Wholesale electricity spot market design: Find a way or make it

Designing wholesale electricity spot markets even after three decades of experience in dozens of countries continues to be a challenging task. The challenges arise from a myriad of options that exist without adequate level of unbiased guidance, and experiences in real-life markets where successes and failures are also open to the interpretation of experts. This is particularly a critical issue in the developing world where wholesale spot markets are yet to be established in 80 % of the countries and the experience in the remaining 20 % countries with an operational market, is mixed. There are certain trends in selection of market design that leave significant room for improvement including fallacies that should be avoided. Implementation of markets have also been problematic in most cases including unrealistic timetable and inadequate consideration of transition mechanism that have also hampered market development. In this article, we present our views on how policy makers, stakeholders and development institutions should think about the choices around market design and some of the common pitfalls around its implementation.     

An approach for applying blockchain technology in centralized electricity markets

The electricity sector has recently begun to undergo a series of changes to improve the generation, transmission, and energy distribution. New technologies have been testing focused on reducing fossil fuels and implementing new tools to create a more efficient energy sector. Blockchain has emerged as one of the most promising tools due to its wide range of utilities, primarily supporting renewable energy, electric mobility, purchase, and energy sale. This technology has been revolutionary, contributing to the significant renewal of the electricity market model. In electricity markets that are not very mature, you can find some centralized activities and competition—creating a commercial monopoly in centralized activities in favor of a specific group. This article aims to analyze the shortcomings of previous research on blockchain in the electricity sector and, through a case study, propose an approach for applying blockchain in centralized electricity markets. Blockchain technology has valuable points, including the excellent security in the transactions carried out in the purchase and sale of energy through a peer-to-peer network stimulating the creation of a decentralized energy trading system.     

Portfolio optimization in electricity markets

In a competitive electricity market, Generation companies (Gencos) face price risk and delivery risk that affect their profitability. Risk management is an important and essential part in the Genco's decision making. In this paper, risk management through diversification is considered. The problem of energy allocation between spot markets and bilateral contracts is formulated as a general portfolio optimization problem with a risk-free asset and n risky assets. Historical data of the PJM electricity market are used to demonstrate the approach.     

Analysis of the effect of voltage level requirements on an electricity market equilibrium model

This paper presents a conjectural-variation-based equilibrium model of a single-price electricity market. The main characteristic of the model is that the market equilibrium equations incorporate the effect of the voltage constraints on the companies’ strategic behavior. A two-stage optimization model is used to solve the market equilibrium. In the first stage, an equivalent optimization problem is used to compute the day-ahead market clearing process. In the second stage, some generation units have to modify their active and reactive power in order to meet the technical constraints of the transmission network. These generation changes are determined by computing an AC optimal power flow.     

应用动态学习改进对竞争对手微增响应猜测的发电公司投标策略

该文应用微增响应猜测(Conjcctural Variation，CV)方法构造一种在不完全信息情况下的寡头竞争电力现货市场中发电公司通过动态学习改进对竞争对手的微增响应猜测的新投标策略，指出当各发电公司采用该文提出的投标策略进行不断重复的投标竞争后，市场所达到的均衡点为：Nash均衡点。算例计算结果说明各发电公司具有进行动态学习的动力，但是当所有公司进行学习时将会降低市场结清电价从而提高电力市场的社会总效益。算例计算结果还说明了电力需求弹性对学习过程的收敛性具有积极作用。     

Distributed resources and re-regulated electricity markets

This paper briefly defines distributed resources (DR) and discusses the current status of DR. The main focus is on discussing the value DR can provide to re-regulated electricity markets. DR can, under certain circumstances, help to significantly reduce the main problem of re-regulation, i.e. market power. Distributed generation and demand-side resources may, even if only small quantities in terms of size of the entire market are utilised, be able to help to significantly reduce market power. This results in a significantly more efficient market. To achieve such benefits, however, DR must be in operation during times when market power issues are likely to arise. To a certain extent this is the case as demand-side resources are usually independent of established generation companies. In addition, many business cases for DG require DG to actually operate to capture the second revenue.     

A PSO based approach for multi-stage transmission expansion planning in electricity markets

This paper presents a particle swarm optimization (PSO) based approach to solve the multi-stage transmission expansion planning problem in a competitive pool-based electricity market. It is a large-scale non-linear combinatorial problem. We have considered some aspects in our modeling including a multi-year time horizon, a number of scenarios based on the future demands of system, investment and operating costs, the N − 1 reliability criterion, and the continuous non-linear functions of market-driven generator offers and demand bids. Also the optimal expansion plan to maximize the cumulative social welfare among the multi-year horizon is searched. Our proposed PSO based approach, namely modified PSO (MPSO), uses a diversity controlled PSO to overcome the problem of premature convergence in basic PSO (BPSO) plus an initial high diversity swarm to cover the search space efficiently. The MPSO model is applied to the Garver six-bus system and to the IEEE 24-bus test system and compared to the BPSO model and a genetic algorithm based model.     

