Central Plant Asset Allocation Under Real-Time Energy Pricing
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Solution Overview
Problem
Central plants face challenges in optimally allocating energy loads across subplants due to real-time pricing fluctuations and the need to minimize production costs while meeting energy demands efficiently.
Innovation Solution
An asset allocator system that identifies sources, subplants, and sinks, generates a cost function, and solves an optimization problem to determine the optimal energy load allocation, considering resource balance constraints, storage, and incentive programs, to minimize economic costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy resources are produced exactly when required by the load, then the energy 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 resources before peak demand periods when pricing is lower. The asset allocator schedules energy production in advance during off-peak hours and stores it in energy storage systems, then discharges the stored energy during high-demand periods when real-time pricing is higher, thereby reducing production costs while maintaining reliable energy supply.
2Ease of operation
If energy loads are allocated without optimization across subplants, then the system operation is simple, but the economic efficiency deteriorates due to inability to minimize production costs
Solution Approach 1:
The asset allocator implements feedback by continuously monitoring real-time pricing signals, energy storage states, subplant operational status, and load demands. It dynamically adjusts energy allocation decisions based on this feedback, optimizing the distribution of energy loads across subplants to minimize production costs while responding to changing economic conditions and system states.
Solution Approach 2:
The system applies dynamics by making the energy allocation strategy adaptive and flexible rather than static. The asset allocator continuously reoptimizes energy distribution across subplants based on real-time pricing fluctuations, storage availability, and load variations, allowing the system to dynamically respond to changing economic and operational conditions.
3Loss of energy
If energy storage is used to manipulate resource consumption timing, then the production cost decreases by producing during low-cost periods, but the system complexity increases due to storage management requirements
Solution Approach 1:
The asset allocator serves multiple functions simultaneously: it optimizes energy allocation across subplants, manages energy storage charging and discharging, responds to real-time pricing signals, and coordinates with incentive programs. By integrating these diverse functions into a single optimization framework, the system reduces overall complexity despite the added capability of storage management.
Data Source
AI summary
A controller for chillers that operate to serve a cooling load of a building or facility obtains a first balance constraint that requires balance between chilled water production and consumption and obtains a second balance constraint that requires balance between water, electricity, or steam consumption and production. The controller solves a control problem using the first balance constraint and the second balance constraint to determine an amount of the chilled water to be produced by the chillers and an amount of the water, electricity, or steam to be consumed by the chillers or other equipment. The controller operates the chillers in accordance with a setpoint to consume the amount of the water, electricity, or steam or produce the amount of the chilled water determined by solving the control problem. The amount of chilled water is delivered for use in serving the cooling load of the building or facility.