Energy Storage Dispatch Using Battery Degradation and Price Forecasts
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Solution Overview
Problem
Existing energy storage management systems lack sophisticated algorithms to optimize charge and discharge times of energy storage devices across multiple power generation sites, considering battery life and energy requirements, leading to inefficiencies and suboptimal revenue generation.
Innovation Solution
An energy storage dispatch optimization algorithm that considers battery life models and energy requirements, determining optimal charge/discharge times across hierarchical power generation structures, incorporating weather forecasts, pricing data, and operational constraints to maximize site revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rules-based methods are used to manage charge and discharge functionality, then the system is simple to implement, but the system becomes unmanageable due to the amount of variables affecting electricity price and storage asset life
Solution Approach 1:
The patent introduces an energy storage dispatch optimization unit as an intermediary component that receives multiple inputs (electricity price forecasts, weather forecasts, storage asset characteristics) and processes them through sophisticated algorithms to generate optimal dispatch instructions. This intermediary handles the complexity of managing multiple variables, relieving the burden from manual rules-based management while maintaining system operability.
Solution Approach 2:
The patent replaces manual rules-based management with automated computational algorithms that process electricity price forecasts, weather forecasts, and storage asset characteristics. This substitution of mechanical/rules-based approaches with intelligent algorithms enables the system to handle complex variables efficiently without becoming unmanageable.
2Productivity
If conventional energy storage devices are installed at power generation sites, then local energy storage is achieved, but it is a non-trivial task to determine the best time-of-day to charge and discharge while maximizing usable life and site revenue
Solution Approach 1:
The optimization unit incorporates feedback loops that continuously monitor electricity price forecasts, weather forecasts, storage asset state of charge, and asset characteristics. This feedback mechanism enables the system to adjust charge/discharge decisions in real-time to maximize revenue while preserving asset life, transforming a complex optimization problem into a manageable iterative process.
Solution Approach 2:
The patent changes the parameters considered in dispatch decisions by incorporating electricity price forecasts, weather forecasts, and storage asset characteristics into the optimization algorithm. This multi-parameter approach enables simultaneous optimization of revenue and asset life, converting a non-trivial task into a systematic optimization process.
3Productivity
If sophisticated algorithms are used to manage charge and discharge, then optimization of electricity price variables and storage asset life is achieved, but the system complexity increases significantly
Solution Approach 1:
The patent segments the complex optimization problem into distinct functional components: an optimization unit that handles revenue optimization by processing electricity price forecasts and generating dispatch instructions, and a separate asset management unit that handles storage asset life preservation by processing weather forecasts and asset characteristics. This segmentation reduces overall system complexity while maintaining sophisticated optimization capabilities.
Data Source
AI summary
A method of energy dispatch for an energy storage device component of a local energy generation plant, the method including obtaining a charge/discharge profile for the energy storage device, quantifying an amount of energy generation available from energy source components of the local energy generation plant, accessing a degradation factor for the energy storage device, forecasting a future cost for storing energy in the energy storage device, evaluating the future cost, providing instruction to an energy storage plant control unit to increase energy storage in the energy storage device based on a result of the evaluation, else, instructing the energy storage plant control unit to decrease energy storage in the energy storage device, and shedding power from the energy source components if a recommendation to shed power was provided. A system for implementing the method and a non-transitory computer-readable medium are also disclosed.