Building control systems with optimization of equipment life cycle economic value while participating in IBDR and PBDR programs
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Solution Overview
Problem
Central plants face challenges in optimizing the allocation of resources, such as electrical and thermal energy, across multiple subplants to minimize energy costs, especially when considering electrical demand charges and equipment efficiency, which complicates the determination of when and how to utilize different subplants effectively.
Innovation Solution
A system and method that utilize electrical energy storage, including batteries, and a controller to optimize the allocation of resources by predicting monetized costs of battery degradation, equipment start/stop penalties, and revenue from incentive-based demand response programs, to determine optimal allocations that maximize the monetary value of operating a building over a time horizon.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If high efficiency equipment is used to reduce energy consumption, then energy efficiency is improved, but equipment cost increases
Solution Approach 1:
The system dynamically changes operational parameters (which subplants to operate, at what capacity) based on real-time conditions such as energy prices, demand charges, and equipment efficiency characteristics. This allows the system to achieve high energy efficiency by selecting optimal equipment combinations without permanently investing in the most expensive high-efficiency equipment for all scenarios.
2Adaptability or versatility
If multiple subplants are used to satisfy energy demand, then energy supply flexibility is improved, but control complexity increases
Solution Approach 1:
The control system continuously monitors energy prices, demand charges, subplant performance, and building energy demand, then uses this feedback to dynamically adjust which subplants operate and at what capacity. This feedback loop enables flexible energy supply adaptation to changing conditions while automating the complex control decisions.
Solution Approach 2:
The system dynamically adjusts operational configurations of multiple subplants based on real-time conditions. Rather than static operation, the system continuously optimizes which subplants are active and at what capacity levels, enabling flexible adaptation to varying energy prices, demand charges, and building needs while distributing the control complexity across multiple controllable units.
3Loss of energy
If electrical energy storage is used to optimize resource allocation, then energy cost is reduced, but battery degradation cost increases
Solution Approach 1:
The system dynamically adjusts battery charge/discharge parameters based on real-time energy prices and demand charges. By changing operational parameters (when to charge, when to discharge, at what rates) rather than fixed schedules, the system maximizes energy cost savings while controlling battery stress and degradation through optimized charge/discharge cycles.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively optimizes resource allocation, reducing energy costs and maximizing the economic value of operating the central plant by considering predicted costs and revenues, thereby improving the efficiency and cost-effectiveness of energy management.
Implementation Method 1
electrical energy storage including one or more batteries configured to store electrical energy purchased from a utility and to discharge the stored electrical energy
Data Source
AI summary
A system for allocating one or more resources including electrical energy across equipment that operate to satisfy a resource demand of a building. The system includes electrical energy storage including one or more batteries configured to store electrical energy purchased from a utility and to discharge the stored electrical energy. The system further includes a controller configured to determine an allocation of the one or more resources by performing an optimization of a value function. The value function includes a monetized cost of capacity loss for the electrical energy storage predicted to result from battery degradation due to a potential allocation of the one or more resources. The controller is further configured to use the allocation of the one or more resources to operate the electrical energy storage.