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 asset purchases that are either insufficient or excessive due to reliance on rough approximations rather than optimal 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 suboptimal and can be either insufficient or excessive
Solution Approach 1:
The system transforms the asset sizing problem from a static estimation exercise into a dynamic optimization problem by introducing a cost function that varies parameters such as asset size, energy load setpoints, and operational strategies. The controller adjusts these parameters to minimize total cost while meeting building energy demands, thereby achieving precise asset size determination through mathematical optimization rather than rough approximations.
2Measurement precision
If a detailed optimization analysis is performed to determine optimal asset sizes, then asset size accuracy is improved, but the computational complexity and time required increase
Solution Approach 1:
The system performs preliminary actions by pre-defining the cost function structure, identifying all relevant cost components (purchase cost, operational cost, maintenance cost), and establishing the mathematical relationships between asset size and costs before actual optimization begins. This preparation enables the optimization algorithm to efficiently converge to the optimal solution without requiring extensive real-time computation, thereby reducing the time loss while maintaining precision.
3Loss of energy
If the cost function includes both initial purchase cost and operational cost effects, then the overall cost optimization is improved, but the complexity of the cost function increases
Solution Approach 1:
The system merges the initial purchase cost and operational cost into a unified cost function that treats both as interconnected components of total cost. The controller simultaneously optimizes asset size to minimize both capital expenditure and operational expenditure, recognizing that these costs are interrelated - larger assets may reduce operational costs but increase purchase costs, and vice versa. This merging enables comprehensive cost optimization while the mathematical formulation keeps the function manageable through systematic organization of cost terms.
Data Source
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.