1^st International Conference on Smart Grid Technologies September 11-12, 2017 Singapore

Biography:

Li Kaicheng has completed his PhD in 1998 from Huazhong University of Science and Technology. He is the Professor of Huazhong University of Science and Technology and mainly focuses on research on electromagnetic measurement, power quality analysis and control, electronic transformer, intelligent instrument, etc. He teaches courses such as signals and systems, sensors and automatic measurement, weak signal detection and so on. He has published more than 100 papers and obtained 10 patents and 5 government awards.

Abstract:

With the development of power system and wide use of power electronics, power quality becomes poor and poor, which increasingly attracts the attention of people. In order to improve power quality, the efficient and accurate disturbance detection and classification from massive power quality data is necessary for us to realize power quality analysis and control. This paper proposed a real time power quality disturbances classification by using a hybrid method based on S transform and dynamics. Classification accuracy and runtime are mainly concerned. The hybrid method firstly uses dynamics to identify the location of the signal components in the frequency spectrum yielded by Fourier transform, and then uses inverse Fourier transform to only some of the signal components. By the hybrid method, runtime of the application has been greatly reduced with satisfactory classification accuracy. In order to reduce the influence of Heisenberg's uncertainty, we firstly proposed that different signal components are windowed by different Gaussian windows, which brings better adaption and flexibility. Then features from Fourier transform, S transform and dynamics are selected and decision tree is used to classify the types of power quality disturbance. A DSP-FPGA based hardware platform is adopted to test the runtime and correctness of the proposed method under real standard signals. Finally, field signal tests are presented. Both simulations and experiments validate the feasibility of the new method.

Biography:

Takashi Iwamoto is a Project Professor of Graduate School of Business Administration, Keio University and is an Expert Researcher of Industry and Business Producing. Prior to joining Keio University, he was the Manager of Nokia Research Center and an R&D Engineer of Lucent Technologies (Bell Laboratories) and Motorola (Semiconductor Sector). He has received his PhD and MS (Master of Science) degrees in Materials Science and Engineering from the University of California, Los Angeles (UCLA) and his BE (Bachelor of Engineering) degree in Metallurgy from the University of Tokyo.

Abstract:

The author started activities to develop businesses using smart grid technologies in 2009. Involvement of government is important in this field and thus, the activities were started with development of public policies. After the public policies were developed, the national project to develop smart grid businesses was executed from April 2010 to March 2015. The Japan Smart Community Alliance (JSCA) was also established in April 2010 with the aim of resolving and overcoming the obstacles of individual organizations through collaboration of the public and private sectors and is supporting the private companies to develop businesses using smart grid technologies. Researches on how to develop business using smart grid technologies have been conducted since 2009 and key points for success were extracted through the researches done over the past 8 years. The first point is to involve government in creating new industries and public policies, should be aligned with the business models. The second point is project management in developing the business models. The difficulty is that companies from various industries join the project and capabilities to manage those players with different backgrounds are necessary and important. The third point is monetization and now is in the phase to monetize various smart grid technologies and each company is making various efforts. In this talk, what activities have been done to develop smart grid businesses and the key points for success extracted through the research are presented.

Biography:

Qing Gao has completed her PhD at the age of 26 years from University of Chinese Academy of Sciences. Now she works on electricity big data analysis and household behavior studies in her postdoctoral studies from Fudan University, School of Science and Technology. Now she is focusing on energy disaggregation on whole-home data.

Abstract:

Energy consumption feedback on separate device can lead to energy-saving behavior, but current electricity meters only report whole-home data. Energy disaggregation is to separate the aggregated energy signal into component appliance contributions, which can achieve appliance specific energy information feedback for consumers. In tradition, most analyses are based on high frequency (over kHz) data and a dictionary of appliances, which is not suitable for wide spread. Recently, hourly whole consumption and disaggregation data for over 500 buildings in Shanghai and same frequency weather information are collected. In this paper, by comparing different algorithms, we develop a method based on Hidden Markov model that can combine robust information, like weather, to disaggregate hourly whole building energy consumption data. In the disaggregation, the behavioral patterns of different appliance types are separated and then, according to the behavioral patterns, the aggregated data is separated into component device. The method is able to separate main appliances perfectly, such as lighting, air conditioning and the method has the potential to be generally utilized in building load monitoring system.

