Hybrid Power System Optimization via Model Predictive Control
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Solution Overview
Problem
Current hybrid power systems lack efficient long-term optimization strategies, relying on heuristic control strategies that do not guarantee maximum efficiency due to the complexity of managing diverse power sources and loads in buildings, leading to suboptimal energy use and increased costs.
Innovation Solution
Implementing a hybrid model predictive control (MPC) framework that captures both continuous and discrete dynamics within the system, enabling online optimization with real-time processing and future information integration, such as weather and load forecasts, to enhance micro-grid efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If heuristic control strategies are used to manage hybrid power systems, then the system is easy to design and implement, but the system does not achieve maximum efficiency
Solution Approach 1:
The patent replaces heuristic control strategies (mechanical/system-based approach) with model predictive control using online optimization algorithms (computational/mathematical approach). The MPC framework formulates the energy management problem as an optimization task that maximizes system efficiency by dynamically determining optimal power distribution from multiple sources based on predicted future conditions, while the online optimization component enables real-time computation of these optimal solutions.
2Productivity
If model predictive control with online optimization is implemented, then maximum efficiency is achieved, but computational complexity increases
Solution Approach 1:
The patent segments the control system into distinct functional modules: the MPC framework module that formulates the optimization problem, the online optimization module that solves it in real-time, and the execution module that implements control actions. This segmentation allows each component to be optimized independently and facilitates real-time processing by distributing computational tasks across different processing levels.
Solution Approach 2:
The MPC framework performs preliminary actions by predicting future system states and conditions (such as forecasted power generation from renewable sources and anticipated load demands) before making control decisions. This predictive capability allows the online optimization to work with pre-processed information, reducing the real-time computational burden while maintaining optimal performance.
3Ease of operation
If diverse power sources and loads are managed independently, then system operation is simple, but coordinated operation and efficiency are lost
Solution Approach 1:
The patent merges the control of diverse power sources (photovoltaic, wind, fuel cell, battery) and loads into a unified MPC framework that optimizes the entire hybrid power system as an integrated whole. The online optimization component calculates coordinated power distribution across all sources and loads simultaneously, ensuring they operate in a synergistic manner to maximize overall system efficiency rather than independently.
Data Source
AI summary
An apparatus optimizes a hybrid power system with respect to long-term characteristics of the hybrid power system. The apparatus includes a real-time controller of the hybrid power system and a processor. The processor cooperates with the real-time controller and is structured to input current measurements of information from the hybrid power system and hybrid dynamics information including continuous dynamics and discrete time dynamics that model the hybrid power system. The processor provides online optimization of the hybrid power system based upon the input, and outputs a power flow reference and a number of switch controls to the real-time controller based upon the online optimization. The processor is further structured to provide at least one of: real-time forecasts or real-time prediction of future information operatively associated with the hybrid power system as part of the input, and real-time processing of the online optimization.