Abstract:
In order to reduce the utilization of fossil energy and promote the utilization of renewable energy, energy storage microgrids have gradually gained widespread application. This paper delves into the research on a multi-agent coordination control strategy for microgrid energy storage systems, aiming to enhance system stability, improve energy utilization efficiency, and achieve balanced state-of-charge (SoC) among energy storage units. Through comprehensive analysis and simulation, this paper proposes an innovative control strategy, laying a solid foundation for the practical application of microgrid energy storage systems.
I. Introduction
With the increasing awareness of environmental protection and sustainable development, renewable energy sources such as solar and wind power have been widely utilized. However, due to their intermittent nature, energy storage systems are indispensable for ensuring the stable operation of microgrids. This paper focuses on the research of microgrid energy storage systems, particularly on the coordination control strategy among multiple energy storage units.
II. Overview of Microgrid Energy Storage Systems
A microgrid is a small-scale power grid that can operate independently or in conjunction with the main grid. Energy storage systems play a crucial role in microgrids, providing energy buffering, peak shaving, and frequency regulation functions. The main components of a microgrid energy storage system include energy storage batteries, power conversion devices, and control systems.
Table 1: Components of Microgrid Energy Storage Systems
| Component | Description |
|---|---|
| Energy Storage Battery | Stores energy and provides power when needed |
| Power Conversion Device | Converts DC/AC power and ensures stable power output |
| Control System | Monitors system status and implements control strategies |
III. Research on Multi-Agent Coordination Control Strategy
To address the issues of unequal SoC among energy storage units, this paper proposes a multi-agent consensus algorithm-based coordination control strategy. This strategy correlates the SoC of each energy storage unit, ensuring balanced energy distribution and improved system stability.
3.1 Multi-Agent Consensus Algorithm
The multi-agent consensus algorithm allows multiple agents (in this case, energy storage units) to reach a consensus on a certain variable (SoC) through local communication and coordination. The algorithm can be represented by the following equation:
3.2 Event-Triggered Communication Strategy
Traditional periodic communication methods can lead to unnecessary waste of communication resources. Therefore, this paper proposes an adaptive event-triggered communication strategy. When the system state changes significantly, the communication between energy storage units is triggered, significantly reducing the number of communications and saving resources.
Table 2: Comparison of Communication Strategies
| Communication Strategy | Description | Advantages | Disadvantages |
|---|---|---|---|
| Periodic Communication | Communication occurs at fixed intervals | Simple implementation | Waste of communication resources |
| Event-Triggered Communication | Communication occurs when the system state changes significantly | Efficient use of communication resources | Complex implementation |
IV. Research on SoC Balancing Strategy
SoC balancing is crucial for ensuring the stable operation and prolonging the lifespan of energy storage batteries. This paper proposes a SoC balancing strategy based on the multi-agent consensus algorithm, which mainly includes centralized, decentralized, and distributed control strategies.
4.1 Centralized Control Strategy
In centralized control, a central controller collects and processes system information, formulates corresponding strategies based on the system’s operation, and optimizes the system. Although this strategy offers high control accuracy, it has drawbacks such as excessive communication volume, cumbersome communication networks, and over-reliance on the central controller.
4.2 Decentralized and Distributed Control Strategies
To overcome the limitations of centralized control, decentralized and distributed control strategies have been proposed. These strategies allow energy storage units to communicate and coordinate locally, reducing dependence on a central controller and improving system robustness.
Table 3: Comparison of Control Strategies
| Control Strategy | Description | Advantages | Disadvantages |
|---|---|---|---|
| Centralized Control | Relies on a central controller for decision-making | High control accuracy | Excessive communication volume |
| Decentralized Control | Energy storage units communicate and coordinate locally | Reduced dependence on central controller | Lower control accuracy compared to centralized control |
| Distributed Control | Combines advantages of centralized and decentralized control | High robustness and scalability | Complex implementation |
V. Simulation Results and Analysis
To verify the effectiveness of the proposed control strategy, simulations were conducted using MATLAB/Simulink. The simulation results demonstrate that the multi-agent coordination control strategy can achieve balanced SoC among energy storage units, improve current distribution accuracy, and reduce system fluctuations.
5.1 SoC Balancing Effect
The simulation results show that the SoC of each energy storage unit gradually converges to a balanced state under the proposed control strategy. This indicates that the strategy can effectively address the issue of unequal SoC among energy storage units.
5.2 Current Distribution Accuracy
The simulation results also demonstrate that the proposed control strategy can improve current distribution accuracy among energy storage units. This is crucial for ensuring the stable operation of the microgrid and prolonging the lifespan of energy storage batteries.
VI. Conclusion
This paper proposes a multi-agent coordination control strategy for microgrid energy storage systems, addressing the issues of unequal SoC among energy storage units and improving system stability. Through comprehensive analysis and simulation, the effectiveness of the proposed strategy has been verified. Future research will focus on optimizing the control strategy further, improving system robustness and adaptability, and exploring practical applications in various scenarios.
In summary, the research on multi-agent coordination control strategies for microgrid energy storage systems is of great significance for promoting the utilization of renewable energy, improving energy utilization efficiency, and ensuring the stable operation of microgrids. With continuous advancements in technology and research, microgrid energy storage systems will play an increasingly important role in the future energy system.