Demonstration of the Multi-Agent Simulator of Competitive Electricity Markets

Electricity markets are complex environments with very particular characteristics. A critical issue concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, performed so that the competitiveness could be increased, but with exponential implications in the increase of the complexity and unpredictability in those markets’ scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behavior. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This paper presents the Multi-Agent System for Competitive Electricity Markets (MASCEM) – a simulator based on multi-agent technology that provides a realistic platform to simulate electricity markets, the numerous negotiation opportunities and the participating entities.

Elspot: Nord Pool Spot Integration in MASCEM Electricity Market Simulator

The energy sector in industrialized countries has been restructured in the last years, with the purpose of decreasing electricity prices through the increase in competition, and facilitating the integration of distributed energy resources. However, the restructuring process increased the complexity in market players' interactions and generated emerging problems and new issues to be addressed. In order to provide players with competitive advantage in the market, decision support tools that facilitate the study and understanding of these markets become extremely useful. In this context arises MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), a multi-agent based simulator that models real electricity markets. To reinforce MASCEM with the capability of recreating the electricity markets reality in the fullest possible extent, it is crucial to make it able to simulate as many market models and player types as possible. This paper presents a new negotiation model implemented in MASCEM based on the negotiation model used in day-ahead market (Elspot) of Nord Pool. This is a key module to study competitive electricity markets, as it presents well defined and distinct characteristics from the already implemented markets, and it is a reference electricity market in Europe (the one with the larger amount of traded power).

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.

Negotiation context analysis in electricity markets

Contextualization is critical in every decision making process. Adequate responses to problems depend not only on the variables with direct influence on the outcomes, but also on a correct contextualization of the problem regarding the surrounding environment. Electricity markets are dynamic environments with increasing complexity, potentiated by the last decades' restructuring process. Dealing with the growing complexity and competitiveness in this sector brought the need for using decision support tools. A solid example is MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), whose players' decisions are supported by another multiagent system – ALBidS (Adaptive Learning strategic Bidding System). ALBidS uses artificial intelligence techniques to endow market players with adaptive learning capabilities that allow them to achieve the best possible results in market negotiations. This paper studies the influence of context awareness in the decision making process of agents acting in electricity markets. A context analysis mechanism is proposed, considering important characteristics of each negotiation period, so that negotiating agents can adapt their acting strategies to different contexts. The main conclusion is that context-dependant responses improve the decision making process. Suiting actions to different contexts allows adapting the behaviour of negotiating entities to different circumstances, resulting in profitable outcomes.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

Ontologies for the interoperability of multiagent electricity markets simulation platforms

Electricity markets worldwide are complex and dynamic environments with very particular characteristics. These are the result of electricity markets’ restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. The rising complexity and unpredictability in electricity markets has increased the need for the intervenient entities in foreseeing market behaviour. Market players and regulators are very interested in predicting the market’s behaviour. Market players need to understand the market behaviour and operation in order to maximize their profits, while market regulators need to test new rules and detect market inefficiencies before they are implemented. The growth of usage of simulation tools was driven by the need for understanding those mechanisms and how the involved players' interactions affect the markets' outcomes. Multi-agent based software is particularly well fitted to analyse dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. Still, they have a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow market players from different systems to interact in common market environments. This dissertation proposes the development and implementation of ontologies for semantic interoperability between multi-agent simulation platforms in the scope of electricity markets. The added value provided to these platforms is given by enabling them sharing their knowledge and market models with other agent societies, which provides the means for an actual improvement in current electricity markets studies and development. The proposed ontologies are implemented in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) and tested through the interaction between MASCEM agents and agents from other multi-agent based simulators. The implementation of the proposed ontologies has also required a complete restructuring of MASCEM’s architecture and multi-agent model, which is also presented in this dissertation. The results achieved in the case studies allow identifying the advantages of the novel architecture of MASCEM, and most importantly, the added value of using the proposed ontologies. They facilitate the integration of independent multi-agent simulators, by providing a way for communications to be understood by heterogeneous agents from the various systems.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

MASCEM - an electricity market simulator providing coalition support for virtual power players

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data.

Data mining applications in power systems - case-studies and future trends

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

An integrated system to support electricity tariff contract definition

This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.

Defining Electricity Tariffs Using the Knowledge About the Consumers Profiles in ELECON Project

The increasing importance of the integration of distributed generation and demand response in the power systems operation and planning, namely at lower voltage levels of distribution networks and in the competitive environment of electricity markets, leads us to the concept of smart grids. In both traditional and smart grid operation, non-technical losses are a great economic concern, which can be addressed. In this context, the ELECON project addresses the use of demand response contributions to the identification of non-technical losses. The present paper proposes a methodology to be used by Virtual Power Players (VPPs), which are entities able to aggregate distributed small-size resources, aiming to define the best electricity tariffs for several, clusters of consumers. A case study based on real consumption data demonstrates the application of the proposed methodology.

Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.