Building Energy Asset Sizing for Cost and Incentive Optimization
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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 insufficient or excessive asset purchases due to reliance on rough approximations rather than optimal calculations.
Innovation Solution
An energy cost optimization system that includes a controller capable of optimizing energy load setpoints and asset sizes by modifying a cost function to account for initial purchase costs and operational effects, incorporating revenue terms from incentive programs, and using stochastic optimization processes to minimize risk and costs.
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 approximation into a dynamic optimization by changing the parameter representation. It uses a cost function with multiple variables including asset size, energy load setpoints, and incentive program participation, allowing the system to explore different parameter combinations to find the optimal asset size that minimizes total cost while meeting building needs.
Solution Approach 2:
The controller acts as an intermediary between the building's energy needs and the asset purchase decision. It modifies the cost function to incorporate multiple factors (initial purchase cost, operational cost, incentive revenue) and performs stochastic optimization to determine the optimal asset size, serving as a mediator that translates complex trade-offs into a specific asset size recommendation.
2Reliability
If asset size is increased to ensure sufficient capacity, then energy load satisfaction is improved, but initial purchase cost and operational cost increase
Solution Approach 1:
The system applies partial action by determining that the optimal asset size may be less than the maximum possible size. Through stochastic optimization of the cost function, it identifies the precise asset size needed to meet energy loads with acceptable reliability, avoiding the excessive action of oversizing assets while still ensuring sufficient capacity through probabilistic analysis.
Solution Approach 2:
The system introduces dynamics by using stochastic optimization rather than a fixed sizing rule. The asset size determination becomes a dynamic process that accounts for uncertainty in energy loads and operational conditions, allowing the system to find the optimal balance between reliability and cost rather than relying on static conservative estimates.
3Quantity of substance
If asset size is decreased to reduce purchase cost, then initial investment is reduced, but energy load satisfaction may become insufficient
Solution Approach 1:
The system uses feedback through the cost function modification process. The controller evaluates how different asset sizes affect both the initial purchase cost and the operational cost, including potential incentive program revenue. This feedback loop allows the system to identify the optimal asset size where further reduction would compromise reliability while additional size would not provide sufficient benefit to justify the increased cost.
4Quantity of substance
If optimization considers multiple factors including incentive programs and operational costs, then overall cost minimization is improved, but the complexity of cost function modification increases
Solution Approach 1:
The system merges multiple cost factors into a single modified cost function. It combines the initial purchase cost, operational cost, and incentive program revenue into one unified function that can be optimized simultaneously. This merging approach simplifies the overall optimization process by treating multiple competing objectives as a single composite objective function rather than separate conflicting goals.
Data Source
AI summary
A controller is configured to obtain a cost function defining a cost of operating building equipment over a time period. The cost function includes a revenue term defining revenue to be obtained by operating the equipment to participate in an incentive program over the time period. The controller is configured to modify the cost function to account for an initial purchase cost of a new asset to be added to the equipment and an effect of the new asset on the cost of operating the equipment. The initial purchase cost of the new asset and the effect of the new asset on the cost of operating the equipment are functions of asset size variables. The controller is also configured to perform an optimization of the modified cost function to determine values for energy load setpoints, the asset size variables, and participation in the incentive program over the time period.