Trading Agent Competition Research Articles

The 2020 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2020 (Power TAC 2020). 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; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, for rectifying supply-demand imbalances, for contributions to peak demand, and for customer connections. 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 approximate the effects of locational-marginal pricing through manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of whom have production capacity such as solar panels or wind turbines. 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. A distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by 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. Changes for 2020 are focused on stability and on making customer evaluation of regulation rates more realistic, and are highlighted by change bars in the margins. See Section 4.1.1 for details.

A competitive and profitable multi-agent autonomous broker for energy markets

Free and competitive energy markets are a recent and increasing phenomenon in several countries. Understanding these new energy markets and estimating their possible evolutions are current challenges of the research community. To avoid real market risks, the research community has developed autonomous traders and tested them in the Power Trading Agent Competition (Power TAC), a sophisticated energy market simulator. In this paper, we present COLDPower’16, a competitive autonomous trader composed of expert agents in specific kinds of markets and customers that combines local strategies into a global strategy to maximize profit. The local strategy of each tariff expert agent uses reinforcement learning algorithms, while the local strategy of the wholesale expert agent estimates future energy prices and the amount of energy that can be negotiated to buy energy when prices are low and sell energy when prices are high. COLDPower’16 was tested in Power TAC 2016. It achieved 2nd place in the final round of this international competition with 7 autonomous agent brokers.

CrocodileAgent 2018: Robust agent-based mechanisms for power trading in competitive environments

Besides the smart grid, future sustainable energy systems will have to employ a smart market approach where consumers are able choose one of many different energy providers. The Power Trading Agent Competition (Power TAC) provides an open source, smart grid simulation platform where brokers compete in power brokerage. This paper presents CrocodileAgent, which competed in the Power TAC 2018 finals as a broker agent. The main focus in the design and development of CrocodileAgent 2018 was the creation of smart time-of-use tariffs to reduce peak-demand charges. CrocodileAgent 2018 was ranked third in Power TAC 2018 Finals, with a positive final profit and a positive result in each of three game types. In addition, CrocodileAgent 2018 had the highest percentage of ?profitable games? (91%) from among all competing agents, the second highest level of ?net profit per standard deviation? (0.48) and the third highest ?net profit per subscriber? (79 monetary units).

Application of adaptive strategy for supply chain agent

With the tremendous increase in the globalization of trade the corresponding supply chains supporting the manufacture, distribution and supply of goods has become extremely complex. Intelligent agents can help with the problem of effective management of these complex supply chains. In this paper we introduce the design, implementation and testing of an intelligent agent for handling procurement, customer sales, and scheduling of production in a stylized supply chain environment. The supply chain environment used in this paper is modeled after the trading agent competition that is held annually to choose the best agent for managing a supply chain. Our supply chain agent, which we call SCMaster, uses dynamic inventory control and various reinforcement learning techniques like Q-learning, Softmax, e-greedy, and sliding window protocol to make our agent adapt dynamically to the changing environment created by competing agents. A multi-agent simulation environment is developed in Java to test the efficacy of our agent design. Two competing agents are created modeled after the winners of past trading agent competitions and are tested against our agent in various experimental designs. Results of simulations show that our agent has better performance compared to the other agents.

The 2017 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2017 (Power TAC 2017). 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; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. 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 approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. 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. Real-time balancing of supply and demand is managed by 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. Changes for 2017 are focused on a more realistic wholesale market, reducing the market power of brokers by making the simulation scenario into a relatively small part of a larger market, and are highlighted by change bars in the margins. See Section 5.3 for details.

The 2018 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2018 (Power TAC 2018). Power TAC is a competitive simulation that models a liberalized retail electrical energy market, where competing business entities or 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; the winner of an individual game is the broker with the highest bank balance at the end of a simulation run. 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 approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. 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. Real-time balancing of supply and demand is managed by 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. Changes for 2018 are focused on stability, on simplifying interaction with the balancing market, and on encouraging more vigorous competition, and are highlighted by change bars in the margins. See Sections 3.1.2, 6, and 8.5 for details.

Market-Based Multi-Agent Coordination to Manage Energy Balance in Smart Grids

Widespread adoption of sustainable energy sources is driving electricity grid operators to supplement hierarchical control regimes with market-based control that better motivates stakeholder involvement. However, to prevent market failures, such controls require testing before real-world implementation. The Power Trading Agent Competition is a competitive simulation of distribution grids that mirrors real-world scenarios and tests alternative policy and business scenarios. In Power TAC, broker agents acquire energy through bidding in a forward wholesale market to satisfy their customers’ overall demand on an hourly basis. In addition, a balancing market is intended to resolve real-time energy imbalances caused by broker prediction errors using demand response resources. As part of the annual alignment process, we discovered that brokers in the 2015 competition were persistently buying insufficient energy on the wholesale market to satisfy their customer demand. Instead, the balancing market made up the deficit, charging brokers a premium over the wholesale price. Also, demand response resources were heavily underused. We studied the economic impact of this systematic imbalance on brokers and discovered that they were behaving rationally, given the prices they faced in the two markets. We present the process and results of this analysis, and show how the balancing market’s pricing mechanism can be adjusted for the 2016 competition to make it rational for brokers to achieve an overall neutral imbalance.

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex sociotechnical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating these problems requires research methods that scale beyond the traditional areas of inquiry of information systems (IS) individuals, organizations, and markets and that deliver solutions in addition to insights. We describe an approach to address these challenges through competitive benchmarking (CB), a novel research method that helps interdisciplinary research communities tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions.

