03506nam a22001577a 4500999001700000082001200017100008000029245010400109260003000213300001200243500290800255700004203163856003303205942001103238952009903249  c66881d66878  aR/IEL21  aAamir Alia(Roll No. PEL-004/7)aSupervisor Prof. Dr. Muhammad Usman Keerio  aOptimal Site Size of Distributed Generation in Power System Using Evolution Algorithms (PhD Thesis)  aNawabshah:bQUEST,c2021.  a219p. ;  aABSTRACT

The Optimal integration of renewable distributed generation (DG) in the power system is gaining attention due to their network support capability, modular design, sustainable and emission-free and supply reliability of network with ever-increasing load. Optimization of DG integration is a mixed-integer, non-linear, and non-convex optimization problem. In the literature, various classical optimization techniques such as linear programming, mixed-integer linear programming, quadratic programming, and non-linear programming are used to integrate DG in a power system. These methods are unable to find the optimal global solution to such problems. Various evolutionary algorithms (EAs) introduced the revolution in numerical optimization methods during the last three decades. These methods can successfully overcome classical practices such as avoiding trap into local optima and efficiently searching for optimal global solutions. Therefore, in this dissertation, a new problem formulation is considered to optimize the site and size of DG in power system incorporating various components. The Optimization above problem is solved by considering the different recent variants of differential evolution (DE) such as composite differential DE (CoDE) and constrained CoDE (C2ODE) along with various constraint handling technique (CHT) for the single-objective optimization. Afterwards, the optimal DG allocation problem is solved by considering multiple conflicting objective functions simultaneously. To find the better trade-off between these multi-objective functions, recent multi-objective evolutionary algorithms such as weighted sum approach, improved decomposition-based evolutionary algorithm (I-DBEA), non-dominated sorting genetic algorithms II (NSGA-II), hybrid C2oDE and NSGA-II called two-phase (ToP), constrained coevolutionary optimization algorithm (CCMO), and improved decomposition-based evolutionary algorithm (I-DBEA) are incorporated for finding the solution of DG allocation problem. The proposed single and multi-objective EAs are combined with the various CHTs these include epsilon constraint method (ECM), adaptive trade-off model (ATM), and constrained domination principal (CDP) to discard the infeasible solutions during optimization process. These CHTs and single and multi-objective EAs successfully implemented to find the optimal renewable DG allocation solution considering uncertainties in load demand and renewable generation. These uncertainties are modeled by using a probabilistic approach. Regarding power system planning considering renewable DG units, theoretical analysis is carried out to investigate the impact of active and reactive power injection (by DGs) considering various conflicting objective functions. 
Keywords: Evolutionary algorithms, renewable distributed generation, transmission network, distribution network, optimal power flow.   aDepartment of Electrical Engineering   uhttps://tinyurl.com/2smy4e5m  cTHESIS  00104070aRESEARCHbRESEARCHd2021-03-03l0oR/IEL21pPP/58-496r2021-03-03 00:00:00yTHESIS