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This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

Renewable energy sources have recently been integrated into microgrids that are in turn connected to electric vehicle (EV) charging stations. In this regard, the optimal planning of microgrids is challenging with such uncertain generation and stochastic charging/discharging EV models. To achieve such ambitious goals, the best sites and sizes of photovoltaic and wind energy units in microgrids with EV are accurately determined in this work using an optimization technique. This proposed technique considers 1) generation profile uncertainty in photovoltaic and wind energy units as well as the total load demand, 2) photovoltaic and wind generation units' DSTATCOM operation capability, and 3) various branch and node constraints in the microgrid. Most importantly, the possible EV requirements are also taken into account, including initial and predetermined state of charge (SOC) arrangements, arrival and departure hours, and diverse regulated and unregulated charging strategies. A bi‐level metaheuristic‐based solution is established to address this complex planning model. The outer level and inner‐level functions optimize renewable energy sources and EV decision variables. Sub‐objectives to be optimized voltage deviations as well as grid power. The results demonstrate the effectiveness of the introduced method for planning renewable energy sources and managing EV to effectively achieve autonomous microgrids. [ABSTRACT FROM AUTHOR]

In this paper, novel and efficient analytical closed-form expressions are proposed for the optimal allocation of multiple capacitors in distribution systems to maximize the total cost reduction (CR) while considering power losses. The proposed expressions are novel since they can directly solve the allocation problem without requiring iterative processes or optimization algorithms. Specifically, two analytical closed-form expressions are introduced to determine the optimal number, locations, and sizes of multiple capacitors. The first analytical expression computes directly the optimal sizes of multiple capacitors where it is employed for the optimal sizing of capacitors for all possible combinations of locations. In turn, the best combination is then assigned by using a second analytical expression which directly evaluates all the combinations in terms of their contribution in CR. Unlike the existing methods/expressions that utilize sensitivity factors or optimize each capacitor individually, the proposed analytical closed-form expressions involve a unified mathematical model for multiple capacitors. The proposed direct approach is tested using a 69-bus distribution system. The accuracy and efficacy of the proposed analytical closed-form expressions are verified by comparisons with existing methods and intensive simulations of various allocation scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal voltage regulation scheme (OVRS) for distribution systems with rich photovoltaic (PV). Various regulation devices are optimally controlled in a coordinated manner: PV inverter, D-STATCOM, step voltage regulator (SVR), and on-load-tap-changer (OLTC). A data structure algorithm is proposed to split the distribution system into layered zones considering the radial structure of the system. The solution process of the proposed scheme is accomplished by a meta-heuristic optimizer. OVRS addresses the voltage violations while yielding a coordinated operation of the various control devices. The proposed OVRS involves three control levels to completely prevent voltage violations. In the first control level, the PV inverter and D-STATCOM mitigate rapidly the local voltage deviation through injecting/absorbing optimized reactive power. The second control level is a decentralized-based control scheme that utilizes the voltage control devices in each zone to handle the voltage violations if any. For each zone, the control devices in the upper-stream zones (parent zones) are managed by the third control level to ensure cooperative control actions. The simulation results on the 119-bus distribution system, with clear, low fluctuation, and high fluctuation of solar radiation profiles, demonstrate the effectiveness of the proposed OVRS. [ABSTRACT FROM AUTHOR]

Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimization-based approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids. [ABSTRACT FROM AUTHOR]

This study presents an efficient power flow method for analysing distribution systems. The proposed method utilises efficient quadratic‐based (QB) models for various components of distribution systems. The power flow problem is formulated and solved by a backward/forward sweep (BFS) algorithm. The proposed QBBFS method accommodates multi‐phase laterals, different load types, capacitors, distribution transformers, and distributed generation. The advantageous feature of the proposed method is robust convergence characteristics against ill conditions, guaranteeing lower iteration numbers than the existing BFS methods. The proposed method is tested and validated on several distribution test systems. The accuracy is verified using OpenDSS. Comparisons are made with other commonly used BFS methods. The results confirm the effectiveness and robustness of the proposed QBBFS at different conditions. [ABSTRACT FROM AUTHOR]

An efficient analytical (EA) method is proposed for optimally installing multiple distributed generation (DG) technologies to minimize power loss in distribution systems. Different DG types are considered, and their power factors are optimally calculated. The proposed EA method is also applied to the problem of allocating an optimal mix of different DG types with various generation capabilities. Furthermore, the EA method is integrated with the optimal power flow (OPF) algorithm to develop a new method, EA-OPF which effectively addresses overall system constraints. The proposed methods are tested using 33-bus and 69-bus distribution test systems. The calculated results are validated using the simulation results of the exact optimal solution obtained by an exhaustive OPF algorithm for both distribution test systems. The results show that the performances of the proposed methods are superior to existing methods in terms of computational speed and accuracy. [ABSTRACT FROM PUBLISHER]

