Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Market power analysis in the Iberian electricity market using a conjectural variations model

In the last years the electricity industry has faced a restructuring process. Among the aims of this process was the increase in competition, especially in the generation activity where firms would have an incentive to become more efficient. However, the competitive behavior of generating firms might jeopardize the expected benefits of the electricity industry liberalization. The present paper proposes a conjectural variations model to study the competitive behavior of generating firms acting in liberalized electricity markets. The model computes a parameter that represents the degree of competition of each generating firm in each trading period. In this regard, the proposed model provides a powerful methodology for regulatory and competition authorities to monitor the competitive behavior of generating firms. As an application of the model, a study of the day-ahead Iberian electricity market (MIBEL) was conducted to analyze the impact of the integration of the Portuguese and Spanish electricity markets on the behavior of generating firms taking into account the hourly results of the months of June and July of 2007. The advantages of the proposed methodology over other methodologies used to address market power, namely Residual Supply index and Lerner index are highlighted. (C) 2014 Elsevier Ltd. All rights reserved.

How market power affects the behaviour of a pumped storage hydro unit in the day-ahead electricity market?

The integration of large amounts of wind energy in power systems raises important operation issues such as the balance between power demand and generation. The pumped storage hydro (PSH) units are seen as one solution for this issue, avoiding the need for wind power curtailments. However, the behavior of a PSH unit might differ considerably when it operates in a liberalized market with some degree of market power. In this regard, a new approach for the optimal daily scheduling of a PSH unit in the day-ahead electricity market was developed and presented in this paper, in which the market power is modeled by a residual inverse demand function with a variable elasticity. The results obtained show that increasing degrees of market power of the PSH unit correspond to decreasing levels of storage and, therefore, the capacity to integrate wind power is considerably reduced under these circumstances.

Price forecasting in the day-ahead Iberian electricity market using a conjectural variations Arima model

Price forecast is a matter of concern for all participants in electricity markets, from suppliers to consumers through policy makers, which are interested in the accurate forecast of day-ahead electricity prices either for better decisions making or for an improved evaluation of the effectiveness of market rules and structure. This paper describes a methodology to forecast market prices in an electricity market using an ARIMA model applied to the conjectural variations of the firms acting in an electricity market. This methodology is applied to the Iberian electricity market to forecast market prices in the 24 hours of a working day. The methodology was then compared with two other methodologies, one called naive and the other a direct forecast of market prices using also an ARIMA model. Results show that the conjectural variations price forecast performs better than the naive and that it performs slightly better than the direct price forecast.

Short-term electricity prices forecasting in a competetive market by a hybrid Pso-Anfis approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn.

(Virtual) Agents for running electricity markets

This paper describes a multi-agent based simulation (MABS) framework to construct an artificial electric power market populated with learning agents. The artificial market, named TEMMAS (The Electricity Market Multi-Agent Simulator), explores the integration of two design constructs: (i) the specification of the environmental physical market properties and (ii) the specification of the decision-making (deliberative) and reactive agents. TEMMAS is materialized in an experimental setup involving distinct power generator companies that operate in the market and search for the trading strategies that best exploit their generating units' resources. The experimental results show a coherent market behavior that emerges from the overall simulated environment.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

Commercial agents portfolio optimization in electricity markets

As it is well known, competitive electricity markets require new computing tools for power companies that operate in retail markets in order to enhance the management of its energy resources. During the last years there has been an increase of the renewable penetration into the micro-generation which begins to co-exist with the other existing power generation, giving rise to a new type of consumers. This paper develops a methodology to be applied to the management of the all the aggregators. The aggregator establishes bilateral contracts with its clients where the energy purchased and selling conditions are negotiated not only in terms of prices but also for other conditions that allow more flexibility in the way generation and consumption is addressed. The aggregator agent needs a tool to support the decision making in order to compose and select its customers' portfolio in an optimal way, for a given level of profitability and risk.

Multi-agent Simulation of Bilateral Contracting in Competitive Electricity Markets

Traditional vertically integrated power utilities around the world have evolved from monopoly structures to open markets that promote competition among suppliers and provide consumers with a choice of services. Market forces drive the price of electricity and reduce the net cost through increased competition. Electricity can be traded in both organized markets or using forward bilateral contracts. This article focuses on bilateral contracts and describes some important features of an agent-based system for bilateral trading in competitive markets. Special attention is devoted to the negotiation process, demand response in bilateral contracting, and risk management. The article also presents a case study on forward bilateral contracting: a retailer agent and a customer agent negotiate a 24h-rate tariff. © 2014 IEEE.

