Electrical Energy Storage Allocation Using 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, especially when considering electrical demand charges and incentive-based demand response programs, due to the complexity of determining when and how to utilize various subplants effectively.
Innovation Solution
A central plant system equipped with an electrical energy storage subplant and a controller that determines an optimal allocation of resources by considering statistical estimates of incentive-based demand response event probabilities, expected revenue, and costs to optimize the total monetary value of operating the central plant over a time horizon, including the allocation of stored electrical energy to subplants and incentive-based demand response programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple subplants are used to satisfy resource demand, then resource supply flexibility is improved, but determining optimal allocation becomes more complex
Solution Approach 1:
The system changes the parameter of resource allocation by using statistical estimates of IBDR event probabilities to dynamically adjust the optimal allocation of electrical energy to different subplants. This allows the system to adapt to varying demand response conditions while managing optimization complexity through probabilistic modeling rather than exhaustive analysis.
Solution Approach 2:
The system performs preliminary actions by pre-calculating statistical estimates of IBDR event probabilities and expected revenues before making real-time allocation decisions. This advance preparation simplifies the real-time optimization process by having pre-computed probability distributions and revenue expectations ready for integration into the allocation algorithm.
2Quantity of substance
If electrical energy storage is allocated to IBDR programs, then expected revenue is improved, but energy available for building loads may be reduced
Solution Approach 1:
The system applies dynamics by making the electrical energy storage allocation flexible and adjustable based on real-time conditions. The controller dynamically determines the optimal amount of energy to allocate to IBDR programs versus building loads by integrating statistical estimates of IBDR event probabilities with current energy prices and demand conditions, allowing continuous adaptation between revenue generation and load satisfaction.
Solution Approach 2:
The system changes the parameter of energy allocation by using statistical probability estimates of IBDR events to dynamically adjust the proportion of stored electrical energy directed to IBDR programs versus building loads. This probabilistic approach allows the system to optimize expected revenue while maintaining sufficient energy availability for building needs based on predicted demand response event likelihoods.
3Quantity of substance
If statistical estimates of IBDR event probabilities are used, then revenue optimization is improved, but computational requirements increase
Solution Approach 1:
The system uses copying by creating simplified statistical models and probability distributions that replicate the complex IBDR event patterns without requiring full computational simulation of each possible scenario. By using historical data to generate statistical estimates of event probabilities and clearing prices, the system captures the essential revenue optimization opportunities while avoiding the computational burden of exhaustive scenario analysis.
Data Source
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.