ESS Dispatch Scheduling With Forecast Uncertainty Control
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Solution Overview
Problem
Current energy storage system (ESS) control models lack accuracy and certainty, failing to optimally predict future operational parameters and adapt to dynamic conditions, leading to suboptimal performance and inefficiencies in energy management.
Innovation Solution
A methodology for creating and executing an optimal dispatch schedule using various forecasting techniques and algorithms, which calculates and compensates for real-time forecast uncertainty, enabling adaptive scheduling and dispatching of ESS to track evolving electrical parameters, and utilizes parallel processing for faster computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sophisticated control models with abstract representations are used, then the system can provide a framework for ESS operation, but the forecast accuracy and certainty deteriorate because these models assume perfect foresight and do not reflect dynamic operational conditions
Solution Approach 1:
The patent implements dynamic forecasting that continuously updates predictions based on real-time operational conditions rather than relying on static abstract models. The system adapts to changing grid conditions, weather patterns, and load demands by recalculating forecasts as new data becomes available, resolving the contradiction between model adaptability and forecast accuracy.
Solution Approach 2:
The system incorporates feedback loops where actual operational data is continuously compared with forecasted values, and the discrepancies are used to refine future predictions. This feedback mechanism allows the system to learn from past performance and improve forecast accuracy while maintaining adaptability to dynamic conditions.
2Measurement precision
If multiple forecasting techniques and parallel processing are implemented, then the accuracy and certainty of ESS control parameters are improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent divides the forecasting process into multiple independent parallel computations, each handling specific aspects of prediction (e.g., load forecasting, generation forecasting, price forecasting). These segmented computations can be executed simultaneously across multiple processors, reducing overall computational complexity while maintaining high accuracy through comprehensive analysis.
Solution Approach 2:
The system merges results from multiple forecasting techniques and data sources through weighted aggregation or ensemble methods. By combining the strengths of different forecasting approaches (statistical models, machine learning algorithms, expert systems), the system achieves superior forecast accuracy without requiring any single complex model to handle all aspects independently.
3Reliability
If real-time forecast uncertainty quantification and remediation strategies are implemented, then the reliability of ESS operation is improved, but the computational time and processing overhead increase
Solution Approach 1:
The system pre-calculates remediation strategies and contingency plans based on predicted uncertainty scenarios before they are needed. When uncertainty thresholds are exceeded, the system can immediately implement pre-planned corrective actions rather than computing new strategies in real-time, thus maintaining high reliability while minimizing computational time delays.
Solution Approach 2:
The patent dynamically adjusts operational parameters such as state of charge targets, power output limits, and dispatch schedules based on quantified forecast uncertainty. By changing these parameters in response to uncertainty levels, the system maintains reliable operation without requiring extensive real-time computations for every scenario.
Data Source
AI summary
Systems and method for optimal control of one or more energy storage systems are provided. Based on live, historical, and/or forecast data received from one or more data sources, one or more forecasts of one or more parameters relating to the operation of the one or more energy storage systems and an associated forecast uncertainty may be calculated by various forecasting techniques. Using one or more optimization techniques, an optimal dispatch schedule for the operation of the one or more energy storage systems may be created based on the forecasts. The optimal dispatch schedule may be used to determine one or more energy storage system parameters, which are used to control the operation of the energy storage systems.