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To take information of system operation and meteorological conditions into state estimation analysis, three classical algorithms for association rule mining are discussed in Sheng et al. The rule mining methods are combined with probabilistic graphical model for potential failure prediction. In most commercial buildings, the building automation system BAS are designed and adopted to control the heating, ventilating and airing conditioning HVAC system to maintain proper temperature and humidity for the occupants.

If the indoor smart grids can be monitored on a continuous or regular basis, a proper operation strategy may be proposed for the improvement of energy efficiency, fault diagnosis and system reliability. In Allen et al.

How Blockchain Technology Can Revolutionize Smart Grids

The fault signatures for various fault types are generated by the ANN classification technique. As the rising number of aging assets in power system is becoming a potential threat to the safety operation, a lot of failure models are proposed focusing on variables of aging time or conditions.

Reference Murthy et al. In order to make the best use of these data, the stratified proportional hazards model PHM is developed as a nonparametric regression method to process and classify the lifecycle data into multi-type recurrent events quantitatively Qiu et al. The potential risk problem and health condition can be predicted with the help of this PHM method Colombo et al. As a worldwide issue, Electric power quality PQ refers to the magnitude, frequency and waveform of voltage and current in power system and highly related to the safe operation of power grid as well as the satisfaction of consumers.

With the increasing application of nonlinear and power electronics based loads and generators, the harmonic distortions and instable situations frequently appears in power grid. Instead of sampling the voltage data of the PQ event data like the existing analysis methods, the image files of the three-phase PQ events are processed for classification by deep learning techniques.

Due to the high cost for installation of advance metering devices, the conventional electromechanical analog meters still work in some residential areas and the data analytics-based PQ analysis cannot be properly utilized. Reference Tang et al. The power consumption information can then be collected to a cloud server through online data exchange. Under the consideration of balance between computation capability and the satisfactory performance of the algorithm, a compact method is presented in Borges et al. A robust and fast processing pattern recognition algorithm is proposed in power quality events PQE classification is illustrated in Ferhat et al.

The features highly correlated to the PQE are extracted with the discrete wavelet transform-entropy and basic statistical criteria for the establishment of ELM classifier. Taking the advantage of information layers in smart grid is an effective means to approach the challenges from the renewable energy sources RES in distribution network.

The measurement, monitoring, communication and control of smart grids by advanced sensors and devices are making the complex network sensible and perceptible. The randomness of RES and uncertainty of the load are increasing the urgency and necessity for a comprehensive decision based on huge volume of data collecting and processing. Since the network-constrained economic dispatch problems are supposed to be solved by the real-time electricity process in a contemporary whole-sale electricity market, the potential of recovering the topology of a grid is explored with market data in Kekatos et al.

Grapy theory and probabilistic DC optimal power flow are adopted for building the network model. With the purpose for a greener society, the low carbon technologies LCTs are driven by the government by application of heat pumps, photovoltaic, electric vehicles and other smart appliances in low voltage LV distribution networks. Therefore, the visualization of LV networks with limited metering and data acquisition equipment attracts increasing research interests.

The network load profiling based on the identification of representative load profiles of LV systems is an economical alternative method. A novel three-stage network load profiling method proposed in Li et al.

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The first two stages are used to identify the load conditions of unmonitored LV systems with similar fixed data to those monitored LV substations. The contribution factor for each LV template is then determined by the cluster-wise weighted constrained regression algorithm. The abundant and environmental friendly RES such as wind and photovoltaic energies are supposed to be the dominant energy source for the next generation of power grid.

However, the randomness and intermittent characteristics are always obstacles for a large-scale utilization of RES in a stable way. To deal with such enormous challenges and get an improved dispatch planning, maintenance scheduling as well as regulation, an accurate and reliable RES forecasting approach has become the hot spot around the world Ak et al. The meteorological information in historical records are used for clustering approach to classify the days into different categories.

