Grid Users Research Articles

Revenue-Based Allocation of Electricity Network Charges for Future Distribution Networks

This paper investigates the economic implications that high penetrations of distributed energy resources (DER) have in future distribution networks, and proposes a novel scalable scheme for the assignment of use of network charges based on individual participant nodes’ revenue. For validation purposes, a techno-economic simulation is proposed to understand how power and revenue flows will change. A year-long high-resolution quasi-static time series (QSTS) simulation, two price schemes, four trading environments, and four DER allocation methods from the literature are used to study economic benefits for individual participants and the supplier. Testing is performed using the IEEE 33-bus and 123-bus networks, and an Irish urban medium voltage feeder. Revenue flow is presented as an indicator of which participant nodes are profiting more from grid usage, and therefore should be responsible for greater network charges, this is validated against traditional and alternative schemes. Important reductions in use of network charges are seen especially by participant nodes with a higher PV generation-to-load and self-consumption rates. The proposed method is only relevant when dynamic tariffs are in place and/or local trading is enabled. Ultimately, results suggest that the income from network charges received by the supplier is increased when dynamic tariffs are used.

Energy optimization mechanism using metastable-smart grid (MSSG)

The Smart Grid model uses energy from renewable sources and utilities to manage energy in a way that ensures uninterrupted power supply for users. In order to operate independently from the main power grid, a DC micro grid is a small power system that generates and uses its own DC electricity. Solar panels, fuel cells, and wind turbines are the most typical power sources for DC micro grids, with batteries used to store any extra energy. The independence of the power grid is one feature of a DC micro grid that promotes increased lifetime. Building owners have more latitude to pursue their sustainability goals since they have control over the production, delivery, and consumption of power. Super-twisting provides a dependable method and an effective instrument for the control of uncertain nonlinear systems by addressing the basic flaws of conventional sliding mode control, notably large control effort and chattering. Fractional order controllers provide greater design freedom than conventional integer order controllers. The purpose of this work was to create a metastable-smart grid(MSSG) user end model that could dynamically manage and optimize energy produced by renewable energy sources and the utility to ensure that customers always have access to electricity. Controlling these many power sources using MSSG and delivering energy in accordance with each user’s unique power plan is the responsibility of the central control unit of the distribution grid. The supply is only switched to solar by the central control unit if a user’s power consumption falls below a predetermined threshold. Giving consumers with less purchasing power more clout as a result.

Assessment of energy losses in power distribution systems with individual prosumers and energy communities

AbstractAchieving the energy transition target will require the extensive engagement of consumers and the private sector in investment and operation of renewable‐based power systems. Support of individual prosumers and proliferation of energy communities are efficient ways to implement this engagement. To facilitate smooth integration of prosumers with photovoltaic (PV) installations, while satisfying effective operation of the power distribution system (PDS), it is important to fundamentally assess energy losses for different grid development scenarios. In this paper, the energy losses are compared for two alternatives: (a) when prosumers with PV installations act as individual grid users, and (b) when prosumers become participants of a solar energy community. To achieve this goal, modeling of loads, prosumers’ behavior and PV generation was performed. Furthermore, it has been analyzed how the physical topology of the distribution network can be harmonized with the underlying bidirectional power flows for each alternative, while complying with system constraints. The IEEE 123 Node Test Feeder was employed to capture the performance of the PDS and to estimate the energy loss with various penetration levels of prosumers. The results indicate that reduction of energy losses is only possible until a certain PV penetration level, after which additional PV capacities cause increase in energy losses and complicate the PDS's operation. Lower energy losses were supervised for the cases with dispersed PV generation, compared to the cases focused on energy communities. This means that the PDS demonstrates better tolerance to evenly distributed prosumers, rather than to single sources of a larger scale installed in one node. The outputs of this study will help the energy planners and utilities in improving performance of the PDS in terms of loss minimization and loadability maximization.

Power grid user asset value evaluation method and application under the background of artificial intelligence and smart grid

In the context of artificial intelligence and smart grids, the decision-making of power grid users’ asset acceptance by power grid companies is critical to their operation and development. The research adopts the method of modeling analysis, and aims to study the method and application of power grid user asset value evaluation in the context of smart grid. Based on the analysis of the characteristics of user assets, the economic impact of user asset value evaluation, the characteristics of power grid user asset value evaluation methods, and the fuzzy comprehensive evaluation method, combined with the principle of constructing power grid user assets evaluation index, a fuzzy comprehensive evaluation method construction model is constructed. Finally, this method is used to evaluate the value of power grid users’ assets. The evaluation results show that the total amount of power assets in the government park is the highest, followed by company assets. From another perspective, the impact of residential parks on the social value of user assets is more prominent, but its economic significance and strategic value are relatively weak. The development and in-depth research is expected to further enhance the customer analysis and accurate power supply capabilities of power companies.

