Power Reserve Capacity from Virtual Power Plants with Reliability and Cost Guarantees

TL;DR

The paper tackles the challenge of procuring power reserve capacity from distributed energy resources via virtual power plants under forecasting uncertainty. It introduces a two-step framework: first, a MILP-based maximum flexibility problem to determine the reliability-adjusted maximum reserve $q^p$ a VPP can offer; second, an epsilon-constrained MILP to build a risk-adjusted supply curve that quantifies costs across reserve quantities. A novel subset-simulation based extreme-quantile estimator is proposed to efficiently enforce the reliability target $R^p$, significantly reducing computational burden. The approach is demonstrated on a representative Swiss low-voltage network with a diversified DER mix, revealing that VPPs can reliably supply reserve and that opportunity costs dominate pricing, with product requirements strongly influencing achievable reserve. The findings offer practical guidance for VPP managers and policymakers in bidding strategies and DER-focused ancillary service product design.

Abstract

The growing penetration of renewable energy sources is expected to drive higher demand for power reserve ancillary services (AS). One solution is to increase the supply by integrating distributed energy resources (DERs) into the AS market through virtual power plants (VPPs). Several methods have been developed to assess the potential of VPPs to provide services. However, the existing approaches fail to account for AS products' requirements (reliability and technical specifications) and to provide accurate cost estimations. Here, we propose a new method to assess VPPs' potential to deliver power reserve capacity products under forecasting uncertainty. First, the maximum feasible reserve quantity is determined using a novel formulation of subset simulation for efficient uncertainty quantification. Second, the supply curve is characterized by considering explicit and opportunity costs. The method is applied to a VPP based on a representative Swiss low-voltage network with a diversified DER portfolio. We find that VPPs can reliably offer reserve products and that opportunity costs drive product pricing. Additionally, we show that the product's requirements strongly impact the reserve capacity provision capability. This approach aims to support VPP managers in developing market strategies and policymakers in designing DER-focused AS products.

Figures (5)

• Figure 1: High-level architecture of the two-step method for determining the supply curve of power reserve capacity products. Rectangles represent algorithms/models, parallelograms represent data, and dashed lines indicate repeated actions, with the number of executions specified by corresponding dashed arrows.
• Figure 2: Case study network map showing the 97 buses, lines, and substation location.
• Figure 3: Maximum product quantity histograms for the three subset simulation levels. The intermediate quantiles are reported as $q^{\text{p},\text{*}}_1$ and $q^{\text{p},\text{*}}_2$, while $q^{\text{p},\text{max}}$ is the target quantile that fulfils the reliability requirement.
• Figure 4: Supply curve of the power reserve capacity product provided by the virtual power plant.
• Figure 5: Impact of the product's technical requirements on the maximum power reserve product quantity the virtual power plant can provide.