In this work, an efficient analytical method is proposed for optimally allocating distributed generations (DGs) in electrical distribution systems to minimize power losses. The proposed analytical method can be employed for obtaining the optimal combination of different DG types in a distribution system for loss minimization. The validity of the proposed method is demonstrated using two test systems with different configurations by comparing with the exact optimal solution obtained from the exhaustive optimal power flow (OPF) algorithm. The calculated results and the comprehensive comparisons with existing methods prove the superiority of the proposed method in terms of accuracy and calculation speed. The proposed loss minimization method can be a useful tool for any general DG allocation problem since it provides effective and fast loss evaluation taking into account other benefits. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

For the past few years, the world has seen a great shift toward renewable energy resources from conventional ones. But the ever‐increasing integration of distributed generation (DG) to the electrical network leads to integration limiting constraints like overvoltage, under voltage, harmonics, equipment ampacity violations, and failure of protection schemes. Therefore, an extensive investigation of the methodologies in which DGs can be incorporated into the electrical network is presented in this manuscript. This article provides an extensive review of all the hosting capacity (HC) terms, references, limiting constraints of the studied networks, geographical segregation, and their determination methodologies. Moreover, the factors defining the HCs of various networks and the architectures employed to increase them, are also explained briefly in the conducted review study. [ABSTRACT FROM AUTHOR]

Distribution system planners and operators have increasingly exposed great attention to maximizing the penetration of renewable energy resources (RERs), and electric vehicles (EVs) toward modern microgrids. Accordingly, intensive operational and economic problems are expected in such microgrids. Specifically, the operators need to meet the increased demand for EVs and increase the dependence on RERs. The charging strategy for EVs and the RER penetration level may result in increased power loss, thermal loading, voltage deviation, and overall system cost. To address these concerns, this paper proposed an optimal planning approach for allocating EV charging stations with controllable charging and hybrid RERs within multi‐microgrids, where the charging strategy in the proposed planning approach contributed to improving power quality and overall system cost, where the voltage deviation, energy not supplied, total cost have been reduced to 26.03%, 49.57%, and 70.45%, respectively. The simulation results are compared with different optimization techniques to verify the effectiveness of the proposed algorithm. The proposed simultaneous allocation approach of EV charging stations and RERs can reduce operating costs for RERs and conventional stations while increasing the charging stations' capacity. [ABSTRACT FROM AUTHOR]

The paper presents an unbalanced three-phase power-flow model for wind turbine generating systems (WTGSs). The model takes into account voltage unbalance factor which exists at the point of common coupling. The developed model is integrated with the unbalanced forward/backward sweep power-flow method. The model comprises of three main components: they are the wind turbine, induction generator, and interface transformer to the grid. Due to their design symmetry, the generator and the transformer are modeled using symmetrical sequence networks. The results show that the developed model has robust convergence characteristics. The solution of the IEEE unbalanced radial feeders shows that the injected powers per phase due to the WTGS are not equal and strongly dependent on the voltage unbalance factor. The results also show that the simplified models based on positive sequence network lead to inaccurate overall power-flow solution. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY The paper presents an unbalanced three-phase power-flow model for wind turbine generating systems (WTGSs). The model takes into account voltage unbalance factor which exists at the point of common coupling. The developed model is integrated with the unbalanced forward/backward sweep power-flow method. The model comprises of three main components: they are the wind turbine, induction generator, and interface transformer to the grid. Due to their design symmetry, the generator and the transformer are modeled using symmetrical sequence networks. The results show that the developed model has robust convergence characteristics. The solution of the IEEE unbalanced radial feeders shows that the injected powers per phase due to the WTGS are not equal and strongly dependent on the voltage unbalance factor. The results also show that the simplified models based on positive sequence network lead to inaccurate overall power-flow solution. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• A novel approach is proposed for maximizing photovoltaic hosting capacity. • The reactive power sources and transformer taps are considered. • The stochastic nature of electric vehicles is considered. • A double-layer metaheuristic optimizer is developed. • IEEE 69-bus distribution system is used for simulation. The interest in maximizing the hosting capacity of photovoltaics is recently being enlarged globally. This paper proposes a novel stochastic approach for maximizing the hosting capacity of photovoltaics in distribution systems. The proposed approach is based on a coordinated management scheme of control devices in distribution systems, i.e. transformer taps and VAr sources. It also considers the promising electrical vehicles with their stochastic nature and comprehensive model, including the arriving and departing times, and initial and preset conditions of their batteries state of charge. Further, the planning model of photovoltaics considers the reactive power support of the photovoltaic inverter based on the recently released IEEE 1547:2018 standard. Compared to existing approaches, the unique merit of the proposed approach is its ability to maximize the hosting capacity of photovoltaics by simultaneous optimization of the different control variables. To accurately solve this stochastic optimization model, a double-layer metaheuristic optimizer is developed for maximizing the hosting capacity of photovoltaics and addressing all constraints. The inner level of the optimizer optimizes the charging/discharging power of electric vehicles, transformer taps, and reactive power support while the outer one maximizes the sizes of photovoltaics. To assess the effectiveness of the proposed approach, various scenarios are performed on the IEEE 69-bus distribution system. The proposed approach can maximize the hosting capacity of photovoltaics while optimally managing transformers, VAr sources, and electric vehicles in a coordinated manner. [ABSTRACT FROM AUTHOR]