Then the bagging algorithm based neural network is trained to get the forecasting results of wind energy. Instead of using the neural network, Ye et al. In Yang et al. Reference Khodayar et al. The forecasting approach of distributed solar energy systems from macro- and micro-aspects is discussed in a general way in ZHAO et al. The data-driven forecasting approach of PV diffusion is proposed based on cellular automation in microscopic analysis. By decomposing the time-series data with discrete wavelet transform, the proposed recurrent neural network RNN model in Nazaripouya et al.

Like the RES prediction, an accurate short-term load forecasting is the essential basis for energy management, system operation and market analysis. With the emerging active role of customers in smart grid, the high efficient dynamic electricity market is also based on a good performance of electricity consumption prediction.

Since electricity consumption is affected by the weather conditions to some extent, reference Liu et al. An extreme learning machine ensembled with a novel wavelet transform is used for electricity consumption in Li et al. To overcome the volatility and uncertainty of load profiles, the recurrent neural network is adopted with a novel pooling layer to avoid overfitting problems in Shi et al. Rather than the aggregated load forecasting, the energy consumption in a single house is usually volatile and difficult to be predicted.

Driven by the recent success of deep learning, a long short-term memory recurrent neural network based framework in Kong et al. Reference Moreno-Munoz et al. Reference Cai et al. Load profiling is a way to describe the typical behavior of electric consumption, which is usually represented in time domain for load forecasting, demand-side management and capital planning Wenhao et al. As an effective method for energy management, the tariff structure designed before is usually based on the type of activity, which is not able to indicate the electrical behavior in a comprehensive way Ahmed et al.

Reference Bo et al. In the first-stage, the load patterns are clustered into different categories according to the evaluation index, and then the customers are classified according to the comprehensive load shape factors defined in the first-stage with SVM algorithm. In contrary to the time domain analysis Al-Otaibi et al. The residential electricity consumption usually can be divided into three parts: fixed, regulable and deferrable loads, which is the theoretical basis for the optimal energy management of the demand response DR mechanism.

Reference Li et al. A learning based DR strategy combining data analytics and optimization is developed for regulatable loads focusing on the residential HVAC Zhang et al. Reference Jindal et al. To better understand the information behind the stochasticity and irregularity of residential energy consumption, an in-depth analysis is presented in Grindrod, with a finite mixture model-based clustering technique. The frequency-domain data analytics in the SOM shows a superiority over the time-domain data with a higher accuracy in new customer classification.

A multi-resolution analysis method based on wavelet analysis is proposed in Li et al. Different permutations of typical load profiles provide a more flexible load profiling with a reduction of computation. With the popularization of electric vehicles EVs , learning the charging load patterns of them is becoming a key step for the stability of power grids.

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Furthermore, the flexibility of the collective EV charging demand is analyzed with Bayesian maximum likelihood. References Tong et al. K-SVD sparse representation technique is used to decompose the load profiles into several partial usage patterns for a linear SVM based method to recognize the type of customers. Load disaggregation is also called non-intrusive load monitoring NILM , aiming to segregate the overall load profiles at household level into the energy consumption of individual appliances. Unlike direct appliance monitoring framework, the NILM from only one smart meter installed in the house is easier to be accepted by the customers Liang et al.

The more effective and complex appliance signatures are then proposed with the harmonics computation of steady-state power or current Berges et al. In Henao et al. As a single channel blind source separation problem, the dictionary learning based approaches can be used in NILM. Compared with HMM, the latter method is not suitable for real-time application. The nontechnical loss NTL , which is probably caused by the electrical theft or errors in accounting, is one of the prominent concerns that have plagued the power system utilities for a long time Leung, ; Zhan et al.

Furthermore, large scale electricity fraudulent behavior may cause severe imbalance problems in power system. Therefore, the effective framework to detect the NTL in the complex power grid has appealed many research interests. DT is trained with various features including heavy appliances, number of persons, weather conditions to get the expected value of electricity consumption for the customer during a particular time. In Zanetti et al. The anomalies in consumption patterns are discovered with the fuzzy clustering algorithm.

