368 research outputs found
Techno-Economic Analysis and Optimal Control of Battery Storage for Frequency Control Services, Applied to the German Market
Optimal investment in battery energy storage systems, taking into account
degradation, sizing and control, is crucial for the deployment of battery
storage, of which providing frequency control is one of the major applications.
In this paper, we present a holistic, data-driven framework to determine the
optimal investment, size and controller of a battery storage system providing
frequency control. We optimised the controller towards minimum degradation and
electricity costs over its lifetime, while ensuring the delivery of frequency
control services compliant with regulatory requirements. We adopted a detailed
battery model, considering the dynamics and degradation when exposed to actual
frequency data. Further, we used a stochastic optimisation objective while
constraining the probability on unavailability to deliver the frequency control
service. Through a thorough analysis, we were able to decrease the amount of
data needed and thereby decrease the execution time while keeping the
approximation error within limits. Using the proposed framework, we performed a
techno-economic analysis of a battery providing 1 MW capacity in the German
primary frequency control market. Results showed that a battery rated at 1.6
MW, 1.6 MWh has the highest net present value, yet this configuration is only
profitable if costs are low enough or in case future frequency control prices
do not decline too much. It transpires that calendar ageing drives battery
degradation, whereas cycle ageing has less impact.Comment: Submitted to Applied Energ
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning
In modern highly interconnected power grids, automatic generation control
(AGC) is crucial in maintaining the stability of the power grid. The dependence
of the AGC system on the information and communications technology (ICT) system
makes it vulnerable to various types of cyber-attacks. Thus, information flow
(IF) analysis and anomaly detection became paramount for preventing cyber
attackers from driving the cyber-physical power system (CPPS) to instability.
In this paper, the ICT network traffic rules in CPPSs are explored and the
frequency domain features of the ICT network traffic are extracted, basically
for developing a robust learning algorithm that can learn the normal traffic
pattern based on the ResNeSt convolutional neural network (CNN). Furthermore,
to overcome the problem of insufficient abnormal traffic labeled samples,
transfer learning approach is used. In the proposed data-driven-based method
the deep learning model is trained by traffic frequency features, which makes
our model robust against AGC's parameters uncertainties and modeling
nonlinearities.Comment: Editor: Geert Deconinck. 18th European Dependable Computing
Conference (EDCC 2022), September 12-15, 2022, Zaragoza, Spain. Fast Abstract
Proceedings - EDCC 202
Resource Aware Run-Time Adaptation Support for Recovery Strategies
The selection of recovery strategies is often based only on the types and circumstances of the failures. However, also changes in the environment such as fewer resources at node levels or degradation of quality-of-service should be considered before allocating a new process/task to another host or before taking reconfiguration decisions. In this paper we present why and how resource availability information should be considered for recovery strategies adaptation. Such resource aware run-time adaptation of recovery improves the availability and survivability of a system
Optimal Power Flow in Four-Wire Distribution Networks: Formulation and Benchmarking
In recent years, several applications have been proposed in the context of
distribution networks. Many of these can be formulated as an optimal power flow
problem, a mathematical optimization program which includes a model of the
steady-state physics of the electricity network. If the network loading is
balanced and the lines are transposed, the network model can be simplified to a
single-phase equivalent model. However, these assumptions do not apply to
low-voltage distribution networks, so the network model should model the
effects of phase unbalance correctly. In many parts of the world, the
low-voltage distribution network has four conductors, i.e. three phases and a
neutral. This paper develops OPF formulations for such networks, including
transformers, shunts and voltage-dependent loads, in two variable spaces, i.e.
current-voltage and power-voltage, and compares them for robustness and
scalability. A case study across 128 low-voltage networks also quantifies the
modelling error introduced by Kron reductions and its impact on the solve time.
This work highlights the advantages of formulations in current-voltage
variables over power-voltage, for four-wire networks.Comment: 10 pages, submitted to Power Systems Computation Conference 202
Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization
Residential demand response programs aim to activate demand flexibility at
the household level. In recent years, reinforcement learning (RL) has gained
significant attention for these type of applications. A major challenge of RL
algorithms is data efficiency. New RL algorithms, such as proximal policy
optimisation (PPO), have tried to increase data efficiency. Additionally,
combining RL with transfer learning has been proposed in an effort to mitigate
this challenge. In this work, we further improve upon state-of-the-art transfer
learning performance by incorporating demand response domain knowledge into the
learning pipeline. We evaluate our approach on a demand response use case where
peak shaving and self-consumption is incentivised by means of a capacity
tariff. We show our adapted version of PPO, combined with transfer learning,
reduces cost by 14.51% compared to a regular hysteresis controller and by 6.68%
compared to traditional PPO.Comment: Submitted to Elsevier Energy and A
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