Solving financial allocation problem in distribution system expansion planning (original) (raw)

Abstract

This paper introduces a new technique to solve financial allocation in Distribution System Expansion Planning (DSEP) problem. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer programming (MIP) problem. It consist the hybridization of Hopfield Neural Network (HNN) and Boltzmann Machine (BM) in first and second phase respectively. During the execution at the first phase, this model will select the feasible units meanwhile the second phase will restructured until it finds the best solution from all the feasible solution. Due to this feature, the proposed model has a fast convergence and the accuracy of the obtained solution. This model can help planners in decision-making process since the solutions provide a better allocation of limited financial resources and offer the planners with the flexibility to apply different options to increase the profit. 1. INTRODUCTION As per present, scenario demand of electric power generation is increasing. Due to the increasing demands, several power supply failures might cause major social losses. Failures are caused by many factors such as type, design, weather condition or geographical location. The distribution system is the most extensive part of the electrical system, and consequently, it is the mainly responsible for energy losses [1-3]. Thus, a meticulous distribution system expansion planning (DSEP) must be provided to supply reliable electricity to consumers. Power system planning is defined as a process of determining a minimum cost strategy for long-range expansion of the generation, transmission and distribution systems so that it is sufficient enough to supply the load forecast within a set of technical, economic and political constraint [4]. As for DSEP, the goal is to fulfill electricity load increment at the lowermost cost and consumer's reliability desires with a level of satisfaction [5]. One of the important factors in the DSEP is included with well-calculated or analyzed investment planning that allowed by the planners. The planners plan a strategic decision related to a whole power system network and also particular individual simultaneously. Nevertheless, planners faced a problem in deciding on how much a portfolio to allocate to the different type of assets. In this case, financial allocation plays a crucial role in solving the planner's problem. Its aim is to balance risk and reward by apportioning a portfolio asset according to an individual goals, risk tolerance and investment horizon [6, 7]. In real situations, financial allocation problems are complicated and non-linear programming problem which is hard to solve. One of the ways of tackling this problem is by using the artificial neural network (ANN) since it is a useful

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References (27)

  1. Carrano EG, Cardoso RTN, Takahashi RHC, et al. Power distribution network expansion scheduling using dynamic programming genetic algorithm. Gener Transm Distrib IET 2008; 2: 444-455.
  2. Trebolle D, Gomez T, Cossent R, et al. Distribution planning with reliability options for distributed generation. Electr Power Syst Res 2010; 80: 222-229.
  3. Valinejad J, Barforoushi T. Generation expansion planning in electricity markets: A novel framework based on dynamic stochastic MPEC. Electr Power Energy Syst 2015; 70: 108-117.
  4. Abadie LM, Chamorro JM. Valuing expansions of the electricity transmission network under uncertainty: The binodal case. Energies 2011; 4: 1696-1727.
  5. Malee RK, Jain P, Gupta PP, et al. Distribution System Expansion Planning Incorporating Distributed Generation. In: 2016 IEEE 7th Power India International Conference (PIICON). Bikaner, 2016, pp. 1-6.
  6. Abu-Elanien AEB, Salama MMA. Asset management techniques for transformers. Electr Power Syst Res 2010; 80: 456-464.
  7. Casamatta F, Cirio D, Lucarella D, et al. Management of interruptible loads for power system security and operation. IEEE Power Eng Soc Summer Meet 2002; 2: 880-885.
  8. Plikynas D, Aleksiejūnas R. Neural Network Based Multiagent System for Simulation of Investing Strategies. Lect Notes Comput Sci 2007; 4676: 164-180.
  9. Altun H, Yalcinoz T. Implementing soft computing techniques to solve economic dispatch problem in power systems. Expert Syst Appl 2008; 35: 1668-1678.
  10. Tabares A, Franco JF, Lavorato M, et al. Multistage Long-Term Expansion Planning of Multiple Alternatives. IEEE Trans Power Syst 2016; 31: 1900-1914.
  11. Shortle J, Rebennack S, Glover FW. Transmission-capacity expansion for minimizing blackout probabilities. IEEE Trans Power Syst 2014; 29: 43-52.
  12. Davidov S, Pantoš M. Stochastic assessment of investment efficiency in a power system. Energy 2017; 119: 1047- 1056.
  13. Abd-el-motalab AM, Alvarado L. Electrical Power and Energy Systems Evaluating the feasibility of operation and planning practices for mutual bene fi ts of DNOs and power developers. Electr Power Energy Syst 2018; 96: 30-42.
  14. Pinto T, Morais H, Sousa T, et al. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation. IEEE Trans Neural Networks Learn Syst 2016; 27: 1720-1733.
  15. Markowitz H. Portfolio Selection. J Finance 1952; 7: 77-91.
  16. Li YZ, Wu QH, Li MS, et al. Mean-variance model for power system economic dispatch with wind power integrated. Energy 2014; 72: 510-520.
  17. Santos-Alamillos FJ, Thomaidis NS, Usaola-García J, et al. Exploring the mean-variance portfolio optimization approach for planning wind repowering actions in Spain. Renew Energy 2017; 106: 335-342. Bulletin of Electr Eng and Inf ISSN: 2302-9285
  19. Silva T, Pinheiro PR, Poggi M. A more human-like portfolio optimization approach. Eur J Oper Res 2017; 256: 252-260.
  20. DeLlano-Paz F, Calvo-Silvosa A, Antelo SI, et al. Energy planning and modern portfolio theory: A review. Renew Sustain Energy Rev 2017; 77: 636-651.
  21. Gasser SM, Rammerstorfer M, Weinmayer K. Markowitz revisited: Social portfolio engineering. Eur J Oper Res 2017; 258: 1181-1190.
  22. Delarue E, De Jonghe C, Belmans R, et al. Applying portfolio theory to the electricity sector: Energy versus power. Energy Econ 2011; 33: 12-23.
  23. Ackley D, Hinton G, Sejnowski T. A learning algorithm for boltzmann machines. Cogn Sci 1985; 9: 147-169.
  24. Yaakob SB, Watada J. Solving bilevel quadratic programming problems and its application. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Germany, pp. 187-196.
  25. Yaakob SB, Watada J, Fulcher J. Structural learning of the Boltzmann machine and its application to life cycle management. Neurocomputing 2011; 74: 2193-2200.
  26. Yaakob SB, Watada J, Takahashi T, et al. Reliability enhancement of power systems through a mean-variance approach. Neural Comput Appl 2012; 21: 1363-1373.
  27. Tahar SHM, Yaakob SB, Ahmed A. An improved Boltzmann machine for strategic investment planning in power system environment. Indones J Electr Eng Comput Sci 2017; 6: 259-267.