Even though there are increasing researches on the big data analytics in smart grids, the deployed applications are few. There are still many open issues needed to be addressed before the techniques can create implications in reality. With the fast deployment of smart meters and advanced sensors, huge amount of data with multiple types and structures from deference sources with a variety of protocols are generated every second. However, the lack of standard data format for the information software and database structures, as well as the issue of interoperability of different information and communication systems deployed in the smart grids, make it complicated and difficult to obtain data for real application.

The traditional way of isolated storage of the data in various systems also increases the barrier for data sharing among applications. As a conventionally sensitive industry, most of the data generated in the smart grid are considered as confidential or related with privacy issues; therefore, it is impractical for researchers to conduct highly relevant studies which can be smoothly transferred later on into deployment.

Thus, most of the researches are still about the algorithms which are tested with ideal data, and hence stay in the Ivory tower. In addition, due to the lack of strategic vision, top design of application, large investment in reality, combined with the short-sighted recognition of the value of the data, the applications of big data in real systems are growing very slow. Even though, the majority of utility companies showed great interests in the big data analytics and their application in their business, they are still waiting to see convincing results before they are willing to put more efforts and investment.

Last but not least, the big data analytics in smart grids is a comprehensive and complicated field, which does not only depend on the mathematic algorithms or techniques, it also depends on the operation of the systems, the behaviors of vast number of autonomous users, the ICT technologies, the expertise of the field, etc. Therefore, it needs the synergy among experts from different fields if we would like to see the benefits of it in the smart grids.

In this article, the big data in smart grid and the corresponding state-of-the-art analysis methods have been reviewed and discussed. The data which may contain valuable information are collected from smart meters installed in the power system, electricity market, GIS, meteorological information system, social media, and so on.

The purpose of advanced ICT technology in power system is to associate the traditional physical parameters in power system to the external variables to discover potential regulations and scientific problems. After extracting the useful features from raw information with the background knowledge of electrical engineering, typical data analytics methods, such as neural network, k-means, and support vector machine, could be widely applied. Secure and efficient operation strategies as well as optimal business decisions are supposed to be made with the data analytics from a more unified view.

With more advanced ICT technologies applied in power system, the fast and efficient data analytics framework for huge volume of data would become a challenging requirement. Moreover, the cyber security and privacy protection could become as important as a relay protection in power system. Even though the interactive communication with customers provides a potential solution for more accurate demand response, it also increases the difficulties in consumption behavior analysis at the same time.

As the application of data analytics in smart grids is a comprehensive and complicated field, involving mathematics, ICT technologies, computer science, electrical engineering, etc. Alam Mollah Rezaul, Kashem M. Muttaqi, Abdesselam Bouzerdoum. Evaluating the effectiveness of a machine learning approach based on response time and reliability for islanding detection of distributed generation. IET renewable power generation volume: 11, Issue: 11, Shiraz, Iran. Baimel D, Tapuchi S, Baimel N Smart grid communication technologies- overview, research challenges and opportunities. Balouji E, Salor O Classification of power quality events using deep learning on events images.

Bauman K, Tuzhilin A, Zaczynski R Using social sensors for detecting emergency events: a case of power outages in the electrical utility industry. Big Data analytics and energy consumption. Wiley, New York. Cai Y, Chow M-Y Exploratory analysis of massive data for distribution fault diagnosis in smart grids. Tech Rep. Journal of Industrial Information Integration — Renew Sust Energ Rev — Claessens BJ, Vrancx P, Ruelens F Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load control.

De Santis E, Livi L, Sadeghian A, Rizzi A Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification. Decis Support Syst — Computer Science Review — Executive Office of the President Economic benefits of increasing electric grid resilience to weather outages. In: USA. In: Energy and Buildings, vol , pp — IEEE Trans. Gungor V et al Sep.

