Power System Management via Continuous-Time Load Forecasting
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Solution Overview
Problem
Conventional power system management techniques fail to adequately consider inter-temporal and continuous-time characteristics of net load and resources, leading to scarcity events and inaccurate valuation metrics due to inadequate scheduling and resource utilization.
Innovation Solution
The system determines a net load forecast by modeling non-linear variance and configuring power generation and storage resources to satisfy inter-temporal and continuous-time characteristics, using cubic spline functions and unit commitment models to optimize resource allocation and valuation metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional power system management techniques are used, then system operation can be managed with simple methods, but scarcity events occur due to inadequate consideration of inter-temporal and continuous-time characteristics
Solution Approach 1:
The patent applies dynamics by transitioning from static, discrete-time management to dynamic, continuous-time management. The system continuously monitors and adjusts generation and storage resources in real-time, adapting to changing load conditions and resource availability. This dynamic approach prevents scarcity events by ensuring resources are optimally allocated at all moments, not just at discrete intervals.
Solution Approach 2:
The patent implements preliminary action through advance planning and commitment of generation and storage resources. The system determines optimal generation trajectories and storage schedules in advance, considering inter-temporal characteristics, to prevent scarcity events before they occur. This proactive approach allows the system to prepare adequate resources ahead of time rather than reacting to shortages.
2Measurement precision
If discrete-time net load forecasts are used, then forecasting is simpler, but non-linear variance within time intervals is not captured leading to inaccurate resource scheduling
Solution Approach 1:
The patent applies dimensionality change by moving from discrete-time points to continuous-time representation. Instead of treating load forecasts as separate discrete values at specific intervals, the system represents load as a continuous function across time, capturing non-linear variations within intervals. This additional temporal dimension enables precise modeling of load behavior between forecast points.
Solution Approach 2:
The patent implements parameter changes by transforming the mathematical representation of net load from discrete values to continuous functions with multiple parameters. The system uses functional forms with adjustable parameters to model non-linear variance, allowing flexible and accurate representation of load characteristics. This parameter-rich approach captures complex load patterns that simple discrete forecasts miss.
3Productivity
If energy storage resources are not incorporated into scheduling, then resource management is simpler, but storage availability is wasted and scarcity events cannot be prevented
Solution Approach 1:
The patent applies merging by integrating energy storage resources with generation resources into a unified management system. Instead of treating storage separately, the system combines generation and storage trajectories, optimizing them together to maximize overall system productivity. This integrated approach allows storage to complement generation, preventing scarcity events while improving total system efficiency.
Solution Approach 2:
The patent implements universality by creating a management framework that handles multiple resource types (generation and storage) with a single unified approach. The system uses consistent mathematical tools and optimization methods for both generation and storage resources, enabling flexible and efficient utilization of all available resources. This universal framework maximizes productivity by appropriately deploying any available resource to meet load demands.
Data Source
AI summary
An operating configuration for a power system during a particular time period may be derived from a net load forecast for the power system during the particular time period. The operating configuration may be based on inter-temporal and/or continuous-time characteristics of the net load forecast. A power system manager may schedule power generation and/or energy storage units to satisfy the net load forecast at minimal cost. The power generation and/or energy storage units may be scheduled in accordance with inter-temporal and/or continuous-time characteristics of the net load. The schedule may comply with generation trajectory and/or ramping constraints of the power generating units, power trajectory and/or ramping constraints of the energy storage units, and so on.