Balancing market integration in MASCEM electricity market simulator

With the restructuring of the energy sector in industrialized countries there is an increased complexity in market players’ interactions along with emerging problems and new issues to be addressed. Decision support tools that facilitate the study and understanding of these markets are extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent simulator for competitive electricity markets. It is essential to reinforce MASCEM with the ability to recreate electricity markets reality in the fullest possible extent, making it able to simulate as many types of markets models and players as possible. This paper presents the development of the Balancing Market in MASCEM. A key module to the study of competitive electricity markets, as it has well defined and distinct characteristics previously implemented.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Multi-agent electricity market simulation with dynamic strategies & virtual power producers

Distributed energy resources will provide a significant amount of the electricity generation and will be a normal profitable business. In the new decentralized grid, customers will be among the many decentralized players and may even help to co-produce the required energy services such as demand-side management and load shedding. So, they will gain the opportunity to be more active market players. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. In this paper we propose the improvement of MASCEM, a multi-agent simulation tool to study negotiations in electricity spot markets based on different market mechanisms and behavior strategies, in order to take account of decentralized players such as VPP.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.

Multi-agent based electricity market simulator with VPP: Conceptual and implementation issues

This paper presents a new architecture for the MASCEM, a multi-agent electricity market simulator. This is implemented in a Prolog which is integrated in the JAVA program by using the LPA Win-Prolog Intelligence Server (IS) provides a DLL interface between Win-Prolog and other applications. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets.

Dgenco market balancing - cost repartition under light regulation markets

In this paper we present a new methodology, based in game theory, to obtain the market balancing between Distribution Generation Companies (DGENCO), in liberalized electricity markets. The new contribution of this methodology is the verification of the participation rate of each agent based in Nucléolo Balancing and in Shapley Value. To validate the results we use the Zaragoza Distribution Network with 42 Bus and 5 DGENCO.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

AI in power systems and energy markets

In a world increasingly conscientious about environmental effects, power and energy systems are undergoing huge transformations. Electric energy produced from power plants is transmitted and distributed to end users through a power grid. The power industry performs the engineering design, installation, operation, and maintenance tasks to provide a high-quality, secure energy supply while accounting for its systems’ abilities to withstand uncertain events, such as weather-related outages. Competitive, deregulated electricity markets and new renewable energy sources, however, have further complicated this already complex infrastructure.Sustainable development has also been a challenge for power systems. Recently, there has been a signifi cant increase in the installation of distributed generations, mainly based on renewable resources such as wind and solar. Integrating these new generation systems leads to more complexity. Indeed, the number of generation sources greatly increases as the grid embraces numerous smaller and distributed resources. In addition, the inherent uncertainties of wind and solar energy lead to technical challenges such as forecasting, scheduling, operation, control, and risk management. In this special issue introductory article, we analyze the key areas in this field that can benefi t most from AI and intelligent systems now and in the future.We also identify new opportunities for cross-fertilization between power systems and energy markets and intelligent systems researchers.

Abordagem à previsão do preço de energia elétrica via métodos de suavização exponencial

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Energy resource management in smart grids considering an Intensive use of electric vehicles

The introduction of electricity markets and integration of Distributed Generation (DG) have been influencing the power system’s structure change. Recently, the smart grid concept has been introduced, to guarantee a more efficient operation of the power system using the advantages of this new paradigm. Basically, a smart grid is a structure that integrates different players, considering constant communication between them to improve power system operation and management. One of the players revealing a big importance in this context is the Virtual Power Player (VPP). In the transportation sector the Electric Vehicle (EV) is arising as an alternative to conventional vehicles propel by fossil fuels. The power system can benefit from this massive introduction of EVs, taking advantage on EVs’ ability to connect to the electric network to charge, and on the future expectation of EVs ability to discharge to the network using the Vehicle-to-Grid (V2G) capacity. This thesis proposes alternative strategies to control these two EV modes with the objective of enhancing the management of the power system. Moreover, power system must ensure the trips of EVs that will be connected to the electric network. The EV user specifies a certain amount of energy that will be necessary to charge, in order to ensure the distance to travel. The introduction of EVs in the power system turns the Energy Resource Management (ERM) under a smart grid environment, into a complex problem that can take several minutes or hours to reach the optimal solution. Adequate optimization techniques are required to accommodate this kind of complexity while solving the ERM problem in a reasonable execution time. This thesis presents a tool that solves the ERM considering the intensive use of EVs in the smart grid context. The objective is to obtain the minimum cost of ERM considering: the operation cost of DG, the cost of the energy acquired to external suppliers, the EV users payments and remuneration and penalty costs. This tool is directed to VPPs that manage specific network areas, where a high penetration level of EVs is expected to be connected in these areas. The ERM is solved using two methodologies: the adaptation of a deterministic technique proposed in a previous work, and the adaptation of the Simulated Annealing (SA) technique. With the purpose of improving the SA performance for this case, three heuristics are additionally proposed, taking advantage on the particularities and specificities of an ERM with these characteristics. A set of case studies are presented in this thesis, considering a 32 bus distribution network and up to 3000 EVs. The first case study solves the scheduling without considering EVs, to be used as a reference case for comparisons with the proposed approaches. The second case study evaluates the complexity of the ERM with the integration of EVs. The third case study evaluates the performance of scheduling with different control modes for EVs. These control modes, combined with the proposed SA approach and with the developed heuristics, aim at improving the quality of the ERM, while reducing drastically its execution time. The proposed control modes are: uncoordinated charging, smart charging and V2G capability. The fourth and final case study presents the ERM approach applied to consecutive days.

