78 research outputs found

    Agent-based Artificial Markets

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    This study is composed of three essays. The first essay adapts the particle swarm optimization algorithm (PSO) to find the equilibrium in an agent-based artificial market. The simulated agents follow simple behavioral rules. The PSO algorithm is compared to a genetic algorithm (GA) under a Cournot game. The second essay uses the agent-based model with PSO algorithm to find equilibrium in a price-quantity competition game. In this game, agents purchase products from sellers and sell processed goods to the retail market using both bid price and capacity as their strategies. Simulations consider different numbers of buyers and are performed with and without a capacity cost. The third essay uses an agent-based model to estimate the impact of captive supplies on the spot market price under long run and short run assumptions in fed cattle markets. Packers purchase cattle from feeders both under captive supply contracts and in the spot market. Captive contracts are assumed fixed in the short run and flexible in the long run. Packers have one choice variable, procurement quantity in the spot market, in the short run; and have an additional choice variable, number of captive contracts in the long run.\nFindings and Conclusions: The first essay successfully adapts the PSO algorithm to solve dynamic economic games. PSO gives faster convergence and more precise answers than the genetic GA methods used by some previous economic studies. The agent-based model is new to agricultural economics and suitable to study complex economic problems that are hard to solve with mathematical methods. The simulation results of the second essay show that the agent-based model can explain the collusion and competition phenomena observed in previous experimental studies with human subjects which cannot be explained by theories. Under price-quantity competition, prices with one or two firms are at the monopsony level and with four firms prices are always at the perfectly competitive level; but the triopsony market changes from mostly monopsony to perfect competition when capacity cost increases from zero to a higher level. The third essay shows that the price depressing effect of captive supplies found in previous theoretical work is a short run effect and in the long run this phenomenon disappears.Department of Agricultural Economic

    Multi-energy retail market simulation with autonomous intelligent agents

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    Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2005. Faculdade de Engenharia. Universidade do Port

    Agent-based common value auctions

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    Scope and Method of Study: This research is composed of three essays about agent-based common value auctions. The objective of the first essay is to establish an agent-based first-price common-value auction to determine the impact of a reserve price with two buyers and with three buyers. In the second essay, the agent-based common-value auction model is used to provide theoretical insight into the likely change in beef packers' market power before and after the Livestock Mandatory Price Reporting Act. The objective in the third essay is to determine if a first-price common-value auction with a reserve price or a posted-price market provides a seller with the larger expected revenue using agent-based models. In these three essays several theoretical contributions are made to the auction literature, and developing an agent-based common-value auction extends the agent-based modeling literature.Findings and Conclusions: Results from these essays provide unique insight into auction theory, agent-based modeling, and federal agricultural policy. From the first essay, a reserve price increases the equilibrium winning bid price and decreases the probability that the item is sold in the two and three buyer auctions. Additionally, a reserve price increases the winning bid price more than an additional buyer and no reserve price. In the second essay, results provide a unique theoretical argument that the Livestock Mandatory Price Reporting Act benefits producers by reducing beef packers' market power. Results from the third essay show the seller is indifferent between a posted price and auctioning an item when the seller and the buyers have similar noisy signals. However, when the seller has perfect information or buyers have less uncertainty than the seller, the seller prefers the posted-price market

    Dynamic pricing and learning: historical origins, current research, and new directions

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    A Review on Multi-Agent Technology in Micro-Grid Control

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    Micro-Grid (MG) integrates renewable generation, storage devices and controllable generations, it provides efficent utilization of clean energy while keeping stable external characteristics. Capability of continuous power supply, high scalability and flexible operation modes can satifiy the current demand of joint operation of renewable generation and Macro-Grid, and will provide a solid foundation for smart grid technology in the future. Thus, MG is an excellent integration of renewable energy utilization with a bright future, Multi-Agent System (MAS) is a new hierarchical control platform and can completely cover all the devices within a MG, its flexible control modes meet the needs of various operations of MG, and the capability of distributed computing supports intelligent functions of MG in the future. Therefore, developing premium functions for MAS in MG control will promote the development of both MG and Smart Grid technologies. This paper reviews the current applications of MAS technology for MG both in basic and advanced control demands. For basic demands concerning safe operations for MG, functions of MAS are available, but a further improvement of performance is essential for future researches to increase penetration of MAS in MG control; For advanced demands, MAS should increase calculation speed to meet the complex need of MG. In the last part, the future focuses are also depicted

    Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.

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    Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

    Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions

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    Building and investigating generators' bidding strategies in an electricity market

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    In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings
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