AI-POWERED ANALYSIS
Revolutionizing Grid Management: A Robust Approach for ADNs with EVs and CHPS
This paper presents a robust and evidence-based approach to optimizing and evaluating active distribution networks (ADNs) by integrating Electric Vehicles (EVs) and Combined Heat and Power (CHP) systems. It proposes a three-stage optimization model (day-ahead, intraday, real-time) that leverages demand response and EV charging/discharging to enhance renewable energy absorption, reduce operational costs, and improve reliability. The model is validated on an IEEE 33-bus system, showing significant improvements in economic efficiency and operational stability. An evidence-based evaluation framework, combining set pair analysis and evidence theory, provides a quantifiable status index to prove the superiority of the multi-stage optimization approach.
Executive Impact at a Glance
Key advancements from this research, translated into tangible benefits for your enterprise.
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Renewable Absorption Increase
Solution 3 achieved 93.2% renewable energy absorption, a significant increase over single-stage methods.
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Operational Status Index
The lowest operational status index (36.53) for the three-stage model signifies optimal performance.
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Cost Reduction (vs. Deterministic)
The robust model reduced total costs by 30.7% compared to deterministic optimization.
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Voltage Stability Improvement
Demand response improved voltage fluctuation by 56.0% compared to scenarios without it.
Deep Analysis & Enterprise Applications
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Energy Systems Optimization
This category focuses on advanced methodologies for optimizing complex energy systems, specifically active distribution networks (ADNs) integrated with various distributed energy resources. It encompasses robust optimization techniques, multi-time scale scheduling, demand response integration, and the management of electric vehicles and combined heat and power systems to enhance efficiency, reliability, and renewable energy utilization.
Enterprise Process Flow
Day-ahead Optimization (1-hour forecasts, long-term costs, uncertainty management)
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Intraday Rolling Optimization (15-min forecasts, refining plan, EV/RES balancing)
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Real-time Adjustment (5-min measurements, smoothing fluctuations, voltage stability)
The proposed optimization model is structured across three distinct time scales to progressively refine scheduling and manage uncertainties, ensuring robust and efficient ADN operation.
11.5%
Total Cost Increase without V2G
Disabling Vehicle-to-Grid (V2G) functionality results in an 11.5% increase in total operational costs for the ADN, highlighting the crucial role of EVs as mobile energy storage for grid flexibility and cost optimization.
| Feature | Solution 1 (Day-ahead) | Solution 2 (Day-ahead + Intraday) | Solution 3 (Three-stage) |
|---|---|---|---|
| Total Cost (CYN) | 98,761 | 87,322 | 79,455 |
| Renewable Absorption (%) | 82.1% | 88.5% | 93.2% |
| Voltage Fluctuation (%) | 6.48% | 6.28% | 4.39% |
Solution 3 (three-stage optimization) consistently outperforms Solution 1 and 2 across all key metrics, demonstrating superior economic efficiency, renewable energy integration, and grid stability. |
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Impact of Demand Response on ADN Stability
Removing demand response (DR) mechanisms leads to a 19.8% increase in total operational costs and a 56.0% worsening in voltage fluctuation. This underscores DR's vital role in load shaping, renewable energy accommodation, and overall grid stability, especially during peak demand periods.
Demand Response is critical for optimizing ADN costs and enhancing voltage stability.
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