Assessment of the barriers towards more rapid development of solar power: the case of Lithuania

Many countries in the world pay special attention to the development of energy from renewable energy sources. However, the efforts made are still insufficient to ensure the desired pace of development. Moreover, independence from fossil fuels is more important than ever in the context of the war in Ukraine. Solar-photovoltaic energy production solutions are particularly attractive for achieving the desired scale of development due to their relatively simple deployment. However, to involve a larger number of prosumers – individuals and communities, it is necessary to further reduce the barriers to such an activity. Although researchers pay considerable attention to the development of energy from renewable energy sources, the topic of solar energy is not so well explored. In addition, it is noted that research on barriers that prevent development should be linked to the situation in a particular country. Therefore, the goal of the research presented in this work is to assess the importance of solar-photovoltaic energy development barriers specific to Lithuania. The literature review was carried out to consider specific barriers to solar energy development and to analyse barriers typical for the development of other sorts of renewable energy. This review allowed us to distinguish barriers relevant to more rapid solar-photovoltaic energy development in Lithuania, dividing them into five groups: (1) economic and financial, (2) policy and regulation, (3) institutional and administrative, (4) information, awareness, and social, and (5) technological barriers. An assessment of the barriers was based on the expert interview method. The individual evaluations of barriers and their rating determined their importance toward more rapid solar-photovoltaic power development in Lithuania. The results obtained during the research made it possible to single out the following main barriers specific to Lithuania: grid capacity and integration into electricity distribution grids; spatial planning and/or zoning rules; permitting, licensing, and approval procedures; changing and/or unclear policies; grid usage fees and their regulation; low electricity price and/or cost of other sources of electricity. In addition, insights into to the ways of their neutralisation or at least reduction were provided pointing out that it should be considered at both the state and municipal levels.

Non-Cooperative Games for Privacy-Preserving and Cost-Efficient Smart Grid Energy Management

In this paper, we design privacy-preserving and cost-efficient energy management strategies for smart grid users that are equipped with renewable energy sources. The adversary is assumed to employ a factorial hidden Markov model based inference for load disaggregation, and the corresponding joint log-likelihood of the model is utilized as the privacy measure. The studied dynamic pricing model is applicable to a commodity-limited market, where the price of unit amount of energy is determined by the users’ aggregated power request. The users’ energy management strategies are designed under a non-cooperative game framework, where each user aims to optimize a weighted sum objective of both privacy measure and energy cost saving. The users’ non-cooperative game is shown to admit a unique pure strategy Nash equilibrium. As an extension, a computational-efficient distributed Nash equilibrium energy management strategy seeking method is proposed, which also avoids the privacy leakage due to the sharing of payoff functions between users. The performance of practical designs of the energy management strategies in the equilibrium is finally illustrated by numerical experiments.

Technical note: Four-year experience with utilization of DICOM metadata analytics in clinical digital radiography practice.

Digital radiography (DR) still presents many challenges and could have complex imaging acquisition and processing patterns in a clinical practice hindering quality standardization. This technical note aims to report the 4-year experience with utilizing a custom DICOM metadata analytics program in clinical DR at a large institution. Thirty-eight DR systems of three vendors at multiple locations were configured to automatically send clinical DICOM images to a DICOM receiver. A suite of custom MATLAB programs was established to extract and store public and private header data weekly. Specific use cases are provided for systematic image acquisition investigation, image processing harmonization, exposure index (EI) longitudinal monitoring and EI target optimization. For systematic acquisition investigation, an example of adult lumbar spine exam analysis was provided with statistics on manual acquisition versus the use of automatic exposure control (AEC, including AEC dose level, active cell, and backup timer), grid usage, and collimation for various projections. For processing harmonization, up to 12.6% of protocols were revealed to have processing parameter differences in an example of a mobile radiography fleet. In addition, inconsistent use of a post-acquisition image size function was also demonstrated, which resulted in anatomy size display variations. Bimonthly monitoring of median EI values showed expected trends, including changes after an AEC dose level adjustment for adult posterior-anterior chest imaging on a scanner system. An example of adult axillary shoulder EI target refinement was shared using the median value, eμ , based on the lognormal EI data distribution after parsing down to acquisitions with appropriate techniques. This analytics program enables systematic analysis of image acquisition and processing details. The information provides invaluable insights into real practice patterns, which can support data-driven quality standardization and optimization.