This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency. [ABSTRACT FROM AUTHOR]

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters' data. The data monitoring is carried based on the industrial digital twins' platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0. [ABSTRACT FROM AUTHOR]

• A three-level control strategy is proposed for PV-Rich distribution systems. • The proposed control strategy can effectively minimize voltage deviations and flickers. • New analytical expressions are proposed to quantify and treat operational problems. • Realistic high-resolution solar radiation datasets are utilized. • PV power curtailment and tap operations of transformers are optimized. Voltage deviation (VD) and voltage flicker (VF) are considered common operational problems associated with high photovoltaic (PV) penetrated distribution systems. In this paper, an optimal control strategy is proposed for minimizing VD and VF in PV-rich distribution systems. The control strategy is based on proposed analytical expressions that minimize both voltage problems by optimizing the smart functions of the PV inverters and control devices simultaneously. The proposed analytical expressions are formulated based on voltage sensitivities with respect to the active and reactive power injections of PV. Specifically, a three-level control strategy with different time resolutions is proposed to significantly alleviate voltage deviation/flicker while minimizing PV active power curtailments and tap movements for transformers. These control levels are (1) local control (LC), (2) area control (AC), and (3) coordinated control (CC). LC provides rapid local control actions to minimize VD and VF, AC minimizes VD within the corresponding area individually, and CC plays a vital role to coordinate between the various control units. The proposed control strategy is assessed using high PV penetration with realistic high-resolution very-variable solar radiation datasets (10 ms). To demonstrate the accuracy and efficiency of the proposed analytical expressions, the calculated results have been compared with existing methods. Results demonstrate that the proposed control strategy effectively coordinates between the various voltage control units while minimizing VD and VF. [ABSTRACT FROM AUTHOR]

For the past few years, the addition of renewable energy sources has been on the rise, but the unregulated addition of these sources can cause severe harm to the grid. Therefore, it is necessary to have a predefined limit for a grid, beyond which no further addition of renewables should be allowed without reinforcement. That limit is called the hosting capacity (HC), which is addressed in the literature by search‐based methods with heavy computational burdens. This manuscript first presents a framework to find the HC for a grid. The same framework is then applied to 503 different simulated but realistic LV distribution feeders in Finland to find their HCs and the most effective network parameters in defining a network specific HC. Next, different machine learning models, that is, Decision Tree (DT), Random Forest (RF), Linear Regression (LR), K nearest neighbours (KNN), Logistic Regression, and Support Vector Machine (SVM) are implemented on the generated data. For the classification case, the accuracy values for logistic regression, KNN, and SVM were 0.89, 0.84, and 0.81, respectively. The findings demonstrate that the developed machine learning based technique will enable distribution network operators in finding the HC without applying any deterministic or probabilistic approaches. [ABSTRACT FROM AUTHOR]

The rapid increase in the installation of renewable energy sources, particularly solar photovoltaic (PV) sources associated with unbalanced features of distribution systems (DS), disturbs the classic control strategy of voltage regulation devices and causes voltage violation problems. This paper proposes an effective control strategy for voltage regulators in the DS based on the voltage sensitivity using a multi-agent system (MAS) architecture. The features of the unbalanced distribution system (UDS) with the PV and different types and configurations of voltage regulators are considered in the proposed strategy. The novelty of the proposed method lies in realizing both the control optimality of minimizing voltage violations and the flexibility to accommodate changes in the DS topology using an MAS scheme. An advantageous feature of using the MAS scheme is the robust control performance in normal operation and against system failure. Simulation studies have been conducted using IEEE 34-node and 123-node distribution test feeders considering high PV penetration and different sun profiles. The results show that the proposed voltage control strategy can optimally and effectively manage the voltage regulators in the UDS, which decrease their operation stresses and minimize the overall voltage deviation. [ABSTRACT FROM AUTHOR]