J Strateg Inf Syst — Hashemi F, Mohammadi M, Kargarian A Islanding detection method for microgrid based on extracted features from differential transient rate of change of frequency. IEEE Access — Jiye Q, Zhixiang J, Mengjie S et al Scenario analysis and application research on big data in smart power distribution and consumption systems. Proceedings of the CSEE 35 8 — In: Proc. Keyan L, Wanxin S, Dongxia Z et al Big data application requirements and scenario analysis in smart distribution network.

Proceedings of the CSEE 35 2 — Li S, Wang P, Goel L May a A novel wavelet-based ensemble method for short-term load forecasting with hybrid neural networks and feature selection. Liang Jian, Simon K. Ng, Gail Kendall, and John W. Rel Eng Syst Safety 86 3 — PR Newswire. Neurocomputing — Sheng G, Hou H, Jiang X, Chen Y A novel association rule mining method of big data for power transformer state parameters based on probabilistic graph model. Singh S, Yassine A Mining energy consumption behavior patterns for households in smart grid. Accident Analysis and Prediction, volume — SmartGrids European Tech.

SmartGrids SDD]. Sultanem F Using appliance signatures for monitoring residential loads at meter panel level. Swetapadma A, Yadav A Data-mining-based fault during power swing identification in power transmission system. Power System Technology 38 12 — Proceedings of the CSEE,36 ;18 pp: —, Power System Technology, Vol.

Smart Grids for the City of Praia: Benefits and Challenges

Yu C Pan Heping. Business intelligence and its key technology Application Research of Computers — Zhang Zhen. Public Utilities Fortnightly, , 1, IEEE Access 5: —, Zikopoulos P, C. Download references. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

EB carried out the smart grid studies, participated in the concepts of big data in power system and the structure of the paper. TH participated in the frontier technologies in smart grid and drafted part of the manuscript. YZ participated in the data analysis application survey in smart grid and drafted the manuscript. All authors read and approved the final manuscript. Correspondence to Tao Huang. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Reprints and Permissions. Search all SpringerOpen articles Search. Abstract Data analytics are now playing a more important role in the modern industrial systems. Introduction With the fast development of digital technology and cloud computing, more and more data are produced through digital equipment and sensors, such as smart phones, computers, advanced measuring infrastructures, etc. Big data in smart grid Concept of big data The definition of big data is not very clear and uniform at present. Concept of smart grids Smart grid is the power system embedded with an information layer that allows for two-way communication between the central controllers and local actuators as well as logistic units to respond digitally to urgent situations of physical elements or quickly changing of electric demand.

Table 1 Quantification of collected data in different sampling rates Big Data analytics and energy consumption, Full size table. Data Sources of the Grids. Full size image. Table 2 Intelligent data collection devices in smart grid Full size table. Smart grid communication infrastructure. Table 3 Summary of communication infrastructure in smart grid Full size table. Data analysis techniques The most important stage of the big data processing system is data analysis, which is the basis for discovering valuable information and supporting the decision-making Fan et al.

Table 4 Concepts related to data analysis Full size table. Data Pre-processing Techniques. Table 5 Data Analytics Algorithms Full size table. Example of big data analytic procedures in smart grid.


Big data analytics in smart grid Fault detection The carbon emission reduction and sustainability of environment are the driving force and construction purpose of smart grid, which is designed in a decentralized structure. Transient stability analysis Transient stability is a critical issue closely related to the safely operation of power system. Power quality monitoring As a worldwide issue, Electric power quality PQ refers to the magnitude, frequency and waveform of voltage and current in power system and highly related to the safe operation of power grid as well as the satisfaction of consumers.

It is one of the easiest and most cost effective ways to combat climate change, improve the competitiveness of businesses, and reduce energy costs for consumers. Energy markets have a substantial amount of economic potential for improving energy efficiency, but many cost-effective energy-efficiency measures are not being deployed.

