Differentiated Pricing-Mechanism Design for Renewable Energy with Analytical Uncertainty Representation

With the integration of high-penetration renewable energy, existing uniform marginal pricing mechanisms face critical challenges, including difficulty in recovering flexibility resource capacity costs and free-riding phenomena caused by renewable energy’s variability. To address these issues, this paper proposes a differentiated pricing mechanism for renewable energy based on analytical uncertainty representation to avoid marginal price distortion and promote the rational allocation of ancillary service costs. Firstly, a joint clearing model for energy and reserve ancillary service is developed, incorporating a distributional robust chance constraint based on moment information to model the uncertainty of renewable energy. Then, the composition structure of the nodal marginal price for ancillary service demand is redefined, offering clearer and more explicit price signals compared with traditional uniform marginal pricing. After that, quantification of the impact of energy storage on renewable energy forecast errors and ancillary service pricing is conducted, with a systematic analysis of its role in reducing ancillary service costs and optimizing generation revenue. Simulation results on the modified IEEE 30-bus system demonstrate significant advantages over traditional uniform pricing: the proposed mechanism ensures fair cost allocation, effectively mitigates free-riding problems, and provides clear economic signals. With energy storage units regulating renewable power output, it could lead to a 12.9% reduction in ancillary service costs while increasing total generation revenue by 6.73%.

Authors

Institutions

Publication Details

Journal
Energies
Published
2025-09-16
DOI
https://doi.org/10.3390/en18184922
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Differentiated Pricing-Mechanism Design for Renewable Energy with Analytical Uncertainty Representation

Xianzhuo Liu, Qi An, Yuan Xue, Jiale Liu
Energies
Smart Grid Energy Management
article

Differentiated Pricing-Mechanism Design for Renewable Energy with Analytical Uncertainty Representation

Xianzhuo Liu, Qi An, Yuan Xue, Jiale Liu
article en

Abstract

With the integration of high-penetration renewable energy, existing uniform marginal pricing mechanisms face critical challenges, including difficulty in recovering flexibility resource capacity costs and free-riding phenomena caused by renewable energy’s variability. To address these issues, this paper proposes a differentiated pricing mechanism for renewable energy based on analytical uncertainty representation to avoid marginal price distortion and promote the rational allocation of ancillary service costs. Firstly, a joint clearing model for energy and reserve ancillary service is developed, incorporating a distributional robust chance constraint based on moment information to model the uncertainty of renewable energy. Then, the composition structure of the nodal marginal price for ancillary service demand is redefined, offering clearer and more explicit price signals compared with traditional uniform marginal pricing. After that, quantification of the impact of energy storage on renewable energy forecast errors and ancillary service pricing is conducted, with a systematic analysis of its role in reducing ancillary service costs and optimizing generation revenue. Simulation results on the modified IEEE 30-bus system demonstrate significant advantages over traditional uniform pricing: the proposed mechanism ensures fair cost allocation, effectively mitigates free-riding problems, and provides clear economic signals. With energy storage units regulating renewable power output, it could lead to a 12.9% reduction in ancillary service costs while increasing total generation revenue by 6.73%.

EnergiesVol. 18(18)
North China Electric Power University (CN), China Southern Power Grid (China) (CN)
Openalex Percentile: Top 76%
Smart Grid Energy Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.