This work was supported by Grant-in-aid for scientific research from the National Natural Science Foundation for the Youth of China (No. 71703027) and China Postdoctoral Science Foundation Funded Project (Project No. 2016M600270 and No. 2017T100257).

Biography:

Yang Zhou is currently pursuing his PhD at Fudan University, School of Economics. His team focuses on electricity big data analysis and household behavior studies. He has published one smart-grid complex network paper in China.

Abstract:

Dichotomous choice contingent value method (DC CVM) is used for valuing a wide variety of nonmarket goods because of its simplicity and non-incentives for strategic responses, among which double-bounded dichotomous choice is one of the most efficient used method. However, this method may show a strong starting-point bias due to lack of information of respondents about the non-market goods. Information will change respondents willingness to pay (WTP) in two ways. First, information will directly change respondents’ WTP. Second, the combination and sequence of information may cause different effect on WTP. This paper designed a survey experiment to estimate the combined information change on WTP. The survey experiment conducted in Shanghai covered 3208 respondents. We randomly assigned the respondents into two sub-samples with and without information. In information part, we reassigned the respondents into three sub-groups with different starting information. In both groups, we asked the respondents to answer “yes” or “no” to the bid price which is a double-bounded dichotomous choice process. Then we stopped the survey by interrupting with other information and did a second DB DC CVM. As result, we found a strong starting-point bias and information change on WTP. The estimation of the WTP in Shanghai for green power is about 18 Chinese cents per kWh with single information. What’s more, we find that different sequence of information combination will cause different change on WTP. The time of cost information given may cause a strong change on WTP.

This work was supported by Grant-in-aid for scientific research from the National Natural Science Foundation for the Youth of China (No. 71703027) and China Postdoctoral Science Foundation Funded Project (Project No. 2016M600270 and No. 2017T100257).

Biography:

Min Su Kim has received his BS degree in the Department of Chemical Engineering at Pohang University of Science and Technology (POSTECH), South Korea. He is currently pursuing MS-PhD integrated course in Chemical Engineering at POSTECH. His current research focuses on development and application of functional nanomaterials.

Abstract:

Employing graphene as an electrode material is advantageous for energy storage devices because it is flexible, highly electrically conductive and chemically stable and has large theoretical surface area. However, graphene generally suffers from serious agglomeration and re-stacking due to its strong π-π stacking and van der Waals interactions between graphene nanosheets during charge-discharge process. To fully use the potential merits of graphene and improve the kinetics of electrode materials in energy storage devices, composites of graphene and metal oxides have been tested as electrode. Metal oxides in the composites, prevent the re-stacking of graphene. Graphene layers in the composites also suppress the volume change and agglomeration of metal oxide and provide a highly conductive matrix for metal oxide. We report a general method to synthesize mesoporous metal oxide at N-doped macroporous graphene composite by heat treatment of electrostatically co-assembled amine-functionalized mesoporous silica/metal oxide composite and graphene oxide and subsequent silica removal to produce mesoporous metal oxide and N-doped macroporous graphene simultaneously. Four mesoporous metal oxides (WO_3-x, Co₃O₄, Mn₂O₃ and Fe₃O₄) were encapsulated in N-doped macroporous graphene. Used as an anode material for sodium-ion hybrid supercapacitors (Na-HSCs), mesoporous reduced tungsten oxide at N-doped macroporous graphene (m-WO_3-x at NM-rGO) gives outstanding rate capability and stable cycle life. Ex situ analyses suggest that the electrochemical reaction mechanism of m-WO_3-x at NM-rGO is based on Na⁺ intercalation/de-intercalation. This is the first report on Na⁺ intercalation/deintercalation properties of WO_3-x and its application to Na-HSCs.

Biography:

Sowjanya Dundhigal has completed BE in Electrical & Electronics Engineering as major, MS in Real Time Embedded Systems from Coventry University and Masters of Research (MRes) in 2011 at Glyndwr University, UK with Electrical Engineering as specialization. She has worked as Business Analyst and Research Embedded Engineer in UK. She is currently working as an Associate Professor and EPICS Program Manager at CMR College of Engineering & Technology, Hyderabad, India. She has published more than 5 papers in reputed journals and has been serving as an Editorial Board Member of CMRJET (CMR Journal of Engineering & Technology). 

