Load Power Scheduling Under Fixed-Duration Power Option Agreements
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Solution Overview
Problem
The volatility in market prices for power supplied to the grid, particularly for renewable energy sources like wind and solar, leads to inefficient power generation and management, often resulting in power being sold at low or negative prices, and curtailment of renewable energy output.
Innovation Solution
Implementing a system with behind-the-meter (BTM) loads that can utilize power received from generation stations before it enters the grid, allowing generation stations to selectively direct power to either the grid or BTM loads based on economic and operational considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If renewable energy generation is increased to meet growing power demand, then power supply capacity is improved, but market price volatility increases and leads to power being sold at low or negative prices
Solution Approach 1:
The system performs preliminary actions by identifying periods of low or negative power prices before they occur, and pre-scheduling energy storage charging during these unfavorable periods. This allows the system to avoid selling power at loss-making prices and instead store it for later discharge at more favorable rates, thus resolving the contradiction between increasing power supply capacity and avoiding economic losses from negative pricing.
Solution Approach 2:
The system implements continuous feedback mechanisms by monitoring real-time power prices, generation levels, and storage status. This feedback enables dynamic adjustment of operating strategies, allowing the system to respond to changing market conditions and automatically shift between generation, storage charging, and grid discharge modes to maximize economic returns while maintaining power supply capacity.
2Loss of energy
If renewable energy output is curtailed to avoid negative pricing, then economic loss is reduced, but power generation efficiency deteriorates
Solution Approach 1:
The system introduces an intermediary energy storage component between the renewable generation and the grid. This storage intermediary allows the system to maintain full generation output (avoiding curtailment) while decoupling the timing of generation from the timing of grid delivery. Power can be generated continuously and stored locally, then discharged to the grid at favorable prices, thus eliminating the need to curtail generation to avoid negative pricing.
Solution Approach 2:
The system dynamically changes operational parameters by adjusting the state of charge of energy storage based on power prices and generation conditions. When prices are negative or low, the system changes from grid discharge mode to storage charging mode, effectively changing the parameter of power flow direction. This allows full generation utilization while avoiding economic losses, resolving the contradiction between maintaining generation efficiency and avoiding negative pricing losses.
3Loss of energy
If energy storage systems are deployed to manage price volatility, then revenue maximization is improved, but system complexity increases
Solution Approach 1:
The system implements multi-functionality by designing the energy storage system to perform multiple roles: arbitrage between high and low prices, provision of frequency regulation services, voltage support, and backup power supply. This universal approach allows a single storage deployment to address multiple challenges simultaneously, maximizing revenue opportunities while avoiding the need for separate specialized systems for each function, thus managing complexity through consolidation.
Solution Approach 2:
The system employs self-service mechanisms through automated control algorithms that independently manage charging, discharging, and grid interaction based on real-time conditions. The system autonomously makes decisions about when to charge or discharge without requiring complex manual intervention or centralized coordination, reducing operational complexity while still achieving optimal revenue management through continuous adaptive control.
Data Source
AI summary
Examples relate to adjusting load power consumption based on a power option agreement. A computing system may receive power option data that is based on a power option agreement and specify minimum power thresholds associated with time intervals. The computing system may determine a performance strategy for a load (e.g., set of computing systems) based on a combination of the power option data and one or more monitored conditions. The performance strategy may specify a power consumption target for the load for each time interval such that each power consumption target is equal to or greater than the minimum power threshold associated with each time interval. The computing system may provide instructions the set of computing systems to perform one or more computational operations based on the performance strategy.