Stochastic Optimization with Risk Aversion for Virtual Power Plant Operations: A Rolling Horizon Control

Castillo, Andrea; Flicker, Jack; Johnson, Jay; Hansen, Clifford W.; Watson, Jean -Paul

doi:10.1049/iet-gtd.2018.5834

Stochastic Optimization with Risk Aversion for Virtual Power Plant Operations: A Rolling Horizon Control

Journal Article · Mon Nov 26 23:00:00 EST 2018 · IET Generation, Transmission, & Distribution

DOI:https://doi.org/10.1049/iet-gtd.2018.5834· OSTI ID:1485463

Castillo, Andrea ^[1]; Flicker, Jack ^[1]; Johnson, Jay ^[1]; Hansen, Clifford W. ^[1]; Watson, Jean -Paul ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

While the concept of aggregating and controlling renewable distributed energy resources (DERs) to provide grid services is not new, increasing policy support of DER market participation has driven research and development in algorithms to pool DERs for economically viable market participation. Sandia National Laboratories recently undertook a three-year research program to create the components of a real-world virtual power plant (VPP) that can simultaneously participate in multiple markets. Our research extends current state-of-the-art rolling horizon control through the application of stochastic programming with risk aversion at various time resolutions. Our rolling horizon control consists of (1) day-ahead optimization to produce an hourly aggregate schedule for the VPP operator and (2) sub-hourly optimization for real-time dispatch of each VPP subresource. Both optimization routines leverage a two-stage stochastic program (SP) with risk aversion, and integrate the most up-to-date forecasts to generate probabilistic scenarios in real operating time. Our results demonstrate the benefits to the VPP operator of constructing a stochastic solution regardless of the weather. In more extreme weather, applying risk optimization strategies can dramatically increase the financial viability of the VPP. As a result, the methodologies presented here can be further tailored for optimal control of any VPP asset fleet and its operational requirements.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1485463

Alternate ID(s):: OSTI ID: 1786814

Report Number(s):: SAND--2018-12376J; 668891

Journal Information:: IET Generation, Transmission, & Distribution, Journal Name: IET Generation, Transmission, & Distribution Journal Issue: 11 Vol. 13; ISSN 1751-8687

Publisher:: Institution of Engineering and TechnologyCopyright Statement

Country of Publication:: United States

Language:: English

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