Stochastic optimisation with risk aversion for virtual power plant operations: a rolling horizon control. Issue 11 (2nd February 2019)

• Record Type: Journal Article
• Authors: Castillo, Anya; Flicker, Jack; Hansen, Clifford W.; Watson, Jean‐Paul; Johnson, Jay
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 11(2019)
• Page Start: 2063
• Page End: 2076
• Publication Date: 2019-02-02
• Subjects: distributed power generation -- stochastic programming -- power markets -- optimisation -- stochastic processes -- risk management -- power generation economics -- renewable energy sources -- power generation scheduling; stochastic optimisation -- risk aversion -- virtual power plant operations -- rolling horizon control -- renewable distributed energy resources -- grid services -- policy support -- DER market participation -- economically viable market participation -- Sandia National Laboratories -- real‐world virtual power plant -- multiple markets -- stochastic programming -- time resolutions -- day‐ahead optimisation -- hourly aggregate schedule -- VPP operator -- sub‐hourly optimisation -- real‐time dispatch -- VPP subresource -- optimisation routines leverage -- two‐stage stochastic programme -- operating time -- stochastic solution -- risk optimisation strategies -- optimal control -- VPP asset fleet -- operational requirements -- time 3.0 year; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.5834
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : 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 3 year research programme to create the components of a real‐world virtual power plant (VPP) that can simultaneously participate in multiple markets. The authors' research extends current state‐of‐the‐art rolling horizon control through the application of stochastic programming with risk aversion at various time resolutions. Their rolling horizon control consists of day‐ahead optimisation to produce an hourly aggregate schedule for the VPP operator and sub‐hourly optimisation for the real‐time dispatch of each VPP subresource. Both optimisation routines leverage a two‐stage stochastic programme with risk aversion and integrate the most up‐to‐date forecasts to generate probabilistic scenarios in real operating time. Their results demonstrate the benefits to the VPP operator of constructing a stochastic solution regardless of the weather. In more extreme weather, applying risk optimisation strategies can dramatically increase the financial viability of the VPP. The methodologies presented here can be further tailored for optimal control of any VPP asset fleet and its operational requirements.