Long-Duration Energy Storage With Stochastic Weather Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing power system designs and operations fail to adequately account for the multi-day nature of long duration energy storage (LDES) and the uncertainty of weather impacts on renewable energy production, leading to inefficiencies in managing energy supply and demand.
Innovation Solution
A stochastic model, such as a Markov chain model, is used to optimize the level of energy stored in a power system by considering probabilistic variability in weather over extended time horizons, treating stored energy as inventory rather than a source of energy generation, and minimizing the impact of renewable energy production variation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If traditional Security Constrained Unit Commitment and hour by hour optimization approaches are used to design and operate power systems, then the system can be optimized for short-duration energy storage (less than 12 hours), but the multi-day nature of long duration energy storage cannot be adequately accounted for
Solution Approach 1:
The patent changes the fundamental parameter of time horizon from hourly to multi-day scales, and transforms the deterministic weather assumptions into stochastic weather models. This allows the system to properly account for long-duration energy storage needs while maintaining design reliability through probabilistic analysis of weather variability over extended periods.
Solution Approach 2:
The patent introduces dynamic stochastic weather modeling that captures the evolving nature of weather patterns over multiple days. Instead of static deterministic assumptions, the system dynamically adjusts to probabilistic weather scenarios, enabling reliable multi-day storage design by accounting for weather uncertainty and variability throughout the storage duration.
2Productivity
If deterministic weather models are used to optimize energy storage schedules, then the optimization can be computationally tractable, but the uncertainty in renewable energy production due to weather variability is not realistically accounted for
Solution Approach 1:
The patent introduces stochastic weather models as an intermediary layer between deterministic optimization algorithms and real-world weather variability. This intermediary probabilistic framework allows the optimization to remain computationally tractable while realistically accounting for weather uncertainty, bridging the gap between simplified models and complex reality.
3Adaptability or versatility
If energy storage is treated as another source of energy generation in traditional paradigms, then it can be integrated into existing power system optimization frameworks, but the unique characteristics of multi-day storage cannot be properly captured
Solution Approach 1:
The patent applies dynamic stochastic modeling to capture the evolving characteristics of multi-day energy storage systems. By modeling weather and storage dynamics probabilistically over extended time horizons, the system accurately represents the unique behavior of long-duration storage while remaining compatible with existing optimization frameworks through standardized interface protocols.
Data Source
AI summary
An electric power system (10) is supplied at least in part by renewable energy sources (12). A method for managing storage of energy in such a system includes obtaining a stochastic model (16) that models probabilistic variability (22) in weather (20) across a sequence of time periods (P-1 . . . P-N) within a time horizon (18), each time period (P-n) being at least one day in duration. The method further includes determining, using the stochastic model (16), one or more values (24V) for one or more design or operational parameters (24) of the electric power system (10) that optimize a level of energy (L-1 . . . . L-N) stored by the electric power system (10) at each time period (P-1 . . . P-N) by minimizing an expected impact of renewable energy production variation occurring over the time horizon (18) due to the modeled probabilistic variability (22) in weather (20).