http://repub.eur.nl/res/aut/7884/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/40138/ 2013-05-22T00:00:00Z This is the specification for the Power Trading Agent Competition for 2013 (Power TAC 2013). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins. http://repub.eur.nl/res/pub/37192/ 2012-09-10T00:00:00Z This is the specification for the Power Trading Agent Competition for 2012 (Power TAC 2012). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins. http://repub.eur.nl/res/pub/30683/ 2011-12-14T00:00:00Z This is the specification for the Power Trading Agent Competition for 2012 (Power TAC 2012). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given timeslot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modelling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins. http://repub.eur.nl/res/pub/32044/ 2011-12-01T00:00:00Z Within the next years, consumer households will be increasingly equipped with smart metering and intelligent appliances. These technologies are the basis for households to better monitor electricity consumption and to actively control loads in private homes. Demand side management (DSM) can be adopted to private households. We present a simulation model that generates household load profiles under flat tariffs and simulates changes in these profiles when households are equipped with smart appliances and face time-based electricity prices.We investigate the impact of smart appliances and variable prices on electricity bills of a household. We show that for households the savings from equipping them with smart appliances are moderate compared to the required investment. This finding is quite robust with respect to variation of tariff price spreads and to different types of appliance utilization patterns.Finally, our results indicate that electric utilities may face new demand peaks when day-ahead hourly prices are applied. However, a considerable amount of residential load is available for shifting, which is interesting for the utilities to balance demand and supply. http://repub.eur.nl/res/pub/23339/ 2011-05-16T00:00:00Z Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime” models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches. http://repub.eur.nl/res/pub/23263/ 2011-05-10T00:00:00Z This is the specification for the Power Trading Agent Competition for 2011 (Power TAC 2011). Agents are simulations of electrical power brokers, who must compete with each other for both power production and consumption, and manage their portfolios. http://repub.eur.nl/res/pub/31589/ 2010-12-01T00:00:00Z Enterprises continuously seek decision support tools that can help automate and codify business decisions. This is particularly true in the business of consumer electronics manufacturing where components are often interchangeable and several manufacturers can supply the same component over the life of a product. In this kind of dynamic environment, businesses are faced with the choice of signing long-term (possibly quite risky) contracts or of waiting to procure necessary components on the spot market (where availability may be uncertain). Having analytical tools to analyze previous and forecast future market conditions is invaluable. We analyze a supply chain scenario from an economic perspective that involves both component procurement and sales uncertainties. The data we analyze comes from a multi-agent supply chain management simulation environment (TAC SCM) which simulates a one-year product life-cycle. The availability of simulation logs allows us access to a rich set of data which includes the requests and actions taken by all participants in the market. This rich informational access enables us to calculate supply and demand curves, examine market efficiency, and see how specific strategic behaviors of the competing agents are reflected in market dynamics. http://repub.eur.nl/res/pub/31739/ 2010-12-01T00:00:00Z Information systems are crucial for effective supply chain management in today's complex supply chains for durable goods. Complex decision making processes on strategic, tactical, and operational level require substantial support in order to contribute to the agility of organizations. Supply chain regimes, i.e., regimes encompassing both the sales and the procurement market in a complex supply chain, provide a way of intuitively and meaningfully characterizing and modeling supply chain market conditions without a need for explicit modeling of individual aspects of the market. T his paper makes a first explorative step towards a model incorporating such regimes, while maintaining the dynamics which enable the model to be utilized in the sales process, e.g., for dynamic product pricing. Initial results show that supply chain regimes have feasible characteristics, based on both sales and procurement market indicators. Taking into consideration these regimes enables a more deliberate sales model. http://repub.eur.nl/res/pub/38579/ 2010-12-01T00:00:00Z Business intelligence (BI) takes many different forms, as indicated by the varying definitions of BI that can be found in industry and academia. These different definitions help us understand of what BI issues are important to the main players in the field of BI; users, suppliers and academics. The goal of this research is to discover gaps and trends from the standpoints of BI users, BI suppliers and academics, and to examine their effects on business and academia. Consultants also play an important role since they can be seen as the link between users and suppliers. Two research methods are combined to accomplish this goal. We examine the BI focus of users and suppliers through a survey, and we gain insight to the BI focus of academics, vendor-neutral consultants (typical representatives like Forrester, Gartner and IDC) and vendor- specific consultants (typical representatives like IBM, Information builders, Microsoft, Oracle and SAP) through their publications. Previous studies indicate that similar article analyses often focus on academic research methods only. That means that the results so far often reveal the academic perspective. Unlike these previous studies, the perspective of this research is not limited to academics. Our results provide insight of the BI trends and BI issue ranking of BI users, suppliers, academics, vendors neutral consultants and vendor specific consultants. Copyright 2012 ACM. http://repub.eur.nl/res/pub/21307/ 2010-11-11T00:00:00Z Sustainable energy systems of the future will need more than efficient, clean, low-cost, renewable energy sources; they will also need efficient price signals that motivate sustainable energy consumption as well as a better real-time alignment of energy demand and supply. http://repub.eur.nl/res/pub/26827/ 2010-08-01T00:00:00Z Over the years, competitions have been important catalysts for progress in Artificial Intelligence. We describe one such competition, the Trading Agent Competition for Supply Chain Management (TAC SCM). We discuss its significance in the context of today’s global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past six years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets. Since its introduction in 2003, the competition has attracted over 150 entries and brought together researchers from AI and beyond in the form of 75 competing teams from 25 different countries. http://repub.eur.nl/res/pub/21712/ 2010-01-01T00:00:00Z An effective Decision Support System (DSS) should help its users improve decision making in complex, information-rich environments. We present a feature gap analysis that shows that current decision