Building Battery Planning Tool for Demand Response Bidding
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Solution Overview
Problem
Energy storage systems, particularly electrical energy storage systems, face challenges in efficiently controlling and allocating assets to optimize participation in incentive-based demand response programs, making it difficult for customers to estimate the benefits of investing in battery systems.
Innovation Solution
An energy storage system with a planning tool that identifies building electric loads and selects functionalities to optimize the cost of operating the system by determining optimal battery power setpoints, considering historical data, revenue from incentive programs, and asset sizing, using linear programming to minimize costs and maximize revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If energy storage systems are used to participate in incentive-based demand response programs, then revenue generation is improved, but difficulty in controlling and allocating assets increases
Solution Approach 1:
The patent introduces a planning tool as an intermediary system that mediates between the energy storage assets and the complex incentive-based demand response programs. This planning tool automatically performs optimization calculations, generates bids, and manages asset allocation, thereby capturing revenue opportunities while eliminating the complexity of manual control and allocation decisions.
Solution Approach 2:
The planning tool dynamically changes operational parameters such as charge/discharge rates, bid prices, and asset allocation based on real-time market conditions, incentive program requirements, and asset state. This automated parameter adjustment enables the system to navigate complex demand response programs without manual intervention.
2Loss of energy
If battery systems are invested in for energy storage, then utility cost reduction is improved, but difficulty in estimating benefits increases
Solution Approach 1:
The planning tool performs preliminary optimization calculations and benefit estimations before actual battery system deployment or investment decisions. By simulating various operational scenarios and calculating projected utility cost reductions and revenue generation, the system provides investors with advance information about expected benefits, eliminating the uncertainty of post-deployment evaluation.
3Measurement precision
If optimization calculations are performed for the entire planning period, then computational accuracy is improved, but calculation time increases
Solution Approach 1:
The patent segments the planning period into smaller time blocks and performs optimization calculations iteratively for each block. This segmentation approach maintains optimization accuracy by considering the full planning horizon while reducing computational burden by processing smaller time increments sequentially, thereby decreasing overall calculation time.
Solution Approach 2:
The optimization process is executed periodically in iterative steps rather than as a single continuous calculation. The planning tool performs optimization for one time block, shifts the planning window forward, and repeats the process, creating a periodic action pattern that balances accuracy with computational efficiency.
Data Source
AI summary
An energy storage system for a building includes a battery asset configured to store electricity and discharge the stored electricity for use in satisfying a building electric load. The system includes a planning tool configured to identify one or more selected functionalities of the energy storage system and generate a cost function defining a cost of operating the energy storage system over an optimization period. The cost function includes a term for each of the selected functionalities. The planning tool is configured to generate optimization constraints based on the selected functionalities, attributes of the battery asset, and the electric energy load to be satisfied. The planning tool is configured to optimize the cost function to determine optimal power setpoints for the battery asset at each of a plurality of time steps of the optimization period.