Building energy cost optimization system with asset sizing
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Solution Overview
Problem
Existing energy cost optimization systems for buildings and central plants face challenges in determining optimal asset sizes and energy load setpoints, often resulting in inefficient asset purchases due to reliance on rough approximations rather than precise calculations.
Innovation Solution
An energy cost optimization system that uses a controller to generate a cost function accounting for both initial purchase costs and operational effects of assets, optimizing asset sizes and energy load setpoints through financial metrics like net present value and internal rate of return, utilizing mixed integer linear programming to determine optimal values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional guidelines and rough approximations are used for asset purchase decisions, then the decision-making process is simple and quick, but the asset size determined is not optimal and can be insufficient or excessive
Solution Approach 1:
The patent transforms the asset sizing problem from a static approximation into a dynamic optimization by changing the parameter representation. It introduces a cost function with multiple parameters including initial purchase cost, operational costs, and financial metrics (NPV, IRR, payback period) that are optimized simultaneously to determine the optimal asset size, thereby achieving precise determination while managing complexity through structured parameterization
Solution Approach 2:
The patent replaces the traditional mechanical/guideline-based asset sizing approach with a mathematical optimization system. Instead of using rule-based methods or rough approximations, it substitutes a computational optimization framework that uses cost functions and financial metrics to automatically determine optimal asset sizes, achieving higher precision through algorithmic rather than heuristic methods
2Reliability
If asset size is increased to ensure sufficient capacity, then the asset can meet the energy load requirements, but the initial purchase cost and operational costs increase
Solution Approach 1:
The patent introduces dynamics into the asset sizing decision by making the asset size a variable in the optimization rather than a fixed parameter. The optimal asset size dynamically adjusts based on the balance between meeting energy load requirements (reliability) and minimizing costs (purchase and operational), allowing the system to find the precise point where sufficient capacity is achieved without excessive investment
Solution Approach 2:
The patent performs preliminary optimization analysis before asset purchase by evaluating multiple asset size scenarios using the cost function and financial metrics. This preliminary action allows decision-makers to identify the optimal asset size in advance, avoiding both insufficient capacity and excessive purchase costs by pre-calculating the most economically viable option that satisfies reliability requirements
3Measurement precision
If optimization is performed over a longer period to capture more operational costs, then the financial metrics become more accurate, but the computational complexity and time required increase
Solution Approach 1:
The patent applies partial action by allowing the optimization period to be flexibly selected based on the specific application requirements. Rather than always performing exhaustive long-term optimization, the system can adjust the optimization horizon to achieve sufficient financial metric accuracy for the decision context, balancing computational time against the need for precise long-term cost evaluation
Solution Approach 2:
The cost function framework is designed to be universal and adaptable to different optimization periods and financial metrics. The same optimization structure can evaluate assets over various time horizons (annual, multi-year, lifecycle) and incorporate multiple financial metrics (NPV, IRR, payback period), making the system versatile for different decision-making contexts without requiring separate specialized models for each scenario
Data Source
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AI summary
An energy cost optimization system for a building includes HVAC equipment and a controller. The controller is configured to generate a cost function defining a cost of operating the HVAC equipment as a function of one or more energy load setpoints. The controller is configured to modify the cost function to account for both an initial purchase cost of a new asset to be added to the HVAC equipment and an effect of the new asset on the cost of operating the HVAC equipment. Both the initial purchase cost of the new asset and the effect of the new asset on the cost of operating the HVAC equipment are functions of one or more asset size variables. The controller is configured to perform an optimization using the modified cost function to determine optimal values for decision variables including the energy load setpoints and the asset size variables.