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ردیف | عنوان | نوع |
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1 |
Quantum-Inspired Power System Reliability Assessment
ارزیابی قابلیت اطمینان سیستم قدرت با الهام از کوانتومی-2022 To enable an in-depth study of power system operation and planning, the assessment of standard reliability indices
is inevitable. The Monte Carlo Simulation (MCS) approach is
a broadly used method in replacing the analytical methods in
reliability indices assessment. The accuracy of MCS, however,
highly depends on the sampling size, and hence, a complicated
system with large number of components requires a large sampling
size and daunting computational effort. To address this shortcoming, this paper attempts to take advantage of potentials of the
quantum computing (QC) for power system reliability assessment
by realizing the following contributions: 1) an innovative quantum
model designed for reliability assessment; 2) a quantum circuit
that achieves the quadratic speed up compared to the classical
MCS method; 3) an efficient quantum amplitude estimation
(QAE) algorithm to accurately evaluate the reliability indices.
The accuracy and efficacy of the quantum reliability method are
extensively verified and demonstrated on both radial and mesh
distribution systems.
Index Terms—Quantum computing | Quantum amplitude estimation | Reliability assessment | Distribution systems |
مقاله انگلیسی |
2 |
Quantum computing in power systems
محاسبات کوانتومی در سیستم های قدرت-2022 Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to
successfully operating large transmission and distribution systems. However, today’ s power systems are suffering from everincreasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources,
as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support
the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes
of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally
intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the
newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing
quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic
analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations,
hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure
communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will
hopefully stimulate increasing attention to the development of quantum-engineered smart grids.
keywords: Quantum computing | power system | variational quantum algorithms | quantum optimization | quantum machine learning | quantum security. |
مقاله انگلیسی |
3 |
Quantum Distributed Unit Commitment: An Application in Microgrids
تعهد واحد توزیع شده کوانتومی: یک کاربرد در ریزشبکه ها-2022 The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment
problem complexity is expected to increase in the future because
of the trend toward the increase of penetration of intermittent
renewables. Even though quantum computing has proven effective
for solving a host of problems, its applications for power systems’
problems have been rather limited. In this paper, a quantum unit
commitment is innovatively formulated and the quantum version
of the decomposition and coordination alternate direction method
of multipliers (ADMM) is established. The above is achieved by
devising quantum algorithms and by exploiting the superposition
and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible
solutions. The main contributions of this paper include: 1) the
innovative development of a quantum model for Unit Commitment;
2) development of decomposition and coordination-supported
framework which paves the way for the utilization of limited
quantum resources to potentially solve the large-scale discrete
optimization problems; 3) devising the novel quantum distributed
unit commitment (QDUC) to solve the problem in a larger scale
than currently available quantum computers are capable of solving.
The QDUC results are compared with those from its classical
counterpart, which validate the efficacy of quantum computing.
Index Terms: Microgrids | quantum computing | quantum distributed optimization | unit commitment. |
مقاله انگلیسی |
4 |
Annealing-based Quantum Computing for Combinatorial Optimal Power Flow
محاسبات کوانتومی مبتنی بر بازپخت برای جریان قدرت بهینه ترکیبی-2022 This paper proposes the use of annealing-based
quantum computing for solving combinatorial optimal power
flow problems. Quantum annealers provide a physical com-
puting platform which utilises quantum phase transitions to
solve specific classes of combinatorial problems. These devices
have seen rapid increases in scale and performance, and are
now approaching the point where they could be valuable for
industrial applications. This paper shows how an optimal power
flow problem incorporating linear multiphase network modelling,
discrete sources of energy flexibility, renewable generation place-
ment/sizing and network upgrade decisions can be formulated as
a quadratic unconstrained binary optimisation problem, which
can be solved by quantum annealing. Case studies with these
components integrated with the ieee European Low Voltage
Test Feeder are implemented using D-Wave Systems’ 5,760
qubit Advantage quantum processing unit and hybrid quantum-
classical solver. Index Terms— Distribution Network | D-Wave | Electric Vehicle | Optimal Power Flow | Power System Planning | Quantum Annealing | Quantum Computing | Smart Charging. |
مقاله انگلیسی |
5 |
Federated learning with hyperparameter-based clustering for electrical load forecasting
یادگیری فدرال با خوشهبندی مبتنی بر فراپارامتر برای پیشبینی بار الکتریکی-2022 Electrical load prediction has become an integral part of power system operation. Deep learning
models have found popularity for this purpose. However, to achieve a desired prediction
accuracy, they require huge amounts of data for training. Sharing electricity consumption data
of individual households for load prediction may compromise user privacy and can be expensive
in terms of communication resources. Therefore, edge computing methods, such as federated
learning, are gaining more importance for this purpose. These methods can take advantage of
the data without centrally storing it. This paper evaluates the performance of federated learning
for short-term forecasting of individual house loads as well as the aggregate load. It discusses the
advantages and disadvantages of this method by comparing it to centralized and local learning
schemes. Moreover, a new client clustering method is proposed to reduce the convergence time
of federated learning. The results show that federated learning has a good performance with a
minimum root mean squared error (RMSE) of 0.117 kWh for individual load forecasting.
