Battery Energy Storage Scheduling With Carbon-Aware Echo State Networks
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Solution Overview
Problem
Existing battery energy storage systems face challenges in optimizing energy availability and carbon emission reduction, as treating these as separate objectives can lead to economic losses and conflict with each other, and conventional methods fail to consider the dynamic and non-linear nature of renewable energy sources and carbon credits effectively.
Innovation Solution
A dynamic non-linear optimization method using an echo state network that integrates load, renewable, and non-renewable energy data, carbon emissions, and credits to generate a day-ahead schedule, combining forecasting and optimization functions, and dynamically retraining the model to minimize costs and maximize revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If carbon emission reduction is treated as a separate optimization objective from economic optimization goals, then carbon footprint reduction is improved, but economic performance deteriorates due to conflicting objectives and inability to recover costs
Solution Approach 1:
The patent combines carbon emission reduction and economic optimization into a unified multi-objective optimization framework. The echo state network simultaneously optimizes both carbon footprint reduction and economic performance by integrating carbon costs, carbon credits, and energy costs into a single optimization model, eliminating the conflict between separate objectives.
Solution Approach 2:
The patent transforms carbon emissions from a separate constraint into an economic parameter by incorporating carbon costs and carbon credits into the optimization objective function. This allows carbon reduction to be evaluated in economic terms, enabling the system to recover costs through carbon credit trading while reducing emissions.
2Device complexity
If conventional optimization methods are used that treat energy availability and carbon reduction as separate objectives, then each objective can be optimized independently, but the system fails to capture the dynamic and non-linear interactions between renewable energy sources, load demands, and carbon credit markets
Solution Approach 1:
The patent implements a dynamic optimization approach using an echo state network that continuously adapts to changing conditions. The system processes real-time inputs including renewable energy generation forecasts, load demands, carbon prices, and carbon credit values to dynamically adjust battery charging/discharging decisions, capturing the non-linear interactions between these variables.
Solution Approach 2:
The system incorporates feedback mechanisms by using the echo state network to process historical and real-time data about energy generation, consumption, carbon prices, and credit values. This feedback loop enables the system to learn from past performance and continuously improve its optimization decisions in response to changing market and operational conditions.
3Ease of operation
If simple switching strategies are used where batteries charge when renewable energy is available and discharge when it is not, then operational simplicity is improved, but economic optimality deteriorates because the strategy does not consider dynamic energy costs and carbon credit values
Solution Approach 1:
The system performs preliminary action by forecasting renewable energy generation, load demands, carbon prices, and carbon credit values in advance. The echo state network uses these forecasts to pre-determine optimal battery charging and discharging schedules, allowing the system to proactively respond to future market conditions rather than simply reacting to current renewable availability.
Data Source
AI summary
A system and method for optimizing a battery energy storage system (BESS), can involve: inputting data to an echo state network, the data relating to an operation of a battery energy storage system and including one or more of load data, renewable energy data, non-renewable energy data, carbon emissions, carbon credits and a weighted cost of energy; and optimizing the operation of the battery energy storage system based on the data input to and processed by the echo state network and which relates to the operation of the battery energy storage system.