Micro-grid Energy Management via Two-layer MPC Control
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Solution Overview
Problem
Micro-grids face challenges in managing energy supply and demand due to the intermittent nature of renewable energy sources, leading to uncertainty and inefficiency, and batteries, being the most expensive component, have a shortened lifespan due to irregular usage patterns, necessitating a multi-objective management system that optimizes both energy cost and battery lifetime.
Innovation Solution
A two-layer control method using Model Predictive Control (MPC) for long-term scheduling and real-time balancing, which generates a battery power cost model and dispatches energy sources to minimize operational costs while maximizing battery lifetime, ensuring robustness against forecasting errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery is used to meet supply shortage in grid-tied micro-grids, then energy supply reliability is improved, but battery lifetime is shortened due to irregular usage patterns
Solution Approach 1:
The patent implements a two-layer control system with dynamic switching between advisory layer (long-term scheduling) and real-time controller (short-term dispatching). The real-time controller dynamically adjusts battery charging/discharging decisions based on forecasted renewable generation and actual system conditions, transforming the static battery usage pattern into a dynamic adaptive strategy that extends battery lifetime while maintaining supply reliability.
Solution Approach 2:
The advisory layer performs long-term scheduling in advance by forecasting renewable energy generation and load demands over extended periods. This preliminary action optimizes battery charge/discharge patterns proactively, preventing irregular usage that would shorten battery life, while ensuring energy supply reliability is maintained through advance planning.
2Ease of operation
If conventional unit commitment is used for micro-grid management, then operational simplicity is maintained, but accuracy and reliability deteriorate due to intermittent nature of distributed generations
Solution Approach 1:
The patent segments the micro-grid management system into two distinct layers: advisory layer for long-term scheduling and real-time controller for short-term dispatching. This segmentation allows each layer to specialize in its time horizon and function, improving overall accuracy while maintaining operational simplicity through clear division of responsibilities and modular architecture.
Solution Approach 2:
The patent adds the time dimension by implementing multi-time horizon forecasting and scheduling. The advisory layer operates on long-term forecasts while the real-time controller handles short-term adjustments, transforming the conventional single-time-horizon unit commitment into a multi-dimensional temporal framework that improves accuracy without sacrificing operational simplicity.
3Reliability
If battery is discharged irregularly to meet sudden supply-demand unbalances, then energy balance reliability is improved, but battery lifetime is reduced due to depth of discharge and discharge power stress
Solution Approach 1:
The real-time controller dynamically adjusts battery discharge strategies based on forecasted renewable generation, load demands, and current battery state. This dynamic approach smooths discharge patterns, avoiding sudden deep discharges that stress the battery, while maintaining energy balance reliability through real-time monitoring and adaptive control.
Solution Approach 2:
The advisory layer performs preliminary long-term scheduling that optimizes battery charge/discharge patterns in advance. By planning battery usage over extended horizons and forecasting conditions, it prevents sudden irregular discharges, reducing depth of discharge stress and extending battery lifetime while ensuring energy balance reliability.
Data Source
AI summary
Systems and methods are disclosed for multi-objective energy management of micro-grids. A two-layer control method is used. In the first layer which is the advisory layer, a Model Predictive Control (MPC) method is used as a long term scheduler. The result of this layer will be used as optimality constraints in the second layer. In the second layer, a real-time controller guarantees a second-by-second balance between supply and demand subject to the constraints provided by the advisory layer.