Enhancing Flexibility in the Power Grid: A Study on Determining the Optimal Battery Size for Existing Wind Farms
(2025) MVKM01 20251Department of Energy Sciences
- Abstract
- The rapid expansion of intermittent renewable energy sources has introduced substantial challenges to both the stability of the electrical grid and the economic viability of incumbent wind farms. In light of recent regulatory amendments within the energy market, stakeholders are compelled to adjust their operational strategies. Concurrently, the expansion of wind power has exacerbated price cannibalization effects and increased price volatility, thereby heightening the imperative for enhanced system flexibility.
This thesis seeks to augment grid flexibility by determining the optimal integration of a Battery Energy Storage System (BESS) within existing wind-farm infrastructure, with the dual objective of improving producers’... (More) - The rapid expansion of intermittent renewable energy sources has introduced substantial challenges to both the stability of the electrical grid and the economic viability of incumbent wind farms. In light of recent regulatory amendments within the energy market, stakeholders are compelled to adjust their operational strategies. Concurrently, the expansion of wind power has exacerbated price cannibalization effects and increased price volatility, thereby heightening the imperative for enhanced system flexibility.
This thesis seeks to augment grid flexibility by determining the optimal integration of a Battery Energy Storage System (BESS) within existing wind-farm infrastructure, with the dual objective of improving producers’ profitability. To this end, a Mixed‐Integer Linear Programming (MILP) model is developed to identify both the optimal capacity and the dispatch strategy of a retrofit BESS. The model’s objective function is formulated to maximize annual profit by (i) exploiting price differentials in the day‐ahead and intraday markets, (ii) participating in Frequency Containment Reserve for disturbance (FCR-D) and normal operation (FCR-N) ancillary-service markets, and (iii) enforcing a stringent connection‐point constraint that precludes charging from the grid. Six scenarios are systematically evaluated: a reference case; three variants prioritizing ancillary-service revenue streams; a scenario exploring variations in state‐of‐charge (SoC) operating ranges; and a case incorporating reduced BESS capital costs.
The results demonstrate the techno‐economic feasibility of retrofitting BESS to existing wind farms. Under the reference scenario, the model identifies an optimal BESS rating of 15 MW/15 MWh for a size 200 MW wind farm, which cycles approximately 577 times per year. This configuration yields a 6.2 percentage‐point improvement in capture rate, a Levelized Cost of Storage (LCOS) of €109 per MWh, and a net present value of €2.0 million, corresponding to a payback period of less than ten years. These findings substantiate the potential for BESS deployment to enhance both grid flexibility and wind‐farm profitability under current market and regulatory conditions. (Less) - Popular Abstract
- Sweden has committed to achieving full carbon neutrality by 2045, and renewable energy, especially wind power, will be central to meeting that goal. Carbon emission free resources driving a growing share of electricity generation, wind farms have become increasingly attractive. Yet this very advantage introduces a critical challenge: wind output is inherently unpredictable, creating unpredictable highs and lows in production that are difficult to manage. This intermittency not only complicates grid planning but also raises questions about how to integrate larger volumes of wind energy without compromising system stability.
As wind capacity grows, another problem emerges: when numerous wind farms feed the grid simultaneously, they push... (More) - Sweden has committed to achieving full carbon neutrality by 2045, and renewable energy, especially wind power, will be central to meeting that goal. Carbon emission free resources driving a growing share of electricity generation, wind farms have become increasingly attractive. Yet this very advantage introduces a critical challenge: wind output is inherently unpredictable, creating unpredictable highs and lows in production that are difficult to manage. This intermittency not only complicates grid planning but also raises questions about how to integrate larger volumes of wind energy without compromising system stability.
As wind capacity grows, another problem emerges: when numerous wind farms feed the grid simultaneously, they push down the market price for electricity, a phenomenon known as price cannibalization. In turn, individual farms find it harder to sell power at favourable rates, squeezing their profitability.
The solution lies in shifting electricity delivery to times when demand and prices are higher, and that’s where Battery Energy Storage Systems (BESS) come in. By storing excess power when production is high and releasing it when the market rewards it most, a BESS can smooth out production peaks, buffer against price slumps, and create flexibility into the grid.
