Browsing by Author "Azevedo, Filipe"
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- Analysis of Electricity Market Prices Using Multidimensional ScalingPublication . Azevedo, Filipe; Machado, J. A. TenreiroThis paper studies the impact of the energy upon electricity markets using Multidimensional Scaling (MDS). Data from major energy and electricity markets is considered. Several maps produced by MDS are presented and discussed revealing that this method is useful for understanding the correlation between them. Furthermore, the results help electricity markets agents hedging against Market Clearing Price (MCP) volatility.
- Analysis of Electricity Markets Using Multidimensional ScalingPublication . Azevedo, Filipe; Machado, J. A. TenreiroThis paper studies the impact of the energy upon electricity markets using Multidimensional Scalling (MDS). MDS is a computational and statistical technique that produces a spatial representation of similarity between objects through factors of relatedness. MDS represents in a low dimensional map data points whose similarities are defined in a higher dimensional space. Data from major energy and electricity markets is considered. Several maps produced by MDS are presented and discussed revealing that this method is useful for understanding the correlation between them. Furthermore, the results help electricity markets agents hedging against Market Clearing Price (MCP) volatility.
- A Clustering Neural Network Model Applied to Electricity Price Range ForecastPublication . Azevedo, Filipe; Vale, Zita; Oliveira, P. B. MouraWith electricity markets birth, electricity price volatility becomes one of the major concerns for their participants and in particular, for the producers. Whether or not to hedge, what type of portfolio is ade-quate, and how to manage that portfolio are important considerations for electricity market agents. To achieve that, load and electricity price forecast have a high impor-tance. This paper provides an approach applied to price range forecast. Making use of artificial neural networks (ANN), the methodology presented here has as main con-cern finding the maximum and the minimum System Mar-ginal Price (SMP) for a specific programming period, with a certain confidence level. To train the neural networks, probabilistic information from past years is used. To in-crease accuracy and turning ANN training more efficient, a K-Means clustering method is previously applied. Re-sults from real data are presented and discussed in detail.
- A decision-support system based on particle swarm optimization for multiperiod hedging in electricity marketsPublication . Azevedo, Filipe; Vale, Zita; Oliveira, P. B. MouraThis paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.
- Decision-Support Tool for the Establishment of Contracts in the Electricity MarketPublication . Azevedo, Filipe; Vale, Zita; Vale, António A.The Pool, in many countries, was adopted for the participants of the electricity market to trade the electrical energy in a basis of each half-hour or one hour of the next day. However, like the traditional markets, the agents of electrical market are now exposed to the volatility of market price. In some countries, to face that problem and to turn the market more liquid, the derivatives markets – futures and options - were introduced to negotiate products with electrical energy as underlying active. In this context, there is a need of decisionsupport tools to assist those agents for the use of derivatives markets with the objective of practicing the hedge. In this paper, we present a decision model that supports producers to establish contracts with the objective to maximize the profit expected utility.
- Electricity Price Forecasting Methods Applied to Spanish Electricity MarketPublication . Ortiz, M.; Ukar, O.; Azevedo, Filipe; Mugica, A.Forecasting electricity prices is a fundamental task for all type of markets participants including electricity markets. There are factors that bring unce1tainty to price formation, such as demand forecasting, fuel prices, player's strategies, regulatory changes, weather conditions and technical restrictions and generation availability. In addition, the particular characteristics of electricity (supply must be in balance with demand) make this task more complicated. So, it is necessary to develop accurate and robust techniques on a sho1t-term (days) and long-term basis (months). This work presents two methodologies to be applied to long-term electricity prices forecasting (months) in Spanish electricity market for a glven period. A study case with real data is presented and discussed in detail.
- Forecasting Electricity Prices with Historical Statistical Information using Neural Networks and Clustering TechniquesPublication . Azevedo, Filipe; Vale, ZitaFactors such as uncertainty associated to fuel prices, energy demand and generation availability, are on the basis of the agents major concerns in electricity markets. Facing that reality, price forecasting has an increasing impact in agents’ activity. The success on bidding strategies or on price negotiation for bilateral contracts is directly dependent on the accuracy of the price forecast. However, taking decisions based only on a single forecasted value is not a good practice in risk management. The work presented in this paper makes use of artificial neural networks to find the market price for a given period, with a certain confidence level. Historical information was used to train the neural networks and the number of neural networks used is dependent of the number of clusters found on that data. K-Means clustering method is used to find clusters. A study case with real data is presented and discussed in detail.
- Hedging Using Futures and Options Contracts in the Electricty MarketPublication . Azevedo, Filipe; Vale, Zita; Vale, António A.Since the 80’s with the experience of Chile, the electric sector has suffered, in many counties, a process of deregulation and liberalization. In almost of the countries, that process originated the appearance of a Pool where the participants of the market trade the electrical energy on a basis of half-hour or one hour of the next day. However, like the traditional markets, the agents of electricity markets are now exposed to the volatility of market price, so far inexistent in those markets. In some countries, to face that problem and to turn the market more liquid have been introduced derivatives markets – futures and options, to negotiate products with underlying active the electrical energy. In this context, there is a need of decision-support tools that allow those agents to use derivatives markets with the objective of practicing the hedge and simultaneously increase their results. In this paper, we present a decision model that supports producers in the establishment of contracts with the objective to maximize the profit expected utility. The paper presents a group of examples of the use of this decision-support system.
- Long-term price range forecast applied to risk management using regression modelsPublication . Azevedo, Filipe; Vale, Zita; Oliveira, P. B. MouraLong-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.
- A long-term risk management tool for electricity markets using swarm intelligencePublication . Azevedo, Filipe; Vale, Zita; Oliveira, P. B. Moura; Khodr, H. M.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.
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