Abstract
In recent years, the increasing demand for sustainable energy has driven the development of renewable energy sources. However, the intermittent and volatile nature of these sources poses significant challenges to the stable operation of power grids. To address these issues, microgrids equipped with battery energy storage systems (BESS) have emerged as a viable solution. This paper focuses on the development of multi-agent coordination control strategies for BESS in microgrids, aiming to ensure the stable and efficient operation of these systems. The primary objectives include the equalization of the state of charge (SoC) among multiple storage units, improvement of the current distribution accuracy, and minimization of bus voltage fluctuations. The proposed strategies incorporate dynamic consistency algorithms and adaptive event-triggered communication mechanisms to enhance system performance while minimizing communication overhead. Simulation results and experimental verifications demonstrate the effectiveness of the proposed approaches in achieving these objectives.
Introduction
With the escalating concerns over climate change and the depletion of fossil fuels, there is a growing emphasis on renewable energy sources. However, the integration of renewable energy sources, such as solar and wind, into power grids introduces significant variability and uncertainty due to their intermittent nature. Microgrids, small-scale power systems capable of operating both in grid-connected and islanded modes, offer a promising solution to this challenge. By integrating renewable energy sources, energy storage systems, and local loads, microgrids can provide reliable and resilient power supply during grid outages or when renewable energy sources are insufficient.
Keywords: Microgrid, battery energy storage system (BESS), State of Charge (SoC), Multi-Agent Consistency Algorithm, Event-Triggered Communication
1. Background and Motivation
1.1 The Need for Energy Storage in Microgrids
Microgrids often rely on renewable energy sources such as solar photovoltaic (PV) systems and wind turbines, which are inherently intermittent and unpredictable. Energy storage systems, particularly battery energy storage systems (BESS), play a crucial role in mitigating these fluctuations by storing excess energy during periods of high generation and releasing it during periods of low generation or high demand. However, operating multiple storage units in parallel can lead to issues such as overcharging or overdischarging of individual units, thereby shortening their lifespans and jeopardizing the stability of the microgrid.
1.2 State of Charge (SoC) Equalization
SoC equalization among storage units is essential to ensure balanced utilization and extended lifetimes. When the SoC of individual units varies significantly, some units may be overcharged or overdischarged, leading to premature aging and failure. Therefore, effective control strategies are required to balance the SoC among all storage units.
1.3 Communication Overhead in Microgrids
Traditional periodic communication schemes can result in significant communication overhead, particularly in large-scale microgrids with numerous distributed energy resources. This not only increases operational costs but also imposes a heavier burden on the communication infrastructure. Adaptive communication strategies that minimize unnecessary communication while maintaining system performance are highly desirable.
2. System Modeling and Analysis
2.1 Microgrid Architecture
A typical microgrid architecture consists of renewable energy sources (e.g., PV systems), energy storage systems (e.g., batteries), loads, and control systems. The simplified microgrid architecture with multiple battery energy storage system (BESS) units connected to a common DC bus.
2.2 Battery Modeling
The battery model captures the dynamic behavior of the storage units, including their charging and discharging characteristics. A commonly used equivalent circuit model for a battery.
The state of charge (SoC) of a battery is defined as the ratio of its remaining capacity to its total capacity and can be mathematically expressed as:
textSoC(t)=QtotalQremaining(t)
where Qremaining(t) is the remaining capacity at time t, and Qtotal is the total capacity of the battery.
2.3 Photovoltaic (PV) System Modeling
The output of a PV system is highly dependent on environmental conditions such as solar irradiation and temperature. The PV cell’s I-V characteristic curve is typically modeled using a single-diode model.
The output current of a PV cell can be mathematically represented as:
I=Iph−I0[exp(nkTq(V+IRs))−1]−RshV+IRs
where Iph is the photogenerated current, I0 is the reverse saturation current, V is the voltage across the cell, Rs and Rsh are the series and shunt resistances, respectively, q is the elementary charge, k is the Boltzmann constant, T is the absolute temperature, and n is the ideality factor.