Hirst and Brown 8 noted that only half of the economic potential for US energy efficiency is likely to be realized over the next 20 years. Achieving these three goals will require complex linking of public and private actors, governments and regulators, economic and social factors, national resources, environmental concerns, and individual behaviors.

Various strategies which can be adopted to combat global warming can be classified under the following three categories: 9. In view of the second point, a conventional model for power systems is widely seen as untenable and Smart Grid initiatives have been launched worldwide as a response to these developments. Smart grid technologies offer great potential for reduction of emissions. The emission benefits of the Smart Grid are difficult to quantify because they depend on how enabling technologies function within the system. Estimates in the literature vary both in magnitude and in source of emission abatement: electric vehicles EVs displacing oil consumption, price transparency stimulating conservation, enhanced energy efficiency reducing energy consumption, or renewable energy displacing conventional fuels through increased system flexibility.

The Information and Communications Technology ICT sector is uniquely placed to partner with power companies to optimize the existing electricity grid to allow more efficient power distribution and enable more renewable or green power. Although all mentioned sources of carbon pollution can contribute to the benefits that Smart Grid technologies deliver, without careful design and well elaborated metrics for success, the risks are outweighing the benefits on the table. Failing to do so would risk forfeiting the potential benefits of Smart Grid deployment, for example through uncoordinated planning or deployment of infrastructure that lacks the capability to provide flexibility in the relevant timescales.

We conclude with a view of recent progress which confirms that Smart Grid can become an integral part of future clean energy solutions. Economic development involves the increased use of highly energy intensive materials, such as steel, cement, glass, and aluminum. These materials are necessary for the construction and development of transport, energy, housing, and water management infrastructure. Coal is the most widely used source of energy in energy-intensive industries and is important in the development of modern infrastructure in growing economies. Notes: CO 2 levels have been climbing steadily in the atmosphere for the last 55 years.

According to The Netherlands Environmental Assessment Agency statistics, since approximately billion tons of CO 2 were cumulatively emitted due to human activities. Global CO 2 emissions in approached 30 Gt, followed by a record high of Figure 2 Global CO 2 emissions per region from fossil-fuel use and cement production. Adapted from Global CO 2 emissions per region from fossil-fuel use and cement production. Authoritative sources such as the World Resources Institute and International Energy Agency claim that atmospheric CO 2 can only be stabilized by deploying a range of measures, which include significant increases in energy efficiency and conservation, wider reliance on renewable energy, and the use of carbon capture and storage.

Regarding atmospheric CO 2 levels, some countries are witnessing a true paradox. For example, Germany, a country whose goal is to have no electricity production from fossil fuels at all by , increased its electricity production from coal in Coal plants are making up for the bulk of the energy production lost due to the shutdown of eight nuclear plants, while gas plants, which emit less CO 2 but are more expensive to run, are barely profitable at present.

The energy carriers in the primary energy supply all show continuous increases over the past decade, except nuclear energy, which decreased since , after the Fukushima accident. However, the good news is that renewable energy has shown an accelerated increase since for example, the use of hydropower increased by 4. Early indications suggest that in CO 2 emissions continued to decline in the Organization for Economic Co-operation and Development countries, more than offset by a rapid increase in non-Organization for Economic Co-operation and Development countries.

For the medium term, the World Energy Outlook projects that global CO 2 emissions from fuel combustion will continue to grow unabated, albeit at a lower rate, reaching This is an improvement over the World Energy Outlook Current Policies Scenario, but still leads to a long-term temperature increase of 3. The electric grid is traditionally divided into three stages: generation, transmission, and distribution. Generation is the stage where the electricity is created, for example, a power plant.

Transmission is the stage that transfers the electricity across great distances from the generation location to an area of demand, and is comprised of high-voltage cables, step-up substations and step-down substations. The distribution stage includes transferring electricity from a substation to the end consumer.

