Investigation of Optimal PV Allocation to Minimize System Losses and Improve Voltage Stability for Distribution and Transmission Networks Using MATLAB and DigSilent - Rasheed, Mohamed Abdul; Verayiah, Renuga (Frontiers in Energy Research, 2021)
Electricity generation from renewable energy such as solar is an emerging sustainable solution. In the last decade, this sustainable source is not only being used as a source of power generation but as a Distributed Generations (DG). Many literatures have been published in this field with the objective to minimize losses by optimizing DG size and location. System losses and voltage profile goes hand in hand, as a result, when system losses are minimized eventually voltage profile improves. With the improvement in inverter technologies, PV DG units do not have to operate at unity power factor. Majority of proposed algorithms and methods do not consider power factor optimization as a necessary optimization. This paper aims to optimize size, location and power factor of PV DG units. The simulations are performed on IEEE 33 bus radial distribution network and IEEE 14 bus transmission network. The methodologies developed in this paper is divided into two sections. The first section aims to optimize PV DG size and location. A multi-objective function is developed by using system losses and voltage deviation index. Genetic Algorithm (GA) is used to optimize the multi-objective function. Next, analytical process developed for verification. The second section aims to further enhance the PV DG by optimizing the power factor of the PV DG. The simulation is performed for static load for both system, which are IEEE 33 bus radial distribution network and IEEE 14 bus transmission network. A mathematical analytical method was developed and it was found to be sufficient to optimize the power factor of PV DG unit. The results obtained shows that VSIs help to minimize the computation time by determining the optimal locations for DG placement in both networks. In addition, the GA method attained faster convergence as compared to analytical method, and hence the best optimal sizing for both test systems with minimum computation time. Additionally, optimization of power factor for both the test systems has demonstrated further improvement to the voltage profile and loss minimization. In conclusion, the proposed methodology has shown promising results for both transmission and distribution network.