Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment
|Title:||Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment||Authors:||Soroudi, Alireza
|Permanent link:||http://hdl.handle.net/10197/6204||Date:||Jan-2011||Abstract:||In deregulated power systems, distribution network operators (DNO) are responsible for maintaining the proper operation and efficiency of distribution networks. This is achieved traditionally through specific investments in network components. The event of distributed generation (DG) has introduced new challenges to these distribution networks. The role of DG units must be correctly assessed to optimise the overall operating and investment cost for the whole system. However, the distributed generation owners (DGOs) have different objective functions which might be contrary to the objectives of DNO. This study presents a long-term dynamic multi-objective model for planning of distribution networks regarding the benefits of DNO and DGOs. The proposed model simultaneously optimises two objectives, namely the benefits of DNO and DGO and determines the optimal schemes of sizing, placement and specially the dynamics (i.e. timing) of investments on DG units and network reinforcements over the planning period. It also considers the uncertainty of electric load, electricity price and wind turbine power generation using the point estimation method. The effect of benefit sharing is investigated for steering the decisions of DGOs. An efficient two-stage heuristic method is utilised to solve the formulated planning problem and tested on a real large-scale distribution network.||Type of material:||Journal Article||Publisher:||Institute of Engineering and Technology (IET)||Copyright (published version):||2011 The Institution of Engineering and Technology||Keywords:||Distributed generation; Immune algorithm; Dynamic planning; Multi-objective optimization; Point estimate method||DOI:||10.1049/iet-gtd.2010.0721||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Electrical and Electronic Engineering Research Collection|
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