Satish Kansal

Cost–benefit analysis for optimal distributed generation placement in distribution systems

This paper presents an optimisation method to determine optimal allocations of distributed generation (DGs) and capacitors based on maximisation of a profit/worth analysis approach. The optimal locations and sizes of DGs and capacitors have been determined by minimising the power distribution loss. This method considers various technical and economic factors such as line losses, sizes of DGs and capacitors at optimal locations, investment costs, operating costs and maintenance costs of DG and capacitor to achieve the objective for a predetermined number. The electricity market price of grid power has been considered to recover initial investment in a specified time period. The improvement in the voltage profile of the system has also been considered in this work. The particle swarm optimisation technique has been used to solve the optimal placement of DGs and capacitors to maximise the profit. The proposed technique is tested on 33-bus and 69-bus test systems.

Hybrid approach for congestion management using optimal placement of distributed generator

ABSTRACT Congestion management (CM) in a large power system network is a difficult task which can be solved by placing one or more distributed generators (DGs) on congested lines. The first concern is to determine the exact location of congested line for the placement of optimal size of DG so that cost can be minimised. In this work, hybridisation of firefly technique and differential evolution optimisation search has been proposed, which manages congestion effectively by rescheduling of generators satisfying the system constraints both technically and economically in the deregulated market scenario. To validate the proposed hybrid approach, results have been compared with firefly optimisation technique results. It is observed that the hybrid approach is an efficient tool in handling CM resulting in a secure operation to reduce flows in the heavily loaded lines with low system loss and increasing power capability with improved stability of network by controlling power flows in the network.

Application of PSO Technique in Multiarea Automatic Generation Control

This paper proposes particle swarm optimization (PSO) technique to optimize the gains of an integral controller for automatic generation control (AGC) of a three unequal area thermal power system. Every control area takes into consideration dynamics of the thermal systems. Load frequency of interconnected multiarea thermal power system is also controlled for obtaining a better steady-state response of system. Further, results of PSO technique are compared with the bacterial-foraging (BF) technique that reveals superior performance of PSO technique over BF technique.

Optimal Placement of Distributed Generation Using Genetic Algorithm Approach

Nowadays there is increasing number of small scale power generations which are connected to distribution networks termed as Distributed Generation (DG). The allocation of DG in distribution networks is intended for power flow control, improvement in stability and voltage profile, power factor correction, and reduction of line losses. This paper presents an optimization approach using Genetic Algorithm (GA) to find optimal location and size of DG in radial distribution system. The objective is to minimize the active power loss keeping the voltage profile in distribution system within defined limits. The effectuality of proposed approach is checked on IEEE 33 bus and 69 bus test systems.

Optimal installation of multiple type DGs considering constant, ZIP load and load growth

ABSTRACT Distributed generation (DG) has drawn the attention of researchers and industrialists for quite a time now due to its numerous advantages. This paper proposes an analytical approach for optimal installation of multiple type DGs in a radial distribution network with consideration of constant, ZIP load model (combination of constant impedance, current, power load models) and load growth. It mainly emphasises on: (i) optimal installation of multiple type DGs with a constant load, (ii) impact of considering ZIP load model on optimal installation of DGs, (iii) proposing load growth factor with load growth rate of 7.5% for the planning period of five years, (iv) impact of considering ZIP load model and load growth simultaneously and (v) cost–benefit analysis. The results are compared for IEEE 33 bus distribution network.