Modeling spot price dependence in Australian electricity markets with applications to risk management

We examine the dependence structure of electricity spot prices across regional markets in Australia. One of the major objectives in establishing a national electricity market was to provide a nationally integrated and efficient electricity market, limiting market power of generators in the separate regional markets. Our analysis is based on a GARCH approach to model the marginal price series in the considered regions in combination with copulae to capture the dependence structure between the marginals. We apply different copula models including Archimedean, elliptical and copula mixture models. We find a positive dependence structure between the prices for all considered markets, while the strongest dependence is exhibited between markets that are connected via interconnector transmission lines. Regarding the nature of dependence, the Student-t copula provides a good fit to the data, while the overall best results are obtained using copula mixture models due to their ability to also capture asymmetric dependence in the tails of the distribution. Interestingly, our results also suggest that for the four major markets, NSW, QLD, SA and VIC, the degree of dependence has decreased starting from the year 2008 towards the end of the sample period in 2010. Examining the Value-at-Risk of stylized portfolios constructed from electricity spot contracts in different markets, we find that the Student-t and mixture copula models outperform the Gaussian copula in a backtesting study. Our results are important for risk management and hedging decisions of market participants, in particular for those operating in several regional markets simultaneously.     

Using Mathematical Programming for Electricity Spot Pricing

Recent moves around the world to introduce competition into electricity markets have created a need for mechanisms to determine electricity spot prices which provide good incentives for market coordination. Duality theory suggests that such prices can be found by solving a mathematical program. We derive implicit prices corresponding to an actual half-hourly dispatch of a full a.c. power system, and discuss the application of spot pricing in New Zealand and the United States.     

Bidding strategies in dynamic electricity markets

In this paper the problem of developing bidding strategies for the participants of dynamic oligopolistic electricity markets is studied. Attention is given to strategic bidding of load serving entities (LSE) in these markets. We model oligopolistic electricity markets as non-linear dynamical systems and use discrete-time Nash bidding strategies. We assume a Cournot model for our game, where the LSEs decide on demand quantities and the market price is the marginal cost of producing electricity.Attention is given to a problem, where the objective functions are quadratic in the deviations of trajectories from desired trajectories and quadratic in the control deviations from the nominal controls. It is assumed that each power marketer can estimate his/her competitors' benefit function coefficients.The optimal bidding strategies are developed mathematically using dynamic game theory. We deal with games that are non-linear in the state equations. We linearize these equations for complex non-linear oligopolistic electricity multi-markets and use discrete-time Nash strategies. We show that the actual dynamic excursions from the operating point where we linearize are small so that the linearization is valid. The developed algorithm is applied to an IEEE 14-bus power system. We show that the LSEs' expected profits are higher for our method than those for other methods in the literature (F. Wen, A.K. David, Optimal bidding strategies and modeling of imperfect information among competitive generators. IEEE Transactions on Power Systems, Vol. 16, No. 1, pp.15–21, Feb. 2001.     

Transmission pricing and recovery of investment costs in the deregulated power system based on optimal circuit prices

Transmission pricing has become a major issue in the discussions about the deregulated electricity markets.Consequently,open access to the transmission system is one of the basic topics to allow competition among participants in the energy market.Transmission costs have an important impact on relative competition among participants in the energy market as well as on short-and long-term economic efficiencies of the whole electricity industry,although they represent only close to 10% of the energy market price.This paper deals with the design and tests of a transmission pricing method based on the optimal circuit prices derived from the economically adapted network(EAN).Prices derived from the EAN have the advantage of being in tune with the maximum revenue allowed to the owner of transmission assets and simplifying the optimal allocation of transmission costs among participants.Beginning from the conceptual design,the proposed method is tested on a three-bus network and on the IEEE 24-bus reliability test system.     

MASCEM: a multiagent system that simulates competitive electricity markets

Around the world, the electricity industry, which has long been dominated by vertically integrated utilities, is experiencing major changes in the structure of its markets and regulations. Owing to new regulations, it's evolving into a distributed industry in which market forces drive electricity's price. The industry is becoming competitive; a market environment is replacing the traditional centralized-operation approach. This transformation is often called the deregulation of the electricity market. MASCEM, a multiagent simulator system, is a valuable framework for evaluating new rules, new behavior, and new participants in the numerous electricity markets that are moving toward liberalization and competition.     

Trading hubs construction in electricity markets using evolutionary algorithms

The trading hubs construction problem for electricity markets under locational marginal prices is considered. Given historical prices for all nodes of the electricity grid and for all market participants over a sufficiently long period of time, the problem is to choose a required number of node clusters (hubs) and to assign market participants to hubs so as to minimize the deviation of hub prices from the prices of participants under certain constraints. In view of problem complexity, two evolutionary algorithms are proposed: a genetic algorithm and a hybrid local search heuristic. It is proved that the proposed genetic algorithm converges to optimum almost surely. The algorithms are tested and compared on the real-life data. The structure of the fitness landscapes is analyzed using multiple restarts of the local search and the behavior of the evolutionary algorithms is explained on the basis of this analysis.     