电力市场现货电价预测方法研究状况综述

电力工业从垄断走向市场,使得电价不再由政府确定,而是在市场机制下产生。电价波动会影响市场参与者的经济利益。时电力市场参与者而言,准确地预测电价具有非常重要的意义。详细分析和研究了电力市场现货电价的预测方法及其技术发展,阐述了各种电价预测方法的种类、预测原理、优缺点及其适用范围。     

解除管制的电力零售市场实时电价研究

零售侧打破垄断引入竞争是电力市场发展的趋势,制定合理的零售电价是市场开放的关键.根据电力零售市场的特点,基于负荷价格响应的定义以及边际成本定价理论,推导出了考虑负荷价格响应的有功实时电价的表达式.以社会成本最大化为目标函数,建立了考虑负荷价格响应特性的最优潮流模型,从而求解出未来引入竞争的电力零售市场中电力库交易模式下的实时电价.采用32节点配电系统对所提方法进行仿真,结果证明为促进零售市场竞争、引导用户合理用电提供了合理有效的定价方法.     

Influence of TCSC on congestion and spot price in electricity market with bilateral contract

This paper presents a quantitative analysis of the effect of TCSC on congestion and spot price in deregulated electricity markets. The paper could also be considered as a comprehensive tutorial on the influence of TCSC in electricity market. A voluntary pool market, where the market participants can trade electricity either via a pool or through bilateral contracts is considered. The electricity market is modeled in an optimal power flow framework with the objective of maximizing the social welfare. In such formulation, the Lagrange operators associated with the equality constraints associated with real power balance give the spot prices of energy at each bus of the system. Studies are carried out with and without TCSC at peak and low loading conditions to capture the influence and to see the effectiveness of TCSC at different loading conditions. The paper further explores the effect of TCSC compensation level on the spot prices and the congestion under varying pool and bilateral loading conditions. A 5-bus test system is used for numerical studies and to showcase the influence of TCSC in an electricity market environment.     

构造发电公司投标策略的一种新方法-微增响应猜测法初探

介绍了微增响应猜测法(Conjectural Variation，CV)的基本概念，提出了基于这种方法构造发电公司在不完全信息情况下的最优投标策略。通过理论分析和算例计算，结果表明基于经典博弈理论构造的发电公司的最优投标策略是该方法的一种特例。采用该方法进行市场模拟时，对各发电公司不同的微增响应猜测进行组合，可以得到不同的市场均衡点，且均为Nash均衡点。     

A time series spot price forecast model for the Nord Pool market

We present three relatively simple spot price forecast models for the Nord Pool market based on historic spot and futures prices including data for inflow and reservoir levels. The models achieve a relatively accurate forecast of the weekly spot prices. The composite regression model achieves a mean absolute percentage error (MAPE) of around 7.5% and under-forecasts the actual spot price by some 1.4 NOK/MW h in the sample period. Out of sample testing achieves a MAPE of around 7.4% including a match of the actual spot price. A myopic model using the previous week’s spot price as a predictor for the next week’s spot price achieves a MAPE of 7.5% and under-forecasts the actual spot price by some 0.9 EUR/MW h. A futures model using the futures price for next week as a predictor for next week’s spot price achieves a MAPE of 5.3% and over-forecast the actual spot price by some 4.3 EUR/MW h.     

A game theory simulator for assessing the performances of competitive electricity markets

In the last years, electricity markets were created all over the world following different basis concepts. Market structure, market rules, demand levels, market concentration and energy sources to produce electricity have a strong influence on market performances. Modifications on these aspects may significantly affect market outcomes. Sensitivity analyses need proper simulation tools. In this paper a medium run electricity market simulator (MREMS) based on game theory is presented. This simulator incorporates two different games, one for the unit commitment of thermal units and one for strategic bidding and hourly market clearing. Either a Forchheimer (one leader) or Bertrand (all player are leaders) or even intermediate model with a whatever number of leaders can be selected, in dependence on the strategic behavior of the producers, allowing for the simulation of markets with different levels of concentration. The simulator was applied to analyse producers’ behavior during the first operative year of the Italian power exchange. A comparison between simulation and true market results was carried out in order to test the simulator and validate its simplifying hypotheses. MREMS, yet capable to be used stand-alone, was conceived as the heart of a long-term market simulator (LREMS) allowing to simulate the long-run evolution of the generation park (investments in new plants, refurbishment and dismission of older ones). LREMS is a hierarchic simulator: a long-term “outer” game takes yearly investment decisions based on mid-term price projections provided by MREMS. Although this paper is mainly devoted to describe MREMS, one specific section will provide an overview of the “outer” game implemented by LREMS.     

