Building Equipment Curves for Convex Load Cost Control
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Solution Overview
Problem
Central plants face challenges in optimally allocating energy loads across subplants due to real-time pricing fluctuations, making it difficult to determine when to produce and consume energy at low costs.
Innovation Solution
A method involving the analysis of subplant performance data to identify points where the convexity metric exceeds a threshold, creating convex regions, and using these regions to control the campus subplant for optimal energy distribution and consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy is produced exactly when required by the load, then the load demand is met reliably, but the production cost increases due to real-time pricing fluctuations
Solution Approach 1:
The system performs preliminary action by producing and storing energy (in thermal storage tanks or ice tanks) during periods when utility pricing is low, before the actual load demand occurs. This allows the central plant to meet load demands during peak pricing periods using pre-stored energy rather than producing energy at high cost during peak times.
Solution Approach 2:
The system introduces energy storage tanks as intermediary components between the utility grid and the load. These tanks act as mediators that decouple the timing of energy production from energy consumption, allowing energy to be produced when cheap and stored for later use when expensive, thereby resolving the contradiction between reliability and cost.
2Loss of energy
If energy storage is used to manipulate consumption timing, then production costs are reduced by producing energy when costs are low, but the system complexity increases
Solution Approach 1:
The central plant system is designed with multi-functionality, where the same infrastructure (chillers, boilers, pumps) serves both immediate load demands and energy storage functions. The thermal storage tanks and ice tanks are integrated into the existing HVAC system, allowing the system to perform both cooling/heating and energy storage without requiring entirely separate systems.
Solution Approach 2:
The system incorporates self-service mechanisms through automated control algorithms that monitor utility pricing signals and automatically dispatch energy storage assets without manual intervention. The asset allocator and optimization software enable the system to autonomously make decisions about when to charge/discharge storage tanks based on real-time pricing, reducing operational complexity.
3Productivity
If the asset allocator optimizes energy load distribution across subplants, then operational efficiency is improved, but the computational complexity and difficulty of allocation increase
Solution Approach 1:
The asset allocator divides the central plant into discrete, controllable subplants and individual assets (chillers, boilers, pumps, storage tanks). Each asset is modeled with its own performance characteristics and constraints, allowing the optimization algorithm to systematically evaluate and allocate loads across multiple segmented components rather than treating the entire plant as a single complex system.
Solution Approach 2:
The optimization software dynamically adjusts operational parameters (temperature setpoints, flow rates, asset on/off states) based on real-time conditions including utility pricing signals and load demands. By changing parameters rather than reconfiguring the physical system, the allocator achieves flexible optimization without permanent structural changes, reducing allocation complexity.
Data Source
AI summary
A method includes finding, in equipment performance data points defined by values of an independent variable and values of a dependent variable, a first point for which a convexity metric of the dependent variable with respect to the independent variable satisfies a convexity criterion, creating an equipment curve by providing a first convex region on a first side of the first point and a second convex region on a second side of the first point, and controlling the building equipment based on the equipment curve.