Optimizing distributed generation parameters through economic feasibility assessment

Abstract

To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method. Abstract -To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.2

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

“…Economic indicators are the most important for DG energy system planning since its investment potential is underlain by economic feasibility [283]. Finance-oriented DG optimisation often results in configuration with minimum capital investment, operational expenditure and cost of energy, subsequently leading to the greatest appeal for prospective investors and electricity buyers.…”

“…Optimal DG siting is based on voltage sensitivity and apparent power load; Specific DG operational schedule is considered; Optimised DG system with lagging power factor exhibits better technical and economic performances Gampa Muttaqi et al [283] Minimum operation and power reserve costs, and emission-related environmental cost Market balance, DG generation capacity, spinning reserve limit, power flow balance, demand reserve limit, and load shedding limit Load and reserve power ratings of multiple DG technologies Uncertainty in power dispatch scheduling associated with renewable variability is incorporated into cost function; Concurrent scheduling of electric vehicle (EV) and responsive load assists in peak shaving, load shifting, reserve power supply, operating cost and emission reduction, and system reliability improvement Rabiee et al [266] Stochastic (improved) PSO Hybrid PSO -SFLA Minimum real power loss, voltage deviation, and CCT index DG power limit, voltage limit, branch power flow constraint, and power flow balance Size and capacity of (multiple) microturbine DG PSO is coupled with SFLA to overcome its premature convergence problem; CCT index is introduced to quantify power system transient stability, and serves to minimise short-circuit fault in the system design; More security-based objective functions are anticipated Nayeripour et al [275] Hybrid In addition, as inspired by multi-modelling regression approach, development of a multi-algorithm optimisation framework for energy planning context could provide an alternative solution besides model hybridisation. This method shall embed different algorithms with varying computational time, exploration and exploitation skill levels.…”

Hybrid renewable energy-based distribution system for seasonal load variations

“…4,5 The DG location and sizing is performed by following analytical method with exact loss formula. 20 The placement and sizing of DG is followed by the type of DG used in distribution system. 7 The techniques are further extended to integrate different types of DG in smart distribution network.…”

“…19 The cost for integrating DG is considered with investment, maintenance, and operation cost for DG placement. 20 The placement and sizing of DG is followed by the type of DG used in distribution system. The abundant renewable sources are diverted along distribution system as DG.…”

A wolf pack hunting strategy based virtual tribes control for automatic generation control of smart grid

“…This drives the current energy management system (EMS) [3,4] to evolve from a centralized coordinated control into a decentralized autonomous control. In general, the generation dispatch and control of EMS are achieved by the cooperation of automatic generation control (AGC) [5,6], plant controller (PLC) and generator speed governor, among which AGC plays a very crucial role for active power balance and autonomous frequency control of an island power grid.…”