Achieving the goal of sustainable development is dependent on the widespread integration of renewable energy sources, energy storage systems (ESSs), and electric vehicles (EVs). However, a continuous increase in the penetration of such elements would bring more complexities to the distribution network. Accordingly, this paper presents a unified planning model comprising renewable energy-based distributed generation (DG), ESS, and electric vehicle charging stations (EVCSs). In this regard, a Latin Hypercube Sampling method is utilized to generate multi-scenario for describing the uncertainty of renewable energy and load demand. The stochastic EV charging behaviors are represented by various probability density functions (PDF). In addition, an exploitable capacity loss of ESS and EV batteries is calculated by the battery degradation model based on the depth of discharge (DOD). Furthermore, the battery degradation cost is incorporated into the objective of the planning model to identify the optimal decision for candidate assets. A piecewise linearization approach is introduced to convert the problem into a mix-integer linear programming (MILP) model. Numerical results demonstrate that the exploitable capacity loss of batteries plays a key role in asset planning and provides potential contributions to the optimal decisions of the distribution network. In the meantime, by considering battery degradation in the optimization model, the sustainability and lifetime of the battery can be preserved. • Uncertainties include the renewable energy, load demand, and behaviors of electric vehicles are considered. • Renewable energy-based DG, ESS, and EVCS are involved in the unified planning model. • The degradation model is proposed to analyze the exploitable capacity loss of ESS and EV battery. • The enhancement of investment decision while maintaining the lifetime of battery is achieved. [ABSTRACT FROM AUTHOR]

Highlights • A method is proposed to calculate the optimal oversize of the DG inverters. • The reactive power capability and active power curtailment are considered. • Different control schemes of the inverter are considered in the proposed method. • The effect of OLTC operation on the total costs and inverter oversize is studied. • IEEE 69-bus and IEEE 33-bus distribution systems are used for simulation. Abstract The penetration of renewable distributed generations (DG) is recently being increased in distribution systems. Due to the intermittent nature of these generation units, several technical problems are expected based on the environmental conditions and load profiles, such as voltage rise and increasing losses. A traditional way to alleviate these technical problems is to oversize their interfacing inverters for releasing their capacities to inject/absorb further reactive power. In the literature, the DG inverter is normally oversized by a certain percentage (e.g., 10%) for this purpose. In turn, an optimization-based method is proposed in this paper to calculate the optimal oversize of the interfaced inverter employed in various utility-owned DG types to regulate voltages and to reduce losses with minimum total costs. The proposed method considers the active power curtailment (APC) feature in the DG inverter and the transformer taps. Different control schemes of the interfaced inverter are considered and incorporated in the proposed optimization model. The simulation results demonstrate the effectiveness of the proposed method compared with existing methods. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy sources (RES) has been increased throughout the world. The main characteristic of RESs is that their generating powers are intermittent and unpredictable. This paper presents an interval optimization method to optimally schedule electric vehicles (EV) with considering the uncertainty of RES generation and loads. For this purpose, the RES generation (including photovoltaic and wind power) and loads are considered as interval parameters, and the charging/discharging power of EV is expressed as an interval variable to be optimally computed. The capability of RES inverters to regulate voltages is also considered in the interval optimization model. The objective function is to minimize the network active power losses and total voltage magnitude deviation with considering overall system constraints. The proposed method is tested on a 33-bus distribution system with uncertain RESs and loads, and the optimal day-ahead scheduling of EV is performed. Different case studies are carried out to test the effectiveness of the proposed method. It is demonstrated that the proposed interval optimization method can accurately represent the uncertain problem, and it provides further information compared with the deterministic optimization. [ABSTRACT FROM AUTHOR]

Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours. [ABSTRACT FROM AUTHOR]

As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy‐saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I‐GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I‐GWO has a movement strategy called dimension learning‐based hunting for enhancing the balance between global and local search and maintaining diversity. The proposed (I‐GWO‐TS) algorithm can solve mixed‐integer programming to achieve the planning and the optimal load flow objectives. The proposed method can be applied to a real Egyptian distribution system that is heavily loaded, with poor voltage regulation, and also has high‐power losses. The obtained results demonstrate the capability of the proposed approach to determine optimal series capacitors' location and sizing for maximization of energy saving. Further, the proposed method improves the network performance regarding the voltage profile and power losses, although the limitations of including series compensation were considered in the distribution system. [ABSTRACT FROM AUTHOR]