Power Producers Trading Electricity in Both Pool and Forward Markets

The electricity industry throughout the world, which has long been dominated by vertically integrated utilities, has experienced major changes. Deregulation, unbundling, wholesale and retail wheeling, and real-time pricing were abstract concepts a few years ago. Today market forces drive the price of electricity and reduce the net cost through increased competition. As power markets continue to evolve, there is a growing need for advanced modeling approaches. This article addresses the challenge of maximizing the profit (or return) of power producers through the optimization of their share of customers. Power producers have fixed production marginal costs and decide the quantity of energy to sell in both day-ahead markets and a set of target clients, by negotiating bilateral contracts involving a three-rate tariff. Producers sell energy by considering the prices of a reference week and five different types of clients with specific load profiles. They analyze several tariffs and determine the best share of customers, i.e., the share that maximizes profit. © 2014 IEEE.

Micro-generation Evaluation of the Zero Emissions Technologies in the Portuguese Market

Micro-generation is the small scale production of heat and/or electricity from a low carbon source and can be a powerful driver for carbon reduction, behavior change, security of supply and economic value. The energy conversion technologies can include photovoltaic panels, micro combined heat and power, micro wind, heat pumps, solar thermal systems, fuel cells and micro hydro schemes. In this paper, a small research of the availability of the conversion apparatus and the prices for the micro wind turbines and photovoltaic systems is made and a comparison between these two technologies is performed in terms of the availability of the resource and costs. An analysis of the new legal framework published in Portugal is done to realize if the incentives to individualspsila investment in sustainable and local energy production is worth for their point of view. An economic evaluation for these alternatives, accounting with the governmentpsilas incentives should lead, in most cases, into attractive return rates for the investment. Apart from the attractiveness of the investment there are though other aspects that should be taken into account and those are the benefits that these choices have to us all. The idea is that micro-generation will not only make a significant direct contribution to carbon reduction targets, it will also trigger a multiplier effect in behavior change by engaging hearts and minds, and providing more efficient use of energy by householders. The diversified profile of power generation by micro-generators, both in terms of location and timing, should reduce the impact of intermittency or plant failures with significant gains for security of supply.

Risk Analysis and Behavior of Electricity Portfolio Aggregator

The scope of this paper is to adapt the standard mean-variance model of Henry Markowitz theory, creating a simulation tool to find the optimal configuration of the portfolio aggregator, calculate its profitability and risk. Currently, there is a deep discussion going on among the power system society about the structure and architecture of the future electric system. In this environment, policy makers and electric utilities find new approaches to access the electricity market; this configures new challenging positions in order to find innovative strategies and methodologies. Decentralized power generation is gaining relevance in liberalized markets, and small and medium size electricity consumers are also become producers (“prosumers”). In this scenario an electric aggregator is an entity that joins a group of electric clients, customers, producers, “prosumers” together as a single purchasing unit to negotiate the purchase and sale of electricity. The aggregator conducts research on electricity prices, contract terms and conditions in order to promote better energy prices for their clients and allows small and medium customers to benefit improved market prices.

A trader portfolio optimization of bilateral contracts in electricity retail markets

Electricity markets are systems for effecting the purchase and sale of electricity using supply and demand to set energy prices. Two major market models are often distinguished: pools and bilateral contracts. Pool prices tend to change quickly and variations are usually highly unpredictable. In this way, market participants often enter into bilateral contracts to hedge against pool price volatility. This article addresses the challenge of optimizing the portfolio of clients managed by trader agents. Typically, traders buy energy in day-ahead markets and sell it to a set of target clients, by negotiating bilateral contracts involving three-rate tariffs. Traders sell energy by considering the prices of a reference week and five different types of clients. They analyze several tariffs and determine the best share of customers, i.e., the share that maximizes profit. © 2014 IEEE.

Investment focused strategies in electricity markets using the ITEM-game simulator

This paper presents a methodology to establish investment and trading strategies of a power generation company. These strategies are integrated in the ITEM-Game simulator in order to test their results when played against defined strategies used by other players. The developed strategies are focused on investment decisions, although trading strategies are also implemented to obtain base case results. Two cases are studied considering three players with the same trading strategy. In case 1, all players also have the same investment strategy driven by a market target share. In case 2, player 1 has an improved investment strategy with a target share twice of the target of players 2 and 3. Results put in evidence the influence of the CO2 and fuel prices in the company investment decision. It is also observed the influence of the budget constraint which might prevent the player to take the desired investment decision.