In this thesis, we explore how incorporating a BESS into a preexisting wind park not only tackles intermittency and cannibalization but also maximizes economic returns, inviting you to discover the optimal storage strategy that could reshape Sweden’s wind-energy landscape.
This culminated in a question which became the driving force of this study, what is the optimal battery size for existing wind farms? To answer the research question, a computer model was developed that tests a range of battery sizes and operational strategies to identify which configuration maximizes a park’s profitability and determines its optimal battery capacity. For this model, historical market prices and wind-power production data from the park were used throughout the thesis. The battery can generate revenue in three main ways:
1. Buy low, sell high (Energy arbitrage): Charge when wind is plentiful and prices are low, then discharge when demand spikes and prices rise.
2. Standby support (Ancillary services): Keep some charge in reserve and get paid to stabilize the grid during sudden dips or surges.
3. Avoiding low-price sales: If selling at a very low price isn’t worth it, for example if a storm drives prices near zero, the battery simply waits for a better offer later.
Our results demonstrate that retrofitting existing wind farms with a Battery Energy Storage System unlocks significant economic and technical benefits. Beyond boosting profitability, a wind-battery hybrid enhances grid reliability and resilience by acting as a buffer against intermittency. Our computer model reveals that a BESS configuration can dramatically improve overall performance, capturing low-price surplus energy, delivering ancillary services, and avoiding bargain sales. With ongoing market reforms and steady technological advances, the outlook for wind-battery hybrids has never been more promising. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9199345
- author
- Liljenberg, Elias and Linné Westman, Felix LU
- supervisor
- organization
- course
- MVKM01 20251
- year
- 2025
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Keywords: Battery Energy Storage System, Energy markets, Wind farm integration, Grid flexibility, Mixed-integer linear programing, Renewable Energy
- report number
- ISRN: LUTMDN/TMPH-25/5615-SE
- ISSN
- 0282-1990
- language
- English
- additional info
- Made in collaboration with Bodecker Partners.
- id
- 9199345
- date added to LUP
- 2025-06-18 09:01:32
- date last changed
- 2025-06-18 09:01:32
@misc{9199345,
abstract = {{The rapid expansion of intermittent renewable energy sources has introduced substantial challenges to both the stability of the electrical grid and the economic viability of incumbent wind farms. In light of recent regulatory amendments within the energy market, stakeholders are compelled to adjust their operational strategies. Concurrently, the expansion of wind power has exacerbated price cannibalization effects and increased price volatility, thereby heightening the imperative for enhanced system flexibility.
This thesis seeks to augment grid flexibility by determining the optimal integration of a Battery Energy Storage System (BESS) within existing wind-farm infrastructure, with the dual objective of improving producers’ profitability. To this end, a Mixed‐Integer Linear Programming (MILP) model is developed to identify both the optimal capacity and the dispatch strategy of a retrofit BESS. The model’s objective function is formulated to maximize annual profit by (i) exploiting price differentials in the day‐ahead and intraday markets, (ii) participating in Frequency Containment Reserve for disturbance (FCR-D) and normal operation (FCR-N) ancillary-service markets, and (iii) enforcing a stringent connection‐point constraint that precludes charging from the grid. Six scenarios are systematically evaluated: a reference case; three variants prioritizing ancillary-service revenue streams; a scenario exploring variations in state‐of‐charge (SoC) operating ranges; and a case incorporating reduced BESS capital costs.
The results demonstrate the techno‐economic feasibility of retrofitting BESS to existing wind farms. Under the reference scenario, the model identifies an optimal BESS rating of 15 MW/15 MWh for a size 200 MW wind farm, which cycles approximately 577 times per year. This configuration yields a 6.2 percentage‐point improvement in capture rate, a Levelized Cost of Storage (LCOS) of €109 per MWh, and a net present value of €2.0 million, corresponding to a payback period of less than ten years. These findings substantiate the potential for BESS deployment to enhance both grid flexibility and wind‐farm profitability under current market and regulatory conditions.}},
author = {{Liljenberg, Elias and Linné Westman, Felix}},
issn = {{0282-1990}},
language = {{eng}},
note = {{Student Paper}},
title = {{Enhancing Flexibility in the Power Grid: A Study on Determining the Optimal Battery Size for Existing Wind Farms}},
year = {{2025}},
}