Abstract
Today’s electrical power system became more complex interconnected network that is expanding every day. The transmission lines of the power system are more severely loaded than ever before. Hence, the power system is facing many problems such as power losses increasing, voltage instability, line overloads, etc. The optimization of real and reactive powers due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile especially, for congested networks. As a result, the optimal power flow problem (OPF) is considered more important tool for the processes of planning and operation of power systems. OPF is a very significant tool for power system operators to meet the electricity demand of the consumers efficiently, and for the reliable operation of the power system. However, the incorporation of renewable energy sources (RESs) into the electrical grid is a very challenging problem due to their intermittent nature. In this paper, the proposed power flow model contains three different types of energy sources: thermal power generators representing the conventional energy sources, wind power generators (WPGs), and solar photovoltaic generators (SPGs) representing RESs. Uncertain output powers from WPGs and SPGs are forecasted with the aid of Weibull and lognormal probability distribution functions (PDF), respectively. The under and overestimation output powers of RESs are taken into consideration while formulating the objective function through adding a penalty and reserve cost, respectively. Moreover, carbon tax is imposed to the main objective function to help in reducing carbon emissions. A jellyfish search optimizer (JS) is employed to reach optimization in the modified IEEE 30-bus test system to validate its feasibility. To examine the effectiveness of the proposed JS algorithm, its simulation results are compared with the results of four other nature-inspired global optimization algorithms. The developed OPF algorithm considers several practical cases such as generation uncertainty of renewable energy sources, time-varying load and the ramp rate limits of thermal generators. The simulation results show the effectiveness of the JS algorithm in solving the OPF problem in terms of minimization of total generation cost and solution convergence.
Highlights
The results prove the effectiveness of the jellyfish search optimizer (JS) algorithm, fast convergence, and high solution quality compared with the other optimal power flow (OPF) optimization algorithms
In this paper, a new metaheuristic optimization algorithm based on jellyfish search optimizer has been proposed for providing an optimal solution of the OPF problem incorporating with stochastic wind and solar energy sources in the IEEE-30 bus power system
To verify the validity of JS algorithm, four recent optimization algorithms: artificial bee colony (ABC), chaos game optimization (CGO), flower pollination algorithm (FPA), and Giza pyramids construction (GPC) are applied for the modified IEEE-30 bus system
Summary
In [13], a simple genetic algorithm (SGA) has been applied for solving OPF problem, where a sequential GA solution scheme has been employed to achieve suitable control variable resolution without violation of system constraints. [42] proposed a modification on DE called SHADE algorithm where the selection process of future control parameters is guided through the successful control parameters settings to guarantee an appropriate balance concerning the exploration and exploitation phases This helped in achieving comparatively fast convergence rate for OPF problems. The proposed algorithm is applied to IEEE 30 –bus system incorporating with two wind generators and one solar PV generator to verify its validity in obtaining the optimal solution for OPF problem with renewable energy sources during theoretical and practical conditions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
