Renewable Power Asset Management via Segmented Pricing Models
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Solution Overview
Problem
Current systems fail to optimize the design and operation of renewable power generation and transmission, with or without energy storage assets, by maximizing energy generation and dispatch value while minimizing costs and accounting for investor or operator risk and return preferences within a rigorous framework.
Innovation Solution
A processor generates Day-Ahead and Real-Time pricing models for renewable power assets, determining optimal commitments and schedules, and controlling the asset to maximize value and minimize financial risks, balancing risks and returns based on risk preferences, using advanced prediction techniques and mathematical models to forecast prices and optimize energy dispatch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If renewable power sources are paired with bulk energy storage systems to mitigate intermittency, then reliability of power delivery is improved, but device complexity increases
Solution Approach 1:
The system segments the renewable power asset management into distinct time horizons (Day-Ahead and Real-Time) with separate optimization models for each. This segmentation allows the complex problem of managing intermittency and market uncertainty to be divided into manageable components, where each model addresses specific aspects without overwhelming complexity
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between the renewable power source, energy storage system, and electricity market. This intermediary layer processes forecasts, determines optimal commitments and schedules, and coordinates power delivery strategies, thereby managing the complexity of integrating multiple components while maintaining reliability
2Productivity
If optimal dispatch is determined in a price environment with Day-Ahead and Real-Time prices, then productivity is improved, but measurement precision deteriorates due to forecast uncertainty
Solution Approach 1:
The system performs preliminary actions by determining Day-Ahead commitments before the actual operating day, using forecasted prices and production volumes. This advance planning allows the system to lock in optimal dispatch decisions early, improving productivity while accounting for forecast uncertainty through probabilistic modeling and scenario analysis
Solution Approach 2:
The patent implements a dynamic two-stage optimization approach where Day-Ahead commitments are determined based on forecasts, and then Real-Time schedules are adjusted based on actual conditions. This dynamic adaptation allows the system to maintain high productivity by continuously optimizing dispatch decisions as new information becomes available, effectively managing forecast precision limitations
3Productivity
If optimal scheduling is performed in a regulated environment with requirements to deliver reliable power at the lowest cost, then productivity is improved, but measurement precision deteriorates due to forecast uncertainty of demand and supply
Solution Approach 1:
The system incorporates feedback mechanisms where Real-Time market prices and actual production/demand data feed back into the optimization process. This feedback loop allows the system to learn from forecast errors and adjust subsequent Day-Ahead and Real-Time scheduling decisions, improving productivity while progressively enhancing measurement precision through data-driven adjustments
Data Source
AI summary
Systems, methods, and devices may enable management of a renewable power asset. A control device may generate a Day-Ahead (DA) pricing model, a Real-Time (RT) pricing model and a renewable generation model for the renewable power asset. Optimal DA commitments may be determined, and an optimal RT schedule estimated. A DA power delivery strategy and an RT power delivery strategy may be determined. The determined DA and RT power delivery strategies may be evaluated based on obtained real power prices. The DA and RT power delivery strategies may be redetermined, and the renewable power asset may be controlled to deliver power the DA and RT power delivery strategies. The value of the renewable power asset may be maximized while bounding financial risks and returns associated with scheduling the renewable power asset as tailored to risk preferences of the renewable power asset owner or operator.