3. Multi-Agent Coordination Control Strategies
3.1 Multi-Agent System Overview
A multi-agent system comprises multiple autonomous agents that interact and coordinate with each other to achieve common goals. In the context of microgrids, each battery energy storage system (BESS) unit can be modeled as an agent, capable of communicating with neighboring units to coordinate their operation.
3.2 Consistency Algorithm for SoC Equalization
A consistency algorithm is employed to equalize the SoC among multiple battery energy storage system (BESS) units. A commonly used approach is the distributed consensus algorithm, where each agent updates its state based on information received from its neighbors. The update rule for the i-th agent can be expressed as:
xi(k+1)=xi(k)+j∈Ni∑aij(xj(k)−xi(k))
where xi(k) is the SoC of the i-th unit at time step k, Ni is the set of neighbors of the i-th unit, and aij are the weights of the communication links.
To achieve faster SoC equalization, a variable regulation factor is introduced, which is dynamically adjusted based on the current SoC difference. The regulation factor α can be defined as:
alpha=α0+βexp(−γ∣ΔSoC∣)
where α0, β, and γ are design parameters, and ΔSoC is the SoC difference between neighboring units.
3.3 Adaptive Event-Triggered Communication
To reduce communication overhead, an adaptive event-triggered communication scheme is proposed. Instead of communicating at fixed intervals, agents communicate only when certain conditions are met. The event-triggering condition for the i-th agent can be defined as:
∣ei(t)∥≥δi(t)∥qi(t)∥
where ei(t) is the measurement error, qi(t) is the control input, and δi(t) is a dynamically adjusted threshold. The threshold δi(t) is adapted based on the current state of the system to ensure stable and efficient operation.
4. Simulation Results and Analysis
4.1 Simulation Setup
Simulations are conducted using MATLAB/Simulink to evaluate the performance of the proposed control strategies. The microgrid consists of three battery energy storage system (BESS) units connected to a common DC bus. The key system parameters are summarized in Table 1.
Table 1: Key System Parameters
| Parameter | Value |
|---|---|
| Battery capacity | 100 Ah |
| Initial SoC of battery energy storage system (BESS)1 | 70% |
| Initial SoC of battery energy storage system (BESS)2 | 75% |
| Initial SoC of battery energy storage system (BESS)3 | 80% |
| DC bus voltage | 400 V |
| Communication topology | Undirected graph |
4.2 SoC Equalization Results
The SoC trajectories of the three battery energy storage system (BESS) units under the proposed control strategy. It can be observed that the SoCs of all units converge to a common value, indicating successful SoC equalization.
4.3 Communication Overhead Reduction
The communication overhead under periodic communication and the proposed adaptive event-triggered communication scheme. It is evident that the event-triggered scheme significantly reduces the number of communication events, thereby minimizing communication overhead.
5. Experimental Verification
To further validate the proposed control strategies, an experimental setup consisting of two battery energy storage system (BESS) units and a PV system is constructed. The experimental platform, utilizes RT-LAB for real-time simulation and data acquisition.
Experimental results demonstrate that the proposed control strategies effectively equalize the SoC of the battery energy storage system (BESS) units and maintain stable bus voltage under various operating conditions.
6. Conclusion
This paper proposes multi-agent coordination control strategies for battery energy storage system (BESS) in microgrids, focusing on SoC equalization and communication overhead reduction. A consistency algorithm with a variable regulation factor is employed to achieve fast and accurate SoC equalization among multiple storage units. Furthermore, an adaptive event-triggered communication scheme is introduced to minimize unnecessary communication while maintaining system performance. Simulation results and experimental verifications demonstrate the effectiveness of the proposed strategies in achieving SoC equalization, improving current distribution accuracy, and minimizing bus voltage fluctuations. Future work will explore the integration of more complex control algorithms and the impact of communication delays on system performance.