Month: November 2023
Driving Energy Flexibility Forward: UL’s Innovative Forecast Models in OPENTUNITY
The OPENTUNITY project aims to empower end-users by leveraging their flexibility assets within the ecosystem, supporting local system operators and creating new revenue streams. Aggregators play a key role by gathering flexibility from various sources and offering it to energy markets, optimizing asset activation when bids are accepted. This is where the University of Ljubljana (UL) contributes significantly.
UL’s main involvement in OPENTUNITY is in Task 4.4, focusing on developing a method for optimal flexibility selection from units of various sizes and types. The outcome will be an algorithm for optimal selection based on market participation (DSO or TSO) and minimal cost criteria.
We’ve adopted a bottom-up approach, splitting the task into:
- Baseline and flexibility forecast models for EV fleet (AVANT) and HEMS fleet (AMIBIT).
- Optimal selection algorithm.
Currently, we’re focused on developing forecast models for EV and HEMS fleets using Python, Keras, and Tensorflow. The analysis of charging point (CP) data for EV fleets shows significant flexibility potential during night hours by postponing charging sessions.
The difference between charging session length can be seen in the figure below.
This flexibility potential is further illustrated in the figure below.
In the coming months, UL will continue to delve into baseline and flexibility forecasting for EV and HEMS fleets, marking the initial step towards developing the optimal selection innovation and empowering end-users within the flexibility ecosystem.
Enhancing Line Rating for Efficient Electricity Market Operation
Enhancing Line Rating for Efficient Electricity Market Operation
06/11/2023
The Line Rating stands as an important factor influencing the efficient operation of the electricity market. Static Line Rating methodologies typically yield conservative capacity estimations, often derived from worst-case weather conditions scenarios. Improving the accuracy of current rating estimations can significantly boost the utilization of distribution system lines, increase the hosting capacity of Renewable Energy Sources (RES), and mitigate the need for costly DSO (Distribution System Operator) upgrades.
Accurately gauging the technical limits of power lines entails measuring air and conductor temperatures, solar heat intensity, and wind speed/direction. Achieving high accuracy and reliability in these estimations typically necessitates the installation of an array of sensors along the line, consequently increasing the cost of such solutions.
In the context of OPENTUNITY, ICCS partner is taking on the task of developing a cost-effective, real-time thermal rating algorithm. ICCS plans to employ machine learning techniques to incorporate numerical weather forecasts and data obtained from sensors within the OPENTUNITY ecosystem, including those in proximity to distribution lines such as RES plants and home automation systems. This approach aims to provide accurate line current rating estimations without the need for multiple sensors spanning the entire line.
ICCS is deeply engaged in research within this field, amalgamating conventional practices related to real-time current ratings derived from industry standards such as CIGRE and IEEE, as well as state-of-the-art research methods. This fusion of established and innovative approaches to real-time current rating is a promising step towards enhancing the efficiency of electricity market operation.