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.

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.

Simulação de um Pool Simétrico com validação técnica do despacho provisório e apresentação de medidas corretivas baseadas num despacho ótimo

Os mercados de energia elétrica são atualmente uma realidade um pouco por todo o mundo. Contudo, não é consensual o modelo regulatório a utilizar, o que origina a utilização de diferentes modelos nos diversos países que deram início ao processo de liberalização e de reestruturação do sector elétrico. A esses países, dado que a energia elétrica não é um bem armazenável, pelo menos em grandes quantidades, colocam-se questões importantes relacionadas com a gestão propriamente dita do seu sistema elétrico. Essas questões implicam a adoção de regras impostas pelo regulador que permitam ultrapassar essas questões. Este trabalho apresenta um estudo feito aos mercados de energia elétrica existentes um pouco por todo o mundo e que o autor considerou serem os mais importantes. Foi também feito um estudo de ferramentas de otimização essencialmente baseado em meta-heurísticas aplicadas a problemas relacionados com a operação dos mercados e com os sistemas elétricos de energia, como é o exemplo da resolução do problema do Despacho Económico. Foi desenvolvida uma aplicação que simula o funcionamento de um mercado que atua com o modelo Pool Simétrico, em que são transmitidas as ofertas de venda e compra de energia elétrica por parte dos produtores, por um lado, e dos comercializadores, consumidores elegíveis ou intermediários financeiros, por outro, analisando a viabilidade técnica do Despacho Provisório. A análise da viabilidade técnica do Despacho Provisório é verificada através do modelo DC de trânsito de potências. No caso da inviabilidade do Despacho Provisório, por violação de restrições afetas ao problema, são determinadas medidas corretivas a esse despacho, com base nas ofertas realizadas e recorrendo a um Despacho Ótimo. Para a determinação do Despacho Ótimo recorreu-se à meta-heurística Algoritmos Genéticos. A aplicação foi desenvolvida no software MATLAB utilizando a ferramenta Graphical User Interfaces. A rede de teste utilizada foi a rede de 14 barramentos do Institute of Electrical and Electronics Engineers (IEEE). A aplicação mostra-se competente no que concerne à simulação de um mercado com tipo de funcionamento Pool Simétrico onde são efetuadas ofertas simples e onde as transações ocorrem no mercado diário, porém, não reflete o problema real relacionado a este tipo de mercados. Trata-se, portanto, de um simulador básico de um mercado de energia cujo modelo de funcionamento se baseia no tipo Pool Simétrico.

Comparing wind generation profiles: A circular data approach

The importance of wind power energy for energy and environmental policies has been growing in past recent years. However, because of its random nature over time, the wind generation cannot be reliable dispatched and perfectly forecasted, becoming a challenge when integrating this production in power systems. In addition the wind energy has to cope with the diversity of production resulting from alternative wind power profiles located in different regions. In 2012, Portugal presented a cumulative installed capacity distributed over 223 wind farms [1]. In this work the circular data statistical methods are used to analyze and compare alternative spatial wind generation profiles. Variables indicating extreme situations are analyzed. The hour (s) of the day where the farm production attains its maximum daily production is considered. This variable was converted into circular variable, and the use of circular statistics enables to identify the daily hour distribution for different wind production profiles. This methodology was applied to a real case, considering data from the Portuguese power system regarding the year 2012 with a 15-minutes interval. Six geographical locations were considered, representing different wind generation profiles in the Portuguese system.In this work the circular data statistical methods are used to analyze and compare alternative spatial wind generation profiles. Variables indicating extreme situations are analyzed. The hour (s) of the day where the farm production attains its maximum daily production is considered. This variable was converted into circular variable, and the use of circular statistics enables to identify the daily hour distribution for different wind production profiles. This methodology was applied to a real case, considering data from the Portuguese power system regarding the year 2012 with a 15-minutes interval. Six geographical locations were considered, representing different wind generation profiles in the Portuguese system.