Transmission Capacity Allocation in Zonal Electricity Markets (original) (raw)
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Estimating zonal electricity supply curves in transmission-constrained electricity markets
Many important electricity policy initiatives would directly affect the operation of electric power networks. 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 conservation 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.
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2021
The use of a zonal structure for energy markets across the globe is expanding; however the debate on how to effectively partition the grid into bidding zones is still open for discussion. One of the factors that needs to be addressed in the process of bidding zones’ delimitation is the transmission system operators control areas. Merging parts of different control areas into one bidding zone can lead to multiple problems, ranging from political, through grid security concerns, to reserve control issues. To address it, this paper presents a novel grid partitioning method aimed at bidding zones delimitation that is based on clustering the power grid using an extended version of the standard agglomerative clustering. The proposed solution adds additional clustering rules when constructing the dendrogram in order to take into account the control areas. The algorithm is applied to the data which represents the locational marginal prices obtained from optimal power flow analysis.
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