Smart Grid Integration Research Articles

IoT based energy efficient architecture for integrated Smart Grid

The emergence of Smart Grid (SG) plays a vital role in energy generation and distribution system. As SG is the association of numerous applications, this can be properly utilized to reduce the energy consumption. Hence, in this work, a new architecture is proposed to optimize the usage of RES effectively. This proposed architecture utilizes IoT for gathering the power consumption profile of the devices. Based on this profile, a schedule for a device is generated by the Micro Grid. The analysis shows the efficiency of proposed architecture.

Efficient Gear Ratio Selection of a Single-Speed Drivetrain for Improved Electric Vehicle Energy Consumption

The electric vehicle (EV) market has grown over the last few years and even though electric vehicles do not currently possess a high market segment, it is projected that they will do so by 2030. Currently, the electric vehicle industry is looking to resolve the issue of vehicle range, using higher battery capacities and fast charging. Energy consumption is a key issue which heavily effects charging frequency and infrastructure and, therefore, the widespread use of EVs. Although several factors that influence energy consumption of EVs have been identified, a key technology that can make electric vehicles more energy efficient is drivetrain design and development. Based on electric motors’ high torque capabilities, single-speed transmissions are preferred on many light and urban vehicles. In the context of this paper, a prototype electric vehicle is used as a test bed to evaluate energy consumption related to different gear ratio usage on single-speed transmission. For this purpose, real-time data are recorded from experimental road tests and a dynamic model of the vehicle is created and fine-tuned using dedicated software. Dynamic simulations are performed to compare and evaluate different gear ratio set-ups, providing valuable insights into their effect on energy consumption. The correlation of experimental and simulation data is used for the validation of the dynamic model and the evaluation of the results towards the selection of the optimal gear ratio. Based on the aforementioned data, we provide useful information from numerous experimental and simulation results that can be used to evaluate gear ratio effects on electric vehicles’ energy consumption and, at the same time, help to formulate evolving concepts of smart grid and EV integration.

Fog Computing for Realizing Smart Neighborhoods in Smart Grids

Cloud Computing provides on-demand computing services like software, networking, storage, analytics, and intelligence over the Internet (“the cloud”). But it is facing challenges because of the explosion of the Internet of Things (IoT) devices and the volume, variety, veracity and velocity of the data generated by these devices. There is a need for ultra-low latency, reliable service along with security and privacy. Fog Computing is a promising solution to overcome these challenges. The originality, scope and novelty of this paper is the definition and formulation of the problem of smart neighborhoods in context of smart grids. This is achieved through an extensive literature study, firstly on Fog Computing and its foundation technologies, its applications and the literature review of Fog Computing research in various application domains. Thereafter, we introduce smart grid and community MicroGrid concepts and, their challenges to give the in depth background of the problem and hence, formalize the problem. The smart grid, which ensures reliable, secure, and cost-effective power supply to the smart neighborhoods, effectively needs Fog Computing architecture to achieve its purpose. This paper also identifies, without rigorous analysis, potential solutions to address the problem of smart neighborhoods. The challenges in the integration of Fog Computing and smart grids are also discussed.

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration.

Research on big data storage and compression method for dual core intelligent meter

With the rapid integration and development of cloud computing, big data and smart grid, not only the type and accuracy requirements of the collected data are increasing, but also the storage depth is getting higher and higher. In hardware, the dual port RAM design is used to improve the real-time performance of reading and writing, digital filter is used for preprocessing in software, and “Equivalent Normalization (EN)” is used to improve storage depth. These schemes save hardware storage space and improve data transmission efficiency in practical application. So the advantages of dual-core intelligent power meter will be further exploited and brought into play.

Iot based sustainable wind green energy for smart cites using fuzzy logic based fractional order darwinian particle swarm optimization

In view of the fast growing countries, most of the people prefer to live in smart cities. Green and sustainable energy is an important factor for creating a smart system because only an integrated smart grid system can be an alternative solution from all the traditional methods. Wind energy is one of the renewable energy sources growing in popularity because of its many advantages such as lower cost of production, sustainability, and being environmentally friendly. There is a specific ratio for each wind turbine, at which the extracted power is maximum. To work at this optimal ratio, Wind Energy Conversion System (WECS) is essential to adjust the power converter duty cycle and rapidly maximize the energy of wind in the system. In this paper, a Maximum Power Point Tracking (MPPT) controller is proposed to achieve utmost available wind power and the controller parameters are optimized by Fractional Order Darwinian Particle Swarm Optimization (FODPSO) based on Fuzzy Logic Controller (FLC) techniques. A wind turbine has a variety of sensors for monitoring and measuring the state of the turbine which are connected to the internet using Internet of Things (IoT). This paper proposed an IoT based communication framework for the purpose of reliable communication between wind turbines and control centre. The proposed FODPSO tunes the FLC continuously for generate duty cycle and adjust the converter switches propositional to the variations in wind speed. This FODPSO technique used in the controller gives high speed and reliability as well as excellent accuracy to track maximum wind power. Proposed FODPSO-FLC extracts 20% and 13% more power than existing FLC and PSO-FLC respectively. Finally Proposed FODPSO-FLC reduces 20% and 9% THD than existing FLC and PSO-FLC respectively. The obtained results show that the new IOT solution can accurately predict and control the wind turbine state.