Voltage fluctuation and voltage rise are common issues associated with the massive integration of photovoltaic (PV) technologies in distribution systems. In this paper, sensitivity-based and optimization-based methods are proposed for mitigating voltage fluctuation and rise in the presence of plug-in hybrid electric vehicles (PHEVs). The concept of these methods is to optimize the reactive power of PV inverter and active power from charging station of PHEVs for matching a target voltage profile. The sensitivity-based method is based on the first-order power flow sensitivities around the desired voltage profile. An optimization model is used in optimization-based to minimize the mismatches between the fluctuating and required voltage profiles. The charging/discharging operation of PHEVs and reactive power of PV inverter are simultaneously controlled for compensating power fluctuation during cloud transients and load fluctuations. The results demonstrate the effectiveness of the proposed methods compared with existing methods. [ABSTRACT FROM AUTHOR]

Recently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility‐owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost‐effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day‐ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self‐adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K‐means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE‐24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near‐optimal day‐ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR. [ABSTRACT FROM AUTHOR]

The unbalanced load distribution, the single-phase connection of renewable energy, and the uncoordinated charging of electric vehicles (EVs) will bring a severe issue corresponding to the three-phase unbalance in modern distribution networks. To deal with this issue, a novel multistage optimal dispatching model with the hybrid energy storage system (HESS), consisting of the battery energy storage system (BESS), EV, and supercapacitor (SC), is proposed in this paper. The first stage is conducted on the day-ahead stage, driven by minimizing the total operation cost and relieving power unbalance, the unbalanced penalty cost is introduced into the optimal dispatching model with the electricity purchasing cost and the degradation cost of HESS. Also, the chance-constraint stochastic programming (SP) model with the coordinated operation of HESS is accommodated to handle the diverse uncertainties of renewable energy, load demand, and EV users' behaviors. In the intra-day operation stage, a rolling optimization-based dispatch model is formulated to re-optimize the operation of the SC for the rapid response of mitigating the real-time three-phase unbalance caused by renewable energy. The numerical experiments are executed on the IEEE 34-bus three-phase test system. Compared with the cases without considering SC and the degradation cost of HESS, the power unbalance in the proposed model is reduced by 3.27% and 3.62% per day at the real-time stage, respectively, while total operation cost is reduced by 46.77 US$ and 350.62 US$ per day, respectively. The results validated that the proposed model is efficient in reducing the three-phase unbalance under multi-time scales, improving the economic operation of the distribution network. • Three-phase power unbalance reduction is achieved by hybrid energy storage system. • Supercapacitor is embedded with BESS and EV in day-ahead and intra-day stages. • Multi-stage optimization scheduling model proposed to minimize power unbalance. • Rolling optimization employed in intra-day stage to address short-term unbalance. [ABSTRACT FROM AUTHOR]

Renewables (i.e., solar and wind power) in the Nordic area have highly seasonal characteristics, which severely restrict the wide development of renewables since they can be excessive in summer but insufficient in winter with intermittent outputs. The conventional battery energy storage system (BESS) with short-term adjustment functionality cannot eliminate the seasonal imbalance of renewables. In this regard, a risk-based bi-level planning model is presented to maximize the hosting capacity (HC) of renewables through configuring seasonal hydrogen storage (SHS) and BESS. Specifically, the upper level applies a multi-objective scheme to maximize HC and minimize the investment cost simultaneously. In turn, the lower level is driven by maximizing the profits of the distribution system operator (DSO) with the implementation of price-based demand response (PBDR). Due to forecasting errors of renewables and load resulting from intermittent output and random behaviors, a stochastic programming (SP) method is developed to address and adapt multiple uncertain fluctuations. Moreover, the conditional value-at-risk (CVaR) is introduced to measure the effect of the risks raised by multiple uncertainties on system operation. Finally, numerical studies are employed to verify the effectiveness of the proposed model. Compared to the cases without considering PBDR and SHS, the total renewable energy HC in the proposed model is increased by 2.30 MW and 0.37 MW, respectively, while the yearly cost benefit of DSO is enhanced by 28063.2 US$ and 17823.7 US$, respectively. The promising results demonstrate the empowerment of SHS can promote the cross-seasonal consumption of renewables, effectively maximizing the HC and improving the economic operation of distribution systems. • The hydrogen storage system is considered in this work for the cross-seasonal balance of renewables. • A bi-level planning model is proposed to improve hosting capacity and economic operation of the system. • The coordinated operation of seasonal hydrogen storage, demand response, and battery storage is designed. • The uncertainties and the operation risk brought by renewables and load are considered. [ABSTRACT FROM AUTHOR]