Abstract:

Efficient energy saving and energy distribution and utilization in a real time environment is the prime area under research. Smart grids offer huge potential in this research area and efficient way to rely on multiple renewable energy sources. Flexible and efficient combination of energy sources, optimal distribution paths and data storage are the features of smart grids. The proposed paper elevates the possible advantages upon developing a network where utilization, distribution and storage of data of multiple smart grids are controlled by central system over the internet as gateway (IoT). In order to achieve the above, smart meter systems and intelligent control systems will be developed with embedded technology. Arduino will be used for the smart meter system development and Raspberry-Pi to develop the intelligent systems which controls the multiple smart meter systems. Through these intelligent systems, the data is analyzed and communicated to the central systems over internet. IoT (Internet of Things) is the technology which connects people to the systems and to the things over internet. Gateway architecture will be designed and developed using embedded communication protocols, which enables the intelligent systems to communicate with the internet over cloud computing. World forum of IoT declared the 7 layered open architecture model for gateway design. With this gateway, the application specific edge software will be developed and later the web user interface is developed for end user. Mobile application can also be developed over the application layer. Thus interoperability between the grids and central distribution, utilization and control systems would be achieved.

Biography:

Tilak Thakur has completed his BSc Engineering and MSc Engineering (Electrical) from BIT Sindri, Ranchi University, India and obtained PhD Engineering (Electronic Instrumentation) from Indian School of Mines, Dhanbad, India. He is a Senior IEEE Member apart from Life Member of Institution of Engineers, India and ISTE Member. He has authored more than 135 research papers in the international and national journals of repute and conferences. Presently, he is the Senior Associate Professor in the Department of Electrical Engineering, PEC University of Technology, Chandigarh. He has been Editorial Member of various international journals and conference. His research interest is in power and energy mechatronics and he has published a book on mechatronic with Oxford University Press. He is also a Member of Board of Studies for Panjab University (PU), Chandigarh and Punjab Technical University (PTU), Jalandhar and India Smart Grid Forum (ISGF).

Abstract:

Power quality issues and energy crises are the major apprehension of this century. Most of the literature in this area of research discusses the problems related to natural resource depletion, environmental impact, the rising demand of new energy resources and the challenging technologies which can overcome these problems. The renewable sources of energy such as solar, wind, biomass, hydro, geothermal and so forth are zero polluting in nature and have large research scope. The output of such energy resources are mostly in the form of DC which needs to be converted into AC for feeding the AC loads. This requires DC to AC converters. Multilevel converters are gaining high reputation because even in high power applications, the renewable energy sources such as photovoltaic arrays, fuel cells and wind turbines etc., can be used as DC input to the multilevel inverter with higher efficiency. Thus, this paper focus on the harmonic elimination of a grid tied multilevel inverter fed with PV arrays. The nonlinear characteristic of PV array and the power electronics switches in the inverter introduces harmonics in the power system. According to IEEE 519 standard, for a 69KV and below system, the Total Harmonic Distortion (THD) value should be less than 5% and the individual harmonics should be less than 3%. Filtering increases the complexity and cost of the system. Therefore, the best alternative is to use suitable low frequency switching scheme for the multilevel inverter to mitigate harmonics. Thus, the investigators propose to use Newton-Raphson based Selective Harmonic Elimination (SHE) technique to eliminate the harmonics and compare it with the conventional PWM technique. The entire system will be simulated using MATLAB^™/SIMULINK

Biography:

Mohammad Rizwan has received his PhD degree in Power Engineering from Jamia Millia Islamia, New Delhi, India in 2011. He is presently associated with Delhi Technological University as an Assistant Professor in the Department of Electrical Engineering. He has published/presented more than 60 research papers in reputed international and national journals and conference proceedings. He has been awarded three research projects in the area of renewable energy systems and power quality. He is the recipient of UGC research award for the period of 2014-2016. Also he has been awarded Raman Fellowship for Post-Doctoral Research for Indian Scholars in USA. Presently he is pursuing his Post-doctoral Research at Virginia Polytechnic Institute and State University, USA. His area of interest includes power system engineering, renewable energy systems particularly solar photovoltaic, building energy management, smart grid and soft computing applications in power systems. He is a Senior Member of IEEE, Life Member of ISTE, Life Member of SSI and many other reputed societies. He is also associated with many journals including IEEE Transactions, International Journal of Electrical Power and Energy Systems, Renewable and Sustainable Energy Reviews (Elsevier), International Journal of Sustainable Energy (Taylor and Francis) in different capacities.