Design and implementation of a decision support system for competitive electricity markets

In this paper we describe software which could be used as a decision support tool for decision makers in competitive electricity markets. The tool is based on the behavior of the competitors in the market, thus many different parameters around them are considered and, additionally, many different scenarios can be used. The possible results are obtained by simulation. Two modes are implemented; one to simulate the functioning of a competitive electricity market and the other, called manual, to evaluate the success of particular strategies by a user. Finally, some computational experience is reported to validate the simulation model.     

Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario.Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.     

A multi-agent approach for planning activities in decentralized electricity markets

Despite the rigour and ability of game theory to cope with oligopolistic electric markets, it fails to model many existing behaviours in the real-world circumstances. The traditional models such as statistical extrapolation or econometrics are not capable to anticipate the changes in the pattern of the market prices due to the future structural changes. Furthermore, in such free and open markets, there is a more intense need for each participant to benefit from a certain level of autonomy, while keeping some capabilities to interact, communicate, collaborate and negotiate with other participants in an efficient way. As a result, there is a need for a novel framework of modelling that could include game theoretical assumptions as well as other more complex assumptions. Agent technologies in general and agent-based simulation in particular offer this possibility. This paper proposes, in a decision-making perspective, a new multi-agent architecture specifically designed to support planning activities in decentralized electricity markets, with a certain level of flexibility. In this model, synthetic agents are created allowing flexible representations of the multi-functional market players and possible mergers and coalitions in the electricity market.     

Electricity price classification using extreme learning machines

Forecasting electricity prices has been a widely investigated research issue in the deregulated power market scenario. High price volatilities, price spikes caused by a number of factors such as weather uncertainty, fluctuating fuel prices, transmission bottlenecks, etc., make the task of accurate price forecasting a formidable challenge for the market participants. A number of models have been proposed by researchers; however, achieving high accuracy is always not possible. In some specific applications such as self-scheduling by demand side participants, certain price thresholds are more useful than accurate price forecasts. In this paper, we have investigated the application of a novel neural network-based technique called extreme learning machine for the problem of classification of future electricity prices with respect to certain price thresholds. Different models corresponding to different lead times are developed and tested with data corresponding to Ontario and PJM markets. It is observed that classification with ELM is fast, less sensitive to user defined parameters and easily implementable.     

Electronic multilateral trade of electricity

This paper presents a technical approach for electronic multilateral trade of electricity in competitive power industries. The trade involves strategic sharing of data among agents in an attempt to provide the opportunity to intelligently discover competitive behavior of peer suppliers. A trading logic is implemented as a specialized software module within the agent. The logic mimics intelligence of the human strategic trade. A time-bounded trade protocol has been introduced as a trading basis among rivalry trade agents in the market. The protocol limits the trade rounds in order to bind the trading process to specific deadlines. The protocol is coded as part of the automated trade server. The results of a generic 3-bus test system show that the electronic multilateral trade logic presented in this paper better distributes market sales, lowers prices and consequently provides higher social welfare compared to the standard Cournot economic model that may be used by the human decision-maker for market trading. Based on a set of test cases with different load profiles, it is noted that the electronic multilateral trade drives the market price closer to the marginal cost of generation supply and far away from the estimated Cournot price.     

Identification of load pockets and market power in electric power systems

In this paper, we present a spectral method for the identification of load pockets and the application of practical techniques for the measurement of market power. Market power is a serious concern in electric energy markets, especially in the area of a load pocket. Common definitions for market power, which rely on a comparison between market prices and a so-called competitive price, are difficult to use in practice because the competitive price is not known when the market is not competitive. The competitive price cannot be computed from data naturally available to the market. Our technique focuses on the identification of participants with the ability to increase revenues by increasing prices, an ability not present in a competitive market. We then propose measures for quantifying the extent to which market power is being exercised. These measures can be computed from data available to the market; they are practical. We present results from a 30 bus, 6 generator system, which illustrates that generators in a load pocket have and can exploit joint market power.     

Price strategies in dynamic duopolistic markets with deregulated electricity supplies using mixed strategies

While effective competition can force service providers to seek economically efficient methods to reduce costs, the deregulated electricity supply industry still allows some generators to exercise market power at particular locations, thereby preventing the deregulated power market to be perfectly competitive. In this paper, we investigate the interdependence of pricing mechanisms and strategy behaviors of the suppliers. A multiperiod dynamic profit-maximizing problem is converted to a bimatrix game that is solved in the framework of mixed strategies. By this procedure, we have at least one Nash solution. Instead of considering only perfectly competitive price and monopoly price, we introduce other prices between these two to simulate the real market better. Numerical examples show that the new entrant that maximizes its profit will not choose the perfectly competitive price even as an entry price.     