Internet Threat Detection in Smart Grids Based on Network Traffic Analysis Using LSTM, IF, and SVM

The protection of users of ICT networks, including smart grids, is a challenge whose importance is constantly growing. Internet of Things (IoT) or Internet of Energy (IoE) devices, as well as network resources, store more and more information about users. Large institutions use extensive security systems requiring large and expensive resources. For smart grid users, this becomes difficult. Efficient methods are needed to take advantage of limited sets of traffic features. In this paper, machine learning techniques to verify network events for recognition of Internet threats were analyzed, intentionally using a limited number of parameters. The authors considered three machine learning techniques: Long Short-Term Memory, Isolation Forest, and Support Vector Machine. The analysis is based on two datasets. In the paper, the data preparation process is also described. Eight series of results were collected and compared with other studies. The results showed significant differences between the techniques, the size of the datasets, and the balance of the datasets. We also showed that a more accurate classification could be achieved by increasing the number of analyzed features. Unfortunately, each increase in the number of elements requires more extensive analysis. The work ends with a description of the steps that can be taken in the future to improve the operation of the models and enable the implementation of the described methods of analysis in practice.

Marginalising household users in smart grids

Unless just and inclusive, transitions to low carbon-energy systems are unlikely to receive sufficient legitimacy and broad public support. While research on inequalities and injustices in energy transitions is growing, these issues remain less explored in the case of the digitalisation of the energy systems through smart grids and smart homes. This paper aims to synthetise our understandings of inequality, exclusion and vulnerability in energy transitions by systemically analysing different dimensions of marginalisation in the transition to a digitalised electricity infrastructure in Sweden. To synthetise understandings of marginalisation and exclusion, conceptualisations of these processes in different social science approaches to energy studies are reviewed. Moreover, an empirical analysis of interviews with a range of Swedish actors such as energy utilities, social housing developers or pensioner organisations is carried out and enriched by previous research on smart grid users. The empirical analysis demonstrates what specific forms that diverse categories of marginalisation, as already identified generally in different social science approaches to energy studies, may take in smart grids. We argue that marginalisation of household users in smart grids may take place along multiple dimensions, relating to matters of literacy, participation, infrastructure, and the economy. The analysis suggests that bundles of marginalisation aspects concerning household users in sustainable energy transitions should be recognised and addressed simultaneously.

Privacy protection methods of location services in big data

Abstract The rapid development of mobile communication technology not only brings convenience and fun to our life, but also brings a series of problems such as privacy disclosure. Therefore, it is very necessary to study the privacy protection method based on location service to strengthen the security of location privacy. The purpose of this work is to improve the security of location privacy and prevent the disclosure of user privacy by studying the characteristics of location services and privacy protection methods. This article first describes the characteristics of the important location privacy protection law, and then studies the structural characteristics and operation process of the location privacy protection law. This work evaluates the advantages and disadvantages of different methods, and finally compares the performance of several privacy protection algorithms through experimental analysis. Through the research of hiding space method, two-level cache method based on user grid, differential privacy protection method and experimental analysis of the algorithm, an effective privacy protection algorithm can be obtained. It can better protect the location privacy of users. For example, dual-active in the hidden space algorithm has the best privacy protection performance. Compared with other algorithms, the success rate of generating hidden space is increased by more than 10%, and the time of generating hidden space is shortened by about a quarter. The algorithm It has certain practical value and significance for use in the privacy protection of users.

Optimal clustering-based operation of smart railway stations considering uncertainties of renewable energy sources and regenerative braking energies

The smart railway stations (SRSs), as prosumer microgrids, are considered active users in smart grids. By utilizing regenerative braking energy (RBE) and renewable energy resources (RERs) along with energy storage systems (ESSs), these SRSs can participate in the prosumer market. The uncertainties of RERs in SRSs due to meteorological factors have been studied in the literature. However, there is a research gap in developing a stochastic method for optimized operating of SRSs considering the RBE uncertainties besides the RER, load, and number of passengers’ uncertainties. In this paper, a new probabilistic clustering-based framework for the optimal operation of SRSs is presented. By applying Monte Carlo Simulations (MCS), several scenarios are generated and then clustered by the k-means algorithm. The introduced method is applied to an actual SRS in Tehran Urban and Suburban Railway Operation Company. The test results of the MCS, deterministic, and proposed scenario-based approaches are compared to illustrate the proposed method. Test results imply that the related error of the scenario-based method under the real-time pricing can be less than 4.4%, while the computation time significantly decreases. Furthermore, sensitivity analysis is done to determine how the exchanging power constraints and ESS capacity might influence the SRS operation.