Abstract:

The large scale penetration of photovoltaic power into sub-transmission and distribution grids can have a significant impact on a power system’s operation and stability because of inherently variable generation and weather dependent energy resources. It is well known that a sudden change in sunlight can initiate a rapid disconnection or reduction in a PV generating capacity. As the penetration of PV increases, this can lead to a problem of voltage variation and transient voltage instability in the case of a weak coupling with the grid. The large-scale penetration of PV units also has an impact on the short-term voltage and transient stability of a system, which is not only restricted to the distribution network but also influences the whole system. Solar photovoltaic forecasting can be used to mitigate these problems and provides the appropriate storage control and reduces the requirements of additional generating stations. In this work, fuzzy logic based one hour ahead short term forecasting of solar photovoltaic (PV) power using meteorological parameters is developed and presented. The solar photovoltaic power is forecasted using solar irradiance, ambient temperature, wind speed, humidity and type of the day or sky conditions as input parameters. The real data of PV plant has been used for the training, testing and validation purpose. The obtained results are evaluated on the basis of statistical indicators including RMSE, ARE, etc. The results of the proposed models are found better as compared to the existing models for different climatic zones. Further, the obtained results have been used for the demand response, storage control applications in the smart grid energy environment.

Biography:

Abel Tablada de la Torre is an Assistant Professor at the Department of Architecture of the National University of Singapore (NUS). His interests cover the topics of tropical net-zero/low-carbon architecture, natural ventilation, building simulation and renewable resources. Presently, he is leading a research on productive facades which integrate solar and farming systems.

Abstract:

Increasing energy and food self-sufficiency in urban areas is crucial to achieve a drastic reduction of carbon footprint and greenhouse gases emissions. This is especially important for Singapore which imports most of the food and energy it consumes. This study discusses the potential energy and food production at urban and building scales in residential districts in Singapore. Radiance-based simulations are conducted to quantify the sunlight availability on a large number of cases with different densities for three building typologies in Singapore. Regression analysis are performed to determine the thresholds of density indices; plot ratio, site coverage and building height, in order to achieve certain percentages of energy and food self-sufficiency. Finally, more detailed facade arrangements are proposed according to the sunlight availability per case as well as a facade prototype to be fabricated and installed at the Tropical Technology Lab at NUS campus. The study shows that full energy and food self-sufficiency can be achieved for districts with average building height <33 m and plot ratios <1.7, respectively. However, 50% of autonomy can be achieved for building height <68 m and plot ratios <2.7. The four main facade orientations are feasible to be used for solar panels and farming systems. However, it is recommended that larger PV surface area is installed on top sections of facades and farming and solar collectors on less exposed surfaces. Finally, a balance between PV dimensions, energy yield, heat gain reduction and daylight requires a precise analysis and design for every specific facade and context.

Biography:

Muhannad Al Jebouri is currently a PhD candidate at Architecture Department, Faculty of Engineering and Built Environment, UKM, Malaysia. He has completed his MSc and has experience in architectural practical field. Presently, he is a Lecturer and has published two technical papers in reputed journals.

Abstract:

Most cities are in a trend of non-sustainability, against that, the concept of sustainability development has immerged and gave rise to Green Building Movement. The Brundtland Report 1987 considered as a Triple Bottom Line (TBL) based on environmental, economic and social issues. Pioneering eco-cities such as Masdar in UAE, Hammarby in Sweden and Dongtan in China have introduced the concept of sustainability, renewable energy sources, zero-carbon and zero-waste ecology. This study is to develop a framework for sustainable building in Oman. The structure of the proposed system development is composed of 5 themes (environmental, economic, social, cultural and governance requirements), 11 categories and 86 indicators. This study involves two main stages; the first stage concentrates on the formulation of the proposed system structure in relation to Oman by reviewing the literature on sustainable development and buildings, as well as analyzing international and regional sustainability-rating systems. The second stage focuses on formulating assessment categories and their relevant performance indicators which are validated through conducting a survey including different stakeholders of the industry from building engineers, building regulators and sustainability experts. Pairwise and direct ranking method comparisons are used as data collection methods to examine the relative importance and weights of each category and each indicator respectively. The analysis of survey data shows that a prominent relative importance and balanced weights are given to indoor environment quality, natural and human resources, social and governance requirements. The research output will help and promote future studies to develop a detailed assessment system for sustainable construction in Oman. The proposed framework is named Oman Building Environment Certificate (OMBEC). It is used as a pilot rating system for homes at present and then to be further expanded in the future to address other building types. The proposed rating system follows a hierarchy of three levels: Categories, indicators and parameters.

