Central Plant Asset Allocation for Real-Time Energy 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 and the need to minimize economic costs, while also considering storage and distribution efficiencies.
Innovation Solution
An asset allocator system that identifies sources, subplants, and sinks, generates a cost function and resource balance constraint, and solves an optimization problem to determine the optimal energy load allocation, incorporating storage elements, distribution costs, and efficiency losses to control central plant equipment effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy resources are produced exactly when required by the load, then the reliability of meeting energy demand is improved, but the economic 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 resources in thermal energy storage systems, avoiding the need to produce expensive energy during high-price periods while still meeting reliability requirements.
2Loss of energy
If energy loads are allocated across multiple subplants and storage systems, then the economic value is maximized through arbitrage opportunities, but the device complexity increases
Solution Approach 1:
The asset allocator acts as an intermediary that manages the complexity of coordinating multiple subplants and storage systems. It performs mixed-integer linear programming optimization to determine optimal energy allocation, automatically handling the complex scheduling decisions without requiring manual intervention in each subsystem, thereby maximizing economic value while managing complexity centrally.
3Speed
If real-time optimization of energy allocation is performed, then the responsiveness to pricing fluctuations is improved, but the computational requirements and system complexity increase
Solution Approach 1:
The system performs preliminary computational work by formulating mixed-integer linear programming problems in advance and solving them before real-time decision points. This allows the asset allocator to pre-determine optimal energy allocation strategies based on forecasted pricing, enabling rapid real-time execution without intensive computational burden during critical pricing events.
Data Source
AI summary
A controller for a system of equipment that operate to produce or consume one or more resources includes one or more processing circuits configured to generate a resource balance constraint using a balance between a first amount of each resource and a second amount of each resource. The first amount of each resource includes at least an amount of the resource produced by the equipment. The second amount of each resource includes at least an amount of the resource consumed by the equipment. The one or more processing circuits are configured to perform a control process using the resource balance constraint to determine amounts of each resource to be produced or consumed by the equipment and operate the equipment to produce or consume the amounts of each resource determined by the control process.