ESS Operating Control Using AI Forecasting for Grid Stability
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Solution Overview
Problem
The volatility of renewable energy sources leads to inconsistencies in power supply and demand, resulting in additional power generation, waste, higher electricity prices, and threats to grid reliability, with existing technologies failing to effectively utilize energy storage systems (ESS) to stabilize energy supply.
Innovation Solution
An ESS operating device uses a deep learning model to forecast electricity information and a reinforcement learning model to derive an operating policy, controlling the ESS to buy electricity at low prices and sell it at high prices, while maintaining optimal charge and discharge levels to maximize revenue and durability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If renewable energy power generation capacity is increased to reduce greenhouse gas, then environmental benefit is improved, but power supply volatility increases leading to grid reliability threats
Solution Approach 1:
The system performs preliminary forecasting of electricity prices and demand using deep learning models before making ESS operation decisions. This advance prediction allows the system to proactively charge during low-price periods and discharge during high-price periods, stabilizing the grid before volatility issues arise rather than reacting after problems occur.
Solution Approach 2:
The energy storage system acts as an intermediary between renewable energy sources and the grid. It absorbs excess energy when supply exceeds demand and releases energy when demand exceeds supply, mediating the volatility between renewable generation and grid requirements, thereby maintaining grid reliability while enabling increased renewable capacity.
2Productivity
If ESS operation is controlled to maximize revenue through arbitrage, then profitability is improved, but system complexity increases due to multiple learning models
Solution Approach 1:
The system merges deep learning models for forecasting with reinforcement learning models for decision-making into a unified ESS control framework. By combining these AI approaches, the system achieves both accurate prediction of price/demand patterns and optimal operation strategies, maximizing revenue while managing complexity through integrated architecture rather than separate standalone systems.
Solution Approach 2:
The reinforcement learning model enables the ESS to automatically derive and execute operating policies based on forecasted information without requiring constant external control or manual intervention. The system self-adjusts its charging and discharging decisions to maximize revenue, reducing operational complexity while maintaining high profitability through autonomous decision-making.
Data Source
AI summary
The present disclosure relates to an operating device and method of an ESS. The ESS operating method may include forecasting electricity information during a predetermined period using a deep learning model generated based on data about an electricity price and an electricity demand, deriving an ESS operating policy by a reinforcement learning model based on the forecasted electricity information and state information of an energy storage device included in the ESS, and controlling the ESS based on the derived ESS operating policy.