Wildfire Resilient Unit Commitment under Uncertain Demand

TL;DR

This paper develops a two-stage mean-$CVaR$ stochastic unit commitment framework for Public Safety Power Shut-offs (PSPS) that jointly addresses demand uncertainty and wildfire risk mapped via Wildland Fire Potential Index (WFPI). By incorporating start-up/shutdown, operating costs, and the value of lost load, and by constraining line energizations with a wildfire-aware tolerance, the model anticipates non-anticipatory day-ahead decisions and real-time adjustments. Results on the IEEE 14-bus system show that the mean-$CVaR$ approach can yield lower total expected costs than risk-neutral or deterministic methods, while enabling risk-averse strategies to reduce tail costs at the cost of some load shedding. The study also compares multiple line-outage strategies and demonstrates that a slack-augmented WFSL approach offers a robust balance between wildfire risk and economic performance, with implications for planning PSPS on real grids.

Abstract

Public safety power shutoffs (PSPS) are a common pre-emptive measure to reduce wildfire risk due to power system equipment. System operators use PSPS to de-energize electric grid elements that are either prone to failure or located in regions at a high risk of experiencing a wildfire. Successful power system operation during PSPS involves coordination across different time scales. Adjustments to generator commitments and transmission line de-energizations occur at day-ahead intervals while adjustments to load servicing occur at hourly intervals. Generator commitments and operational decisions have to be made under uncertainty in electric grid demand and wildfire potential forecasts. This paper presents deterministic and two-stage mean-CVaR stochastic frameworks to show how the likelihood of large wildfires near transmission lines affects generator commitment and transmission line de-energization strategies. The optimal costs of commitment, operation, and lost load on the IEEE 14-bus test system are compared to the costs generated from prior optimal power shut-off (OPS) formulations. The proposed mean-CVaR stochastic program generates less total expected costs evaluated with respect to higher demand scenarios than costs generated by risk-neutral and deterministic methods.

Figures (7)

• Figure 1: Block diagram showing the data inputs and decision outputs for each stage of the two-stage stochastic optimization.
• Figure 2: An IEEE 14-bus system schematic with each transmission line and bus color-coded to depict its wildfire risk value. Note the arbitrary layout of the buses comes from PowerGridLibIEEE14; the distances between buses are not to scale WFPI values were recorded on October 11th, 2015 in the southwestern U.S. WFPI.
• Figure 3: Load scenarios (mean, $\pm$ 1 std. dev. and $\pm$ 2 std. dev.) for the IEEE 14-bus system derived from the tree reduction load profiles for the RTS-GMLC for October 11, 2020. The probabilities of occurrence of each demand scenario are shown in the insert in the upper left corner.
• Figure 4: Effect of the number of active lines on a) demand, b) production costs, c) wildfire risk, and d) commitment costs for the five benchmark optimization approaches described in Table \ref{['table:Benchmark']} at the moment of peak demand (hour 16 of the day).
• Figure 5: SPSPS (a) generation, (b) load shedding, (c) total costs (excluding VoLL), and (d) VoLL costs for the IEEE 14-bus system optimized at the moment of peak demand (hour 16). Left bars are risk-neutral results for $\beta=0$ and right bars are risk-averse results for $\beta=0.8$. Subplots (a)-(d) together show that for the risk-averse strategy, higher production costs in scenarios 4 and 5 are overcome by savings in VoLL.