Renewable Energy Penetration Limit Determination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The unpredictability of renewable energy sources like wind and solar introduces intermittency into the grid, leading to reliability issues and often results in curtailment due to transmission bottlenecks, which affects the grid's stability and efficiency.
Innovation Solution
A computer-implemented method determines intermittent renewable energy penetration limits by analyzing historical and future load data, network parameters, renewable and non-renewable energy sources, and storage capabilities to optimize the integration of renewable energy sources into the grid, minimizing waste and ensuring grid stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If renewable energy sources are increased to reduce carbon emissions, then environmental sustainability is improved, but grid reliability deteriorates due to intermittency
Solution Approach 1:
The system performs preliminary determination of renewable energy penetration limits and storage component sizes before actual grid integration. By pre-calculating optimal configurations based on load forecasts and network parameters, the system prepares the grid to accommodate renewable energy without compromising reliability, thus preventing curtailment while maintaining stability.
Solution Approach 2:
The system determines optimal parameter values including maximum renewable energy source sizes, storage component capacities, and minimum loading levels for non-renewable sources. By optimizing these parameters based on load forecasts and network constraints, the system resolves the contradiction between maximizing renewable energy utilization and maintaining grid reliability.
2Productivity
If renewable energy sources are integrated without limits, then clean energy usage is improved, but transmission bottlenecks worsen causing curtailment
Solution Approach 1:
The system pre-determines the maximum size of renewable energy sources that can be integrated without causing transmission bottlenecks. By calculating penetration limits based on network parameters and load forecasts before integration, the system enables maximum renewable energy productivity while preventing transmission-related curtailment.
Solution Approach 2:
The system introduces storage components as intermediaries between renewable energy sources and the grid. By determining optimal storage sizes that can absorb excess generation during high production periods and release energy during low production periods, the system mediates transmission bottlenecks and eliminates curtailment while maintaining high renewable energy productivity.
3Loss of energy
If storage component size is increased to store more renewable energy, then energy waste is reduced, but system complexity and cost increase
Solution Approach 1:
The system determines the optimal storage component size as a specific parameter based on the maximum renewable energy source size and network constraints. By calculating the precise storage capacity needed to minimize energy waste without excessive oversizing, the system reduces energy loss while avoiding unnecessary complexity and cost associated with oversized storage systems.
Data Source
AI summary
Methods, systems, and computer program products for determining intermittent renewable energy penetration limits in a grid are provided herein. A computer-implemented method includes determining a load forecast for an electrical network based on historical load data and future step load information; determining the maximum size of renewable energy sources that can be added to the network based on the load forecast, parameters pertaining to the network, information pertaining to the renewable energy sources, and information pertaining to non-renewable energy sources; determining a storage component size for storing renewable energy generated by the renewable energy sources based on the maximum size of the renewable energy sources and constraints associated with the network; determining a minimum loading level of the non-renewable energy sources based on the load forecast, intermittency data associated with the renewable energy sources and the parameters pertaining to the network; and configuring the network based on the determinations.