Systems and methods for assessing and controlling sustainability of an energy plant
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Solution Overview
Problem
Existing central plant designs may be energy-efficient but not economically feasible, necessitating an investment appraisal to determine long-term profitability, and current simulation tools lack comprehensive economic feasibility assessment.
Innovation Solution
A system and method for simulating building equipment operations to generate Pareto-optimal points, integrating economic feasibility assessment through user interfaces that allow for dynamic parameter adjustment and automated generation of simulation results, enabling efficient evaluation of plant designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a central plant is designed to maximize energy efficiency, then energy consumption is reduced, but economic feasibility deteriorates due to high implementation costs
Solution Approach 1:
The system changes parameters by introducing multiple control objectives (energy efficiency, economic feasibility, sustainability) and using Pareto-optimal point classification to identify designs that balance competing parameters. This allows transition from single-objective optimization to multi-objective optimization, resolving the contradiction between energy efficiency and economic feasibility.
Solution Approach 2:
The patent adds a new dimension of economic feasibility assessment to the traditional energy efficiency evaluation. By implementing investment appraisal analysis and sustainability scoring, the system transforms the problem from two-dimensional (energy vs. cost) to three-dimensional optimization, enabling comprehensive comparison of plant designs across multiple criteria.
2Measurement precision
If comprehensive economic feasibility assessment is implemented, then investment decision accuracy is improved, but simulation tool complexity increases
Solution Approach 1:
The simulation tool performs self-service by automatically conducting investment appraisal analysis, generating sustainability scores, and identifying Pareto-optimal points without requiring manual intervention. The system autonomously evaluates multiple plant designs against comprehensive criteria, reducing the burden on users while improving assessment accuracy.
Solution Approach 2:
The system implements feedback mechanisms by comparing simulated plant performance against multiple control objectives and providing sustainability feedback scores. This feedback loop enables iterative optimization of plant designs, allowing users to refine designs based on comprehensive economic and environmental performance data.
3Measurement precision
If multiple control objectives are optimized simultaneously, then sustainability assessment accuracy is improved, but computational time increases
Solution Approach 1:
The system performs preliminary action by pre-classifying design points as Pareto-optimal or non-Pareto-optimal before detailed sustainability assessment. This preliminary classification filters out dominated designs, reducing the computational burden of comprehensive multi-objective optimization while maintaining assessment accuracy for promising candidates.
Solution Approach 2:
The patent segments the optimization process into distinct stages: initial Pareto-optimal point identification, sustainability scoring, and detailed investment appraisal. This segmentation allows the system to handle multiple control objectives systematically, reducing computational complexity by breaking down the comprehensive optimization problem into manageable sub-tasks.
Data Source
AI summary
Systems and methods for controlling building equipment include simulating operation of the building equipment at a plurality of initial points to generate corresponding values of a first control objective and a second control objective that competes with the first control objective, automatically generating a new point at which to run a new simulation based on the plurality of initial points and the corresponding values of the control objectives, running the new simulation at the new point to generate corresponding values of the control objectives, classifying a subset of the plurality of initial points and the new point as Pareto-optimal points based on the corresponding values of the control objectives, and operating the building equipment at a Pareto-optimal point selected from the subset of the plurality of initial points and the new point classified as Pareto-optimal points.