19,779 research outputs found
Dynamic Co-Simulation Methods for Combined Transmission-Distribution System and Integration Time Step Impact on Convergence
Combined Transmission and Distribution Systems (CoTDS) simulation for power
systems requires development of algorithms and software that are numerically
stable and at the same time accurately simulate dynamic events that can occur
in practical systems. The dynamic behavior of transmission and distribution
systems are vastly different, especially with the increased deployment of
distribution generation. The time scales of simulation can be orders of
magnitude apart making the combined simulation extremely challenging. This has
led to increased research in applying co-simulation techniques for integrated
simulation of the two systems. In this paper, a rigorous mathematical analysis
on convergence of numerical methods in co-simulation is presented. Two methods
for co-simulation of CoTDS are proposed using parallel and series computation
of the transmission system and distribution systems. Both these co-simulation
methods are validated against total system simulation in a single time-domain
simulation environment. The series computation co-simulation method is shown to
have better numerical stability at larger integration time steps. The series
computation co-simulation method is additionally validated against commercial
EMTP software and the results show remarkable correspondence.Comment: 10 page
European White Book on Real-Time Power Hardware in the Loop Testing : DERlab Report No. R- 005.0
The European White Book on Real-Time-Powerhardware-in-the-Loop testing is intended to serve as a reference document on the future of testing of electrical power equipment, with specifi c focus on the emerging hardware-in-the-loop activities and application thereof within testing facilities and procedures. It will provide an outlook of how this powerful tool can be utilised to support the development, testing and validation of specifi cally DER equipment. It aims to report on international experience gained thus far and provides case studies on developments and specifi c technical issues, such as the hardware/software interface. This white book compliments the already existing series of DERlab European white books, covering topics such as grid-inverters and grid-connected storag
Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems
Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management
SecGrid: A Secure and Efficient SGX-enabled Smart Grid System with Rich Functionalities
Smart grid adopts two-way communication and rich functionalities to gain a
positive impact on the sustainability and efficiency of power usage, but on the
other hand, also poses serious challenges to customers' privacy. Existing
solutions in smart grid usually use cryptographic tools, such as homomorphic
encryption, to protect individual privacy, which, however, can only support
limited and simple functionalities. Moreover, the resource-constrained smart
meters need to perform heavy asymmetric cryptography in these solutions, which
is not applied to smart grid. In this paper, we present a practical and secure
SGX-enabled smart grid system, named SecGrid. Our system leverage trusted
hardware SGX to ensure that grid utilities can efficiently execute rich
functionalities on customers' private data, while guaranteeing their privacy.
With the designed security protocols, the SecGrid only require the smart meters
to perform AES encryption. Security analysis shows that SecGrid can thwart
various attacks from malicious adversaries. Experimental results show that
SecGrid is much faster than the existing privacy-preserving schemes in smart
grid
A Survey of Data Fusion in Smart City Applications
The advancement of various research sectors such as Internet of Things (IoT),
Machine Learning, Data Mining, Big Data, and Communication Technology has shed
some light in transforming an urban city integrating the aforementioned
techniques to a commonly known term - Smart City. With the emergence of smart
city, plethora of data sources have been made available for wide variety of
applications. The common technique for handling multiple data sources is data
fusion, where it improves data output quality or extracts knowledge from the
raw data. In order to cater evergrowing highly complicated applications,
studies in smart city have to utilize data from various sources and evaluate
their performance based on multiple aspects. To this end, we introduce a
multi-perspectives classification of the data fusion to evaluate the smart city
applications. Moreover, we applied the proposed multi-perspectives
classification to evaluate selected applications in each domain of the smart
city. We conclude the paper by discussing potential future direction and
challenges of data fusion integration.Comment: Accepted and To be published in Elsevier Information Fusio
Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms
As automatic optimization techniques find their way into industrial
applications, the behavior of many complex systems is determined by some form
of planner picking the right actions to optimize a given objective function. In
many cases, the mapping of plans to objective reward may change due to
unforeseen events or circumstances in the real world. In those cases, the
planner usually needs some additional effort to adjust to the changed situation
and reach its previous level of performance. Whenever we still need to continue
polling the planner even during re-planning, it oftentimes exhibits severely
lacking performance. In order to improve the planner's resilience to unforeseen
change, we argue that maintaining a certain level of diversity amongst the
considered plans at all times should be added to the planner's objective.
Effectively, we encourage the planner to keep alternative plans to its
currently best solution. As an example case, we implement a diversity-aware
genetic algorithm using two different metrics for diversity (differing in their
generality) and show that the blow in performance due to unexpected change can
be severely lessened in the average case. We also analyze the parameter
settings necessary for these techniques in order to gain an intuition how they
can be incorporated into larger frameworks or process models for software and
systems engineering.Comment: ICAC, 2018, Trent
Energy and Information Management of Electric Vehicular Network: A Survey
The connected vehicle paradigm empowers vehicles with the capability to
communicate with neighboring vehicles and infrastructure, shifting the role of
vehicles from a transportation tool to an intelligent service platform.
