Energy Grid Applications: Comparison
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Please note this is a comparison between Version 2 by Fanny Huang and Version 1 by Yuvaraja TEEKARAMAN.

In the energy usage scenario, the demands on energy load and the tariffs on the usage of electricity are two main areas that require a lot of attention. Energy forecasting is an ideal solution that would help us to better understand future needs and formulate solutions accordingly. Some important factors to investigate are the quantity and quality of smart grids as they are significantly influenced by the transportation, storage, and load management of energy.

  • energy management
  • energy grid

1. Introduction

Vehicle-to-grid transfer, consumption, production, energy management, demand response, and design are the energy grid applications considered. A detailed description of the usages, problems, and challenges faced during the processes involved in energy grid applications are discussed below.

2. Demand Response

Demand response applications are widely used in energy grids and many studies are conducted and organized based on them. In grid-connected systems, the dissemination of power is a result of the due control on the demand side of electrical consumption. Demand response research is reviewed [25][1] for the study of demand response effects in residential houses; load shifting is determined, and its simulation results are then obtained. Therefore, load shifting is the ultimate use among demand response applications [93,102][2][3]. Dynamic costs, incentives, and time-based demand responses comprise the challenges faced in grid-connected system implementation scenarios. Simulation analyses were carried out in [104,107][4][5] on the tariffs paid in controlling peak demands from electrical appliances in houses; it was identified that the tariffs paid are high. Internet of Things applications and smart appliances present new views on energy consumption, production, and management using demand response.

3. Energy Management

The behavior of energy storage and transportation of energy loads influences the quality and quantity of energy used in buildings on an everyday basis; this can be seen in energy management systems. In the present context, energy usage in commercial and residential buildings has significantly increased due to increasing populations [110,113][6][7]. Energy management systems collect, store, and monitor the amount of data that is available about energy use. Analysis and exploitation of data in efficient ways are seen in this application. Data analytics techniques are presently being used to rapidly increase energy efficiency; such research is receiving significant interest and attention [114][8]. Energy storage and controlling energy resources for energy balancing are problems seen in energy management applications. The communication network architectures used in smart grids are considered in [46][9]; here, the intention was to identify energy theft and metering defects, aiming to decrease the non-technical losses that occur in smart grids. A novel real-time energy management strategy is presented in [115][10]; this is proposed to improve fuel consumption in hybrid vehicles through the utilization of different driving strategies. Opportunities and challenges also arise in these techniques, leading to further improvement requirements in computational technologies.

4. Energy Consumption

Practical data-driven models are commonly used in energy consumption applications, especially for forecasting energy consumption [1,64,73][11][12][13]. In the past few years, with the use of conventional sources and increase in demand requirements, energy consumption and CO2 emissions have increased significantly. Energy is the most important part of all our lives in the current context [17,23][14][15]. Data pre-processing is the most significant method for energy prediction, and findings indicate that energy costs can be significantly reduced. Dynamicity is the main problem seen in energy consumption and this can be overcome using prediction analysis. The utilization of energy consumption prediction saves energy cost and avoid wastage of excess energy [78][16]. Various machine learning algorithms can be trained and tested to achieve the best results for energy consumption prediction. Performance measures can be analyzed and evaluated using various data mining tools. By developing and utilizing data-driven models, energy consumption prediction can be improved in the near future [86][17]. Data-driven models can be used to remedy the existing gaps in research fields and future for research.

5. Energy Production

Sources of primary energy are generated from electric power; this process is called energy production. Delivering to the end users is the first stage followed by storage of energy, recovery, transmission, and distribution. The significance of energy production is to generate energy for various purposes, but it is commonly generated for industries. Electric energy is not freely present in nature; therefore, it must be produced in remarkable amounts via energy production [117][18]. Power plants and power stations generally carry out energy production tasks. Electromechanical generators generate a huge amount of electricity using power plants. Energy can be primarily produced through combustion or nuclear fission methods and can also be produced through natural means; for example, kinetic energy can be generated through freely available resources, such as wind or flowing water. Geothermal and photovoltaic resources can also be used as energy sources. Various renewable and non-renewable energy forms can be converted into useful electric energy [48,118][19][20]. Batteries also provide a very small amount of utility in electric power. For utility-scale energy generation, electric generators are rotated or photovoltaic systems are used [50,121][21][22]. The main challenge seen in energy production applications is in smart grid scenarios, where the production completely relies on solar panels; this approach is entirely dependent on the weather and climate.

6. Design

Reduction in harmful gases and their emission must be controlled and, for this reason, efficient models must be designed which address practical necessities, fault detections, and feature weighting in designing any energy-related model. The main usage of design modeling is to create ecofriendly and reliable models. There are various advancements happening very rapidly in the energy field. Good designs are required which utilize renewables-based distributed energy resources. Some examples for renewables-based energy resources are wind and solar systems [38][23]. In smart grids, researchers see the concept of active distribution level for the requirement of resilient power networks, and this can be achieved using renewable-based distributed energy resources. Turbine technologies have rapidly increased in the current context and concepts characterized by minimal land requirements have also been formulated [38,39,109][23][24][25]. The main challenge involved in the design of smart grids is the provision of suitable and safe protection approaches that involve dynamic behavior with weather conditions. The other additional issues faced are mode detection, varying fault scenarios, and section identification. Wind-turbine-based smart grids are very commonly seen; these show the impacts in voltage–current characteristics and consequently provide high wind speed profiles [41][26]. Pre-specified threshold settings are not very sensitive for detecting the faults that could occur with varying wind speeds in cases of conventional over-current relay scenarios.

7. Vehicle-to-Grid Transfer

Plug-in electric vehicles run on batteries, hydrogen fuel, or hybrid sources. These electric vehicles communicate with the smart grid, relaying a supply–demand response to the systems either by regulating their charge rate or by returning energy to the smart grid. Distributed storage units are used in vehicle-to-grid technologies for electric cars. The state of charge in batteries, technical data, and statistical data are seen in the power transfer between vehicles and smart grids. Bidirectional power flow can be seen in vehicle-to-grid transfers [51][27]. Power generation through wind and solar resources is commonly seen in electric vehicles; in smart grids, under normal conditions, power is sent back to the vehicle. The effect of intermittent energy supply is reduced using the distributed storage units in electric vehicles. Efficient utilization of control schemes through optimal charging and discharging is made as cost-effective as possible. The main usage of vehicle-to-grid applications is to store and discharge energy. Intelligent scheduling for charging electric vehicles is an emerging idea for obtaining maximum profits. Computer software is used to analyze and find out the optimization in charging with and without vehicle-to-grid transfer. Peak demand reduction is carried out, and the results show that better performance is obtained through charging optimization with vehicle-to-grid than without vehicle-to-grid. Vehicle-to-grid aggregators are introduced for providing additional frequency regulation services due to rapid deployment of vehicle-to-grid technologies in electric vehicles. Demand from electric vehicle owners is fulfilled by using optimal dispatching strategies of vehicle-to-grid aggregators [52][28]. The challenges faced in vehicle-to-grid applications are battery degradation, investment costs, energy losses, and effects on distribution equipment.

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