Decentralized TSO-DSO coordination for voltage regulation purposes based on renewable energy sources management – Sensitivity and robustness analyses

• A decentralized coordination between a Transmission System Operator (TSO) and a Distribution System Operator (DSO) implemented via a standardized Business Use Case (BUC) for scheduling and deploying optimal reactive power exchanges in the DSO's boundary for improved voltage control in the TSO's networks is proposed. • The interoperability between the TSO, the DSO, and other stakeholders is solved by designing and developing the BUC within the framework of the International Electrotechnical Commission (IEC) Common Information Model (CIM) family of standards IEC61970, IEC61968, and IEC62325. • The standardized BUC is demonstrated on real-world Slovenian TSO's and DSO's networks. On one hand, a sensitivity analysis is conducted to evaluate to which extent different Renewable Energy Sources (RES), such as capacitor banks and different Distributed Generators (DGs), viz. hydropower, photovoltaic (PV), and thermal (co-generation) units, impact on the high voltage (HV) for different network topologies, DGs’ operating scenarios, and capacitor banks’ sizes and locations. On the other hand, a robustness analysis is conducted to evaluate the capability of the proposed approach to regulate the HV voltage by managing the reactive power injected by these different RES in the different simulated scenarios. • The proposed data exchange mechanism based on the standardized BUC can successfully exchange data between the TSO, the DSO, and other stakeholders, such as the Significant Grid Users (SGUs) and Meter Operators, as a CIM Common Grid Model Exchange Standard (CGMES) format. • The proposed approach can manage DGs at the DSO's boundary toward contributing additional (positive or negative) reactive power to reduce the voltage deviations in the grid, improve the power quality at the DSO's boundary by reducing the flow of reactive power from the TSO's to the DSO's networks and vice versa, and keep the HV within safe values. The increasing penetration of Renewable Energy Sources (RES) in distribution networks has led traditional voltage regulation to their boundaries. In order to develop advanced techniques for voltage control in this new context, an adequate and real-time coordination and communication between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) is needed. In this paper, a decentralized TSO-DSO coordination approach for scheduling and deploying optimal reactive power exchanges in the DSO's boundary for improved voltage control in the TSO's networks is proposed. The proposed approach is implemented via a standardized Business Use Case (BUC). The interoperability between the TSO, the DSO, and other stakeholders is solved by designing and developing the BUC within the framework of the International Electrotechnical Commission (IEC) Common Information Model (CIM) family of standards IEC61970, IEC61968, and IEC62325. In view of the lack of pilot tests in the field, the proposed standardized BUC is demonstrated on real-world Slovenian TSO's and DSO's networks. The simulation experiments presented in this paper are twofold. On one hand, the proposed data exchange mechanism based on the standardized BUC demonstrates the feasibility of successfully exchanging data between the TSO, the DSO, and other stakeholders, such as the Significant Grid Users (SGUs) and Meter Operators, as a CIM Common Grid Model Exchange Standard (CGMES) format. On the other hand, the capability of the proposed decentralized TSO-DSO coordination approach to regulate the High Voltage (HV) by managing the reactive power injected by different RES, such as capacitor banks and different Distributed Generators (DGs), viz. hydropower, Photovoltaic (PV), and thermal (co-generation) units, is validated via sensitivity and robustness analyses for different network topologies, DGs’ operating scenarios, and capacitor banks’ sizes and locations. The simulation results show that the proposed approach can manage DGs toward contributing additional (positive or negative) reactive power to reduce the voltage deviations in the grid, improve the power quality at the DSO's boundary by reducing the flow of reactive power from the TSO's to the DSO's networks and vice versa, and keep the HV voltage within safe values. Unfortunately, this is not the case for the capacitor banks, where the capability of the proposed approach to manage their injected reactive power to regulate the HV voltage is highly dependent on their sizes and location, being necessary to be studied on a case-by-case basis.