Biography:

Abstract:

Nowadays, electricity has become essential for improving the quality of life of the population; in developing countries, millions of people do not always have access to electricity. These people mainly reside in rural areas. The classical solution which is unrealistic below a certain power threshold, delays rural electrification and aggravates the exodus towards urban centers already congested and unable to absorb this migration, thus, to combat late electrification, new techniques are required for the countryside. In this paper, we describe the principles of some energy extraction techniques directly from the high voltage lines, profitable methods for the developing countries.

Biography:

Koksal Erenturk has received his BS degree from Yildiz Technical University, Turkey, MS degree from Istanbul University, Turkey and PhD degree from Karadeniz Technical University, Turkey all in Electrical Engineering. His work has focused on the smart grid, renewable energy systems and development and application of control theory to a variety of mechatronic systems with a focus on observation and estimation based control and also on the analysis and control of dynamical systems that arise in engineering applications.

Abstract:

Smart energy systems are defined as an approach in which smart electricity, thermal and gas grids are combined and coordinated to identify synergies between them to achieve an optimal solution for each individual sector as well as for the overall energy system. The challenge of integrating the fluctuating renewable energy power sources such as wind, solar and ocean energy depends strongly on the share of the input. The following three phases of implementing renewable energy technologies can be defined as: (1) The introduction phase, (2) The large-scale integration phase and (3) The 100% renewable energy phase. The introduction phase represents a situation in which there is no or only a small share of renewable energy in the existing energy system. The phase is characterized by marginal proposals for the introduction of renewable energy, e.g., wind turbines are integrated into a system with only a limited share of wind power. The system will respond in the same way during all hours of the year and the technical influence of the integration on the system is easy to identify in terms of saved fuel on an annual basis. Moreover, the input of renewable power does not pose a challenge to the operation of the grid and the electricity balance. The large scale integration phase represents a situation in which there is already a major share of renewable energy in the system, e.g., when more wind turbines are added to a system which already has a high share of wind power. The phase is defined by the fact that further increase in renewable energy penetration will have an influence on the system and this will vary from one hour to another, e.g., depending on whether heat demand is high or low in the given hour, whether a heat storage is full or not or whether the electricity demand is high or low during the given hour. The integration of wind and solar power in the system becomes complex and requires consideration with regard to grid stabilization. The 100% renewable energy phase represents a situation in which the energy system is currently or is being transformed into a system. The system is characterized by the fact that new investments in renewable energy will have to be compared not to nuclear or fossil fuels, but to other sorts of renewable energy system technologies. These include conservation, efficiency improvements and storage and conversion technologies. The influence on the system is complex not only with regard to differences from one hour to another but also with regard to the identification of a suitable combination of changes in conversion and storage technologies. Moreover, the challenge of operating the grid in terms of ensuring frequency and voltage stability is of major importance.

Biography:

Kshitija Bhasme has completed her Bachelor of Technology in the Faculty of Electronics and Communication from Usha Mittal Institute of Technology, SNDT Women’s University, India. She is currently working as a Research Intern in the field of Smart Grid Technologies at Center for Development of Advanced Computing (CDAC) which is an Autonomous Scientific Society of Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology, Government of India. Her past research experiences include a Research Internship with the Bhabha Atomic Research Center (BARC), Government of India where she has worked as a Research Project Trainee with some of the best scientific officers in India.

