POWER SYSTEM OPTIMIZATION CONSIDERING UNCERTAINTY IN RENEWABLE ENERGY GENERATION. 

Abstract:

The integration of renewable energy sources into power systems has gained significant attention due to their environmental benefits and potential for reducing dependence on fossil fuels. However, the intermittent and uncertain nature of renewable energy generation, such as solar and wind, poses challenges for power system operation and planning. To address this issue, power system optimization techniques that consider uncertainty in renewable energy generation have emerged as crucial tools for ensuring reliable and efficient operation.

This paper presents an abstract on power system optimization considering uncertainty in renewable energy generation. The objective is to develop optimization models and algorithms that account for the stochastic characteristics of renewable energy resources and enable the efficient utilization of these resources while maintaining system reliability. The proposed approach aims to minimize the overall cost of power generation, considering the variability and uncertainty associated with renewable energy sources.

The key components of the proposed optimization framework include accurate modeling of renewable energy generation, probabilistic forecasting of renewable energy resources, and robust optimization techniques. The uncertainty in renewable energy generation is modeled using probabilistic methods, such as probability density functions or scenarios, to capture the variability and unpredictability of renewable energy resources. Probabilistic forecasts are utilized to estimate the future states of renewable energy generation, enabling the system operator to make informed decisions in real-time.

The optimization algorithm incorporates the uncertainty in renewable energy generation to determine the optimal generation schedule, unit commitment, and dispatch of conventional and renewable power sources. It takes into account various factors, including generation costs, transmission constraints, energy storage, and demand response, to achieve a balance between cost minimization and reliability of power supply. The optimization framework also considers the integration of energy storage systems and demand response programs to enhance the flexibility and stability of the power system.

The proposed approach is validated through case studies and numerical simulations using real-world data from renewable energy sources and power systems. The results demonstrate the effectiveness of the optimization framework in managing the uncertainty in renewable energy generation, improving the economic efficiency of power systems, and reducing greenhouse gas emissions.

In conclusion, power system optimization considering uncertainty in renewable energy generation offers a promising solution for addressing the challenges associated with the integration of renewable energy sources. The proposed framework enables the efficient utilization of renewable resources, enhances system reliability, and contributes to the overall sustainability of power systems. Further research and development in this area are warranted to refine the optimization techniques and facilitate the large-scale deployment of renewable energy in power systems.

POWER SYSTEM OPTIMIZATION CONSIDERING UNCERTAINTY IN RENEWABLE ENERGY GENERATION.GET MORE MASTERS COMPUTER SCIENCE

Sharing is caring!