Energy Storage Modeling and Control for Grid Applications
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Solution Overview
Problem
Current studies and models are ineffective in providing specific insights into the appropriate specifications and operation of energy storage systems (ESS) for particular applications at specific locations, failing to couple operating modes with economic metrics to determine optimal energy and power characteristics, thus lacking as planning tools for grid participants and regulators.
Innovation Solution
A methodology and real-time control algorithms that calculate an optimal energy storage system operating strategy using historical and projected data, pricing information, and system configurations to maximize asset revenues and profitability, incorporating sophisticated modeling applications and optimization techniques such as Monte Carlo optimization and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple models and studies are used to guide energy storage applications, then overall market insight is provided, but specific insights into appropriate specifications and operation for particular applications at specific locations are not provided
Solution Approach 1:
The patent segments the energy storage analysis into multiple hierarchical levels: (1) general market studies for overall trends, (2) location-specific grid condition analysis, (3) application-specific optimization models, and (4) real-time control strategies. This segmentation allows users to select the appropriate level of detail and complexity for their specific needs, providing specific guidance when required while maintaining simplicity for general overview.
Solution Approach 2:
The patent implements dynamic modeling capabilities that adapt to specific locations and applications. The system uses dynamic optimization algorithms that adjust ESS specifications and operating strategies based on location-specific grid conditions, local energy prices, and application requirements. This dynamic approach provides specific insights tailored to each scenario without requiring permanently complex models for all cases.
2Manufacturing precision
If operating modes are not coupled with economic metrics, then operational simplicity is maintained, but optimal energy and power characteristics cannot be determined
Solution Approach 1:
The patent merges operational models with economic metrics into an integrated optimization framework. The system combines technical parameters (energy capacity, power rating, efficiency) with economic factors (energy prices, capacity payments, incentive structures) to simultaneously optimize both operational performance and financial returns. This integration enables determination of optimal ESS characteristics by coupling operating modes with economic metrics.
Solution Approach 2:
The patent employs parameter optimization techniques that adjust ESS characteristics (energy capacity, power rating, charge/discharge rates) based on the coupled analysis of operational requirements and economic metrics. The system varies these parameters to identify optimal configurations that maximize financial returns while meeting operational goals, thereby achieving precise optimization of energy and power characteristics.
3Reliability
If comprehensive factors are considered for ESS installation and operation, then optimal financial returns are achieved, but analysis and planning complexity increases
Solution Approach 1:
The patent develops a universal planning tool that integrates multiple functions into a single system: technical feasibility analysis, economic evaluation, optimization of ESS characteristics, and generation of operating strategies. This multi-functional tool considers comprehensive factors (costs, revenues, grid conditions, technical constraints) while providing a unified interface that reduces overall planning complexity despite the comprehensiveness of the analysis.
Data Source
AI summary
Systems and methods for optimal planning and real-time control of energy storage systems for multiple simultaneous applications are provided. Energy storage applications can be analyzed for relevant metrics such as profitability and impact on the functionality of the electric grid, subject to system-wide and energy storage hardware constraints. The optimal amount of storage capacity and the optimal operating strategy can then be derived for each application and be prioritized according to a dispatch stack, which can be statically or dynamically updated according to data forecasts. Embodiments can consist of both planning tools and real-time control algorithms.