Optimizing Electric Vehicle Aggregator Bidding in Nordic Frequency Containment Reserve for Disturbances (FCR-D) Markets

Core Concepts

An optimization model is developed to enable an aggregator of electric vehicles (EVs) to optimally bid their flexible capacity into the Nordic Frequency Containment Reserve for Disturbances (FCR-D) markets, while adhering to new market regulations such as the P90 rule and Limited Energy Reservoir (LER) classification.

Abstract

The paper presents an optimization approach for an aggregator of electric vehicles (EVs) to bid their flexible capacity into the Nordic Frequency Containment Reserve for Disturbances (FCR-D) markets. The key highlights are:

The Nordic ancillary service markets have introduced new regulations, the P90 rule and LER classification, to enable more flexible stochastic resources like EVs to participate.

The P90 rule allows for some uncertainty in the capacity bid, while the LER classification relaxes the activation duration requirements for limited energy resources like EVs.

The authors develop a chance-constrained optimization model that incorporates these new market regulations to maximize the aggregator's FCR-D bids, using real-life data of a portfolio of EVs.

Two approximation methods, ALSO-X and Conditional Value at Risk (CVaR), are used to reformulate the chance constraints and make the optimization problem tractable.

The results show a significant synergy effect when aggregating a portfolio of EVs, especially when using the more aggressive ALSO-X approximation method which better exploits the P90 rule.

The LER requirements, particularly the need to reserve 20% of the downward bid in upward flexibility, are found to be a significant constraint limiting the aggregator's bids.

The optimization model with ALSO-X can achieve higher profits for the aggregator compared to CVaR, but CVaR is more conservative and better ensures compliance with the P90 rule.

Stats

"The FCR-D market has seen soaring prices in recent years, even allowing for a pay-back time of almost one year for a one MW/MWh battery."
"Prices in FCR-D up and down-regulation have skyrocketed and since only negligible energy delivery is expected, it serves as an extremely attractive market right now in the Nordics."

Quotes

"The P90 rule has the potential to increase liquidity in these markets by lowering barriers to entry for stochastic flexible resources."
"Accessing the flexibility of already existing infrastructure, such as flexible demands, can help undertake the green transition and make it more efficient."
"Consequently, providing flexibility has rapidly become a hot topic in the electricity-consuming industry."

Key Insights Distilled From

Aggregator of Electric Vehicles Bidding in Nordic FCR-D Markets: A Chance-Constrained Program

by Gustav A. Lu... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12818.pdf

Deeper Inquiries

How could the aggregator further diversify its portfolio of flexible resources beyond just EVs to reduce the constraints imposed by the LER requirements?

To reduce the constraints imposed by the LER requirements, the aggregator could further diversify its portfolio of flexible resources. One way to achieve this is by including a mix of different types of flexible resources that complement each other. Some potential options for diversification include:

Battery Energy Storage Systems (BESS): BESS can provide fast response times and can be used to store excess energy during low-demand periods for later use during peak demand times. They can help balance the grid and provide additional flexibility.

Demand Response Programs: Engaging in demand response programs with industrial or commercial customers can provide additional flexibility by adjusting their energy consumption based on grid needs. This can help in reducing the reliance on a single type of resource.

Renewable Energy Sources: Integrating renewable energy sources like solar and wind power into the portfolio can add variability and diversity to the mix. These sources can contribute to the overall flexibility of the aggregator's portfolio.

Flexible Loads: Including flexible loads such as smart appliances, HVAC systems, and electric water heaters can add to the overall flexibility of the portfolio. These loads can be controlled to adjust energy consumption based on grid conditions.

By diversifying the portfolio with a mix of these resources, the aggregator can reduce the constraints imposed by the LER requirements and enhance the overall flexibility and resilience of its offerings.

How could the system operator consider to preserve the security of supply while also increasing market liquidity, beyond the current LER requirements?

To preserve the security of supply while increasing market liquidity beyond the current LER requirements, the system operator could consider the following alternative approaches:

Dynamic LER Requirements: Implementing dynamic LER requirements that adjust based on real-time grid conditions and demand patterns can help balance the need for security of supply with market liquidity. This flexibility can ensure that resources are available when needed while allowing for more participation in the market.

Incentivizing Flexibility: Offering incentives for resources that provide flexibility beyond the LER requirements can encourage participation and increase market liquidity. Rewards for fast response times, accurate forecasting, and availability during critical grid events can help maintain security of supply while enhancing market dynamics.

Collaborative Partnerships: Establishing partnerships with neighboring TSOs or aggregators to share resources during peak demand or emergencies can improve security of supply. By collaborating and sharing resources, the system operator can enhance resilience and reduce the burden on individual participants.

Advanced Forecasting and Monitoring: Investing in advanced forecasting tools and real-time monitoring systems can improve grid management and decision-making. By accurately predicting demand and supply fluctuations, the system operator can optimize resource allocation and maintain security of supply while maximizing market liquidity.

By adopting these alternative approaches, the system operator can strike a balance between security of supply and market liquidity, ensuring a reliable and efficient energy system.

How could the probabilistic modeling of the EV flexibility be improved to better capture the uncertainty and further increase the aggregator's bidding capacity?

To improve the probabilistic modeling of EV flexibility and better capture uncertainty, the aggregator can consider the following strategies:

Advanced Data Analytics: Implementing advanced data analytics techniques, such as machine learning algorithms, can help in analyzing historical EV charging patterns and predicting future flexibility with higher accuracy. By leveraging big data analytics, the aggregator can improve the probabilistic modeling of EV flexibility.

Scenario Analysis: Conducting scenario analysis based on different demand scenarios, grid conditions, and market dynamics can enhance the probabilistic modeling of EV flexibility. By considering a range of possible outcomes, the aggregator can better capture uncertainty and make more informed decisions.

Real-Time Data Integration: Integrating real-time data from EV charging stations and grid sensors can provide up-to-date information on EV flexibility. By incorporating real-time data into the probabilistic model, the aggregator can adjust bidding strategies dynamically based on current conditions.

Stochastic Optimization: Utilizing stochastic optimization techniques can help in optimizing bidding strategies under uncertainty. By incorporating probabilistic constraints and objectives into the optimization model, the aggregator can better account for the variability in EV flexibility and maximize bidding capacity.

Collaboration with EV Manufacturers: Collaborating with EV manufacturers to access detailed data on EV performance, battery health, and charging behavior can improve the accuracy of the probabilistic model. By leveraging manufacturer insights, the aggregator can enhance the modeling of EV flexibility.

By implementing these strategies, the aggregator can enhance the probabilistic modeling of EV flexibility, capture uncertainty more effectively, and increase bidding capacity in the market.