Optimization of social welfare and transmission losses in offshore MTDC networks through multi-objective genetic algorithm

A multi-objective approach, for an envisioned future DC independent system operator (ISO), on how to optimally operate an offshore multi-terminal DC network is presented in this paper. A pool market is used, in which the ISO receives the bids, of both producers and consumers connected to the offshore network, and determines the electricity spot price. A trade-off between maximization of the social welfare and the minimization of transmission losses is analyzed. The offshore multi-terminal DC (MTDC) network here implemented is based on recent studies [1]. The multi-objective optimization algorithm (MOOA) determines an optimal power flow (OPF), which guarantees the network constrains - e.g. DC voltages boundaries, maximum DC cable current and power produced at the offshore wind farms - as defined by the ISO are all respected. Even on DC Networks the system losses, capitalized over a year, can be in the range of tenths of millions of euros [2]. Consequently, a fair power losses allocation among loads and generators has an important impact on their benefits. Therefore, a losses allocation technique is implemented in the algorithm. System security is also taken into consideration. In order to enhance the DC system stability with regard to predictable changes - demand and generation evolution - and unpredictable events - e.g. an outage at of one of the DC voltage controlling stations - the results of the OPF are also tested to make sure that the offshore network always remains at least N-1 secure [3].

[1]  Lamine Mili,et al.  Economic Market Design and Planning for Electric Power Systems , 2009 .

[2]  A. Osyczka,et al.  A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm , 1995 .

[3]  Ujjwal Maulik,et al.  Multiobjective Genetic Algorithms for Clustering - Applications in Data Mining and Bioinformatics , 2011 .

[4]  J. Stonham,et al.  Decomposition model and interior point methods for optimal spot pricing of electricity in deregulation environments , 2000 .

[5]  Federico Milano,et al.  Multiobjective optimization for pricing system security in electricity markets , 2003 .

[6]  Goran Strbac,et al.  Contributions of individual generators to loads and flows , 1997 .

[7]  Jizhong Zhu,et al.  Optimization of Power System Operation , 2009 .

[8]  Vassilios Petridis,et al.  Optimal power flow by enhanced genetic algorithm , 2002 .

[9]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[10]  T. Numnonda,et al.  Optimal power dispatch in multinode electricity market using genetic algorithm , 1999 .

[11]  Marcelino Madrigal Optimization models and techniques for implementation and pricing of electricity markets , 2001 .

[12]  D.S. Linden,et al.  Using a real chromosome in a genetic algorithm for wire antenna optimization , 1997, IEEE Antennas and Propagation Society International Symposium 1997. Digest.

[13]  A. Conejo,et al.  Transmission Loss Allocation: A Comparison of Different Practical Algorithms , 2002, IEEE Power Engineering Review.

[14]  Syed Nasar,et al.  Electric Energy Systems , 1995, Energy for Sustainable Society.

[15]  Antonio J. Conejo,et al.  Z-Bus Loss Allocation , 2001 .

[16]  Janusz Bialek,et al.  Tracing the flow of electricity , 1996 .

[17]  H. Rudnick Chile: Pioneer in deregulation of the electric power sector , 1994, IEEE Power Engineering Review.