- The paper proposes a Stackelberg game model for optimal scheduling of integrated energy systems under renewable uncertainty, incorporating integrated demand response including user thermal comfort.
- This framework transforms uncertain chance constraints into deterministic forms using Sequence Operation Theory and solves the bi-level problem with KKT conditions, demonstrating effective coordination and enhanced renewable consumption in case studies.
- The method provides a practical approach for balancing operator profits and user costs in real-world multi-energy systems by integrating district heating network flexibility and user comfort considerations.
A Stackelberg Game Approach for Optimal Scheduling in Integrated Energy Systems with Uncertain Renewable Generations
The growing emphasis on renewable energy sources due to environmental concerns and energy crises has led to significant advancements in integrated energy systems (IES). The optimal integration of renewable energy within IESs poses complex challenges, primarily due to the uncertainties inherent in renewable generation and the need to balance diverse stakeholder interests. The paper "Optimal Scheduling of Integrated Demand Response-Enabled Integrated Energy Systems with Uncertain Renewable Generations: A Stackelberg Game Approach" introduces a novel framework for addressing these challenges using a Stackelberg game-based approach.
Framework Overview
The research presents a two-tier Stackelberg game model where the integrated energy operator (IEO) acts as the leader by setting energy prices, and users act as the followers by adjusting their consumption strategies to minimize costs. This model simultaneously considers the uncertainties of renewable energy generation, portrayed as probabilistic spinning reserves through chance constraints, and integrates the dynamic characteristics of district heating networks (DHNs).
Crucially, the research incorporates the predicted mean vote (PMV) index into integrated demand response (IDR) to account for users' thermal comfort variability. By applying sequence operation theory (SOT), the authors convert the chance constraints into deterministic forms, enabling the mixed-integer quadratic programming approach to find the Stackelberg equilibrium efficiently. The Karush–Kuhn–Tucker (KKT) conditions play a pivotal role in addressing the bi-level programming challenge.
Numerical Results and Analysis
The paper substantiates the robustness of the proposed framework through two case studies—theoretical analysis on a conceptual IES and practical validation using a real-world system in China. The results reveal that the Stackelberg game-based method achieves effective coordination between renewable generation and IDR, optimizing both the IEO's profit and users' costs. Specifically, the real-world application elucidated how the proposed method enhances renewable energy consumption while maintaining thermal comfort effectively.
Significantly, mode-based comparisons indicated that the joint optimization strategy (mode 3) harnesses the full potential of renewable consumption more efficiently compared to independent optimizations, underscoring the value of considering user dynamics and DHN characteristics.
Theoretical and Practical Implications
From a theoretical perspective, this research enriches the literature on game-theoretic approaches in IES management, providing a structured method for integrating variable renewable energy with demand-side management strategies. The paper underlines how incorporating the flexibility of DHNs and user comfort into the game-theoretic framework yields a balanced outcome for both operators and consumers.
Practically, the paper demonstrates the applicability of such models in real-world energy systems, offering insights into the operational improvements and cost efficiencies achievable through strategic pricing and consumption adjustments. The methodology presented here is versatile, laying foundational work for future applications in broader systems, including integrating electrical, heating, and gas networks.
Future Prospects
While the research provides valuable insights and improvements in IES scheduling, it leaves open avenues for further exploration. Future studies might focus on expanding this framework to incorporate additional layers of complexity in multi-energy systems or enhancing computational efficiencies of the optimization solver. The assumption of ignored secondary heating networks could also be revisited to simulate more realistic scenarios, thus extending the framework's applicability.
In conclusion, the paper provides a methodologically sound, practically relevant solution for IES scheduling issues amidst the uncertainty of renewable resources, contributing constructively to the advancements in integrated operations of mixed-energy systems.