Stochastic Model Predictive Control for Building Energy Systems
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Solution Overview
Problem
Building energy management systems face challenges in optimizing resource allocation across equipment to minimize costs while mitigating demand charges and participating in frequency regulation markets, due to uncertainties in energy loads and prices.
Innovation Solution
A stochastic model predictive control system that generates multiple scenarios for energy load allocation, considering uncertainties in energy consumption, prices, and market incentives, and optimizes an overall cost function to determine optimal resource allocation across equipment, including battery systems, to balance resource supply and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a deterministic optimization approach is used for energy load allocation, then the computational complexity is low, but the system cannot account for uncertainties in energy loads and prices
Solution Approach 1:
The patent segments the uncertain future into multiple discrete scenarios, each representing a possible realization of uncertain parameters (loads, prices, incentives). By dividing the continuous uncertainty space into discrete scenarios, the system can apply standard optimization techniques to each scenario while capturing uncertainty effects, thus improving reliability without excessive computational complexity.
Solution Approach 2:
The patent implements dynamic scenario generation and weighting, where scenarios are created based on predicted uncertainties and their probabilities are dynamically adjusted. The optimization problem is reformulated to incorporate scenario probabilities, creating a probabilistic objective function that adapts to changing uncertainty conditions, thereby improving reliability while managing computational complexity through efficient scenario management.
2Measurement precision
If multiple scenarios are generated for optimization, then the accuracy of cost prediction improves, but the computational burden increases
Solution Approach 1:
The patent generates multiple scenarios but applies partial action by using scenario reduction techniques and selective scenario generation. Not all possible scenarios are created equally - instead, the system focuses computational resources on generating and optimizing for the most probable and impactful scenarios, achieving good cost prediction accuracy without the exponential computational burden of exhaustive scenario analysis.
Solution Approach 2:
The patent changes the parameter representation by introducing scenario probabilities as weighting factors in the objective function. Instead of treating all scenarios equally, the system transforms the optimization problem to account for scenario likelihoods, improving cost prediction accuracy while managing computational time through efficient parameterization of the probabilistic model.
3Productivity
If optimal resource allocation is pursued across all equipment, then operational costs are minimized, but the system complexity and control difficulty increase
Solution Approach 1:
The patent creates a universal optimization framework that can handle multiple equipment types (chillers, boilers, batteries, co-generation systems) and multiple objectives (cost minimization, demand charge mitigation, frequency regulation participation) through a single integrated model. This multi-functional approach improves operational efficiency by coordinating all equipment optimally while managing system complexity through unified formulation rather than separate control systems.
Solution Approach 2:
The patent implements feedback mechanisms where the optimization results from one time step inform the constraints and parameters of the next time step. The system uses predicted future loads and prices along with actual system state feedback to dynamically adjust optimal allocations, improving operational efficiency while managing complexity through recursive control that builds on previous decisions rather than requiring complete re-optimization.
4Productivity
If demand charges are mitigated through load management, then energy costs are reduced, but the flexibility in meeting load demands decreases
Solution Approach 1:
The patent applies preliminary action by proactively managing loads and resource allocations in advance of peak demand periods. The optimization framework predicts future demand charges and proactively adjusts equipment operation and resource allocation to avoid high-cost periods, reducing overall energy costs while maintaining load flexibility through advance planning rather than reactive control that would limit adaptability.
Data Source
AI summary
A building energy system includes equipment and an asset allocator configured to determine an optimal allocation of energy loads across the equipment over a prediction horizon. The asset allocator generates several potential scenarios and generates an individual cost function for each potential scenario. Each potential scenario includes a predicted load required by the building and predicted prices for input resources. Each individual cost function includes a cost of purchasing the input resources from utility suppliers. The asset allocator generates a resource balance constraint and solves an optimization problem to determine the optimal allocation of the energy loads across the equipment. Solving the optimization problem includes optimizing an overall cost function that includes a weighted sum of individual cost functions for each potential scenario subject to the resource balance constraint for each potential scenario. The asset allocator controls the equipment to achieve the optimal allocation of energy loads.