Smart Power Electronics–Based Solutions to Interface Solar-Photovoltaics (PV), Smart Grid, and Electrified Transportation: State-of-the-Art and Future Prospects

The need to reduce the use of fossil fuels and greenhouse gas (GHG) emissions produced by the transport sector has generated a clear increasing trend in transportation electrification and the future of energy and mobility. This paper reviews the current research trends and future work for power electronics-based solutions that support the integration of photovoltaic (PV) energy sources and smart grid with charging systems for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV). A compressive overview of isolated and non-isolated DC–DC converters and AC–DC converter topologies used to interface the PV-grid charging facilities is presented. Furthermore, this paper reviews the modes of operation of the system currently used. Finally, this paper explores the future roadmap of research for power electronics solutions related to photovoltaic (PV) systems, smart grid, and transportation electrification.

Development of Smart Grid technology for maintaining the functioning of a biogas cogeneration system

The integrated Smart Grid System of harmonization of production and consumption of electric power and heat with the use of heat-pumping power supply of the biogas plant, which uses fermented wort as a low-potential source of power, was developed. A change in the power factor of the cogeneration system, the temperature of local water is predicted by measuring the voltage at the inlet to the inverter, at the outlet from the inverter and voltage frequency. In the engine cooling circuit, the temperature of cooling water at the inlet to the heat exchanger, at the outlet from the heat exchanger, and the return water temperature are measured. It was proposed to estimate a change in the ratio of voltage at the inlet to the inverter and at the outlet from the inverter. Making forestalling decisions to change the power of the heat pump and the number of plates in the heat exchanger of the engine cooling circuit makes it possible to maintain the voltage at the entrance to the inverter and the temperature of the heated local water. The complex mathematical and logical modeling of the cogeneration system, based on the mathematical substantiation of the architecture of the cogeneration system and mathematical substantiation of the maintenance of functioning of the cogeneration system, was performed. Time constants and coefficients of the mathematical models of dynamics regarding the estimation of a change in the power factor of the cogeneration system, temperature of local water, were determined. Functional estimation of a change in power factor of the cogeneration system in the range of 85–95 %, temperature of local water in the range of 30–55 °С at the compensation of reactive power of up to 40 % was obtained. Determining final functional information provides an opportunity to make forestalling decisions on a change in the power of a heat pump and a change in the number of plates in the heat exchanger of the engine cooling circuit to maintain the functioning of the cogeneration system

Efficient Energy of Smart Grid Education Models for Modern Electric Power System Engineering in Iraq

Smart grid integrates the use of high end technologies so that the generation of energy as well as its delivery can be done with greater performance to the multiple locations and regions. As Iraq is one of the elevating countries in the energy sector and enormous projects, the need to integrate the smart grids arise. This manuscript underlines the assorted perspectives of the smart grid technologies with the integration of solar system and the implementation aspects in Iraq. With the integration of smart grid based architecture and cumulative formulation of the effectual approaches the overall energy can be optimized and higher degree of throughput can be achieved with greater values in the overall performance. In Iraq, there are many projects which are under development and many development using which the overall energy based projects are getting successful in Iraq with the overall elevated growth of the country in multiple sectors including power, security, corporate, international relations, finance and many others. In this research manuscript, the assorted factors and the projects with the multiple dimensions of energy optimization and power saving in Iraq are presented with the pragmatic results.

A mitigation scenario for Latin American power-related carbon dioxide emissions

ABSTRACT The International Energy Agency (IEA) has proposed an ambitious carbon dioxide emission mitigation scenario with specific recommendations for Latin American and the Caribbean (LAC) countries. However, this mitigation scenario entails significant limitations. The expected electricity production for the region is quite low compared to global average figures. This situation could endanger the sustainable development of the region. In this paper, we discuss an alternative mitigation scenario that seeks the compliance of both climate objectives and future energy requirements. This new scenario is based on the widespread integration of smart grids and renewable energies. High capacity smart transmission grids could enlarge existing transnational electricity markets. Results show that Latin America could contribute with 4.1% of the global mitigation effort up to 2040 (the IEA’s 450 S scenario for the region is 3.6%) with an additional investment of 7% in large-scale renewable-based generation plants and HDVC transmission links. As a main result, future regional generation output is aligned with the requirements of national policy scenarios outlined at the climate convention.