The electric grid has an interconnected mesh structure whereas major populated areas and generation plants are interconnected via more than one path, creating necessary redundancies. The Smart Grid conceptually consists of three main layers: Rather than replacing existing infrastructures, new smart capabilities are made possible by integrating new applications into transmission and distribution grids. The document Framework and Roadmap for future Smart Grids, 22 by National Institute of Standards and Technology NIST identifies seven domains within the Smart Grid — transmission, distribution, operations, bulk generation, markets, customer, and service provider.

A Smart Grid domain is a high-level grouping of organizations, buildings, individuals, systems, devices, or other actors with similar objectives and relying on, or participating in similar types of applications. Across the seven domains, numerous actors will capture, transmit, store, edit, and process the information necessary for Smart Grid applications. Some of the important changes include more focus on distributed generation, incorporating distributed energy resources and cybersecurity developments.

Future developments include transactive energy and microgrids which are getting a lot more attention as a result of the deployment of coupled solar and storage solutions. In Release 3. Figure 3 shows the conceptual model with key linkages across entities that will comprise the Smart Grid. Figure 3 Interaction of roles in different Smart Grid domains through secure communication.

Accessed January 10, The International Energy Agency identified eight main Smart Grid technology areas whose functions can be described as follows: Some of the technologies from Figure 4 are actively being deployed and are considered mature in both their development and application, while others require further development and demonstration.

A fully optimized electricity system will deploy all the technology areas from Figure 4.

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[Full text] Smart Grid and nanotechnologies: a solution for clean and sustainable | EECT

Figure 4 Smart Grid technology areas. Notes: Smart Grid technology areas — each consisting of sets of individual technologies — span the entire grid, from generation through to transmission, and distribution, to various types of electricity consumers. Licence: www. The mechanisms and their impacts — a reduction in electricity use and CO 2 emissions by — are analyzed in Table 1.

Reproduced from Pacific Northwest National Laboratory. Smart grids are also about enabling users to improve their power usage profile and make it smarter, reducing power consumption when energy is scarcely available while allowing higher consumption when there is more on offer. Depending on the time of equipment use, energy efficiency measures can produce significant reductions in peak demand. The ability to optimize energy usage at all levels of the supply chain will become an important sustainability issue. Smart energy management systems are key enablers of the envisioned efficiencies both on the demand and supply sides of the smart energy grids.

Some organizations argue that a Smart Grid might be able to deal with the extra load without adding significant generating capacity by acting as a mediator to smooth out the peaks in consumption when there is high demand. The precise control over capacity promised by a Smart Grid will allow utilities to meet occasional peaks in demand without continuously running huge installed bases of oil, coal or gas-fired power plants.

Energy utilities, including transmission and distribution providers, are beginning to generate massive volumes of data in Smart Grids. This data, when applied effectively with analytics, can help energy companies evaluate the returns being generated against the sizable investments in Smart Grid technologies. In addition, big data analytics can help power providers evaluate the areas within their Smart Grid networks that can be refined or improved and help assess the business benefits being achieved as a result of Smart Grid.

Cloud based solutions are assisting utilities with the management and merging of Smart Grid data with other forms of data to deliver information that can help a utility save money or improve operations — ranging from grid controls to customer engagement. Cloud also provides a scalable solution, unlimited data access, computation systems to analyze raw data and cost savings, in addition to the reduced need for data storage and server configurations. Combined with an online interface, cloud computing gives utilities and customers access to accurate consumption data at any time via smart phones, tablets, desktops, and laptops.

Cloud also provides computational power for complex Smart Grid projects. Companies that host their services in the cloud need to buy sufficient capacity to meet demand, but recently the solutions appeared which allow to choose where in the world cloud servers can be located. Stratus system 31 effectively balances the load between different computer servers located across the globe and allows a company to set out how much importance to attach to cost, greenhouse gas emissions, and network delays involved in servicing Internet load.

