Power Grid Controller Tuning for Cost-Emission Reliability Tradeoffs
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Solution Overview
Problem
Power grids face challenges in balancing electrical supply and demand due to the intermittent nature of renewable energy sources, leading to increased complexity and conflicts between minimizing energy costs and carbon emissions, with dispatchable fossil fuel generation often being expensive and inefficient.
Innovation Solution
A dynamic tuning mechanism for power grid controllers that employs multi-objective compromise optimization, allowing real-time adjustment of priorities based on live operational data and user preferences to optimize for conflicting objectives such as renewable energy utilization, greenhouse gas emissions, and energy costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If renewable energy sources are increased to reduce carbon emissions, then carbon emissions are reduced, but the variability and intermittency of power supply increases
Solution Approach 1:
The controller dynamically adjusts the prioritization of conflicting objectives (carbon emissions vs. power supply stability) based on real-time operational data and grid conditions. This allows the system to adaptively balance renewable energy utilization with reliability maintenance, rather than using fixed priority rules.
Solution Approach 2:
The system changes the parameters of the compromise optimization function by adjusting weightings associated with different objectives based on live operational data. This enables flexible transformation of the optimization criteria to respond to varying grid conditions and renewable energy availability.
2Reliability
If dispatchable fossil fuel generation is used to balance supply and demand, then power supply reliability is maintained, but energy costs and carbon emissions increase
Solution Approach 1:
The system dynamically adjusts the weightings in the compromise optimization function based on real-time data, allowing flexible transformation of optimization criteria. This enables the system to minimize fossil fuel usage and associated emissions while maintaining reliability through optimized dispatch decisions.
Solution Approach 2:
The controller uses real-time operational data as feedback to continuously adjust the prioritization of objectives. This feedback mechanism allows the system to learn from actual grid performance and optimize the balance between reliability and emissions/cost minimization based on current conditions.
3Loss of energy
If the controller prioritizes minimizing energy costs, then energy costs are reduced, but carbon emissions may increase
Solution Approach 1:
The system changes the parameters of the optimization function by dynamically adjusting weightings associated with different objectives. This allows the controller to transform the optimization criteria based on real-time conditions, balancing cost minimization with emissions reduction through adaptive parameter modification.
Solution Approach 2:
The prioritization of conflicting objectives is made dynamic rather than static. The controller adapts the relative importance of cost versus emissions based on live operational data, enabling flexible response to changing market conditions and environmental priorities.
4Object-generated harmful factors
If the controller prioritizes minimizing carbon emissions, then carbon emissions are reduced, but energy costs may increase
Solution Approach 1:
The system dynamically adjusts the weightings in the compromise optimization function based on real-time operational data, allowing flexible transformation of optimization criteria. This enables the system to minimize emissions while managing costs through adaptive dispatch decisions.
Solution Approach 2:
The controller makes the prioritization of emissions versus costs dynamic, adapting the relative importance of each objective based on current grid conditions, renewable energy availability, and market prices rather than using fixed priorities.
Data Source
AI summary
Methods and systems relating to improvements in controlling power grid systems are provided. Improvements include dynamic tuning of compromise optimization control in power grid systems. The controlling of assets associated with a power grid system may include optimizing for several conflicting objectives. The performance of the optimization with respect to each objective may be monitored in real-time or near real-time and based on streaming and historic data relating to the system. The optimization may be adjusted in real-time or near real-time when it is determined that the performance of the optimization is not meeting specific levels of performance in regard to one or more of the conflicting objectives. Further, user input may be provided to the system to assign priority levels to one or more of the conflicting objectives.