ABSTRACT
A challenge that consistently arises when reviewing and justifying novel energy models and theorems is the accuracy of the electrical data used. Therefore, this paper presents a dataset representing a complete European residential community based on real-life data. In this case, a community of 250 residential households was constructed with actual energy consumption and photovoltaic generation profiles collected by smart meters in households in different European locations. Additionally, 200 members of the community were ascribed with their photovoltaic generation, while 150 were owners of a battery storage system. New profiles were generated from the sample collected and were randomly given to each end-user according to their previously defined characteristics. Furthermore, one regular and one premium vehicle were allocated to each household - a total of 500 electric vehicles - with information on their capacity, state of charge, and usage. Moreover, data on the location, type, and prices of public electric vehicle chargers were specified.
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.
