Electric Transmission Infrastructure Expansion Considering Property Value Impact on Routing

It has long been agreed upon by the practitioners and researchers in the field of electric power market operations that both pricing and demand must play much more proactive roles in better balancing demands across the hours of the day. A balanced system will mitigate demand and price spikes and thereby reduce the need for expensive reserve generation capacity as well. Balanced demand will also mean increased network reliability. However, proactive management of pricing and demand would require a more upgraded power market infrastructure than what is currently in place in the U.S. Fortunately, increasing availability of advanced metering and power network infrastructure supported by the Internet of energy (IoE) will soon pave the way for the desired upgrade. This will facilitate dynamic pricing of electricity by system operators and intelligent demand response by load schedulers (controllers) in smart and connected consumer communities. A dynamic pricing strategy will offer binding prices for each time interval (perhaps, hourly) to the consumer nodes before loads are scheduled. In response to dynamic pricing, the smart communities will optimize their load schedule for all remaining time intervals of the day, as well as manage the use of renewable power generated by the communities. Dynamic pricing and demand response (DP&DR) in a network will be highly interdependent between actions of the system operator and the community controllers. The dynamic pricing goal for the nonprofit system operator will be to minimize variations between payment received from the consumers and payment made to the generators. The demand response goal will be to minimize the cost of electricity to their communities over a 24 hour period. DP&DR, as described above, have not yet been adopted in power network operations anywhere in the U.S. In this talk, we present a model designed to obtain dynamic pricing strategies as well as intelligent demand response strategies that work together to reduce cost to the consumers and reduces demand and price spikes. Results from a sample numerical problem are discussed. Continuous-time Marginal Pricing in Electricity Markets Masood Parvania, and Roohallah Khatami Electrical and Computer Engineering, University of Utah * Presenting Author The current practice of discrete-time electricity pricing starts to fall short in providing an accurate economic signal reflecting the continuous-time variations of load and generation schedule in power systems. This presentation will introduce the fundamental mathematical theory of continuous-time marginal electricity pricing. We first formulate the continuous-time unit commitment (UC) problem as a constrained variational problem, and subsequently define the continuous-time economic dispatch (ED) problem where the binary commitment variables are fixed to their optimal values. We then prove that the continuous-time marginal electricity price equals to the Lagrange multiplier of the variational power balance constraint in the continuous-time ED problem. The proposed continuous-time marginal price is not only dependent to the incremental generation cost rate, but also to the incremental ramping cost rate of the units, thus embedding the ramping costs in calculation of the marginal electricity price. Using the numerical results, we demonstrate that the continuous-time marginal price manifests the behavior of the constantly varying load and generation schedule in power systems. Sensitivity and covariance in a large-scale Stochastic Complementarity problem using first order approximation Sriram Sankaranarayanan∗1, Felipe Feijoo, and Sauleh Ahmad Siddiqui Department of Civil Engineering, Johns Hopkins Unversity, MD Pacific Northwest National Laboratory, MD