support technologies lack important qualities for a new generation of agile business models that require easy, temporary integration across organisational boundaries. We enumerate these qualities as DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. To address this gap, we describe a new design approach that enables users to compose decision behaviours from separate, configurable components, and allows dynamic construction of analysis and modelling tools from small, single-purpose evaluator services. The result is what we call an ‘evaluator service network’ that can easily be configured to test hypotheses and analyse the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management. http://repub.eur.nl/res/pub/17337/ 2009-11-25T00:00:00Z The energy sector will undergo fundamental changes over the next ten years. Prices for fossil energy resources are continuously increasing, there is an urgent need to reduce CO2 emissions, and the United States and European Union are strongly motivated to become more independent from foreign energy imports. These factors will lead to installation of large numbers of distributed renewable energy generators, which are often intermittent in nature. This trend conflicts with the current power grid control infrastructure and strategies, where a few centralized control centers manage a limited number of large power plants such that their output meets the energy demands in real time. As the proportion of distributed and intermittent generation capacity increases, this task becomes much harder, especially as the local and regional distribution grids where renewable energy generators are usually installed are currently virtually unmanaged, lack real time metering and are not built to cope with power flow inversions (yet). All this is about to change, and so the control strategies must be adapted accordingly. While the hierarchical command-and-control approach served well in a world with a few large scale generation facilities and many small consumers, a more flexible, decentralized, and self-organizing control infrastructure will have to be developed that can be actively managed to balance both the large grid as a whole, as well as the many lower voltage sub-grids. We propose a competitive simulation test bed to stimulate research and development of electronic agents that help manage these tasks. Participants in the competition will develop intelligent agents that are responsible to level energy supply from generators with energy demand from consumers. The competition is designed to closely model reality by bootstrapping the simulation environment with real historic load, generation, and weather data. The simulation environment will provide a low-risk platform that combines simulated markets and real-world data to develop solutions that can be applied to help building the self-organizing intelligent energy grid of the future. http://repub.eur.nl/res/pub/14082/ 2008-12-05T00:00:00Z An effective Decision Support System (DSS) should help its users improve decision-making in complex, information-rich, environments. We present a feature gap analysis that shows that current decision support technologies lack important qualities for a new generation of agile business models that require easy, temporary integration across organisational boundaries. We enumerate these qualities as DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. To address this gap, we describe a new design approach that enables users to compose decision behaviours from separate, configurable components, and allows dynamic construction of analysis and modelling tools from small, single-purpose evaluator services. The result is what we call an “evaluator service network” that can easily be configured to test hypotheses and analyse the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management. http://repub.eur.nl/res/pub/13547/ 2008-10-20T00:00:00Z We present a computational approach that autonomous software agents can adopt to make tactical decisions, such as product pricing, and strategic decisions, such as product mix and production planning, to maximize profit in markets with supply and demand uncertainties. Using a combination of machine learning and optimization techniques, the agent is able to characterize economic regimes, which are historical microeconomic conditions reflecting situations such as over-supply and scarcity. We assume an agent is capable of using real-time observable information to identify the current dominant market condition and we show how it can forecast regime changes over a planning horizon. We demonstrate how the agent can then use regime characterization to predict prices, price trends, and the probability of receiving a customer order in a dynamic supply chain environment. We validate our methods by presenting experimental results from a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM). The results show that our agent outperforms traditional short- and long-term predictive methodologies (such as exponential smoothing) significantly, resulting in accurate prediction of customer order probabilities, and competitive market prices. This, in turn, has the potential to produce higher profits. We also demonstrate the versatility of our computational approach by applying the methodology to prediction of stock price trends. http://repub.eur.nl/res/pub/14495/ 2008-01-01T00:00:00Z Modern electronic commerce creates significant challenges for decision-makers. The trading agent competition for supply-chain management (TAC SCM) is an annual competition among fully-autonomous trading agents designed by teams around the world. Agents attempt to maximize profits in a supply-chain scenario that requires them to coordinate Procurement, Production, and Sales activities in competitive markets. An agent for TAC SCM is a complex piece of software that must operate in a competitive economic environment. We report on results of an informal survey of agent design approaches among the competitors in TAC SCM, and then we describe and evaluate the design of our MinneTAC trading agent. We focus on the use of evaluators - configurable, composable modules for data analysis, modeling, and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports Sales and Procurement decisions, and how those decision process can be modified in useful ways by changing evaluator configurations. © 2008 Elsevier B.V. All rights reserved. http://repub.eur.nl/res/pub/14805/ 2008-01-01T00:00:00Z An autonomous trading agent is a complex piece of software that must operate in a competitive economic environment. We report results of an informal survey of agent design approaches among the competitors in the Trading Agent Competition for Supply Chain Management (TAC SCM). http://repub.eur.nl/res/pub/10719/ 2007-11-19T00:00:00Z An autonomous trading agent is a complex piece of software that must operate in a competitive economic environment and support a research agenda. We describe the structure of decision processes in the MinneTAC trading agent, focusing on the use of evaluators – configurable, composable modules for data analysis and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports sales and procurement decisions, and how those decision processes can be modified in useful ways by changing evaluator configurations. To put this work in context, we also report on results of an informal survey of agent design approaches among the competitors in the Trading Agent Competition for Supply Chain Management (TAC SCM). http://repub.eur.nl/res/pub/10594/ 2007-10-19T00:00:00Z We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.