Adaptive Portfolio Optimization for Multiple Electricity Markets Participation

Tiago Pinto, Hugo Morais, Tiago M. Sousa, Tiago Sousa, Zita Vale, Isabel Praca, Ricardo Faia, Eduardo Jose Solteiro Pires

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.
Original languageEnglish
JournalI E E E Transactions on Neural Networks and Learning Systems
Volume27
Issue number8
Pages (from-to)1720-1733
Number of pages14
ISSN2162-237X
DOIs
Publication statusPublished - 2016

Keywords

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Adaptive learning
  • artificial neural network (NN)
  • electricity markets
  • multiagent simulation
  • portfolio optimization
  • swarm intelligence
  • Commerce
  • Costs
  • Electric power transmission networks
  • Energy efficiency
  • Energy resources
  • Financial data processing
  • Financial markets
  • Investments
  • Neural networks
  • Particle swarm optimization (PSO)
  • Renewable energy resources
  • Smart power grids
  • Bilateral contracts
  • Competitive electricity markets
  • Distributed Energy Resources
  • Electricity market
  • Evolutionary particle swarm optimizations
  • Market opportunities
  • Portfolio optimization
  • Renewable energy source
  • Power markets
  • smart power grids
  • optimisation
  • power markets
  • evolutionary particle swarm optimization process
  • adaptive portfolio optimization
  • multiple electricity markets participation
  • distributed energy resources
  • smart grid concept
  • energy efficiency
  • day-ahead spot markets
  • balancing markets
  • intraday negotiations
  • bilateral contracts
  • artificial neural networks
  • Electricity supply industry
  • Optimization
  • Artificial neural networks
  • Context
  • Databases
  • Portfolios
  • Adaptation models
  • Power system management, operation and economics

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