks are used to verify the effectiveness of proposed hybrid scheme against the gravitational search algorithm (GSA), a combination of PSO and GSA (PSOGSA), cuckoo search algorithm (CSA), teaching learning-based optimization (TLBO), and flower pollination algorithm (FPA). The results highlight the advantage of the proposed hybrid optimization scheme over the other optimization algorithms

PV Inverter Based Volt/Var Control of Active Distribution Networks with Inverter Reliability Assessment and Enhancement

Traditional passive distribution networks can not sufficiently handle the voltage stability issues brought by the increasingly integrated PV systems, while an active distribution network, which features active management of distributed energy resources, can flexibly utilise PV inverters to provide a volt/var control (VVC) function to regulate the network voltage. However, PV inverters are vulnerable power electronics devices and utilising them for additional VVC support can further degrade their reliability, leading to shortened inverter lifetime and impaired economic benefits. In this regard, the thesis focuses on addressing the PV inverter reliability issues in VVC methods, via assessing the PV inverter reliability in VVC and proposing advanced PV inverter based VVC methods considering inverter reliability enhancement. The thesis consists of four stages of my research. Firstly, a comprehensive PV inverter reliability assessment approach is developed to evaluate inverter lifetime when used for VVC functions, and the impacts of the PV inverter based VVC on inverter lifetime are successfully quantified. Secondly, a PV inverter reliability constrained VVC method is proposed in which the constraints to enhance inverter reliability are developed with a restriction factor to regulate inverter apparent power outputs. This method can efficiently minimise network power loss and curtailed PV power, while guaranteeing long inverter lifetime. Thirdly, a PV inverter reliability constrained VVC approach with power smoothing is proposed, in which an inverter power smoothing scheme with high control flexibility is developed by utilising a power smoothing factor to constrict variations of inverter apparent power outputs. Additionally, a penalty convex-concave procedure (CCP) solution method is developed to solve the non-convex optimisation problem with high computing efficiency. Fourthly, a multi-objective PV inverter based VVC method is proposed to simultaneously minimise network power loss and inverter apparent power output, and a Pareto front analysis method is developed to select a solution to achieve efficient power loss reduction with expected inverter lifetime. All the proposed methods apply advanced network operating models and optimisation methods to address uncertainties. These methods have been successfully demonstrated and tested through comprehensive case studies, and numerical simulation results verified the feasibility and high efficiency of the proposed methods