Enterprise AI Analysis
A Two-Stage Intelligent Reactive Power Optimization Method for Power Grids Based on Dynamic Voltage Partitioning
This paper introduces a sophisticated two-stage intelligent optimization method for grid reactive power, leveraging dynamic voltage partitioning and enhanced deep reinforcement learning. It addresses critical challenges such as reactive power fluctuations and insufficient local support in new power systems with large-scale renewable energy integration. By decoupling large-scale optimization problems and improving agent training efficiency, the method ensures voltage security and optimal grid losses.
Key Metrics & Impact
Our analysis highlights the quantifiable benefits of this innovative approach, demonstrating significant improvements in operational efficiency and reliability in modern power grids.
2.5h
Training Time (39-Bus)
0
Constraint Violations (39-Bus)
31.86MW
Network Loss (39-Bus)
99.06%
Voltage Qual. Rate (118-Bus)
Deep Analysis & Enterprise Applications
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Dynamic Voltage Partitioning
The core of this method lies in its dynamic voltage partitioning strategy. Traditional methods often miss the mark by relying on static electrical distances. Our approach introduces a comprehensive indicator system covering reactive power margin, regulation capability, and geographical distance. This multi-dimensional assessment allows for adaptive adjustment of partition results based on real-time grid operating states, especially crucial with fluctuating renewable energy outputs. Leveraging an adaptive MOPSO-K-means algorithm, we optimize cluster centers to effectively decouple large-scale optimization problems into manageable sub-regions, significantly reducing computational complexity and enhancing adaptability.
Enhanced Deep Deterministic Policy Gradient (DDPG)
For intra-region optimization, we construct a Markov Decision Process (MDP) model for each dynamically formed partition. A key innovation is our reward function, which embeds a dynamic penalty mechanism for safety constraint violations. This ensures that the system not only optimizes for efficiency but strictly adheres to operational safety limits by penalizing violations proportionally to their severity. Furthermore, the Deep Deterministic Policy Gradient (DDPG) algorithm is significantly enhanced through a multi-experience pool with hierarchical probabilistic replay and sampling mechanisms. This strategic experience management prioritizes high-reward and safety-critical experiences, drastically improving learning efficiency and convergence speed for regional agents.
Superior Overall Performance
The proposed two-stage framework demonstrates superior overall performance compared to state-of-the-art DRL algorithms like TD3, PPO, and SAC. Across rigorous simulations on IEEE 39-bus and 118-bus systems, our method consistently achieved faster training times (up to 51.9% reduction), higher convergence rates, and significantly improved optimization outcomes. We achieved the lowest network losses (e.g., 31.86 MW in IEEE 39-bus, 120.84 MW in IEEE 118-bus) and the highest voltage qualification rates (e.g., 100% in IEEE 39-bus, 99.06% in IEEE 118-bus) with zero constraint violations. This comprehensive advantage in economy, safety, and stability, even under diverse dynamic scenarios, validates the method's robustness and engineering practicality for modern power grids.
Two-Stage Reactive Power Optimization Process
Construct Comprehensive Partitioning Indicators
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Adaptive MOPSO-K-means for Dynamic Partitioning
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Decompose Global Problem to Sub-Regional Problems
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Establish MDP Model for Each Partition
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Improve DDPG with Multi-Experience Pool & Penalty
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Regional Agents Perform Autonomous Optimization
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Achieve Global Optimization & Voltage Security
| Metric | Proposed Algorithm | TD3 | PPO | SAC |
|---|---|---|---|---|
| Training Time (IEEE 39-Bus) | 2.5 h | 5.2 h | 3.8 h | 3.2 h |
| Convergence Rate (IEEE 39-Bus) | 0.61 | 0.52 | 0.33 | 0.39 |
| Network Loss (IEEE 39-Bus) | 31.86 MW | 32.21 MW | 34.11 MW | 33.87 MW |
| Voltage Deviation (IEEE 39-Bus) | 0.0165 p.u. | 0.0175 p.u. | 0.0187 p.u. | 0.0182 p.u. |
| Constraint Violations (IEEE 39-Bus) | 0 times | 14 times | 5 times | 10 times |
| Training Time (IEEE 118-Bus) | 4.5 h | 8.3 h | 7.8 h | 7.2 h |
| Convergence Rate (IEEE 118-Bus) | 0.44 | 0.12 | 0.23 | 0.19 |
| Average Loss (IEEE 118-Bus) | 120.84 MW | 121.64 MW | 123.03 MW | 124.23 MW |
| Voltage Deviation (IEEE 118-Bus) | 0.0108 p.u. | 0.0124 p.u. | 0.0154 p.u. | 0.0139 p.u. |
| Voltage Qualification Rate (IEEE 118-Bus) | 99.06 % | 97.34 % | 94.22 % | 96.37 % |
51.9%
Faster Training Time vs. TD3 (IEEE 39-bus)
Adaptive Performance in Dynamic Scenarios
The proposed dynamic partitioning algorithm adaptively adjusts to various operating conditions, including high renewable output, renewable outage, load surge, and system faults. It enhances regional reactive power balance and voltage regulation capability, achieving lower regional reactive power balance degree and higher voltage sensitivity compared to conventional methods. This ensures effective voltage adjustment and secure operation under dynamic and uncertain grid states. The method consistently outperforms other algorithms in network loss reduction and voltage qualification rate across scenarios, demonstrating strong practical applicability.
0
Constraint Violations (IEEE 39-bus)
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Annual Savings
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Annual Hours Reclaimed
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