Gas Turbine Dispatch Optimizer Balancing Peak-Fire and Maintenance

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Solution Overview

Problem

Gas turbines in power plants face challenges in maintaining optimal operation due to accelerated parts-life consumption during peak-firing, leading to shortened maintenance intervals and increased maintenance costs, which can result in missed revenue opportunities and suboptimal utilization of assets.

Innovation Solution

A dispatch optimization system that leverages forecast information and asset performance models to balance peak-fire and cold part-load operations, optimizing the creation and consumption of parts-life credits to extend maintenance intervals and maximize profit, while ensuring compliance with target life constraints.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Power

If gas turbines are peak-fired above base capacity during peak demand periods, then extra power output is produced, but parts-life consumption increases and maintenance intervals are shortened

Engineering Contradiction:

Improvepower outputVSAvoidmaintenance interval

Core Design Contradiction:

PowerVSDuration of action of stationary object

Solution Approach 1:

The system performs preliminary analysis of forecast information (weather, fuel prices, power prices) and asset performance models before making dispatch decisions. This allows operators to plan peak-firing activities in advance, understanding the parts-life consumption implications before committing to high-power operation modes.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts operating modes between base capacity and peak-firing based on real-time conditions. By making the power output dynamic rather than static, the system can optimize the balance between generating extra power during peak demand and managing parts-life consumption through adaptive control.

Inventive Principle:

Principle #15Dynamics

2Productivity

If gas turbines are peak-fired often within the maintenance interval, then incremental parts-life consumption occurs, but maintenance schedules are pulled in and extra customer service agreement charges are incurred

Engineering Contradiction:

Improvepower generation flexibilityVSAvoidmaintenance schedule delay

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system incorporates feedback loops that continuously monitor parts-life consumption, maintenance interval status, and operating conditions. This feedback mechanism allows the system to adjust peak-firing frequency and duration in real-time, preventing excessive parts-life consumption that would trigger maintenance events and associated charges.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system enables self-service optimization by automatically analyzing forecast data, asset models, and operational constraints to generate optimized dispatch schedules. This reduces the need for manual intervention and external service calls, allowing the plant to manage its own maintenance timing more effectively.

Inventive Principle:

Principle #25Self-service

3Duration of action of stationary object

If plant asset owners exercise peak-fire mode more conservatively to avoid maintenance costs, then maintenance schedules are maintained, but revenue opportunities are missed

Engineering Contradiction:

Improvemaintenance intervalVSAvoidrevenue opportunity

Core Design Contradiction:

Duration of action of stationary objectVSLoss of energy

Solution Approach 1:

The system changes key parameters including forecast weather conditions, fuel prices, power prices, and asset performance characteristics to evaluate different operating scenarios. By dynamically adjusting these parameters in the analysis, the system identifies optimal peak-firing opportunities that balance maintenance interval preservation with revenue maximization.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system acts as an intermediary between maintenance constraints and revenue opportunities. It processes forecast information and asset models to translate maintenance interval requirements into actionable dispatch recommendations, enabling operators to capture revenue opportunities while respecting maintenance boundaries.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10452041B2Gas turbine dispatch optimizer real-time command and operations

Publication Date: 2019.10.22 GE INFRASTRUCTURE TECH LLC

AI summary

A dispatch optimization system leverages ambient and market forecast data as well as asset performance and parts-life models to generate recommended operating schedules for gas turbines or other power-generating plant assets that substantially maximize profit while satisfying parts-life constraints. The system generates operating profiles that balance optimal peak fire opportunities with optimal cold part-load opportunities within a maintenance interval or other operating horizon. During real-time operation of the assets, the optimization system can update the operating schedule based on actual market, ambient, and operating data. The system provides information that can assist operators in determining suitable conditions in which to cold part-load or peak-fire the assets in an optimally profitable manner without violating target life constraints.