Conjectural variation based learning model of strategic bidding in spot market

In actual electricity market, which operates repeatedly on the basis of one hour or half hour, each firm might learn or estimate other competitors' strategic behaviors from available historical market operation data, and rationally aims at its maximum profit in the repeated biddings. A conjectural variation based learning method is proposed in this paper for generation firm to improve its strategic bidding performance. In the method, each firm learns and dynamically regulates its conjecture upon the reactions of its rivals to its bidding according to available information published in the electricity market, and then makes its optimal generation decision based on the updated conjectural variation of its rivals. Through such learning process, the equilibrium reached in the market is proven a Nash equilibrium. Motivation of generation firm to learn in the changing market environment and consequence of learning behavior in the market are also discussed through computer tests.     

Slack-bus independent penalty factor for regional spot pricing under deregulation

Recently, the deregulation has been a hot issue in electric power industries with the introduction of the bidding system to electric power markets. One of the main topics associated with the deregulation is spot pricing where the exact analysis of transmission loss and the method of calculating reliable penalty factor play an important role. Penalty factors calculated with conventional methods are highly dependent on the location of the slack bus, which brings about unfair competition to Independent Power Producers (IPPs). This paper presents a new method of calculating the exact and reliable penalty factor independent of the change of the slack bus. The proposed method is tested on IEEE 14 bus system, New England 39 bus system and OH-145 bus system. The test results show that the proposed method can give more reliable information of the penalty factors for the regional spot pricing.     

Integrated risk measurement and control for stochastic energy trading of a wind storage system in electricity markets

To facilitate wind energy use and avoid low returns, or even losses in extreme cases, this paper proposes an integrated risk measurement and control approach to jointly manage multiple statistical properties of the expected proft distribution for a wind storage system. First, a risk-averse stochastic decision-making framework and multi-type risk measurements, including the conditional value at Risk (CVaR), value at risk (VaR) and shortfall probability (SP), are described in detail. To satisfy the various needs of multi-type risk-averse decision makers, integrated risk measurement and control approaches are then proposed by jointly considering the expected, boundary and probability values of the extreme results. These are managed using CVaR, VaR and SP, respectively. Finally, the efectiveness of the proposed risk control strategy is verifed by conducting case studies with realistic market data, and the results of diferent risk control strategies are analyzed in depth. The impacts of the risk parameters of the decision maker, the energy capacity of the battery storage and the price diference between the day-ahead and real-time markets on the expected profts and risks are investigated in detail     

A summary of demand response in electricity markets

This paper presents a summary of Demand Response (DR) in deregulated electricity markets. The definition and the classification of DR as well as potential benefits and associated cost components are presented. In addition, the most common indices used for DR measurement and evaluation are highlighted, and some utilities’ experiences with different demand response programs are discussed. Finally, the effect of demand response in electricity prices is highlighted using a simulated case study.     

电力市场下的需求响应研究

介绍了电力市场下的需求响应,包括需求响应的概念及其分类、需求响应成本和需求响应效益.分析了需求响应的短期市场影响效益、长期市场影响效益以及系统可靠性的效益等.同时,介绍了市场下的需求响应在国外的应用情况,并给出了在我国电力市场下实施需求响应的一些想法.     

The effect of wind generation and weekday on Spanish electricity spot price forecasting

This paper empirically compares the predictive accuracy of a set of methods for day-ahead spot price forecasting in the Spanish electricity market. The methods come from time series analysis and artificial intelligence disciplines, and include univariate, multivariate, linear and nonlinear. Within the univariate methods, the double seasonal ARIMA and the recently proposed exponential smoothing for double seasonality are compared and used as benchmarks. They allow us to quantify the improvement on price forecasting when including explanatory variables or using more complex models. Dynamic regression models including the electricity load forecast are then considered. Their good performance in price forecasting has been pointed out by many authors. However, we find evidences of their predictive accuracy can be significantly outperformed by accounting the wind generation forecast provided by the System Operator. Moreover, these forecasts can be even more accurate if changes of price's behavior according with the day of the week are taken into account by means of periodic models. The last of the tested methods are feed-forward neural networks used as multivariate nonlinear regression methods with universal function approximation capabilities. The influence of the wind generation forecast on price prediction is also proved with this approach. Detailed out-of-sample results of the tested methods are given.