Selectivity Characteristics of the Sorting Grid in Shrimp Beam Trawls used to Reduce Bycatch in the Sea of Marmara

Considerable by-catch problems have occurred in the commercial beam trawl fishery targeted at deep water pink shrimp in the Sea of Marmara. In this study, the grid systems were used to reduce the untargeted catch and improve the species' selectivity. By-catch reduction ratios of the beam trawls deployed with and without the grid were compared by the parallel hauling method. Selectivity values of 20 mm bar spacing grid and 24 mm mesh size cod-end were also analyzed by SELNET program. In general, by-catch reduction ratios of the grid beam trawl were determined as 50.6% and 56.9% in terms of number and weight, respectively. The reduction of target catch (Parapenaus longirostris) in the same gear was found to 23.1% in terms of number and 23.2% in terms of weight. In the model for rose shrimp, obtained according to the lowest AIC value, the selectivity parameters of the grid for L50 were 13.51 cm and for SR 4.76 cm. Selectivity parameters of the same species for 24 mm mesh size cod-end were; L50 = 8.99 cm and SR = 5.56 cm. These results indicated that grid usage benefits and contribute to sustainable fishery in the target catch-oriented fishing applications.

Research on feature extraction and classification of load's reactive power based on data-driven

Driven by the advancement of science and technology, the extensive access of high proportion of renewable distributed energy and power electronic equipment has changed the reactive power characteristics of the user side of the urban distribution network. In order to deeply explore the current status of reactive power characteristics on the urban user side, this paper takes the typical users of a regional power grid in Shanghai as the research object, whose power factor data are collected, and the power factor change curves of users are analyzed by combining deep learning and multidimensional clustering. Firstly, the collected data are preliminarily processed to remove the abnormal data in the total sample, and the incomplete data is filled by the interpolation method. Then, aiming at avoiding the influence of the data with an overhigh dimension on the clustering effect, this paper uses a multi-layer autoencoder deep neural network in deep learning to reduce the dimension of the typical daily power factor change curve set of input and extracts its deep features through unsupervised learning. Finally, the K-means clustering algorithm is used to conduct clustering analysis on the daily power factor change curve of typical users with different power consumption types, including large industry, commerce, resident, and charging service industry, etc. So as to summarize various users' reactive power consumption characteristics and reactive power distribution rules. The analysis results show that the shape of the power factor curves of some users vary significantly in different months, and even the factor fluctuates greatly in the same day.

A Three-Phase CDRT Strategy Based on Successive Relay for Smart Grid

In the vision of the future smart grid, the communication is often featured by wide range, massive connect, and low latency, which poses new requirements on the reachable distance and spectral efficiency of wireless communication. In this regard, this paper studies ergodic capacity enhancement by applying successive relay (SR) technology to a non-orthogonal multiple access (NOMA) based Coordinated Direct and Relay transmission (CDRT) system, where a base station (BS) communicates with a near user (NU) directly while communicating with a far user (FU) with the help of a group of relays. We design a novel three-phase CDRT strategy based on SR technology to overcome the half-duplex (HD) constrain without introducing additional noise. The proposed strategy can improve the spectral efficiency while expanding the communication coverage, which to some extent improves the communication quality of the edge users of the smart grid and reduces the communication delay. To analyze the performance of the proposed three-phase CDRT strategy, an exact and closed-form expression for ergodic capacity of the NU, the FU, and the whole system is derived. Finally, the numerical and simulation results validate the analysis results and show that the proposed strategy can improve the ergodic capacity of FU without reducing the capacity scaling of NU.

Analyzing WLCG File Transfer Errors Through Machine Learning

The increasingly growing scale of modern computing infrastructures solicits more ingenious and automatic solutions to their management. Our work focuses on file transfer failures within the Worldwide Large Hadron Collider Computing Grid and proposes a pipeline to support distributed data management operations by suggesting potential issues to investigate. Specifically, we adopt an unsupervised learning approach leveraging Natural Language Processing and Machine Learning tools to automatically parse error messages and group similar failures. The results are presented in the form of a summary table containing the most common textual patterns and time evolution charts. This approach has two main advantages. First, the joint elaboration of the error string and the transfer’s source/destination enables more informative and compact troubleshooting, as opposed to inspecting each site and checking unique messages separately. As a by-product, this also reduces the number of errors to check by some orders of magnitude (from unique error strings to unique categories or patterns). Second, the time evolution plots allow operators to immediately filter out secondary issues (e.g. transient or in resolution) and focus on the most serious problems first (e.g. escalating failures). As a preliminary assessment, we compare our results with the Global Grid User Support ticketing system, showing that most of our suggestions are indeed real issues (direct association), while being able to cover 89% of reported incidents (inverse relationship).