Autonomous Electricity Trading Using Time-of-Use Tariffs in a Competitive Market

This paper studies the impact of Time-Of-Use (TOU) tariffs in a competitive electricity market place. Specifically, it focuses on the question of how should an autonomous broker agent optimize TOU tariffs in a competitive retail market, and what is the impact of such tariffs on the economy. We formalize the problem of TOU tariff optimization and propose an algorithm for approximating its solution. We extensively experiment with our algorithm in a large-scale, detailed electricity retail markets simulation of the Power Trading Agent Competition (Power TAC) and: 1) find that our algorithm results in 15% peak-demand reduction, 2) find that its peak-flattening results in greater profit and/or profit-share for the broker and allows it to win against the 1st and 2nd place brokers from the Power TAC 2014 finals, and 3) analyze several economic implications of using TOU tariffs in competitive retail markets.

A Multiagent Competitive Gaming Platform to Address Societal Challenges

The shift toward sustainable electricity systems is one of the grand challenges of the 21st century. Decentralized production from renewable sources, electric mobility, and related advances are at odds with traditional power systems where central, large-scale generation of electricity follows inelastic consumer demand. Information systems innovations can enable new forms of dynamic electricity trading that leverage real-time consumption information and that use price signals to incentivize sustainable consumption behaviors. However, the best designs for these innovations, and the societal implications of different design choices, are largely unclear. We are addressing these challenges through the Power Trading Agent Competition (Power TAC), a competitive gaming platform on which numerous research groups now jointly devise, benchmark, and improve IS-based solutions to the sustainable electricity challenge. Based on the Power TAC community's results, we give preliminary empirical evidence for the efficacy of competitive gaming platforms, and for the community's contributions toward resolving the sustainable electricity challenge.

The 2016 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2016 (Power TAC 2016). 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; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. 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 approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. 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. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market posi- tions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources.Changes for 2016 are focused on a more realistic cost model for brokers, and are highlighted by change bars in the margins. See Section 7 for details.

Adaptive Tactical Pricing in Multi‐Agent Supply Chain Markets Using Economic Regimes

ABSTRACTIn today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two‐layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions—economic regimes—identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs.

The 2015 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2015 (Power TAC 2015). 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 model locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. 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. Real-time balancing of supply and demand is managed by 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.

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions.

TacTex'13: A Champion Adaptive Power Trading Agent

Sustainable energy systems of the future will no longer be able to rely on the current paradigm that energy supply follows demand. Many of the renewable energy resources do not produce power on demand, and therefore there is a need for new market structures that motivate sustainable behaviors by participants. The Power Trading Agent Competition (Power TAC) is a new annual competition that focuses on the design and operation of future retail power markets, specifically in smart grid environments with renewable energy production, smart metering, and autonomous agents acting on behalf of customers and retailers. It uses a rich, open-source simulation platform that is based on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making, as well as the robustness of proposed market designs. This paper introduces TacTex'13, the champion agent from the inaugural competition in 2013. TacTex'13 learns and adapts to the environment in which it operates, by heavily relying on reinforcement learning and prediction methods. This paper describes the constituent components of TacTex'13 and examines its success through analysis of competition results and subsequent controlled experiments.

The 2014 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2014 (Power TAC 2014). 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 model locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. 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. Real-time balancing of supply and demand is managed by 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.

가상 전력 도매 시장의 최적 경매 가격 예측

Power TAC (Power Trading Agent Competition) is an agent-based simulation for competitions between electricity brokering agents on the smart grid. To win the competition, agents obtain electricity from the electricity wholesale market among the power plants. In this operation, a key to success is balancing the demand of the customer and the supply from the plants because any imbalance results in a significant penalty to the brokering agent. Given the bidding on the wholesale market requires the price and the quantity on the electricity, this paper proposes four different price estimation strategies: exponentially moving average, linear regression, fuzzy logic, and support vector regression. Our evaluations with the competition simulation show which strategy is better than which, and which strategy wins in the free-for-all situations. This result is a crucial component in designing an electricity brokering agent in both Power TAC and the real world.

Agent-assisted supply chain management: Analysis and lessons learned

This work explores “big data” analysis in the context of supply chain management. Specifically we propose the use of agent-based competitive simulation as a tool to develop complex decision making strategies and to stress test them under a variety of market conditions. We propose an extensive set of business key performance indicators (KPIs) and apply them to analyze market dynamics. We present these results through statistics and visualizations. Our testbed is a competitive simulation, the Trading Agent Competition for Supply-Chain Management (TAC SCM), which simulates a one-year product life-cycle where six autonomous agents compete to procure component parts and sell finished products to customers. The paper provides analysis techniques and insights applicable to other supply chain environments.

Autonomous Agents in Future Energy Markets: The 2012 Power Trading Agent Competition

Sustainable energy systems of the future will need more than efficient, clean, and low-cost energy sources. They will also need efficient price signals that motivate sustainable energy consumption behaviors and a tight real-time alignment of energy demand with supply from renewable and traditional sources. The Power Trading Agent Competition (Power TAC) is a rich, competitive, open-source simulation platform for future retail power markets built on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making as well as the robustness of proposed market designs. Power TAC invites researchers to develop autonomous electricity broker agents and to pit them against best-in-class strategies in global competitions, the first of which will be held at AAAI 2013. Power TAC competitions provide compelling, actionable information for policy makers and industry leaders. We describe the competition scenario, demonstrate the realism of the Power TAC platform, and analyze key characteristics of successful brokers in one of our 2012 pilot competitions between seven research groups from five different countries.

Power TAC: A competitive economic simulation of the smart grid

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. The Power Trading Agent Competition (Power TAC) is a rich competitive simulation of future retail power markets. This simulation will help us to understand the dynamics of customer and retailer decision-making and the robustness of market designs, by stimulating researchers to develop broker agents and benchmark them against each other. This will provide compelling, actionable information for policymakers and industry leaders. We describe the competition scenario in detail, and we demonstrate behaviors that arise from the interaction of customer and broker models.