Optimal Solar plant placement using holomorphic embedded power Flow Considering the clustering technique in uncertainty analysis

Increasing energy consumption and reducing fossil energy reserves and its environmental consequences can lead to the use of clean and renewable energy sources such as solar energy. With an inappropriate placement of these units, the system losses is increased and voltage profile will be decreased, So the system efficiency will be decreased. Choosing the best placement of these power plants affects the amount of production of energy and its cost, and consequently amount of the emissions of pollutants. The purpose of this study is to find the best place of solar photovoltaic (SPV) plant to increase energy efficiency by reducing losses and improving voltage profiles. It is crucial task due to the stochastic variation of the PV output power which is related to the solar irradiance variations. In this paper, an improved clustering method with a non-iterative flow approach named as holomorphic embedding method (HEM) are used to solve the problem under the uncertainty condition. It is developed on the base of a tip how it possible to reduce the computation time of calculations. Achieving the mentioned goals has been made faster and easier by reducing the considered scenarios and by using the holomorphic load flow algorithm. Obtaining the solution is possible by using the particle swarm optimization (PSO) algorithm. The decision is based on converting the multi-objective function to a single-objective one. These objective functions are considered to have the lowest line loss and the lowest voltage deviation. The proposed approach is able to include a variety of possible scenarios in the problem analysis and it is applied on IEEE 14-bus test system by considering uncertainty of solar irradiance. Best results are obtained from the placement of PV unit in bus number 3 with operation at -0.27 Pf.