Grid-Scale Energy Storage Management via Forecasting and Degradation Optimization
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Solution Overview
Problem
The integration of renewable energy sources into grid-connected energy storage systems poses challenges due to their intermittent and variable nature, leading to power quality and reliability issues, and existing forecasting methods are inefficient and costly, failing to effectively manage energy distribution and battery degradation in grid-scale Energy Storage Systems (ESSs.
Innovation Solution
A hybrid energy management system that employs time series models for forecasting electricity prices, determines battery life and degradation costs, optimizes bids for energy markets, and dynamically controls energy distribution in ESSs to maximize revenue and minimize costs, using a forecaster, determiner, optimizer, and controller to manage energy flow based on forecasted pricing data and battery health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional forecasting methods are used to predict electricity prices, then forecasting quality can be improved through better model fitting, but processing costs increase and large amounts of data are required
Solution Approach 1:
The patent transforms the forecasting approach by changing the parameter of data requirements from large historical datasets to minimal input parameters. The new system uses simple parameters such as current price, forecasted load, and forecasted generation to generate price forecasts, eliminating the need for extensive historical data storage and complex processing while maintaining forecasting capability
2Productivity
If grid-scale ESSs participate in multiple energy markets, then revenue opportunities increase, but system complexity and control difficulty increase
Solution Approach 1:
The patent implements a universal energy management system that handles multiple energy markets (energy market, frequency regulation market, voltage regulation market) through a single integrated platform. The system uses unified time series forecasting models and optimization algorithms that work across all market types, allowing the ESS to participate in multiple markets without proportionally increasing system complexity
Solution Approach 2:
The patent combines multiple forecasting models (ARX, ARMAX, VARMAX) and multiple market participation strategies into a single integrated optimization framework. The system merges energy market bidding, frequency regulation bidding, and voltage regulation control into one coordinated decision-making process, simplifying management while maximizing revenue across all markets
3Reliability
If battery degradation is considered in market bidding, then long-term profitability improves, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating battery degradation costs and incorporating them into the bidding optimization process before market settlement. The system estimates degradation based on forecasted charge-discharge cycles and integrates these costs into the objective function for market bidding, allowing long-term profitability optimization without requiring complex real-time degradation modeling during market operations
Data Source
AI summary
Systems and methods for energy distribution for one or more grid-scale Energy Storage Systems (ESSs), including generating one or more time series models to provide forecasted pricing data for one or more markets, determining battery life and degradation costs for one or more batteries in or more ESSs to provide battery life and degradation costs, optimizing bids for the one or more markets to generate optimal bids based on at least one of the forecasted pricing data or the battery life and degradation costs, and distributing energy to or from the one or more ESSs based on the optimal bids generated.