Journal article
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2016 |
Pages: | 1720-1733 |
ISSN: | 21622388 , 2162237x , 19410093 and 10459227 |
Types: | Journal article |
DOI: | 10.1109/TNNLS.2015.2461491 |
ORCIDs: | Morais, Hugo |
Adaptation models Adaptive learning Artificial Intelligence Artificial neural networks Bilateral contracts Commerce Competitive electricity markets Computer Networks and Communications Computer Science Applications Context Costs Databases Distributed Energy Resources Electric power transmission networks Electricity market Electricity supply industry Energy efficiency Energy resources Evolutionary particle swarm optimizations Financial data processing Financial markets Investments Market opportunities Neural networks Optimization Particle swarm optimization (PSO) Portfolio optimization Portfolios Power markets Power system management, operation and economics Renewable energy resources Renewable energy source SDG 7 - Affordable and Clean Energy Smart power grids Software adaptive portfolio optimization artificial neural network (NN) artificial neural networks balancing markets bilateral contracts day-ahead spot markets distributed energy resources electricity markets energy efficiency evolutionary particle swarm optimization process intraday negotiations multiagent simulation multiple electricity markets participation optimisation portfolio optimization power markets smart grid concept smart power grids swarm intelligence