Distributed energy resources (DERs), including distributed generation, storage, and demand response, are expanding and decentralizing options for the provision of electricity services. These novel resources compete with one another as well as conventional generation resources and network assets to provide a limited range of important electricity services. Distributed resources derive much of their comparative advantage from the ability to be located in areas of the electrical grid that maximize the “locational value” delivered by these resources. Locational value derives from the provision of electricity services that exhibit location-specific variation in value, including the locational value of electrical energy resulting from impact of transmission and distribution losses and constraints, the potential to defer or avoid network capacity upgrades, or the ability to increase local reliability or resiliency. In addition, many DERs, including solar PV and battery energy storage devices, exhibit economies of unit scale, which result in lower costs for larger installations.
New electricity system planning tools are needed to properly evaluate the value of DERs and provide insights into how, where, and why DERs can be economically attractive contributors to an affordable and reliable electricity system. These methods must be able to capture the key drivers of locational value, including transmission and distribution network losses, constraints, and expansion costs. They must also capture economies of unit scale, which create trade-offs between cheaper, larger-scale systems on the one hand and more expensive, distributed systems that may be able to capture greater locational value on the other hand.
This seminar will describe a new approach to modeling the electricity system planning problem that incorporates novel methods suitable for analyzing the role and value of DERs in power systems. A new approach is developed to derive the aggregate impact of distributed resources on distribution network losses and network upgrade costs from detailed distribution network simulations. The results from these simulations are then used to parameterize a tractable reduced-form representation of distribution network impacts in a new formulation of the capacity planning problem. An initial case study demonstrating the utility of this model for exploring trade-offs between economies of unit scale and locational value and competition amongst distributed and centralized resources will be presented.
Jesse D. Jenkins is a PhD candidate in Engineering Systems at MIT's Institute for Data Systems and Society and a researcher with the MIT Energy Initiative’s Electric Power Systems Center. Jesse harnesses optimization methods and empirical data to improve planning, operations, regulation, and policy in the rapidly evolving electricity sector. He focuses in particular on two important trends: the transition to zero-carbon power systems and the proliferation of distributed energy resources. Jesse earned a S.M. in Technology & Policy at MIT in 2014 and previously directed the Energy and Climate Program at the Breakthrough Institute, a public policy think tank. He has published peer-reviewed papers in Applied Energy, The Energy Journal, Economics of Energy and Environmental Policy, Energy Policy, Nuclear Technology, and WIREs: Climate Change. He has delivered invited testimony before the United States Senate Committee on Energy and Natural Resources, and his research and writing has been featured in major media outlets including NPR, the New York Times, Wall Street Journal, Washington Post and Time Magazine. Jesse has received fellowships from the National Science Foundation, MIT Energy Initiative, and Martin Family Society of Fellows for Sustainability and served for three years as co-president of MIT’s Electricity Students Research Group. Connect with Jesse on LinkedIn at http://linkedin.com/in/jessedjenkins; follow him on Twitter @JesseJenkins; and view his Google Scholar profile at http://bit.ly/ScholarJenkins