PhD Position in Electrical power engineering and mechatronics, Early stage researcher

Tallinn University of Technology, School of Engineering, Department of Electrical Power Engineering and Mechatronics offers a 4-year PhD position in the field of electrical power engineering and mechatronics.

Proposed doctoral thesis topic: "Aggregation of Energy Flexibility in nZED from different flexibility vectors".

Supervisor: Senior Researcher Roya Ahmadiahangar and Professor Argo Rosin

Abstract

Flexibility is of prime importance for current and future Power Systems (PS) with increasing grid integration of renewable Energy sources (RES). In this regard, the main challenge is the management of the increased variability and uncertainty imposed by RES in the power balance. Contribution from the aggregated flexibility of nearly Zero Energy Buildings (nZEB) can significantly increase the flexibility of nearly Zero Energy Districts (nZED). This project aims to increase the ENERGY FLEXIBILITY of nZED as a key enabler of the transformation towards the high integration level of renewable energy resources in the grid. Most attention will be paid to developing a novel machine learning-based method, which accurately characterizes and exploits the aggregated flexibility from the demand-side to avoid excessive investments in conventional power plants and costs for balancing power.

Description

Forecasting the flexibility, especially in nZEB (nearly zero energy building) and aggregated in nZED(nearly zero energy districy) , depends on several parameters, including weather, electricity generation, and demand, grid constraints and market prices aswell as the characteristics of the building, ventilation and heating system, control system, smart appliances, the available capacity of ESS, behaviour of occupants and their willingness to change their usage pattern and share of flexible loads. Conventional approaches like computing the average of the data are not efficient in analyzing this volume of data. Therefore, machine learning (ML) approaches are considered a powerful tool to deal with huge datasets. The power consumption of flexible appliances and the usage behavior of consumers is required in defining the EF of each nZEB . Another issue toward the utilization of the flexibility in nZED, is the lack of standard metrics for characterisation of flexibility.

While most efforts so far targeting characterisation and forecasting of the nZEB flexibility, scaling up to nZED level, and the use of deep learning-based methods increase the characterization accuracy of the available flexibility. The main reason is enabling of accurate aggregation of consumption profiles with different user behaiviour and several flexibility vectors. The main novelty is the aggregation of flexibility vectors and distinguishing the impact of AC and DC grids.

Project tasks:

• Investigation and development of aggregation methods for energy flexibility in nZED, capable of distinguishing flexibility of AC and DC buses within nZEBs and nZED.
• Comparison analysis of AC and DC flexibility
• Development of a novel deep learning-based multi-task learning model capable of forecasting the aggregated flexibility of nZED from different flexibility vectors, considering practical constraints and hybrid vectors.