Microgrid Power Controller Using Dynamic SOC and Peak Shaving
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Solution Overview
Problem
Microgrids face challenges in optimizing power distribution from distributed energy resources (DERs) and battery charging/discharging to efficiently manage peak loads and reduce operational costs, while also considering intermittent renewable sources and the need for uninterrupted electricity supply.
Innovation Solution
A power controller device and method that utilize a processor to determine dynamic peak shaving limits and state of charge (SOC) limits based on load history data, optimizing power distribution by controlling DERs and batteries to provide power to the load, and implementing load curtailment and energy arbitrage strategies, independent of external forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic peak shaving limits and SOC limits are implemented based on load history data, then power distribution optimization is improved, but control system complexity increases
Solution Approach 1:
The system performs preliminary analysis of load history data to determine dynamic peak shaving limits and SOC limits in advance. The power controller device stores load history data and uses it to pre-calculate optimal operating parameters, enabling proactive power distribution optimization rather than reactive control.
Solution Approach 2:
The system implements dynamic adjustment of peak shaving limits and SOC limits based on varying load conditions. The power controller continuously monitors load history data and modifies operational parameters in real-time, transitioning from static to dynamic control to optimize power distribution under different operating conditions.
2Reliability
If real-time control independent of external forecasting is implemented, then system reliability is improved, but computational requirements increase
Solution Approach 1:
The power controller device uses its own stored load history data to generate control decisions independently, without requiring external forecasting services. The system serves itself by utilizing internally stored historical information to make real-time control decisions, enhancing reliability while avoiding dependency on external computational resources.
Solution Approach 2:
Load history data is stored in advance in the power controller device's memory, allowing the system to perform computational analysis using pre-captured data rather than requiring real-time external forecasting. This preliminary data collection enables self-sufficient control operations.
3Productivity
If battery degradation cost is considered in power instruction signals, then long-term operational efficiency is improved, but control algorithm complexity increases
Solution Approach 1:
The power controller incorporates battery degradation cost feedback into its control algorithm. By monitoring battery state and calculating degradation costs based on charge-discharge cycles, the system adjusts power instruction signals to minimize long-term battery wear while maintaining operational efficiency, creating a closed-loop feedback mechanism.
Solution Approach 2:
The system dynamically changes operational parameters including power instruction signals and SOC limits based on battery degradation considerations. By adjusting these parameters in response to battery health status and degradation costs, the system optimizes long-term operational efficiency while accounting for battery lifespan constraints.
Data Source
AI summary
A power controller uses a combination of dynamic state of charge limits, dynamic charge/ discharge rates, battery degradation cost and dynamic peak shave limits to identify the optimal set-points to optimally control load and generating sources while being independent of any external forecasting module. By utilizing these configurable parameters, a power controller provides optimal savings ensuring reliable and sustainable operation of a micro-grid.