Renewable Energy Dispatch Policies for Multi-Market Time Shifting

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Solution Overview

Problem

The integration of renewable energy systems into existing infrastructure faces challenges due to their unpredictable nature and variability, leading to reliability issues in power grids and inefficiencies in energy markets, making manual management unfeasible.

Innovation Solution

A machine learning system is introduced to generate policies for governing renewable energy generation and storage systems, considering local consumer demands and energy market conditions, using reinforcement learning to optimize resource dispatch and storage based on real-time data and historical information, while adhering to physical and technical constraints.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If renewable energy systems are integrated into existing infrastructure, then green energy production increases, but reliability deteriorates due to unpredictable generation variability

Engineering Contradiction:

Improvepower grid reliabilityVSAvoidrenewable generation predictability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary actions by predicting renewable energy generation and storage needs in advance using historical data and machine learning models. The automated system forecasts future energy availability and demand patterns, allowing operators to pre-position storage resources and plan dispatch strategies before actual generation and consumption events occur, thereby maintaining reliability despite renewable variability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where real-time data from renewable generation systems, storage facilities, and demand sources are fed into machine learning models. These models continuously update predictions and adjust dispatch policies based on actual versus predicted performance, enabling dynamic adaptation to changing conditions while maintaining grid reliability through iterative optimization.

Inventive Principle:

Principle #23Feedback

2Productivity

If manual management is used for renewable energy systems, then operational control is maintained, but efficiency deteriorates due to complexity of optimizing across multiple markets and timeframes

Engineering Contradiction:

Improveenergy management efficiencyVSAvoidmanagement system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system enables self-service by implementing automated machine learning models that independently manage the complexity of optimizing across multiple energy markets and timeframes. The system autonomously analyzes data, generates predictions, formulates dispatch policies, and executes trading decisions without requiring manual intervention, thereby dramatically improving management efficiency while containing complexity within the automated system rather than requiring human expertise across all dimensions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical management processes with automated computational systems. Machine learning algorithms substitute for human operators in analyzing market conditions, predicting generation patterns, and making dispatch decisions across multiple timeframes and markets, thereby improving efficiency by processing information faster and more comprehensively than manual systems while consolidating complexity into automated computational infrastructure.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If energy storage systems are added to smooth renewable fluctuations, then reliability improves, but device complexity increases

Engineering Contradiction:

Improvepower supply consistencyVSAvoidsystem configuration complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system achieves universality by designing storage facilities and control mechanisms that serve multiple functions simultaneously. The same storage infrastructure and automated control system optimize for both grid reliability (smoothing fluctuations) and economic efficiency (maximizing revenue across multiple energy markets). The machine learning platform universally manages diverse storage technologies and market participants, reducing overall system complexity through consolidated multi-functional operations rather than separate specialized systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12525808B2Time-shifting optimizations for resource generation and dispatch

Publication Date: 2026.01.13 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

The techniques disclosed herein enable systems to optimize generation and dispatch of renewable energies using data-driven models. In many contexts, a renewable energy system is collocated with a local consumer such as a datacenter, a smart building, and so forth. The objective of the renewable energy system is to meet local power needs while participating in various energy markets of differing trading frequencies. To optimally manage the renewable energy system, a data-driven model is configured to analyze current conditions and generate policies to control renewable energy system operations. For instance, the model can retrieve current market prices, generation capacity, costs associated with generating energy, and so forth. Based on the collected information, the model can generate a policy that maximizes revenue obtained by the renewable energy system while meeting local demand. Through many iterations, the model can determine a realistically optimal policy for managing the renewable energy system.