Meanwhile, the transportation electrification pushes forward the electric
vehicle (EV) commercialization to reduce the greenhouse gas emission by
petroleum combustion. The unstoppable trends of connected vehicle and EVs
transform the traditional vehicular system to an electric vehicular network
(EVN), a clean, mobile, and safe system. However, due to the mobility and
heterogeneity of the EVN, improper management of the network could result in
charging overload and data congestion. Thus, energy and information management
of the EVN should be carefully studied. In this paper, we provide a
comprehensive survey on the deployment and management of EVN considering all
three aspects of energy flow, data communication, and computation. We first
introduce the management framework of EVN. Then, research works on the EV
aggregator (AG) deployment are reviewed to provide energy and information
infrastructure for the EVN. Based on the deployed AGs, we present the research
work review on EV scheduling that includes both charging and vehicle-to-grid
(V2G) scheduling. Moreover, related works on information communication and
computing are surveyed under each scenario. Finally, we discuss open research
issues in the EVN
Distributed Monitoring for Prevention of Cascading Failures in Operational Power Grids
Electrical power grids are vulnerable to cascading failures that can lead to
large blackouts. Detection and prevention of cascading failures in power grids
is impor- tant. Currently, grid operators mainly monitor the state (loading
level) of individual components in power grids. The complex architecture of
power grids, with many interdependencies, makes it difficult to aggregate data
provided by local compo- nents in a timely manner and meaningful way:
monitoring the resilience with re- spect to cascading failures of an
operational power grid is a challenge. This paper addresses this challenge. The
main ideas behind the paper are that (i) a robustness metric based on both the
topology and the operative state of the power grid can be used to quantify
power grid robustness and (ii) a new proposed a distributed computation method
with self-stabilizing properties can be used to achieving near real-time
monitoring of the robustness of the power grid. Our con- tributions thus
provide insight into the resilience with respect to cascading failures of a
dynamic operational power grid at runtime, in a scalable and robust way. Com-
putations are pushed into the network, making the results available at each
node, allowing automated distributed control mechanisms to be implemented on
top
Power-Traffic Coordinated Operation for Bi-Peak Shaving and Bi-Ramp Smoothing -A Hierarchical Data-Driven Approach
With the rapid adoption of distributed photovoltaics (PVs) in certain
regions, issues such as lower net load valley during the day and more steep
ramping of the demand after sunset start to challenge normal operations at
utility companies. Urban transportation systems also have high peak congestion
periods and steep ramping because of traffic patterns. We propose using the
emerging electric vehicles (EVs) and the charing/discharging stations (CDSs) to
coordinate the operation between power distribution system (PDS) and the urban
transportation system (UTS), therefore, the operation challenges in each system
can be mitigated by utilizing the flexibility of the other system. We conducted
the simulation and numerical analysis using the IEEE 8,500-bus for the PDS and
the Sioux Falls system with about 10,000 cars for the UTS. Two systems are
simulated jointly to demonstrate the feasibility and effectiveness of the
proposed approach.Comment: 12 pag
Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection
A significant challenge in energy system cyber security is the current
inability to detect cyber-physical attacks targeting and originating from
distributed grid-edge devices such as photovoltaics (PV) panels, smart flexible
loads, and electric vehicles. We address this concern by designing and
developing a distributed, multi-modal anomaly detection approach that can sense
the health of the device and the electric power grid from the edge. This is
realized by exploiting unsupervised machine learning algorithms on multiple
sources of time-series data, fusing these multiple local observations and
flagging anomalies when a deviation from the normal behavior is observed.
We particularly focus on the cyber-physical threats to the distributed PVs
that has the potential to cause local disturbances or grid instabilities by
creating supply-demand mismatch, reverse power flow conditions etc. We use an
open source power system simulation tool called GridLAB-D, loaded with real
smart home and solar datasets to simulate the smart grid scenarios and to
illustrate the impact of PV attacks on the power system. Various attacks
targeting PV panels that create voltage fluctuations, reverse power flow etc
were designed and performed. We observe that while individual unsupervised
learning algorithms such as OCSVMs, Corrupt RF and PCA surpasses in identifying
particular attack type, PCA with Convex Hull outperforms all algorithms in
identifying all designed attacks with a true positive rate of 83.64% and an
accuracy of 95.78%. Our key insight is that due to the heterogeneous nature of
the distribution grid and the uncertainty in the type of the attack being
launched, relying on single mode of information for defense can lead to
increased false alarms and missed detection rates as one can design attacks to
hide within those uncertainties and remain stealthy
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