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Large-Scale Integration of Deferrable Demand and Renewable Energy Sources in Power Systems

Anthony Papavasiliou, UC Berkeley

Wednesday, Feb 1st, EEB 248, 2:00pm

The large-scale integration of renewable energy sources is hindered by the fact that these resources are neither controllable nor accurately predictable. Our analysis focuses on quantifying the cost of balancing power system operations in the presence of renewable resources and on the amount of capital investment in operating and contingency reserves that is necessary for ensuring the reliable operation of the system. We also explore the extent to which demand-side flexibility can mitigate these impacts. We specifically focus on a contract that couples the operations of renewable energy resources with deferrable loads that can shift a fixed amount of energy demand over a given time window. Various flexible energy consumption tasks can be characterized in this way, including electric vehicle charging or agricultural pumping.

We use a two-stage stochastic unit commitment model for our analysis. The use of this model is justi ed by the fact that it is capable of endogenously quantifying the amount of required reserve capacity by explicitly representing the uncertainty of daily operations in the model. We present a dual decomposition algorithm for solving the model and various scenario selection algorithms for representing uncertainty that are necessary for achieving computational tractability in the stochastic unit commitment formulation. We present results for a reduced network of the California power system that consists of 124 generators, 225 buses and 375 lines. We validate the stochastic unit commitment policy that we derive from the stochastic optimization model by demonstrating that it outperforms deterministic unit commitment rules commonly used in practice. We demonstrate this superior performance for both a transmission-constrained as well as an unconstrained system for various types of uncertainty including network element failures as well as two levels of wind integration that roughly correspond to the 2012 and 2020 renewable energy integration targets of California. We then use the stochastic unit commitment model to quantify the benefits of coupling renewable energy supply with deferrable demand. We compare three fundamental approaches to modeling demand flexibility: central co-optimization of dispatch able generation and deferrable demand by the system operator, demand-side bidding and coupling renewable supply to deferrable demand.

Bio: Anthony Papavasiliou is currently a post-doctoral researcher and was formerly a PhD student in the department of Industrial Engineering and Operations Research at the University of California at Berkeley. He completed his undergraduate degree in Electrical and Computer Engineering at the National Technical University of Athens, Greece. His work is focused on the planning and operation of electric power systems and electricity markets under uncertainty, with a focus on renewable energy and demand response integration in smart grids. Anthony has interned at the Federal Energy Regulatory Commission, the Xerox Palo Alto Research Center and the Energy, Economics and Environmental Modeling Laboratory in the National Technical University of Athens. He is the recipient of the U.C. Berkeley Sustainable Products and Solutions Program Fellowship and three awards from the U.C. Berkeley Venture Lab, the CITRIS Information Technology for Society competition and the U.C. Berkeley Energy and Environmental Innovation competition.

Host: Alex Dimakis, dimakis [at]

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large_scale_integration_of_deferrable_demand_and_renewable_energy_sources_in_power_systems.txt · Last modified: 2016/09/01 19:15 (external edit)