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Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment

Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment

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  • Author(s): A. Soroudi 1 ; M. Ehsan 1 ; R. Caire 2, 3 ; N. Hadjsaid 2, 3
    • View affiliations
    • Affiliations: 1: Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
      2: Department of Electrical Engineering, Grenoble Electrical Engineering Laboratory (G2Elab), Saint Martin d'Hères, France
      3: Department of Electrical Engineering, Inventer la Distribution Electrique de lAvenir (IDEA), Tehran, Iran
  • Source: Volume 5, Issue 9, September 2011, p. 961 – 972
    DOI:  10.1049/iet-gtd.2010.0721 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Published

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.

Inspec keywords: wind power plants; genetic algorithms; distributed power generation; power distribution planning; wind turbines; power markets

Other keywords: wind turbine power generation; long-term dynamic multiobjective model; deregulated power system; point estimation method; distribution network operator; DGO; distribution network planning; DNO; electricity price; distributed generation owner; hybrid immune-genetic algorithm method; deregulated environment; two-stage heuristic method; maximisation; electric load uncertainty

Subjects: Wind power plants; Distribution networks; Power system planning and layout; Distributed power generation; Power system management, operation and economics; Optimisation techniques

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