Grid-Scale Energy Storage Scheduling with Co-Optimization
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Solution Overview
Problem
Grid Scale Energy Storage (GSS) units face uncertainties in frequency regulation, leading to potential violations of state of charge limits, reduced revenue, and equipment lifespan issues due to random frequency regulation signals and limited capacity deployment guarantees, resulting in power quality and reliability concerns.
Innovation Solution
A computer-implemented method and system for generating an optimal GSS schedule using co-optimization to determine optimal capacity deployment factors, track state of charge bounds as hard constraints, and calculate risk indices to minimize penalties and ensure reliable operation, thereby preventing violations and maximizing revenue and lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GSS units continue providing scheduled services beyond their limitations, then service commitment is maintained, but equipment lifespan is adversely affected
Solution Approach 1:
The system performs preliminary actions by calculating risk indices and determining optimal capacity deployment factors before the GSS unit operates. The co-optimization model proactively schedules charging/discharging operations to prevent state of charge violations, thereby avoiding the need to operate beyond limitations and protecting equipment lifespan while maintaining service commitments.
2Duration of action of stationary object
If GSS units stop operating when meeting upper/lower limits, then equipment lifespan is protected, but power system quality deteriorates due to unmet commitments
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring the state of charge and calculating risk indices based on frequency regulation uncertainties. The co-optimization model uses this feedback to adjust capacity deployment factors and scheduling decisions, enabling the GSS unit to operate close to limits without violating them, thus maintaining both equipment lifespan and power system quality.
3Reliability
If GSS units do not provide scheduled services, then operational limits are protected, but revenues are significantly reduced due to penalties
Solution Approach 1:
The system changes parameters by determining optimal capacity deployment factors that adjust the GSS unit's charging/discharging schedule. The co-optimization model modifies operational parameters to maximize revenue while respecting operational limits, calculating risk indices to ensure compliance is maintained without unnecessarily reducing service provision and associated revenues.
Data Source
AI summary
A system and method for management of one or more grid-scale Energy Storage Systems (GSSs), including generating an optimal GSS schedule in the presence of frequency regulation uncertainties. The GSS scheduling includes determining optimal capacity deployment factors to minimize penalties for failing to provide scheduled energy and frequency regulation up/down services subject to risk constraints; generating a schedule for a GSS unit by performing co-optimization using the optimal capacity deployment factors, the co-optimization including tracking upper and/or lower bounds on a state of charge (SoC) and including the bounds as a hard constraints; and calculating risk indices based on the optimal scheduling for the GSS unit, and outputting an optimal GSS schedule if risk constraints are satisfied. A controller charges and/or discharges energy from GSS units based on the generated optimal GSS schedule.