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
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.
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
1. Establishment of resilience quantification metrics for a MES of power and
district heating. Dimension reduction of attributes can be achieved through
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.
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 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
students, such data-driven approaches can be further applied to other areas in the
future, e.g. banking.
I samarbejde medVillum Fonden
Programming (e.g., Python, Matlab), a flair for optimization.