Renewable energies evaluation and linking to smart grid

To tackle the widespread challenges encountered during the management of power flow in electrical networks, the integration of smart grid (S.G) is proposed as a viable solution to address the environmental and economic concerns. In order to enhance overall functionality of the electric power delivery system, operation of the smart grid is governed by data-driven algorithms where various sensors and smart meters are used to collect this data. On other hand, the fluctuating cost of fossil fuels and its negative impact on the environnement has incentivized the exploitation of renewable energies in the smart grid. In the present paper, a hybrid Photovoltaic/Wind (PV/Wind) is suggested to supply a smart grid. The objective of this study is to asses the feasibility of a smart grid supplied solely by renewable energy sources. Conceptual standings of this newly suggested model are validated by simulations.

MANFIS based SMART home energy management system to support SMART grid

With the emergence of the smart grid, the customers get the chance to involve in demand-side management and to combine renewable energy sources. Renewable energy sources integration in smart grid is difficult due to intermittent and fluctuating nature of Renewable energy. The variation in renewable energy generation is smooth out by utilizing the Energy Storage System in a smart house. This paper introduces the Smart Home Energy Management System using Multi-output Adaptive neuro-fuzzy inference system for efficient management of Energy Storage System, scheduled appliances and to integrate Renewable energy. The controller can interrupt or postpone scheduled appliances and trading surplus energy between consumers thereby reducing reverse power flow and electricity cost. The proposed system is tested with daily data of wind speed, temperature, insolation, uncontrollable and controllable appliances power and electrical energy price as inputs to validate the results. The output of the control system decides how to handle energy production, consumption, and scheduling of the appliance. The result shows that the electricity cost reduced by 57.62%, peak power consumption is reduced by 44.4% and peak-to-average ratio is reduced by 73.6% after adopting the proposed strategy.

Toward a hydrogen society: Hydrogen and smart grid integration

Hydrogen has an important role as a smart solution for Smart Grid, as it can play as an energy vector, a storage medium, and a clean fuel cell. The integration of Hydrogen and Smart Grid can minimize the impact on the environment while maximizing sustainability, which indicates that we are developing toward a hydrogen society. There have been already many studies on different aspects of this topic. For a better understanding of the related work, this paper proposed a comprehensive overview of the related work on the integration of Hydrogen and Smart Grid. Related literature is organized and analyzed from four categories, including Hydrogen energy in smart grids, Hydrogen fuel cell electric vehicles, Hydrogen economy in smart grids, and Models for energy system in smart grids. And each subject has been introduced more carefully. What's more, for a clear understanding for readers, we provide overall scenario views for the organization of the related work.

Energy system transformation to meet NDC, 2\xa0\xb0C, and well below 2\xa0\xb0C targets for India

India’s commitment to Paris Climate Change Agreement through its Nationally Determined Contribution (NDC) will require the energy system to gradually move away from fossil fuels. The current energy system is witnessing a transformation to achieve these through renewable energy targets and enhanced energy efficiency (EE) actions in all sectors. More stringent global GHG mitigation targets of 2 °C and well below 2 °C regimes would impose further challenges and uncertainties for the Indian energy systems. This paper provides a quantitative assessment using bottom-up optimization model (AIM/Enduse) to assess these until 2050 for meeting carbon mitigation commitments while achieving the national sustainable development goals. Energy transformation trajectories under five scenarios synchronized with climate mitigation regimes are explored—Business As Usual scenario (BAU), NDC scenario, 2 °C scenarios (early and late actions), and well below 2 °C scenario. The key results from the study include (a) coal-based power plants older than 30 years under NDC and older than 20 years for deeper CO2 mitigation will be stranded before their lifetime, (b) increase in renewables of up to 225–280 GW by 2050 will require battery storage with improved integrated smart grid infrastructure, (c) growth in nuclear to 27–32 GW by 2050 is dependent on nuclear supply availability, (d) gradual shift towards electrification in industry, building, and transport sectors, and (e) installation of CCS technologies in power and industry sectors. Cumulative investments of up to 6–8 trillion USD (approximately) will be required during 2015–2030 to implement the actions required to transform the current energy systems in India.

CO2emission reduction and energy management for an integrated smart grid — Case of study: Rwandan electrical network

Many scholars have been focusing on the energy management by Integrating a smart grid into a conventional electrical grid. They have showed that to meet a certain power demand of the consumers, using energy management, the electric utility can turn on some generators, which may have the least operation cost, while the generators with high operation cost are left to supply extra load demand in specific peak periods. Henceforth, the operation cost of its generation units is minimized. The issue remains at a level of relating the energy management to CO2 emission. The present paper briefly discusses the Rwandan electrical network that still integrates the use of diesel generators. It estimates the amount of CO2 emission that can be avoided once a PV system is integrated into the electrical network. The paper as well proposes an algorithm for energy management with consideration of CO2 emission.

