A long-term risk management tool for electricity markets using swarm intelligence

Abstract

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

“…The inertia weight of the PSO algorithm is dynamically adjusted by a fuzzy evaluation. Methods for supporting players' portfolio decisions using PSO and GA, are respectively presented in [5,6,18].…”

Teaching particle swarm optimization through an open‐loop system identification project

“…Figure 8 presents the evolution of the best model parameters over 100 iterations. In this figure, it is also clear that for all the model parameters the initial random value is in the interval [0,9]. Figure 9 presents the logarithm of the best and mean cost values evolution.…”

“…The best model (G m3 ) achieved is the following: Figure 7 presents one trial results obtained with the swarm randomly initialized in a section of the entire search space. The initialization section considered was approximately one-tenth of the entire space: [9,10], [9,10], and [9,10], for K, T, and L, respectively. The initial space does not contain the optimum values.…”

Towards new competitive electricity markets based on information transparency: Hypaethral market

“…If there is no limitation on the number of changes, the electricity market price reaches the marginal cost of the first loser. Equations (17)(18)(19) present a model in order to optimize the decision for each supplier in the electricity market.…”

“…In , by using information release, the effect of information transparency was addressed in the electricity market that would lead to more trust for all of the buyers and sellers in their financial transactions and decreases the possibility of collusion among suppliers. In , effects of information transparency were investigated in detail including increasing competition, decreasing risks, and selecting profitable strategies. On the other hand, due to problems such as restriction in the number of suppliers, no facility in entering to market, and limitations in the transmission network, the suppliers' strategies play a key role in increasing their profits .…”