Currently, distribution systems are continuously evolving towards modern and flexible structures while integrating promising renewable energy resources (RERs). In this regard, an optimal network constraint-based expansion planning model combined with an optimal integration framework of intermittent RERs is proposed in this work to improve the topological flexibility in modern distribution networks (DNs). Specifically, the best investment locations and times of substations, lines, and RER-based distributed generations (DGs) are jointly taken into consideration. Additionally, the uncertainty-based multiple scenarios are modeled by probability distribution functions to strengthen the robustness and reliability of DNs influenced by the stochastic of renewable energy and load behavior. The novel network constraint is combined with three levels, where the first level introduces the graph theory to guarantee the radiality topology of modern DNs. In the second level of the network constraint, graph theory and fictitious load theory are collaboratively applied to ensure that each subsystem has a reserve connection interconnected to other subsystems. The third level is modifying the conventional fictitious load theory to ensure each subsystem is linked with at least one DG. The proposed planning model is driven by the minimum present value of total cost, including investment cost of branches, DGs, and substations, cost of substations operation, the electricity purchasing cost of substations and DGs, power losses cost, and environmental penalty cost of conventional generators. Numerical results are presented to verify that a more flexible and resilient topology of the DN system is obtained, and criteria evaluation is introduced to validate its higher performance with respect to existing procedures from power supplied quality, environmental burden, and supplied flexibility three aspects. [ABSTRACT FROM AUTHOR]

• Developing a reliable and secure IoT against transformer cyberattacks. • Introducing a deep 1D-CNN for fault classification against uncertainties. • Deep 1D-CNN is combined with the IoT for online transformer monitoring. • The introduced architecture increases the transformer life time. The distribution of the power transformers at a far distance from the electrical plants represents the main challenge against the diagnosis of the transformer status. This paper introduces a new integration of an Internet of Things (IoT) architecture with deep learning against cyberattacks for online monitoring of the power transformer status. A developed one dimension convolutional neural network (1D-CNN), which is characterized by robustness against uncertainties, is introduced for fault diagnosis of power transformers and cyberattacks. Further, experimental scenarios are performed to confirm the effectiveness of the proposed IoT architecture. While compared to previous approaches in the literature, the accuracy of the new deep 1D-CNN is greater with 94.36 percent in the usual scenario, 92.58 percent when considering cyberattacks, and ±5% uncertainty. The proposed integration between the IoT platform and the 1D-CNN can detect the cyberattacks properly and provide secure online monitoring for the transformer status via the internet network. [ABSTRACT FROM AUTHOR]

The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the butterfly optimization algorithm (BOA). The proposed BOA utilizes the neighboring butterflies' co-operation by sharing their knowledge in order to tackle the issue of trapping at the local optima and enhance the global search. Furthermore, the BOA requires few adjustable parameters via other optimization algorithms for the optimal design of the robot-manipulator controller. The BOA is combined with a developed figure of demerit fitness function in order to improve the trajectory tracking, which is specified by the simultaneous minimization of the response steady-state error, settling time, and overshoot by the robot manipulator. Various test scenarios are created to confirm the performance of the BOA-based robot manipulator to track different trajectories, including linear and nonlinear manners. Besides, the proposed algorithm can provide a maximum overshoot and settling time of less than 1.8101% and 0.1138 s, respectively, for the robot's response compared to other optimization algorithms in the literature. The results emphasize the capability of the BOA-based robot manipulator to provide the best performance compared to the other techniques. [ABSTRACT FROM AUTHOR]

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices. [ABSTRACT FROM AUTHOR]

• A novel probabilistic approach for evaluating DERs hosting capacity utilizing large number of feeders. • Monte Carlo simulations are conducted to model the intermittency associated with the DERs' integration in the distribution networks. • Impact of different network factors like node distance, customer count, impedance, and feeder length on hosting capacity are analysed. • Provides a practical lookup table for aiding distribution network operators (DNOs) during feeder planning. Increasing the adoption of renewable energy, especially photovoltaics (PVs), can have a positive impact on the environment and economies. However, the unplanned inclusion of these renewable resources into the grid system can give rise to serious constraint violations (voltage violations, over-currents, overloading of transformers) for the distribution system. In this regard, the primary intention of this study is to firstly identify the type of constraints that may be violated, and then quantify the hosting capacity (HC) for a particular feeder. Monte Carlo simulations were used to counter the uncertainties and variability related to loading behaviour and the randomness in the location and size of PV. The study was conducted on 507 realistic and distinct Finnish low-voltage distribution systems. The occurrence of the limiting constraints on HC and the pattern of their occurrence is explained. In the end, a correlation analysis is also performed using some key parameters of the grid to highlight the factors which influence the HC value the most. [ABSTRACT FROM AUTHOR]

