Review on Economic Loss Assessment of Power Outages

Mao, Shuai; Wang, Chengzhi; Yu, Shiwen; Hao, Gen; Yu, Jufang; Hou, Hui

doi:10.1016/j.procs.2018.04.151

Cited by 69 publications

(29 citation statements)

References 8 publications

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“…Although there has been increasing interest in hardening the power system to be resilient against power outages, the risk of power outages cannot be completely diminished. In addition, power blackouts have resulted in various impacts to residents' living spaces, public services and facilities, and huge damages to the economy [11], [12]. By using publicly available information of historical major power outages, socio-economic data, state-level climatological observations, electricity consumption patterns and landuse data, Mukherjee et al [28] have developed a two-stage hybrid risk estimation model to address power outage risks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The common causes of outages can be categorized into supply side factors, demand side factors and political economy factors. In terms of the different impacts to the residents living in the area and the different damages (economic and others) [8], [11], [12], the power supply reliability in China is divided into three levels [13]: (1) The first level load. The interruption of the power supply at this level can cause personal accidents and large economic losses.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

et al. 2019

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With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackoutsensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers. INDEX TERMS Blackout sensitivity, big data, decision tree, electricity market, Internet of Things, longtailed, Pareto effect. NOMENCLATURE Notations Description Y The dependent variable, or target variable. It can be ordinal categorical, nominal categorical or continuous. If Y is categorical with J classes, its class takes values in C = {1,. .. , }.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

et al. 2019

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“…inevitably depends, on the last instance, on a reliable electrical supply. Grid disconnections are detrimental to both customers and providers [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…(1) high electric efficiency (> 45%); (2) overall efficiency of 85-93%; (3) low noise (silent operation); (4) reduction of air pollution (low emission); (5) high availability (24/7); (6) dispatchable with the capability to follow the load.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Fuzzy Logic-Based Energy Management System for Microgrids with Battery and Hydrogen Energy Storage System

et al. 2020

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This paper proposes a fuzzy logic-based energy management system (EMS) for microgrids with a combined battery and hydrogen energy storage system (ESS), which ensures the power balance according to the load demand at the time that it takes into account the improvement of the microgrid performance from a technical and economic point of view. As is known, renewable energy-based microgrids are receiving increasing interest in the research community, since they play a key role in the challenge of designing the next energy transition model. The integration of ESSs allows the absorption of the energy surplus in the microgrid to ensure power supply if the renewable resource is insufficient and the microgrid is isolated. If the microgrid can be connected to the main power grid, the freedom degrees increase and this allows, among other things, diminishment of the ESS size. Planning the operation of renewable sources-based microgrids requires both an efficient dispatching management between the available and the demanded energy and a reliable forecasting tool. The developed EMS is based on a fuzzy logic controller (FLC), which presents different advantages regarding other controllers: It is not necessary to know the model of the plant, and the linguistic rules that make up its inference engine are easily interpretable. These rules can incorporate expert knowledge, which simplifies the microgrid management, generally complex. The developed EMS has been subjected to a stress test that has demonstrated its excellent behavior. For that, a residential-type profile in an actual microgrid has been used. The developed fuzzy logic-based EMS, in addition to responding to the required load demand, can meet both technical (to prolong the devices’ lifespan) and economic (seeking the highest profitability and efficiency) established criteria, which can be introduced by the expert depending on the microgrid characteristic and profile demand to accomplish.

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“…Several possible problems and difficulties like electrical energy supply interruption that cause remarkable loss of economic profit from the power plant holders' point of view and trouble to electrical energy consumers from the customers' point of view may occur as power transformers work under faulty condition (Shuai et al 2018). Moreover, TPT are significantly expensive and their replacing or repairing is a too remarkably pricey and drawn-out process.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Katooli

Koochaki

2020

J Control Autom Electr Syst

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Three-phase transformers (TPT) play a significant and crucial function in the power networks in order to connect the subsystems and deliver the electrical energy to final customers. The TPT are one of the most high-priced equipment in modern power networks, and therefore their working condition should be constantly monitored to prevent their breakdown, power outages and huge financial damage. Accordingly, this paper presents a hybrid method for detection and classification of incipient faults in TPT using dissolved gas analysis techniques (DGAT) information and rule-based machine learning method. In the developed method, the most informative and important items of DGAT data out of 14 items selected by association rules mining technique (ARMT) are employed as the input of adaptive neuro-fuzzy inference system (ANFIS). The ARMT is implemented to select the items, which have maximum information and can train the ANFIS more accurately. Furthermore, in order to enhance the accuracy of ANFIS and improve its robustness in different implementations, black widow optimization algorithm is applied for ANFIS training. In order to evaluate the performance of developed method on real issues, two industrial data collections obtained from Iran-Transfo Company chemical laboratory and Damavand power substations are used. The obtained results through MATLAB simulations proved that the developed method has high fault detection and classification accuracy, robust function in different implementations, short run time and simple structure.

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Review on Economic Loss Assessment of Power Outages

Cited by 69 publications

References 8 publications

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

Multi-Objective Fuzzy Logic-Based Energy Management System for Microgrids with Battery and Hydrogen Energy Storage System

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

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