A Comprehensive Study on Future Smart Grids: Definitions, Strategies and Recommendations

Anvari‐Moghaddam, Amjad; Seifi, Ali Reza

doi:10.7572/2167-5880-127.1.28

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…This complex power system forms the backbone of modern human civilization. The existing infrastructure uses strategies and technologies that were developed many decades ago, which utilize limited control and digital communication technologies of the 21st century [1]. Moreover, climate change concerns and environmental issues urged many countries to adopt new strategies for reducing the dependency on fossil fuels to reduce carbon dioxide emissions.…”

Section: Introductionmentioning

confidence: 99%

“…The concept of a Smart Grid has emerged as the vision of the future generation of power systems [5]. The smart grid's attributes can be summarized in the following [6]: (1) optimal cost electricity generation considering dispatchable demands, (2) maximizing low-carbon generation, (3) resiliency of both physical and cyber-attacks, (4) ensuring the best power quality for consumers, and (5) monitoring of components and devices to prevent component outages or even system blackouts. It is important to mention herein that the cost of an electric system blackout is very high and results in severe economic and social impacts [7].…”

Section: Introductionmentioning

confidence: 99%

“…The TOU could increase billing during summer to 42~57% higher. In this study, the households were assumed to respond in different ways: (1) regulate their thermostat set-point to lower air conditioning demand, (2) either shift unneeded appliances to different periods of the day, or (3) invest in energy measures. Meanwhile, the SEC has started an ambitious project towards digital transformation by installing 10 million smart meters throughout the Kingdom by the year 2021 G. The most interesting feature is that customers can access real-time data about their consumption and hence, adjust their consumption patterns as needed [21].…”

Section: Introductionmentioning

confidence: 99%

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Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology

et al. 2021

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This study describes the design and control algorithms of an IoT-connected photovoltaic microgrid operating in a partially grid-connected mode. The proposed architecture and control design aim to connect or disconnect non-critical loads between the microgrid and utility grid. Different components of the microgrid, such as photovoltaic arrays, energy storage elements, inverters, solid-state transfer switches, smart-meters, and communication networks were modeled and simulated. The communication between smart meters and the microgrid controller is designed using LoRa communication protocol for the control and monitoring of loads in residential buildings. An IoT-enabled smart meter has been designed using ZigBee communication protocol to evaluate data transmission requirements in the microgrid. The loads were managed by a proposed under-voltage load-shedding algorithm that selects suitable loads to be disconnected from the microgrid and transferred to the utility grid. The simulation results showed that the duty cycle of LoRa and its bit rate can handle the communication requirements in the proposed PV microgrid architecture.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology

et al. 2021

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“…The smart grid is regarded to be the next generation of traditional power system that relies on two-way power flow as well as information exchange competencies [1]. Conventional grid includes power generation, transmission, distribution, control centers, and end users [2].…”

Section: Introductionmentioning

confidence: 99%

A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications

et al. 2020

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This research work puts forward a hybrid AC/DC microgrid with renewable energy sources pertaining to consumer’s residential area for meeting the demand. Currently, the power generation and consumption have experienced key transformations. One such tendency would be integration of microgrids into the distribution network that is characterized by high penetration of renewable energy resources as well as operations in parallel. Traditional droop control can be employed in order to get an accurate steady state averaged active power sharing amongst parallel inverters pertaining to hybrid AC/DC microgrid. It is presumed that there would be similar transient average power responses, and there would be no circulating current flowing between the units for identical inverters possessing the same droop gain. However, the instantaneous power could be affected by different line impedances considerably and thus resulting in variation in circulating power that flows amongst inverters, especially during unexpected disturbances like load changes. This power, if absorbed by the inverter, could result in sudden DC-link voltage rise and trip the inverter, which in turn causes performance degradation of the entire hybrid microgrid. When the hybrid generators act as unidirectional power source, the issue worsens further. In this research work, we have put forward a new distributed coordinated control pertaining to hybrid microgrid, which can be applied for both grid connected and islanded modes that include variable loads and hybrid energy resources. Also, in order to choose the most effective controller scheme, a participation factor analysis has been designed for binding the DC-link voltage as well as reducing the circulating power. Moreover, to both photovoltaic stations and wind turbines, maximum power point tracking (MPPT) techniques have been used in order to extract the maximum power from hybrid power system when there is discrepancy in environmental circumstances. Lastly, the feasibility and effectiveness pertaining to the introduced strategy for hybrid microgrid in various modes are confirmed via simulation results.

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“…Even though the use of computers and digital technology as part of the electrical grid has existed for at least a few decades, this technology has primarily been used for Supervisory Control and Data Acquisition (SCADA) rather than the autonomous intelligent control which is what smart grid paradigms aim for [2]. The smart grid concept is mainly comprised of microgrids (MGs) as key components [3]. These are parts of a central grid that can operate independently from the central utility grid [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid

et al. 2020

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The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.

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A Comprehensive Study on Future Smart Grids: Definitions, Strategies and Recommendations

Cited by 13 publications

References 17 publications

Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology

Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology

A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications

A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid

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