Design of power purchase under transmission congestion

Abstract

PurposeThe non‐storable nature of electricity and the increasing complexity of financial instruments as a tool for hedging against risk make the area of research very useful in the real world. Many power portfolio optimization problems have been developed to combat the issue of risk tolerance, but very few (if any) have included transmission constraints. The purpose of this paper is to consider optimization of portfolios of real and contractual assets, including derivative instruments, in a multi‐period setting where transmission constraints also exist.Design/methodology/approachRather than using a flowgate constraint as a representation of transmission congestion, the authors use fixed transmission rights. A model is introduced that involves a three‐node unidirectional network in order to evaluate the significance of transmission constraints. Data from the PJM, which is located in the eastern USA, were used for model implementation.FindingsThe stochastic nonlinear mixed‐integer model presented shows that transmission constraints and fixed transmission rights can have a significant effect on the choices a utility will make when dealing with power procurement. It is demonstrated that the inclusions drastically decrease the value of the objective function.Research limitations/implicationsConditional value at risk (CVaR) was chosen over VaR as a risk measurement for two different reasons. First, it is important to have a good representation of the trade‐off between the best expected profit and the volatility experienced when obtaining that profit. Second, it provides protection against very undesirable scenarios that may occur with low probability. In order to simplify the fixed transmission rights contracts, a three‐node network is used with unidirectional flow.Practical implicationsWhen markets were regulated, transmission lines were owned and operated by local utilities, and all power sent over the lines was either owned by the operating utility or wheeled for another utility based on existing agreements. With the advent of deregulation, utilities were forced to wheel other companies' power, which introduced more risk in terms of transmission constraints.Originality/valueThe contribution of this research is to help companies not only hedge the risk of unknown power prices but also unknown transmission congestion. One distinctive feature of the authors' research is to expand upon existing “power portfolio optimization with risk” literature by introducing a transmission constraint into the model. Historically, transmission congestion has been modeled in different ways, including flowgates, transmission rents and fixed transmission rights.