Multi-energy retail market simulation with autonomous intelligent agents

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

Foresighted Demand Side Management

We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators' costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators' states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators' states or the ISO's state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregator's energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities

Energy Markets and Economics Ⅱ

This issue brings together a collection of papers that provide economic insights into the modern energy market, which is still dominated by crude oil but has expanded to incorporate new energy sources in the form of coal, natural gas, and a mixture of renewable energy sources. Given the differences in the dynamics at play with different energy sources, particularly in relation to price determination, the impact they have on the environment, their importance in the energy mix and energy policy, and so forth, it has become imperative to check their behavior using economic models. Papers 1–3 provide some perspective on oil price determination by focusing on the time-varying nature of supply shocks linked to oil producers (Paper 1), OPEC’s announcements (2), and the heterogeneous interconnections of supply or demand shocks over time horizons and different countries (3). Papers 4–6 compare different energy sources within the energy market and other markets (4); explore the importance of energy storage in the electricity market (5); and examine the dynamic relationship between prices of substitutes (oil price) on the natural gas market in China (6). The final four studies examine the impact of renewable and nonrenewable energy on the macroeconomy and the environment

Forecasting and Risk Management Techniques for Electricity Markets

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects

Risk Hedging Strategies in New Energy Markets

In recent years, two typical developments have been witnessed in the energy market. On the one hand, the penetration of renewable generations has gradually replaced parts of the traditional ways to generate energy. The intermittent nature of renewable generation can lead to energy supply uncertainty, which might exacerbate the imbalance between energy supply and demand. As a result, the problem of energy price risks might occur. On the other hand, with the introduction of distributed energy resources (DERs), new categories of markets besides traditional wholesale and retail markets are emerging. The main benefits of the penetration of DERs are threefold. First, DERs can increase power system reliability. Second, the cost of transmission can be reduced. Third, end users can directly participate in some of these new types of markets according to their energy demand, excess energy, and cost function without third-party intervention. However, energy market participants might encounter various types of uncertainties. Therefore, it is necessary to develop proper risk-hedging strategies for different energy market participants in emerging new markets. Thus, we propose risk-hedging strategies that can be used to guide various market participants to hedge risks and enhance utilities in the new energy market. These participants can be categorized into the supply side and demand side. Regarding the wide range of hedging tools analyzed in this thesis, four main types of hedging strategies are developed, including the application of ESS, financial tools, DR management, and pricing strategy. Several benchmark test systems have been applied to demonstrate the effectiveness of the proposed risk-hedging strategies. Comparative studies of existing risk hedging approaches in the literature, where applicable, have also been conducted. The real applicability of the proposed approach has been verified by simulation results

Mechanism Design for Fair Allocation

Mechanism design for a social utility being the sum of agents' utilities (SoU) is a well-studied problem. There are, however, a number of problems of theoretical and practical interest where a designer may have a different objective than maximization of the SoU. One motivation for this is the desire for more equitable allocation of resources among agents. A second, more subtle, motivation is the fact that a fairer allocation indirectly implies less variation in taxes which can be desirable in a situation where (implicit) individual agent budgetary constraints make payment of large taxes unrealistic. In this paper we study a family of social utilities that provide fair allocation (with SoU being subsumed as an extreme case) and derive conditions under which Bayesian and Dominant strategy implementation is possible. Furthermore, it is shown how a simple modification of the above mechanism can guarantee full Bayesian implementation. Through a numerical example it is shown that the proposed method can result in significant gains both in allocation fairness and tax reduction

An Evaluation of Overseas Oil Investment Projects under Uncertainty Using a Real Options Based Simulation Model

This paper applies real options theory to establish an overseas oil investment evaluation model that is based on Monte Carlo simulation and is solved by the Least Squares Monte-Carlo method. To better reflect the reality of overseas oil investment, our model has incorporated not only the uncertainties of oil price and investment cost but also the uncertainties of exchange rate and investment environment. These unique features have enabled our model to be best equipped to evaluate the value of oil overseas investment projects of three oil field sizes (large, medium, small) and under different resource tax systems (royalty tax and production sharing contracts). In our empirical setting, we have selected China as an investor country and Indonesia as an investee country as a case study. Our results show that the investment risks and project values of small sized oil fields are more sensitive to changes in the uncertainty factors than the large and medium sized oil fields. Furthermore, among the uncertainty factors considered in the model, the investment risk of overseas oil investment may be underestimated if no consideration is given of the impacts of exchange rate and investment environment. Finally, as there is an important trade-off between oil resource investee country and overseas oil investor, in medium and small sized oil investment negotiation the oil company should try to increase the cost oil limit in production sharing contract and avoid the term of a windfall profits tax to reduce the investment risk of overseas oil fields.Overseas Oil Investment, Project Value, Real Options, Least Squares Monte-Carlo