Keywords: Federated learning | Electricity load forecasting | Edge computing | LSTM | Decentralized learning |
مقاله انگلیسی |
6 |
Solving constrained economic electrical energy generation and CO2 emission dispatch using hybrid algorithm
حل تولید انرژی الکتریکی اقتصادی محدود و ارسال CO2 با استفاده از الگوریتم ترکیبی-2021 Carbon foot print is latest hot discussion and all the countries are started the initiation
to minimize it. One of the main carbon emission industries is thermal electrical
power generating company. For the social welfare the emission has to minimize to
maximum extend. The electrical power generation company as well needs to minimize
the generation cost for the better operation. Minimizing carbon foot print and minimal
power generation cost are opposite to each other. When the optimization focused to
minimize the generation cost it lead to increase the carbon foot print and vice versa.
This paper addresses the constraint the optimization problem which needs the objective
of reducing both emission and generation cost. Electrical power generating cost of
thermal power plant is nonlinear and non-convex in nature. Likewise the emission
produced by the thermal power plant is complex mathematical problem. Intelligent
algorithms are best suitable to solve these types of practical problems. In this paper
hybrid of firefly algorithm and differential evolution technique is adopted to find
the constrained emission minimization and cost minimization. Firefly algorithm works
based on brightness, attractiveness and movement and there is no mutation process.
Differential evolution technique has better mutation process and hence this mutation
operation of differential evolution technique is introduced with firefly algorithm for the
hybrid operation and to get better result. The research work is aimed to reduce the
carbon foot print as well the generation cost of the power system.
Keywords: Economic dispatch | Emission dispatch | Intelligent algorithm | Hybrid algorithm | Firefly algorithm | Differential evaluation | Constraint optimization |
مقاله انگلیسی |
7 |
Information and Measurement System for Electric Power Losses Accounting in Railway Transport
اطلاعات و سیستم اندازه گیری برای حسابداری تلفات برق در حمل و نقل ریلی-2021 The purpose of the presented research is to minimize the loss of electricity during the operation of railway power systems. Losses
are defined as an unbalance between the released and consumed electricity, which is recorded by means of commercial electricity
ccounting. Given that electricity losses are divided into technical and non-technical (commercial) components, there are
currently no technical tools that can analyze the components of electricity losses in detail, and therefore prevent their occurrence.
To achieve this goal, the factors inherent in commercial electricity accounting systems in various areas of production activity that
affect the growth of electricity losses are identified. An algorithm is proposed that allows determining the presence of abnormal
power losses in real time for making organizational and technical decisions to reduce them. A block diagram of the information
and measurement system for accounting of power losses has been developed, which allows using the existing equipment without
replacement or modernization, which allows obtaining new technical capabilities. The method of intellectualization of the
process of classification of factors that cause the growth of abnormal power losses, based on artificial neural networks, is
posed. The intelligent module allows replacing the person who makes organizational and technical decisions, minimizing the
consequences of abnormal situations that lead to the growth of abnormal losses, applying the proposed solutions in departments
that do not have qualified specialists. The results of training an artificial neural network are considered, and the main parameters
of the efficiency of the information and measurement system for loss accounting on a real railway transport object are
determined.
