Estimating zonal electricity supply curves in transmission-constrained electricity markets Mostafa Sahraei-Ardakani a , Seth Blumsack b, * , Andrew Kleit b a School of Electrical, Computer, and Energy Engineering, Arizona State University, USA b John and Willie Leone Family Department of Energy and Mineral Engineering, The Pennsylvania State University, USA article info Article history: Received 13 June 2014 Received in revised form 4 November 2014 Accepted 9 November 2014 Available online 19 December 2014 Keywords: Carbon tax Demand response policy Electricity markets Pennsylvania's Act 129 Transmission constraints Zonal electricity supply curve abstract Many important electricity policy initiatives would directly affect the operation of electric power net- works. This paper develops a method for estimating short-run zonal supply curves in transmission- constrained electricity markets that can be implemented quickly by policy analysts with training in statistical methods and with publicly available data. Our model enables analysis of distributional impacts of policies affecting operation of electric power grid. The method uses fuel prices and zonal electric loads to determine piecewise supply curves, identifying zonal electricity price and marginal fuel. We illustrate our methodology by estimating zonal impacts of Pennsylvania's Act 129, an energy efficiency and con- servation policy. For most utilities in Pennsylvania, Act 129 would reduce the influence of natural gas on electricity price formation and increase the influence of coal. The total resulted savings would be around 267 million dollars, 82 percent of which would be enjoyed by the customers in Pennsylvania. We also analyze the impacts of imposing a $35/ton tax on carbon dioxide emissions. Our results show that the policy would increase the average prices in PJM by 47e89 percent under different fuel price scenarios in the short run, and would lead to short-run interfuel substitution between natural gas and coal. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction Many energy and environmental policy initiatives (including emissions regulations; renewable portfolio standards; and effi- ciency policies) would affect the operation of electric power grid. Analysis of such policies is however difficult in the absence of reliable models of the electric power system. The North American power transmission grid has been called “the largest and most complex machine in the world” [1]. Detailed modeling of the sys- tem requires complete engineering data on every element of the system such as transmission lines, transformers and generators. This engineering approach is often not feasible in the context of policy analysis due to the proprietary nature of the data and en- gineering model complexity. Moreover, policy analysis involves the study of future scenarios. Thus, the inputs to the model should be estimated for the future, which always involves some degree of uncertainty. Since the engineering models need detailed data, the set of input uncertainties becomes extremely large. There exist, many other methods in the literature for forecasting short term electricity prices, including probabilistic estimation of price dura- tion curves [2], short term forecast with fuzzy neural networks [3,4], transfer functions [5], and linear and nonlinear time series [6e9]. These methods are designed to forecast short term prices from hours to a week ahead. They estimate short-term prices well but cannot be used in policy analysis, where acceptable perfor- mance over longer periods of time is needed. Abstract equilibrium models such as [10] can provide insights into strategic gaming and market design efficiency, but cannot be directly applied to real markets. As a result, many policy models in the existing literature neglect the effects of the transmission system and use the relatively simple dispatch curve models [11e 19]. In order to construct a dispatch curve, power plants in a system are sorted according to their marginal cost. The data needed for calculating marginal cost in- cludes heat rate, fuel type and capacity, which are publically available through e-GRID [20] or other similar data sources. Fig. 1 shows an estimated dispatch curve for the PJM Interconnection, a U.S. Regional Transmission Organization whose footprint covers all or parts of thirteen states plus the District of Columbia. The dispatch curve is estimated in a manner similar to [13]. Given data on electricity demand, the dispatch curve can be utilized to deter- mine the marginal unit in the system, as well as the market price in * Corresponding author. Department of Energy and Mineral Engineering, 153 Hosler Building, University Park, PA 16802, USA. Tel.: þ1 814 863 7597. E-mail address: sab51@psu.edu (S. Blumsack). Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy http://dx.doi.org/10.1016/j.energy.2014.11.030 0360-5442/© 2014 Elsevier Ltd. All rights reserved. Energy 80 (2015) 10e19