Building management system with electrical energy storage optimization based on statistical estimates of IBDR event probabilities
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Solution Overview
Problem
Central plants face challenges in optimizing energy distribution and resource allocation across multiple subplants to minimize energy costs, particularly when considering electrical demand charges and the effectiveness of control technologies, as existing systems struggle to determine the optimal usage of subplants and participate in incentive-based demand response programs effectively.
Innovation Solution
A building management system (BMS) with a central plant controller that performs optimization processes to determine the optimal allocation of resources, including thermal energy, water, and electricity, by maximizing economic value through participation in incentive-based demand response programs, using statistical estimates of event probabilities and revenue generation potential, and weighing benefits against costs to determine the best combination of programs to participate in.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If central plants use multiple subplants to serve thermal energy loads, then flexibility and capacity to meet varying demands improve, but determining optimal usage of each subplant becomes difficult and challenging
Solution Approach 1:
The system performs preliminary actions by predicting future thermal energy loads, electricity prices, and demand response event probabilities in advance. This allows the optimization algorithm to pre-determine the optimal usage schedule for multiple subplants before the optimization period begins, resolving the complexity of real-time decision-making while maintaining flexibility to meet varying demands.
Solution Approach 2:
The system dynamically adjusts subplant usage based on varying conditions including predicted thermal energy loads, electricity prices, demand response events, and thermal energy storage capacity. The optimization algorithm continuously adapts the operating schedule of each subplant to maximize economic value while meeting building demands, thus maintaining flexibility without overwhelming complexity.
2Loss of energy
If central plants participate in incentive-based demand response programs, then economic value increases, but optimal participation strategy becomes complicated especially with electrical demand charges
Solution Approach 1:
The system predicts demand response event probabilities and electricity prices in advance of the optimization period. This preliminary action allows the optimization algorithm to incorporate IBDR participation strategies before the actual events occur, simplifying the decision-making process by pre-determining when and how to participate in demand response programs while accounting for demand charges.
Solution Approach 2:
The system introduces thermal energy storage as an intermediary between electricity consumption and thermal energy delivery. By storing thermal energy during periods of low electricity cost or high demand response incentives and delivering it during peak periods, the system decouples the complexity of electricity market participation from the thermal energy delivery requirement, thus simplifying the optimization problem.
3Loss of energy
If thermal energy storage capacity is used to shed power during peak events, then economic value increases, but system complexity increases
Solution Approach 1:
The system performs preliminary prediction of thermal energy storage capacity, IBDR event probabilities, and electricity prices before the optimization period. This allows the optimization algorithm to pre-determine the optimal charging and discharging schedule for thermal energy storage, maximizing economic value by shedding power during peak events while avoiding the complexity of real-time control decisions.
Data Source
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AI summary
A central plant that generates and provides resources to a building. The central plant includes an electrical energy storage subplant configured to store electrical energy purchased from a utility and to discharge the stored electrical energy. The central plant includes a plurality of generator subplants that consume one or more input resources. The central plant includes a controller configured to determine, for each time step within a time horizon, an optimal allocation of the input resources and the output resources for each of the subplants in order to optimize a total monetary value of operating the central plant over the time horizon. The total monetary value includes revenue from participating in incentive-based demand response programs as well as costs associated with resource consumption, equipment degradation, and losses in battery capacity.