DTU Studieprojekt - Quantification of multi-energy system resilience with data-driven approaches

Quantification of multi-energy system resilience with data-driven approaches

Udbyder
Vejleder
Sted
København og omegn
Context
Power system resilience is the ability to withstand and recover from extreme contingencies, which have high impact and low probability (HILP, also known as black swan events). Examples can be extreme windy weather leading to outages of wind turbines and loss of power lines. Such HILP events can contribute to unsecure operation of power system and even system blackouts, which have huge societal impacts.

In order to achieve ambitious renewable energy goals, multi-energy systems (MESs) are investigated to operate all energy sectors as a whole. Resilience of MESs is however overlooked. The HILP events can influence the operation of different energy sectors through coupling technologies. Take the power system and district heating system in Denmark as an example, extreme windy weather can lead to loss of power generation of combined heat and power plants or power lines. Additionally, extreme cold weather can lead to high heat demand supplied by residential heat pumps or combined heat and power plants. Such HILP events can bring impacts on one energy sector then propagated to another. This can lead to unbalanced generation and consumption in both energy sectors and therefore unreliable operation of the MES.

Therefore, novel resilience quantification metrics that can assess and quantify an MES is necessary. Such metrics can help the system operator to understand the system behavior during and after the events, e.g. value of lost load and energy not supplied.

• 
  Objectives
• 
  Methodology
  As a first step, the current scientific literature on the quantitative method of resilience metrics will be reviewed to quantify the MES resilience. Data-driven approaches for dimension reduction e.g. feature selection and extraction, and classification should be developed, in order to reduce the dimension of attributes. Next, the HILP events will be simulated as inputs of system behavior in terms of resilience. Such events should be generated through probabilistic approach considering their distribution and the fragile curve of possible components damages. Then economic dispatch of the MES system should be performed before and after the events. System performance will be evaluated through the resilience metrics.
• 
  Expected results
• Prerequisites
  

1. Establishment of resilience quantification metrics for a MES of power and district heating. Dimension reduction of attributes can be achieved through data-driven approaches.

2. Simulation of HILP events with probabilistic approach, i.e. their distribution and influence on the MES.

3. Simulation of MES operation performance after the HILP events occur and quantify the system resilience with the metrics established.

The expected outcome includes a review of relevant literature, an overview of the theory of the used methods, and documentation of the implementation and results.

Electricity markets, programming (e.g., Python, Matlab), a flair for optimization.

For students, such data-driven approaches can be further applied to other areas in the future, e.g. banking.

I samarbejde med
Villum Fonden

Forudsætninger
Programming (e.g., Python, Matlab), a flair for optimization.

Emneord

• Elektroteknologi
• Energi
• Matematik
• Antenner
• Elektromagnetisme
• Elektronik
• Lyd
• Mikrobølgeteknologi
• Robotteknik og automation
• Bioenergi
• Brændselsceller
• Elforsyning
• Energieffektivisering
• Energilagring
• Energiproduktion
• Energisystemer
• Kraftværker
• Solenergi
• Vindenergi
• Kortlægning og opmåling
• Billedanalyse
• Computerberegning
• Dataanalyse
• Hardware og komponenter
• Software og programmering
• Telekommunikation
• Geometri
• Matematisk analyse
• Matematisk logik
• Matematisk modellering
• Statistik
• Høreapparater
• Medicinske apparater og systemer
• Klimatilpasning
• Rumteknologi og instrumenter
• Satellitter

• energysytem
• MathematicalModelling
• powersystems
• Resilience

Navn
Jiawei Wang

Stilling
Postdoc

Mail
jiawang@elektro.dtu.dk

Vejleder-info
Kandidatuddannelsen i Elektroteknologi
Vejleder
Jiawei Wang

Medvejledere
Pierre Pinson

Type
Kandidatspeciale, Specialkursus

Kandidatuddannelsen i Matematisk Modellering og Computing
Vejleder
Jiawei Wang

Medvejledere
Pierre Pinson

Type
Kandidatspeciale, Specialkursus

Kandidatuddannelsen i Bæredygtig Energi
Vejleder
Jiawei Wang

Medvejledere
Pierre Pinson

Type
Kandidatspeciale, Specialkursus