GOALI: Collaborative Research - A Systems Approach to Modeling Real-Time Market Prices of Electricity

这个研究项目的目的是确定合适的方法来发现在解除管制的市场实时电价的统计分布。它将采用一种自下而上的基于系统的方法，通过生产成本模型来代表市场。该模型的组成部分将是发电机的容量、可靠性和成本特征，包括市场、燃料价格、需求和影响它的气象影响。作为第一个近似，每小时的价格将等于最后一个发电机组用于满足每小时需求的边际成本。最初的努力将集中于根据关于市场供需方面的现有资料，通过生产成本计算模型，找出每小时边际单位的统计分布。然而，经验数据表明，当供给接近需求时，在寡头垄断市场下，现货价格超过了边际生产成本。这一现象将在基于市场生产成本表示的随机框架下使用不同的经济模型进行研究。试图对整个统计分布进行建模，需要考虑与发电机停机和时序负载相关的随机过程。该项目将开发适用于大型系统的近似分析程序，并将使用蒙特卡罗模拟评估这些近似的准确性。将这些模型预测结果与历史数据进行比较，验证计算结果。

The purpose of this research project is to determine suitable approaches for finding the statistical distributions of real-time electricity prices in a deregulated market. It will use a bottoms-up systems based approach in which the market is represented via a production-costing model. The components of this model will be the capacity, reliability, and cost characteristics of the generators that comprise the market, fuel prices, demand, and the meteorological influences that affect it. As a first approximation, the price at every hour will be equated to the marginal cost of the last generating unit used to supply each hour's demand. The initial effort will concentrate on finding the statistical distribution of the marginal unit at each hour via the production-costing model from the available information on the supply and demand sides of the market. Empirical data suggest, however, that when the supply approaches demand and under an oligopoly market, the spot price exceeds the marginal cost of production. This phenomenon will be investigated using different economic models under a stochastic framework based on a production-costing representation of the market. Attempts at modeling the entire statistical distributions require that the stochastic processes associated with the generator outages and chronological load are accounted for. The project will develop approximate analytical procedures that are applicable to large systems and will evaluate the accuracy of these approximations using Monte Carlo simulation. The computations will be validated by comparing the results predicted by these models with historical data.