Keywords: Power Loss | Artificial Neural Networks. |
مقاله انگلیسی |
8 |
Accounting for uncertainties due to high-impact low-probability events in power system development
محاسبه عدم قطعیت های ناشی از رویدادهای با احتمال کم تاثیر زیاد در توسعه سیستم قدرت-2021 In the long-term development of the electric power system, system operators should consider the socio-economic
balance between grid investment costs and security of supply, including the risk of power supply interruptions.
Cost-benefit analyses conducted for this purpose are associated with many uncertainties but have traditionally
focused on the expected value of the net socio-economic benefits of risk-reducing measures. This article focuses
on the large uncertainties that are associated with the possible occurrence of high-impact low-probability
interruption events (HILP events). The objective is to quantify and visualize the implications of uncertainties due
to HILP events in the context of power system development. More specifically, this article describes a method-
ology accounting for uncertainties in socio-economic cost-benefit analysis of measures for reducing the risk of
HILP events. The methodology accounts for the contributions of both aleatory and epistemic uncertainties and
comprises a hybrid probabilistic-possibilistic uncertainty analysis method. Applying the methodology to a real
case involving a grid investment decision, it is demonstrated how it provides additional insight compared to
conventional cost-benefit analyses considering expected values where uncertainties are not accounted for
explicitly. It is furthermore discussed how these results can help to better inform grid development decisions. keywords: برنامه ریزی سیستم قدرت | تحلیل ریسک | آسیب پذیری | قابلیت اطمینان سیستم قدرت | رویدادهای فوق العاده | Power system planning | Risk analysis | Vulnerability | Power system reliability | Extraordinary events |
مقاله انگلیسی |
9 |
Power network robustness analysis based on electrical engineering and complex network theory
تجزیه و تحلیل استحکام شبکه قدرت بر اساس مهندسی برق و نظریه شبکه پیچیده-2021 The growing importance of power systems in the development of modern society
has increasingly focused the attention on the various dangers to which these systems
are exposed. This paper proposes a robust analysis framework based on complex
network theory with the aim of exploring the robustness of the power system from a
methodological perspective. The analysis framework establishes three models: a purely
topological model, an artificial flow model, and a direct current power flow model to
analyze the power system structure and functional robustness. We present different
analysis metrics under different models, simulate three fault scenarios, and conduct
an evaluation and analysis. The validity of the evaluation analysis was further verified
by adopting IEEE300 and two randomly generated 1000-node network models that
meet the characteristics of small world and scale, respectively, for detailed robustness
analysis. The results show that the proposed method can effectively analyze a power
system from the perspectives of pure topology, artificial flow, and direct current power
flow. The case analysis based on the IEEE300 network and systems with different
network characteristics proves that the framework is effective for the evaluation of
power systems with different characteristics.
Keywords: Power network | Robustness | Topological model | Artificial flow | Direct current power flow |
مقاله انگلیسی |
10 |
Modified deep learning and reinforcement learning for an incentive-based demand response model
یادگیری عمیق اصلاح شده و یادگیری تقویتی برای یک مدل پاسخ تقاضای مبتنی بر انگیزه-2020 Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand
during peak period through rewards. In this study, an incentive-based DR program with modified deep
learning and reinforcement learning is proposed. A modified deep learning model based on recurrent
neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by
forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then,
reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can
maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the
proposed modified deep learning model can achieve more accurate forecasting results compared with
some other methods. It can support the development of incentive-based DR programs under uncertain
environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs
while reducing the peak electricity demand. A short-term DR program was developed for peak electricity
demand period, and the experimental results show that peak electricity demand can be reduced by 17%.
This contributes to mitigating the supply-demand imbalance and enhancing power system security. Keywords: Demand response | Modified deep learning | Reinforcement learning | Smart grid |
مقاله انگلیسی |