• Optimization-based method is proposed for voltage fluctuations smoothing due to RES. • The reactive power capability of RES inverters is considered. • The fluctuations of EV powers and their minimum SOC at departure time are considered. • Hull moving average (HMA) is used to mitigate voltage fluctuations. • IEEE 90-bus and IEEE 33-bus distribution systems are used for simulation. Recently, the use of renewable energy sources (RES) and electric vehicles (EVs) has been rapidly increased worldwide. As a result of the highly fluctuating nature of RES, the charging and discharging rates of EVs significantly have to be increased, and so the lifespan of EV batteries decreases. In this paper, an optimization-based method is proposed to smooth voltage fluctuations due to various RES types by optimally controlling the charging and discharging power of EVs and the reactive power of the RES inverters. To extend the lifespan of the EV battery, EV power fluctuations and their minimum preset state of charge (SOC) are considered in the proposed optimization model. For this purpose, a new multi-objective function is formulated, including (1) voltage fluctuations, (2) EV power fluctuations, and (3) the deviation of SOC of EVs from their minimum desired level. The use of the hull moving average (HMA) is proposed to mitigate voltage fluctuations, which eliminates the lag problem of the widely used moving average methods. The gravitational search algorithm (GSA) is utilized to accurately solve the optimization model. The simulation results demonstrate the effectiveness of the proposed method to smooth voltage fluctuations while considering degradation and charging plan of EV batteries. [ABSTRACT FROM AUTHOR]

Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactive power compensation. Nevertheless, the wind farms based on DFIG are expensive compared with the SCIG wind farm, it features by its ability to control the active power independent of reactive power. However, combined wind farm (CWF) has been developed to collect the benefits of SCIG and DFIG wind turbines in the same wind farm. In this article, artificial neural network (ANN) is used to evaluate gain parameters of static synchronous compensator (STATCOM) in order to improve the stability performance of CWF. The impact of tuned STATCOM on the performance of CWF during gust wind speed and during three-phase fault is comprehensively investigated. The performance of CWF with STATCOM tuned by ANN is compared with its performance when the STATCOM tuned by the multiobjective genetic algorithm (MOGA) and whale optimization algorithm (WOA). The results show that the performance of CWF can be enhanced using STATCOM tuned by ANN more than MOGA and WOA. [ABSTRACT FROM AUTHOR]

Reliability improvement is regarded as a crucial task in modern distribution network expansion planning. Compared to previous works, this paper presents a bi-level optimization model to optimize the planning of the distribution network complying with multiple renewable energy and energy storage system (ESS) functionalities to guarantee the economical and reliable operation of the distribution network. The candidate assets include substations, distribution lines, renewable energy-based distributed generations (DGs), and ESSs are systematically involved. The load level affected by seasonal change and the multiple uncertainties, including renewable energy, load fluctuation, and contingency outage, are comprehensively considered. The uncertainties caused by the stochastic of renewable energy and load demand are described using Latin Hypercube Sampling (LHS) method. To address the computational burden and complexities associated with non-linear AC power flow, the mixed-integer linear programming (MILP)-based bi-level model is proposed via piecewise linearization methodology. Therein, the upper-level optimization model is proposed to minimize the total present value cost of the planning scheme in normal operating conditions. The lower level model, which is constrained to investment decision-making of the upper-level framework, aims to minimize the total cost of expected energy not supplied (EENS) considering the uncertainties of the single contingency outage. The effectiveness of the proposed bi-level planning model is validated by numerical studies to guarantee economic and reliability improvement for distribution network. • Multiple uncertainties are comprehensively considered in the proposed model. • Branches and multi-type generators are incorporated to the joint planning model. • Optimal allocation and reliability assessment are coordinated to build bi-level model. • The benefits from ESS for reliability enhancement is analyzed. [ABSTRACT FROM AUTHOR]

