Energy Management System Using Iterative Expert Engine
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Solution Overview
Problem
Current energy management systems in industrial plants are limited in their ability to optimize energy costs and production, as they rely on simplistic rules of thumb and lack the capability to make detailed calculations necessary for optimal energy usage and production, especially in fluctuating market conditions, leading to inefficiencies and increased operational costs.
Innovation Solution
An energy management system utilizing an expert engine and numerical solver to determine optimal operating configurations for energy production, consumption, and storage equipment, minimizing costs while meeting operational constraints, by iteratively calling the numerical solver with different input criteria to optimize equipment usage and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If an energy management system uses detailed analysis and optimization algorithms to determine energy savings, then energy cost optimization improves, but system complexity increases
Solution Approach 1:
The system divides the optimization problem into two distinct modules: an expert engine that handles high-level decision-making about load shedding and restoration based on economic criteria, and a numerical solver that performs detailed optimization calculations. This segmentation allows each component to specialize in specific tasks, reducing overall system complexity while maintaining optimization capability.
Solution Approach 2:
The expert engine acts as an intermediary between the numerical solver and the plant control system. It translates complex optimization results into actionable decisions about load management, and formulates appropriate input criteria for the numerical solver. This intermediary layer simplifies the interface between optimization algorithms and practical control operations.
2Measurement precision
If the system performs detailed optimization calculations for energy management, then optimization accuracy improves, but calculation time increases
Solution Approach 1:
The expert engine performs preliminary analysis by establishing economic criteria and formulating input criteria for the numerical solver before detailed optimization begins. It pre-processes plant configuration data and identifies relevant parameters, so that when the numerical solver executes detailed calculations, it can focus on specific optimization aspects rather than processing all raw data from scratch.
Solution Approach 2:
The system dynamically adjusts the level of optimization detail based on plant configuration and operating conditions. The expert engine determines which plant equipment and parameters should be included in detailed numerical optimization versus those that can be handled by rule-based expert system logic, creating a flexible, adaptive calculation approach.
3Ease of operation
If the system manually restores loads after load shedding, then operational control flexibility improves, but restoration time increases
Solution Approach 1:
The system implements automatic feedback control for load restoration. The expert engine continuously monitors plant operating conditions and energy market conditions, and automatically triggers load restoration when economic criteria are met. This closed-loop feedback mechanism eliminates manual intervention delays while maintaining flexible control based on real-time conditions.
Solution Approach 2:
The energy management system performs self-service by automatically making restoration decisions based on pre-established economic criteria and real-time condition monitoring. The expert engine autonomously determines when and which loads to restore without requiring manual operator intervention, enabling the system to serve its own control needs while maintaining operational flexibility.
Data Source
AI summary
An energy management system uses an expert engine and a numerical solver to determine an optimal manner of using and controlling the various energy consumption, producing and storage equipment in a plant/community. The energy management system operates the various energy manufacturing and energy usage components of the plant to minimize the cost of energy over time, or at various different times, while still meeting certain constraints or requirements within the operational system, such as producing a certain amount of heat or cooling, a certain power level, a certain level of production, etc. In some cases, the energy management system may cause the operational equipment of the plant to produce unneeded energy that can be stored until a later time and then used, or that can be sold back to a public utility, for example, so as to reduce the overall cost of energy within the plant.