Abstract:

Nowadays, power grids are being integrated into the smart grid concept for efficiently managing and delivering electric power. A smart grid provides a two-way dialogue where electricity and the information can be exchanged between the utility and its customers by allowing them to sell and purchase electricity. Attacks on such systems may have catastrophic impact, especially on the customers which is a particular threat in the winter, when people can be left without heat. Hence, the mitigation solutions for these situations are necessary. It is impractical to test attacks and mitigation strategies on real-time networks. Therefore, we propose a predictive analysis to make the smart grid prepared to address power failure faults and attacks in the time of emergencies. We explain different power system scenarios and network designs by simulating IEEE 14 bus system models through Power System Analysis Toolbox (PSAT) in MATLAB for the assessment of power flow and to perform a complete and accurate power system analysis. A data sheet of power system analysis is created and fed into various machine learning algorithms in WEKA data mining tool. The goal is to make our machine learn different power system analysis scenarios in order to be able to detect normal, fault and attack instances in a power system by checking the accuracy of the instances being learnt by the machine and cluster formations of all the correctly classified instances. This can help save millions of dollars and long hours of investigation invested to study and detect unusual occurrences in a power system. We embark upon a journey towards a new era of reliability, availability and efficiency of the smart grid by bringing in the machine learning domain in the assessment process which is a faster and more reliable way of detecting adversities in a power system than the conventional way.

Biography:

Muhammad Kamran has received his BSc and MSc Electrical Engineering (EE) (specialization in power) degree from University of Engineering & Technology (UET), Lahore, Pakistan in 1992 and 1999, respectively. He has completed his PhD in Computer Engineering from Beijing Institute of Technology, China in 2007. He is presently serving as a Professor and Campus Coordinator of a campus of UET, Lahore, Pakistan. He has 53 national and international publications focusing on computers and power engineering. Currently he is dealing with power system protection, power system quality and high voltage engineering at graduate and undergraduate level. He is also a Reviewer of famous international journals and conferences, Member of Pakistan Engineering Council Accreditation Committee, Convener of Higher Education Commission (HEC) and Pakistan Technology Curriculum Development Committee (2015).

Abstract:

With the advancement in smart grids, easy access to load monitoring and management is becoming essential feature of this modern system. In order to comply with this parameter, a novel nonintrusive load monitoring (NILM) technique is proposed in this paper. This technique is found cost effective, state of the art load monitoring and energy disaggregation that can be employed in conjunction with smart grids for smart energy management systems. NILM involves extracting load signatures of individual appliances from composite voltage and current signals and identifying those signatures by employing machine learning techniques. An introduction of novel feature extraction techniques involving fractal analysis has made it novel and comparison is made with other methods involving discrete wavelet analysis. Research has highlighted that fractal analysis has become emerging method for data extraction and analysis even utilizing artificial intelligence and neural networks. The results obtained in this research are appreciable to verify the effectiveness of novel technique of data extraction using fractal analysis which saves the time and cost of data management system and has become valuable feature of smart grids.

Biography:

Geetha S has completed her Post-graduation in Information Technology and pursuing her Doctoral studies. Presently, she is working as an Assistant Professor in an Engineering Institution. Her research interest includes smart grid, natural language processing and soft computing. Her research focuses on developing an optimization problem for demand response in smart grid and trying out its implementation in a real test bed.

Abstract:

Introduction: Smart Grids will be the promising solution to the future energy crisis. One of the most important features of the smart grid is its active Demand Response (DR) model through which the consumer can shape their daily energy consumption patterns so that they can reduce their energy usage and peak to average ratio (PAR).

Aim: In our work, bidirectional communication architecture has been proposed between the end users and the service provider. Real time pricing models (RTP) has been adapted to reflect the fluctuations in the electricity market. Load scheduling method encourages the users to adopt smart usage of electricity. The method aims at scheduling the appliances based on their need, hence to optimize the energy usage and to minimize the usage cost. The optimization problem for load scheduling has been solved using genetic algorithm. The load balancing approach spreads the electrical load over a time horizon in order to appropriately schedule the requests that have been rejected by the admission control, while minimizing a cost function related to the energy price. Every appliance, represented by a task has to be processed in a certain time window according to power load, preemption state and priority characteristics.

Results: Based on the assumptions of consumption patterns of the consumer from an Indian middle class scenario, an approximation of the daily power consumption pattern in the household is made and demand load profile for a day has been computed. The load scheduling algorithm is applied to the residential demand load with the target of scheduling the operation of burst loads found in the household.

Conclusion: From the results we conclude that we get a better dispersive load with our proposed scheduling method which in turn will definitely optimize the usage cost in a real time environment.