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Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management

Daniel Mastropietro, Vyacheslav Kungurtsev

Abstract

High intermittent renewable penetration in the energy mix presents challenges in robustness for the management of power systems' operation. If a tail realization of the distribution of weather yields a prolonged period of time during which solar irradiation and wind speed are insufficient for satisfying energy demand, then it becomes critical to ramp up the generation of conventional power plants with adequate foresight. This event trigger is costly, and inaccurate forecasting can either be wasteful or yield catastrophic undersupply. This encourages particular attention to accurate modeling of the noise and the resulting dynamics within the aforementioned scenario. In this work we present a method for rare event-aware control of power systems using multi-stage scenario-based optimization. A Fleming-Viot particle approach is used to bias the scenario generation towards rare realizations of very low wind power, in order to obtain a cost-effective control of conventional power plants that is robust under prolonged renewable energy shortfalls.

Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management

Abstract

High intermittent renewable penetration in the energy mix presents challenges in robustness for the management of power systems' operation. If a tail realization of the distribution of weather yields a prolonged period of time during which solar irradiation and wind speed are insufficient for satisfying energy demand, then it becomes critical to ramp up the generation of conventional power plants with adequate foresight. This event trigger is costly, and inaccurate forecasting can either be wasteful or yield catastrophic undersupply. This encourages particular attention to accurate modeling of the noise and the resulting dynamics within the aforementioned scenario. In this work we present a method for rare event-aware control of power systems using multi-stage scenario-based optimization. A Fleming-Viot particle approach is used to bias the scenario generation towards rare realizations of very low wind power, in order to obtain a cost-effective control of conventional power plants that is robust under prolonged renewable energy shortfalls.
Paper Structure (10 sections, 5 equations, 1 figure, 1 table)

This paper contains 10 sections, 5 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related work
  3. Problem description
  4. Methodology
  5. Experimental setup and results
  6. Scenario generation
  7. Solution to the multi-stage optimization problem
  8. Evaluation of the solution
  9. Results
  10. Conclusion

Figures (1)

  • Figure 1: Wind power realizations (red) and their closest scenario (blue) in each of the two scenario generation strategies considered in the resolution of the 5-stage optimization problem \ref{['equ:optim_problem']}: Benchmark (left column) and Biased (right column) (see Section \ref{['sec:scenario_generation']}). Each row corresponds to a different Bernoulli probability $q$ of generating a rare wind power change at each stage using Eq. \ref{['equ:wind_power_generation_model']} in Section \ref{['sec:evaluation']}.