Renewable Energy Network Optimization Tool
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Solution Overview
Problem
The renewable energy industry faces challenges in optimizing the selection of sites for wind and solar farms, leading to inefficiencies and lower returns on investment due to non-optimized energy production, which results in variability and load balancing issues for electrical grids.
Innovation Solution
A renewable energy network optimization tool using a hybrid simulated annealing-genetic algorithm to determine optimal site configurations by evaluating candidate sites through scoring metrics, ensuring geographical diversity and maximizing usable power, while considering constraints such as location and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If renewable energy sites are selected without optimization, then site selection process is simple and quick, but energy production efficiency and stability are low
Solution Approach 1:
The patent replaces manual or simple heuristic site selection methods with a computer-based optimization system that uses simulated annealing and genetic algorithms. This computational approach automatically evaluates multiple candidate sites using scoring metrics that consider geographical diversity, resource availability, and grid stability, thereby improving energy production efficiency without requiring manual intervention in the complex optimization process.
Solution Approach 2:
The optimization system changes the parameters of site selection by introducing multiple scoring metrics that evaluate candidate sites based on geographical diversity, resource availability, and grid stability. The simulated annealing and genetic algorithms dynamically adjust selection parameters across iterations, transitioning from random selection to optimized configuration, thereby resolving the contradiction between simple selection and efficient production.
2Productivity
If renewable energy generation is increased to reduce non-renewable dependency, then renewable energy production increases, but variability and grid stability challenges increase
Solution Approach 1:
The patent applies local quality by selecting specific geographical locations for renewable energy sites based on their unique characteristics. The scoring metrics evaluate each candidate site's local conditions including geographical diversity, resource availability, and proximity to grid infrastructure. By optimizing the specific location and configuration of each site, the system maximizes renewable energy production while maintaining grid stability through strategically placed diverse generation sources.
Solution Approach 2:
The optimization system incorporates feedback mechanisms where scoring metrics evaluate candidate networks and the results feed back into the simulated annealing and genetic algorithms. This feedback loop allows the system to learn from previous iterations and adjust site selections to improve both energy production and grid stability, resolving the contradiction between increased renewable production and grid reliability.
3Measurement precision
If more candidate sites are evaluated to find optimal configuration, then network optimization accuracy improves, but computational time and resources increase
Solution Approach 1:
The patent applies preliminary action by pre-defining scoring metrics and evaluation criteria before the optimization process begins. The simulated annealing and genetic algorithms use these pre-established metrics to efficiently evaluate candidate sites without requiring extensive real-time computation. This preliminary preparation enables accurate site selection while reducing computational time during the actual optimization process.
Solution Approach 2:
The system evaluates a partial set of candidate sites that are most likely to yield optimal results, rather than exhaustively evaluating all possible sites. The scoring metrics identify and prioritize promising candidates, allowing the simulated annealing and genetic algorithms to focus computational resources on the most relevant options, thereby achieving high accuracy without excessive computation time.
Data Source
AI summary
Embodiments of a system and method are disclosed for providing a renewable energy network optimization tool. A method for optimizing a renewable energy network determines an initial configuration state, populates a pool of candidate sites for placement of renewable-energy generating units a hybrid simulated annealing-genetic algorithm, constructs a plurality of candidate renewable energy generation networks from the pool of candidate sites using random selection, evaluates the candidate renewable energy generation networks using scoring metrics, ranks the evaluated candidate renewable energy generation networks with respect to each other and prior iteration candidate renewable energy generation networks, adds candidate sites from a top ranked candidate renewable energy generation network to a list of candidate sites to be kept repeats the above until a final best candidate renewable energy generation network of kept sites is determined.