According to a report, 34 M2M-enabled Smart Grid is a significant contributor to the reduction of greenhouse gases. M2M has the potential to enable efficiency gains throughout the global economy that could reduce greenhouse gas emissions by 9. Automobiles play a particular role in the transport sector because they are dominating the street traffic in most countries, and because car sales exhibit the greatest growth rates in the world.

The transition to a sustainable society, particularly efficient mobility technologies are needed worldwide. EVs have been identified as being such a technology. While the electrification of the transportation sector poses numerous challenges, it also presents utilities with a significant opportunity. Transportation electrification presents an opportunity for utilities to have greater contact with customers, creating a stronger relationship.

By planning now for EVs, utilities can maximize the utilization of their infrastructure and leverage EV supply equipment communications investments for other energy initiatives. In transportation, vehicles powered by batteries or other electric technologies have the potential to displace vehicles burning gasoline and diesel fuel, reducing associated emissions and demand for oil.

It is quite well understood that electric cars have the potential to reduce CO 2 emissions, but this potential is dependent on the type of electricity that charges the battery. Given that the vast majority of power generation around the world is grid-tied, where a car is charged plays a large role in determining its carbon emissions. In countries with coal dominated power supplies electric cars generate carbon emissions four times higher than in places with low-carbon electricity.

Energy storage will play a key role in enabling the community to develop a low-carbon electricity system. Energy storage can supply more flexibility and balancing to the grid, providing a back-up to intermittent renewable energy. In this way, it can ease the introduction of renewable sources into the electricity market, accelerate the decarbonization of the electricity grid, improve the security and efficiency of electricity transmission and distribution reduce unplanned loop flows, grid congestion, voltage and frequency variations , stabilize market prices for electricity, while also ensuring a higher security of energy supply.

Solar photovoltaic power is a commercially available and reliable technology with a significant potential for long-term growth in nearly all world regions. Wind power is also very effective at cutting CO 2 emissions, as demonstrated by researchers from Universidad Politecnica de Madrid, Spain, in the report. The new target of 2, GW to 2, GW of installed wind capacity will avoid the emission of up to 4. Cycling is related to the changes produced by gas or carbon plants for diverse reasons, including renewable generation resulting in spending more fuel per MWh.

When the intermittent wind energy source becomes unavailable, energy must be provided by traditional energy sources, and therefore the reduction of CO 2 emission becomes questionable and the cost of MWh rises. Power electronics PE can be broadly defined as solid-state energy conversion technology that enables efficient and fully controllable conversion of electrical power.

However, Si-based semiconductor technology cannot handle the power levels and switching frequencies anticipated by next generation utility applications. The generated power output from renewable energy is often difficult to control, and if adopted in large quantities, may cause frequency fluctuations throughout the entire power system and local voltage fluctuations may occur. With a Smart Grid, a compensating high-speed high-accuracy power supply system must be used to connect renewable energy, for which the generated output power is difficult to control, to the power system, and PE technology play an important role in the realization of such a system.

In particular, many types of distributed power sources generate direct current power, and PE technology for performing power conversion is one of the most important technologies for Smart Grids. From a green angle, the use of advanced PE not only enables the effective integration of renewable resources through generation, but it also eliminates the need for building conventional generation in reactive power deficient load pockets — a net positive impact on reducing carbon emissions.

PE based on wide bandgap WBG semiconductor materials, such as Si-carbide, gallium nitride, and diamond, could increase the reliability and efficiency of the next generation electric grid. These materials are capable of routing power more quickly and handling higher voltages. A number of barriers and challenges exist in utilizing WBG semiconductor based PE to their full potential, including identifying and designing new types of devices that best exploit WBG semiconductor properties and creating cost-effective high-volume manufacturing processes for those devices.

