Distributed Generation Allocation under Different Network Conditions

Abstract

In an electrical power system, maintaining a continuous balance between varying load demand andelectrical generation is a must. Furthermore, it is desirable that the system losses must be minimal. Looking for new sources of energy becomes very important concern in the areas of excessive load growth. Wind and Photovoltaic distributed generators (DGs) that are optimally located and sized in power systems, are a powerful trend in this regard. In this thesis, an algorithm is proposed to estimate the probabilistic optimal power flow analysis (P-OPF) and determine the appropriate location and size of the DGs for the purpose of minimizing the electrical power losses on the distribution system feeders.Many uncertain parameters are taken into consideration when the proposed algorithm is implemented. In the presented case study, renewable energy sources are to be integrated with the grid such as solar and wind energies. The probabilistic nature of wind speed and solar irradiance is taken into account. Modeling of the input data uncertainties such as load demand, wind speed, and solar irradiance is essential. The deterministic optimal power flow analysis OPF consider the maximum load demand as the input power data required by each node which is not accurate as it doesn’t take the uncertainties accompanied with the load demand. The load variations at every node of the grid are not precisely predictable throughout the year to the power utility specially the small ones. In this study load demand is modeled by dividing the year into 12 periods to accommodate the load variations. While for the sake of modeling the generated power from the wind turbines, Weibull probability density function (PDF) is considered a very good expression that is often used to model the wind speed behavior.Regarding modeling of solar arraysoutput power,the probability of solar irradiance occurrence is studied using the data gathered daily per hour over a whole year. Twelve levels are suggested to represent the solar irradiance variations. The deterministic optimal power flow analysis OPF is used to examine the state of the power system under study but the uncertainties accompanied with input data made it impossible for the deterministic OPF to reveal the power system state accurately. Probabilistic evaluation is a majorconcern in this area. One of the main requirements is the computation of the probabilistic optimal power flowP-OPF while planning the power system operation. Carrying out P-OPF analysis for eachlikelyor probable combination of load demand level, distributed generators site and size is impossibleas it requires an extremelyenormous computational effort; for that reason, the optimization techniquesthat havesatisfactory accuracy in addition to a tractable computation are desired for studying the system. Evolutionary algorithms (EAs) are used for the computationally challenging problems as they are stochastic search methods. These algorithms are inspired by the natural biological evolution principles and are soeffectiveinsolving the complex optimization problems. In general, an evolutionary algorithm EA returns a population of solutions and concurrentlydevelops a population of likely solutions. Evolutionary algorithms have so many techniques that include evolution strategies (ES), genetic algorithms (GAs), biogeography-based optimization (BBO),differential evolution (DE), andevolutionary programming (EP). In this thesis comparison between two evolutionary algorithms EAs;genetic algorithm (GA) and differential evolution algorithm (DE) is handled. Allocation of wind farms and photovoltaic arrays is determined for the different scenarios of the case study proposed using evolutionary algorithms; genetic algorithm GA and differential evolution DE algorithm. A comparison is made between the two algorithms from two point of views; the results obtained and the time consumed by each one of the algorithms. The total system losses minimization is assigned as the fitness function FF for this problem. Probabilistic optimal power flow analysis is repeated three times for three different levels of renewable energy penetration to the power system.