Demand-side management in Grid-Connected Energy Storage System using Fuzzy Logic Control
In today's dynamic energy landscape, it's essential to ensure efficient power flow in grid-connected battery systems. In this blog post, we'll explore a comprehensive model and logic for optimizing power flow in a setup comprising residential and commercial loads, a battery, and AI-based control systems.
Understanding the System:
The system in question consists of five residential loads, three commercial loads, a battery, and an AI-based Synergy controller. The goal is to efficiently manage the power flow while considering factors like time of day, state of charge (SOC) of the battery, and commercial load requirements. The Synergy Controller:
The Synergy controller acts as the brain of the system, receiving inputs of time of day and SOC of the battery. It categorizes time of day into different segments, such as early morning, normal hours, peak hours, and evening peak hours. Based on these inputs, it generates power commands for the battery. Charging Modes:
The power commands generated by the Synergy controller dictate whether the battery operates in charging mode, discharging mode, or remains idle. Charging mode implies that the battery receives power from the grid, while discharging mode means it supplies power to the load. In idle mode, no power is exchanged. Logic Model:
To make this work, a logic model is created to manage the battery's state. It consists of 12 rules, which consider the SOC and time of day. The rules determine whether the battery should be in charging or discharging mode. The Control Loop:
The battery controller receives the power command generated by the Synergy controller. It processes this command to generate a current reference. This current reference is then used by the inverter controller, which converts three-phase power into two-phase power. The inverter controller manages the power flow between the battery and the grid. Load Profiles:
The residential and commercial loads have their own demand profiles for 24 hours. These profiles are integrated into the system to ensure proper power distribution. Measurement and Monitoring:
The power flow is continuously monitored within the system. Measurements include grid power, battery power, and individual load power. These measurements help make real-time decisions about power flow. Conclusion:
Efficient power flow management in grid-connected battery systems is crucial for optimizing energy use and cost savings. The use of AI-based Synergy controllers and logic models ensures that power is distributed appropriately based on time of day and battery state of charge. This approach can lead to better energy management and reduced energy costs in various settings.