The PE devices used in Smart Grids are required to have a function that is capable of accommodating fluctuations in frequency or voltage, as well as a function for safely interconnecting with a power system. By taking advantage of technological innovations in semiconductor materials, and microprocessor or digital-based control systems, PE is creating devices that enhance energy generation and delivery systems. The versatility and reliability of lower cost devices combined with advances in circuit topologies and controls has resulted in technologies that replaced what has been traditionally done by electromagnetic and electromechanical systems.

With the development of solid-state-based packages, PE devices can now convert almost any form of electrical energy to a more desirable and usable form. Another benefit of PE is their extremely fast-response times. PE interfaces can respond to power quality events or fault conditions within the subcycle range. Transmission owners have already begun implementing advanced power electronic technologies for a variety of applications. These applications include dynamic reactive power support to ensure satisfactory voltage profiles during system events, and series compensation to effectively increase line capacity without incurring large capital expenditures typically associated with this.

Transmission owners are taking it one step further: they are implementing coordinated schemes using static and dynamic sources of reactive power to strengthen their systems and account for variable power flows that result from wind and solar penetration. Nanotechnology is a platform whereby matter is manipulated at the atomic level. There are various ways that nanotechnology can be applied along the Smart Grid to help reduce CO 2 emissions.

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  5. The major impact of nanotechnology on the energy sector is likely to improve the efficiency of current technologies to minimize use of fossil fuels. Any effort to reduce emissions in vehicles by reducing their weight and, in turn, decreasing fuel consumption can have an immediate and significant global impact. In recognition of the above, there is growing interest worldwide in exploring means of achieving weight reduction in automobiles through use of novel materials.

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    For example, use of lighter, stronger, and stiffer nano-composite materials is considered to have the potential to significantly reduce vehicle weight. Nanotechnology is applied in aircraft coatings, which protect the materials from the special conditions of the environment where they are used instead of the conventional bulk metals such as steel. Since the amount of CO 2 emitted by an aircraft engine is directly related to the amount of fuel burned, CO 2 can be reduced by making the airplane lighter.

    Nanocoatings are one of the options for aerospace developers, but also for automotive, defense, marine, and plastics industries. It is often a matter of only a few grams. However, given 15, to 16, flights a year and an average flight time of about 6 hours, the cumulative effect of a number of grams can quickly add up to tons. The removal of a gram phone handset resulted in jet fuel savings of 3. Nanotechnology is already applied to improve fuel efficiency by incorporation of nanocatalysts. Enercat, a third generation nanocatalyst developed by Energenics, uses the oxygen storing cerium oxide nanoparticles to promote complete fuel combustion, which helps in reducing fuel consumption.

    Reducing friction and improving wear resistance in engine and drive train components is of vital importance in the automotive sector. Cars equipped with category A tires consume approximately 7. Aerogel is a nanoporous super-insulating material with extremely low density; silica aerogel is the lightest solid material known with excellent thermal insulating properties, high temperature stability, very low dielectric constant and high surface area.

    Nanotechnology is positioned to create significant change across several domains, especially in energy where it may bring large and possibly sudden performance gains to renewable sources and Smart Grids. Nanotech enhancements may also increase battery power by orders of magnitude, allowing intermittent sources such as solar and wind to provide a larger share of overall electricity supply without sacrificing stability. Nanotech sensors will also enable Smart Grids and foster more flexible and decentralized electricity management.

    In a somewhat more distant future, we may see electricity systems apply nanotechnology in transmission lines. Research indicates that it is possible to develop electrical wires using carbon nanotubes that can carry higher loads and transmit without power losses even over hundreds of kilometers. The implications are significant, as it would increase the efficiency of generating power where the source is easiest to harness. Semiconductor devices, transistors, and sensors will benefit from nanotechnology especially in size and speed. Nanotech sensors could be used for the Smart Grid to detect issues ahead of time, ie, to measure degrading of underground cables or to bring down the price of chemical sensors already available for transformers.

    Nanotechnology will likely become indispensable for the Smart Grid to fully evolve in the near future. This review demonstrates the potential for reduction of CO 2 emissions that Smart Grids can potentially achieve.