Analysis of Wind Power Integration in Electricity Markets LMP Pricing (2410.11139v1)

Abstract: Wind energy has emerged as one of the most vital and economically viable forms of renewable energy. The integration of wind energy sources into power grids across the globe has been increasing substantially, largely due to the higher levels of uncertainty associated with wind energy compared to other renewable energy sources. This study focuses on analyzing the Locational Marginal Pricing (LMP) market model, with particular emphasis on the integration of wind power plants into substations. Furthermore, it examines a two-stage stochastic model for electricity markets employing LMP pricing, utilizing the Optimal Power Flow (OPF) method for the analysis.

• The paper presents a two-stage stochastic programming model that adjusts LMP pricing to accommodate wind power uncertainties.
• It finds that increased wind uncertainty raises system costs and that line congestion elevates LMPs at affected buses.
• Numerical experiments on the IEEE 24-bus RTS validate that wind integration can reduce LMPs under uncongested conditions while ensuring market reliability.

Analysis of Wind Power Integration in Electricity Markets: LMP Pricing

This paper examines the integration of wind energy into electricity markets through the Locational Marginal Pricing (LMP) model, emphasizing its impact on market dynamics and system reliability. The paper employs a two-stage stochastic programming framework to address the uncertainties inherent in wind energy production.

Key Contributions

The authors explore how LMP can be adjusted to account for the variable nature of wind energy, by integrating uncertainty components such as transmission line overload costs and generation violation costs. The approach aims to enhance economic dispatch while maintaining system reliability.

Methodology

The model introduced in the paper is built around a two-stage stochastic programming approach:

1. First Stage: Represents the electricity market, incorporating constraints related to market balance, generation, wind power production, load, and reserve determination.
2. Second Stage: Captures the operational and physical constraints that depend on specific wind scenarios, using optimal power flow (OPF) methods to determine LMPs.

The numerical experiments utilize the IEEE 24-bus RTS network to validate the model, focusing on how uncertainty in wind production affects the market's objective function and locational marginal prices.

Numerical Results

The experimentation reveals several intriguing insights:

• Increased uncertainty in wind resources raises the expected costs within the system, indicating that higher uncertainty diminishes wind power's cost-reducing potential.
• Line congestion leads to increased LMP at buses connected to congested lines. Connecting wind power sources to these buses can mitigate LMP increases due to congestion.
• In scenarios without congestion, increasing the penetration of wind energy reduces LMP values, particularly during peak load hours.

Implications and Future Research

The findings underscore the importance of considering the stochastic nature of wind power when integrating it into electricity markets. The application of LMPs to account for variability enables better adaptation and pricing strategies.

Future research could expand upon the model to incorporate more complex grid configurations and hybrid systems, potentially including storage solutions to further mitigate the issues arising from the intermittency of wind resources. Additionally, exploring more diverse scenarios could yield enhanced robustness in market settlements under increased renewable penetration.

By refining these pricing mechanisms, there is potential to promote more investment in renewable energy generation and storage solutions, ultimately leading to a more resilient and economically efficient power grid.