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Yao, Yiming

Publications and source records attributed to Yao, Yiming.

The Value of Storage and Demand Response for Renewble Integration

Increased contributions from wind and solar power resources are necessary to meet California’s goal to use 33 percent renewable energy by 2020. Using these renewable resources, however, will substantially increase the variability and uncertainty in electricity generation resources available to California’s electricity grid operators. Automated demand response and energy storage systems can help reduce this variability and uncertainty through energy buying and selling (arbitrage) in day-ahead markets that would levelize loads and prices throughout the day. They could also provide load-following capability through bids in the real-time market and system management (or regulation) services to the system operator. The project identified policies, technologies (energy storage), and control methods (demand response) that could reduce the cost and improve the reliability of electric power for California ratepayers. Data and assumptions describing energy storage and demand response resources were provided by the Electric Power Research Institute, the California Energy Storage Alliance, and the Demand Response Research Center. The California Independent System Operator provided the production simulation model and supporting data.

14 SOLAR ENERGY↗

Large-Scale Stochastic Optimization for Unit Commitment and Economic Dispatch

Our FY 2012 LDRD research on stochastic optimization with its application in power grid planning and operations has advanced the state-of-the-art of this area in the following aspects: (1) Deploying stochastic unit commitment on large real-world applications, (2) Stochastic optimization coupled with real-time production costsimulation (interleaved), and (3) Real-time stochastic optimization with rolling-horizon look-ahead. In conjunction, a number of computational experiments have been performed to evaluate the capability of the latest stochastic optimization software and explore opportunities for future research. In what follows, we discuss each of these contribution areas, including the results of our computational experiments and their implications on the scalability and solvability of large-scale stochastic optimization models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