Abstract
In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives.
Highlights
An electrical power system is comprised of generation sources, transmission lines and distribution system
A multi-constraint multiobjective optimal power flow multi-objective optimal power flow (MOOPF) problem is formed for power system operation with intermittent RESs, and afterwards is solved by the particle swarm optimization(PSO) algorithm hybrid with the probability density function (PDF), penalty function approach (PFA), fuzzy membership function (FMF)
Rather than using true Pareto base optimization, we have developed a computationally efficient approach which finds the global optimal solution of MOOPF problem by using split approach
Summary
An electrical power system is comprised of generation sources, transmission lines and distribution system. Ilyas et al.: MOOPF With Integration of Renewable Energy Sources Using FMF of modern control technologies into optimal power flow (OPF) study. Recent studies show that variable energy resources such as solar and wind pose dynamics that span multiple time scales, affecting different layers of power system’s control These findings illustrate that traditional load flow studies are no longer sufficient to ensure reliability through optimal resource allocation, as penetration of RESs continue to grow [21]. To maintain reliability of the electric power system with the high penetration of RESs, it is required to have system operators to flexibly manage generation resources to handle uncertainty of solar and wind generation In this context, this topic is novel in such a way that equilibrium states have been obtained through mathematical formulation.
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