A Bilevel Optimization Model Based on Edge Computing for Microgrid.

With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments.

Cyber Security of Smart Metering Infrastructure Using Median Absolute Deviation Methodology

To combat cyber threats in the smart grid, an intrusion detection system can be integrated into the advanced metering infrastructure. Anomaly-based intrusion detection can detect even the tiniest changes in the parameter under investigation, whereas signature-based intrusion detection only recognises known attacks. The growing usage of smart grids necessitates the classification, identification, and implementation of countermeasures to threats. At the absolute least, smart grids must be protected against cyberattacks; thus, the highest level of information security must be offered. As a result of digitisation and the usage of more smart applications, the research looked at a variety of attack types, smart grid assaults, and major cyber threats on the voltage regulation. Machine learning techniques that analyse data in real time and formulate patterns to recognise an attack and scan through huge data for anomalies can be implemented into the advanced metering infrastructure (AMI) for intrusion detection for anomaly-based intrusion detection. The comparative test study done for the research found that the proposed method, median absolute deviation for anomaly identification in smart metering datasets, produced the most accurate and precise differentiations with the highest accuracy and precision. The median absolute deviation (MAD) algorithm model is trained using test data, and raw predictions are made, before true data are used to derive final test result parameters, precision, recall, and F1 scores. The methodology of the entire study is discussed in this paper, as well as how the MAD algorithm is best suited for anomaly-based intrusion detection, as well as result comparisons of other machine learning algorithms.

Future of Smart Cities: The Role of Machine Learning and Artificial Intelligence

The goals of "smart cities" include relieving the burden of growing urbanisation, cutting down on energy use, protecting the environment, boosting the local economy and people's standard of living, and facilitating wider access to and use of cutting-edge Information and Communication Technologies (ICT). In smart cities, ICT is essential for policymaking, decision making, plan execution, and the delivery of useful services. The primary goal of this analysis is to investigate the part that AI and machine learning play. Examples of ed tech include Deep Reinforcement Learning (DRL) and Machine Learning (ML). In a complicated smart city setting, the aforementioned methods can be used to develop the best possible rules. Smart transportation, cyber-security, energy-efficient usage of smart grids (SG), efficient use of Unmanned Aerial Vehicles (UAVs) to guarantee the best 5G and beyond 5G (B5G) communications, and a smart health monitoring are all discussed in length in this paper. Finally, we discuss the research challenges that have yet to be met and potential future research directions that could bring the concept of a "smart city" closer to fruition.

A comprehensive optimal energy control in interconnected microgrids through multiport converter under N−1 criterion and demand response program

Nowadays, the local distribution grids have been facing technical, economic, and regulatory challenges, because of the increased integration of renewable energy sources (RESs) and electrification of vehicles. The traditional solutions to the grid expansion, e.g., to build an additional power line, are utility-centered solutions, i.e., the distribution grid operators (DSOs) are the only party involved to tackle grid issues. The DSOs have to engage grid users with technology providers to develop innovative solutions that tackle one problem and overcome several cost-effectively. This paper presents a holistic solution to optimally control cross-sectoral energy flow between interconnected microgrids (MGs) consisting of different RESs, hydroelectric power plant (HPP) and wind turbines (WTs) to meet electric vehicles (EVs), residential, commercial and industrial demands with the main grid contribution. This issue will provide the advantages of community-based MGs for local energy trading which causes for an active and engaged system, however, an adequate control strategy for proper operation is required. The proposed solution is based on a new interconnection line between two MGs through a multiport converter (MPC) with the techno-economic consideration of newly installed components such as MPC, cables and the required battery energy storage system (BESS). The proposed case study is evaluated under three different conditions e.g., load increment, demand response (DR) and N-1 criterion in separate, interconnect and island modes. The CPLEX solver of GAMS software is employed to solve the mixed-integer linear programming model. The results show that the applied interconnection line for MGs compared to the separated operation mode can decrease the system's total costs, reduce the applied peak to the upstream grid, and enhance the system's reliability under different conditions. Furthermore, the applied solution provides the ability for MGs operation even in island mode under different conditions for a full day (24 h).