Energy System Simulation Scheduling for Cost-Adaptive Control
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Solution Overview
Problem
Existing energy management systems fail to adapt to changing energy costs and user preferences, as they do not account for the amount of energy used or its cost, requiring users to manually adjust settings which is inefficient and lacks intelligent adaptation.
Innovation Solution
A facility that uses model-based simulations to identify and optimize energy system components and schedules based on user preferences and environmental factors, generating schedules that minimize energy consumption or cost by analyzing thermal capacitance, weather conditions, and energy pricing, employing meta-heuristic optimization techniques to determine optimal operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated schedules are used to control energy systems, then ease of operation is improved, but adaptability to changing energy costs and user needs deteriorates
Solution Approach 1:
The system transitions from static predetermined schedules to dynamic optimization that continuously adapts to changing conditions. The optimization module dynamically adjusts energy system schedules based on real-time energy costs, user preferences, and environmental conditions, making the system both easy to operate and highly adaptable.
Solution Approach 2:
The system incorporates feedback loops where the optimization module receives information about energy costs, user preferences, and system performance, then uses this feedback to continuously refine and adjust schedules. This feedback mechanism enables the system to adapt to changing conditions while maintaining automated operation.
2Use of energy by moving object
If model-based simulations with meta-heuristic optimization are implemented, then energy consumption is reduced, but device complexity increases
Solution Approach 1:
The optimization module acts as an intermediary layer between the energy management system and the actual energy-consuming devices. It performs complex model-based simulations and meta-heuristic optimization calculations to determine optimal schedules, then translates these into simple control commands for the energy systems, thereby achieving energy reduction without requiring complexity in the energy devices themselves.
Solution Approach 2:
The system creates virtual copies of the energy system through simulation models. These digital twins allow the optimization module to test and evaluate different operating scenarios without affecting the actual physical systems, enabling complex optimization calculations to be performed on copied representations rather than requiring complex modifications to the real devices.
3Ease of operation
If predetermined temperature adjustments are made at fixed times, then ease of operation is improved, but loss of information regarding energy cost and consumption occurs
Solution Approach 1:
The optimization module performs preliminary calculations and simulations to determine optimal schedules before they are implemented. By pre-calculating the most energy-efficient schedules based on forecasted energy costs and user preferences, the system maintains simple automated operation while avoiding the loss of information about energy consumption and costs through informed advance planning.
Solution Approach 2:
The system continuously monitors actual energy consumption and costs, then feeds this information back to the optimization module. This feedback loop ensures that the system maintains ease of operation through automated scheduling while preventing information loss by using actual performance data to refine future schedule optimizations.
Data Source
AI summary
A facility implementing systems and/or methods for achieving energy consumption/production and cost goals is described. The facility identifies various components of an energy system and assesses the environment in which those components operate. Based on the identified components and assessments, the facility generates a model to simulate different series/schedules of adjustments to the system and how those adjustments will effect energy consumption or production. Using the model, and based on identified patterns, preferences, and forecasted weather conditions, the facility can identify an optimal series or schedule of adjustments to achieve the user's goals and provide the schedule to the system for implementation. The model may be constructed using a time-series of energy consumption and thermostat states to estimate parameters and algorithms of the system. Using the model, the facility can simulate the behavior of the system and, by changing simulated inputs and measuring simulated output, optimize use of the system.