考虑节点边际价格的热电联产机组主从博弈竞价策略

本文构建了以热电联产机组(combined heat and power unit,CHP)、电力市场和热力市场为参与者的主从博弈模型,并基于电力市场中节点边际电价(locational marginal electricity price,LMEP)的概念,提出了节点边际热价(locational marginal heat price,LMHP)的概念.在节点边际电价的求解中,采用了支路潮流(branch power flow,BPF)模型,考虑了配电网中的网络损耗从而可以得到更精确的计算结果.在节点边际热价的求解中,考虑了管道热损耗,并基于管道损耗方程分析了节点边际热价的分布规律.在此基础上,采用变步长迭代寻优算法求解热电联产机组、电力市场、热力市场各自最优出力和最优报价策略.最后,通过一个6节点电网–4节点热网的算例对所构建的主从博弈模型及热电联产机组的竞价策略进行了验证.     

一种双边可选择电力远期合同的定价模型

激烈竞争的电力市场需要有效的风险管理工具来回避易变的价格风险，结合金融期权交易思想的电力远期合同（或称可选择远期合同）因其灵活性和多样性而将发挥重要作用，提出一种合同双方均有选择权的双边可选择电力远期合同模型，根据期权定价思想给出了合同价格计算方法，并通过一个合同买卖双方各自追求最大期望报酬的均衡模型，给出了有关期权敲定价的均衡选择，比较分析表明，这种双边可选择远期合同模型具有良好的特性。     

Dynamic Spatial Auction Market Models with General Cost Mappings

The deregulation of electricity markets in Europe has deeply changed the organization of this sector. Vertically integrated generating companies have been unbundled to create competition and to increase the competitiveness of electricity markets. Directive 96/92/EC was issued by the European Commission to liberalize electricity markets and to pave the way for the creation of the Internal Electricity Market. In particular, this Directive aimed at promoting the competition in the activities of electricity generation and wholesale through the creation of a “marketplace” and the maximization of transparency and efficiency. Competition in European day-ahead electricity markets has been established through auction markets where electricity producers and consumers offer/bid prices and volumes. This paper suggests a dynamic equilibrium model for a system of auction markets linked by transmission lines and subject to energy balance and transmission constraints, such as those characterizing restructured electricity markets. This model is treated as a system of variational inequalities with arbitrary monotone mappings. An inexact splitting type method is proposed to find its solution. Numerical experiments are conducted on the Italian day-ahead electricity market.     

Combining Energy Networks

Electricity markets depend on upstream energy markets to supply the fuels needed for generation. Since these markets rely on networks, congestion in one can quickly produce changes in another. In this paper we develop a combined partial equilibrium market model which includes the interactions of natural gas and electricity networks. We apply the model to a stylized representation of Europe??s electricity and natural gas markets to illustrate the upstream and downstream feedback effects which are not obvious on first sight. We find that both congestion and loop-flow effects in electricity markets impact prices and quantities in markets located far from the initial cause of the market changes.     

A web-based platform for experimental investigation of electric power auctions

This paper describes the architecture and uses of an Internet-based software platform called PowerWeb. PowerWeb was designed and implemented as a simulation environment for evaluating various power exchange auction markets through experimental investigation. It is designed to host simulations of a competitive ‘day-ahead' electric energy market in which the participants interact by submitting bids or offers into a market via a web-based user interface. The PowerWeb environment is meant to be flexible so as to allow investigation of various market mechanisms. In this paper we describe its interactive, distributed and web-based character.     

LEARNING OLIGOPOLISTIC COMPETITION IN ELECTRICITY AUCTIONS

This paper addresses the problem of auction markets efficiency within the context of recently liberalized electricity markets. Two different auction mechanisms, i.e., the uniform and the discriminatory price setting rules, have been employed worldwide in designing electricity markets. In this paper, we study the relative efficiency of the two auction mechanisms in the framework of the learning-in-games approach. The behavior of electricity suppliers are modeled by means of an adaptive learning algorithm and the demand is assumed to be constant and inelastic, according to a common hypothesis in electricity market modeling. Computational experiments results are interpreted according game theoretical solutions, i.e., Nash equilibria and Pareto optima. Different economic scenarios corresponding to a duopoly and a tripoly competition with different level of demand are considered. Results show that in the proposed conditions, sellers learn to play competitive strategies, which correspond to Nash equilibria. Finally, this study establishes that, in the presented computational setting and economic scenarios, the discriminatory auction mechanism results more efficient than the uniform auction one.     

基于多智能集成学习的中短期电煤价格预测

为扩大电力市场交易量与下调市场电价,需要提升电煤价格预测的可靠性与准确性.为此本文提出了多智能集成学习的中短期电煤价格预测方法.首先,阐述了Stacking集成学习的结构和原理;然后,介绍了数种智能电煤价格的预测模型,并通过算例证明了不同单智能模型对数据的感知能力存在差异性;进而,通过比较单智能模型预测结果的差异值均差,筛选出预测性能优异并且数据感知角度差异性明显的智能模型组.为了充分发挥个模型感知能力差异性的优势,利用Stacking融合各模型,得到一种适用于电煤价格滚动预测的集成模型.最后,通过滚动预测2019至2020年的电煤价格,对集成模型的有效性进行验证.