• Development of random forest algorithm for fault identification of transformers. • Machine-learning model reduces limitation and complexity of implementing graphical DPM. • Proposed RF + SMOTE perform satisfactorily in diagnosing faults for evaluation large dataset. • Proposed RF + SMOTE compared with previously methods (Duval Triangle, SVM, Rogers' and IEC Refined). • High accuracy of proposed RF + SMOTE based DPM2 algorithm equal to 96.5%. Power transformers are considered one of the power system's most critical and expensive assets. In this regard, it is vital to assess the fault within the power transformer considering numerous operational aspects. In the literature, dissolved gas analysis (DGA) is the routine in-service test for power transformers and one of the most important tests to ensure sufficient system reliability. Specifically, this test can detect dissolved gases in transformer oil which are then interpreted to detect the fault type of the transformer. Previous studies reported that the graphical Duval pentagon is one of the most accurate and consistent DGA interpretation techniques. However, it still has limitations on the complexity of the implementation in large amounts of data. To cover these issues, this study mitigates the limitation and complexity of implementing the graphical Duval Pentagon Method (DPM) in large amounts of data. To reach this goal, we develop a precise machine-learning-based fault identification model by employing the Random Forest algorithm with Synthetic minority over-sampling technique (SMOTE) preprocessing. The proposed Random Forest models with SMOTE perform satisfactorily in diagnosing faults for the evaluation dataset, with a total accuracy of 96.2% for DPM1 and 96.5% for DPM2. The proposed models were also compared to other machine learning algorithms, performing better both in classification accuracy and consistency due to uncertainty. [ABSTRACT FROM AUTHOR]

• FDM-FMG is proposed for assessing electrostatic precipitator designers. • FMG decreases the truncation error for finite difference approximations. • FMG is time-efficient as it converges fast on one finer computational domains. • FDM-FMG is an excellent prediction tool for clean and dust air precipitator. Worldwide, electrostatic precipitators (ESP) have been extensively utilized to separate fine particles for diverse large-scale industrial applications. In this regard, this paper presents a novel approach for modeling the dust-loaded ESP on the fine computational domain where the need for a fast solver arises. Unlike the previously published numerical techniques, the finite difference method (FDM) integrated with a full multi-grid method (FMG), labeled FDM-FMG, is developed to resolve Poisson and continuity equations on one fine computational domain. For clean and dust-loaded ESP, the proposed FMG is checked versus successive over-relaxation (SOR) on fine domains where the proposed one is greatly transcendent in terms of convergence characteristics and hence the computational performance (CPU time). For the first time, two major issues are highlighted and solved: the first concerning issue is the chosen ion mobility as an important factor in the simulation results and the second one is choosing an optimal computational grid for dust loaded precipitators that grantees both low truncation and roundoff errors, results in well-matched with experimental measurements nominated in the previous publishing. The novel idea of working on various grid sizes and tracking the optimal ones gives the FDM-FMG an advantage of predicting a precise picture for the electrical situations in industrial ESP over the other numerical techniques. After all, the impact of changing the spacing between the different wires and the height of the ionized wires on the distributions of current, ion, and particle charge densities on the ground are deeply simulated and presented in dust-loaded ESP. The proposed FDM-FMG can be a promising tool for the designers and manufacturers of precipitators, thanks to its superior computational performance. [ABSTRACT FROM AUTHOR]

• FDM-FMG is proposed for assessing HVDC bundled conductors. • FMG decreases the truncation error for finite difference approximations. • FMG is time-efficient as it converges fast on finer computational domains. • Numerical outcomes concurred well with past and present laboratory results. This paper presents an intensive measurement and analysis of monopolar ionized fields in bundled high voltage direct current (HVDC) conductors using the finite difference method based on the full multigrid technique. The positive feature of this study is that it considers the comprehensive representation of the bundle conductor, unlike the existing studies that approximate the bundle conductor with an equivalent conductor radius. Firstly, the proposed method is compared with previous experimental results. Secondly, a flexible laboratory model for the bundled HVDC conductors is constructed. Thirdly, the laboratory model is exploited to validate the numerically computed current density distribution on the ground plane and corona current for different bundles' numbers and different distances between bundles. Bundles of one, two, and four conductors are adopted in the experimental setup. For the same applied voltage, the results verified that the corona current decreases by increasing the bundles' number and/or minimizing the spacing between bundles. Consequently, the obtained results confirmed that corona power losses can be minimized, without needing the traditional procedures that involve increasing either the conductor radius or its height above the ground. The results of the proposed numerical approach concurred well with the present and past laboratory results. [ABSTRACT FROM AUTHOR]

In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems. [ABSTRACT FROM AUTHOR]

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques. [ABSTRACT FROM AUTHOR]