Integration of Smart Grids and PV System Total Harmonics Distortion (THD) Reduction

Integration of Smart Grids and PV System Total Harmonics Distortion (THD) Reduction

Technologies of Engagement: How Battery Storage Technologies Shape Householder Participation in Energy Transitions

The transition to a low-carbon energy system goes along with changing roles for citizens in energy production and consumption. In this paper we focus on how residential energy storage technologies can enable householders to contribute to the energy transition. Drawing on literature that understands energy systems as sociotechnical configurations and the theory of ‘material participation’, we examine how the introduction of home batteries affords new roles and energy practices for householders. We present qualitative findings from interviews with householders and other key stakeholders engaged in using or implementing battery storage at household and community level. Our results point to five emerging storage modes in which householders can play a role: individual energy autonomy; local energy community; smart grid integration; virtual energy community; and electricity market integration. We argue that for householders, these storage modes facilitate new energy practices such as providing grid services, trading, self-consumption, and sharing of energy. Several of the storage modes enable the formation of prosumer collectives and change relationships with other actors in the energy system. We conclude by discussing how householders also face new dependencies on information technologies and intermediary actors to organize the multi-directional energy flows which battery systems unleash. With energy storage projects currently being provider-driven, we argue that more space should be given to experimentation with (mixed modes of) energy storage that both empower householders and communities in the pursuit of their own sustainability aspirations and serve the needs of emerging renewable energy-based energy systems.

A novel approach for harmonic tidal currents constitutions forecasting using hybrid intelligent models based on clustering methodologies

Forecasting of renewable energy resources and their output power is playing a key role to improve the grid energy efficiency by making some load generation management. Tidal currents output power is depending on the tidal currents constitutions (speed magnitude and direction) forecasting. The accuracy of the tidal currents forecasting models is very important especially when we deal with smart grid and renewable energy integration. Many models are proposed in the literature for tidal currents forecasting but most of the models are not able to control the requirements of the smart grid due to their accuracy. This paper is proposing hybrid approaches for harmonic tidal currents constitutions forecasting based on clustering approaches to improve the system accuracy. These hybrid models involve various combinations of Wavelet and Artificial Neural Network (WNN and ANN) and Fourier Series Based on Least Square Method (FSLSM) techniques. The proposed work is validated by using two different datasets; one for tidal currents speed magnitude and the other one for tidal currents direction as well as K-fold cross validation. Simulations results prove the importance of the proposed models to improve the system performance. The proposed models are tested based on actual tidal currents data collected from the Bay of Fundy, Canada in 2008.

A big data anonymous batch verification scheme with conditional privacy preservation for power injection over vehicular network and 5G smart grid slice

Abstract Internet of Things (IoT) is a new paradigm that has changed the way we live in our society by providing many intelligent applications such as smart healthcare, precision agriculture, smart city, smart supply chain, smart home, smart grid, and intelligent transportation. As an integration of smart grid and intelligent transportation, vehicle-to-grid (V2G) technology can make the power grid to communicate with the plugin electric vehicles (EVs), allowing the EVs to store greenpower energy from renewable energy sources and inject overplus power back to the power grid during the peak demand hours for financial gains. Nevertheless, security of transmitted information and users’ privacy preservation are very crucial to ensure fairness in the energy market and enhance users’ acceptability of this propitious technology. For instance, there is need to expeditiously authenticate massive power bids injected by EVs without exposing the owners’ private information. Therefore, a big data anonymous batch verification for injection of power over vehicular network and 5G smart grid slice based on a new ultraefficient certificateless aggregate signature (CL-AS) algorithm is proposed. The proposed scheme achieves batch-verifiable authentication of all the injected bids in an anonymous manner without compromising the privacy of participants. Moreover, we prove that the scheme is secure, assuming the Discrete Logarithm Problem (DLP) is intractable. The performance evaluation demonstrates its superiority in terms of efficiency over the related schemes.

Smart Assimilation of Renewable Based Microgrids

Expansion from the existing power transmission and distribution with close coactions with energy, control and communication is possible with the help of Smart grid. Microgrid plays an important part in the coalition of this integration of smart grid. Using both kinds of energy systems a proximity with the loads and actuation using power electronic devices and systems can be achieved with power processing. There is a challenge in such type of interactive, buoyant and sustainable model. In this paper, a unique modeling and control prototype is conferred for the integration of renewable based Microgrid into the smart grid. More renewable energy resources can be added into the Microgrid considering the design requirements. The proposed model versatility is experimented in MATLAB/ Simulink platform to demonstrate the transition of states in Microgrid and overall power conditioning with transient response of the system.