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Renewable-based distributed generation (DG) offers economic and environmental benefits, but integrating it into the distribution network (DN) poses technical challenges. Hosting capacity (HC) defines the maximum amount of DG that can be safely integrated into the DN without violating technical constraints, with various methods available to enhance the DN and increase HC. This article proposes an optimization-based demand response (DR) program using dynamic pricing to enhance the HC. A hierarchical bi-level optimization framework is developed for the DR program, where the upper level optimizes the hosting capacity depending on the optimal load profiles optimized in the lower level. Additionally, DR is combined with smart inverters’ (SIs) optimal Volt/VAR control to further improve the HC. The pelican optimization algorithm (POA) is employed to solve the optimization problem and is compared with four other optimization techniques, demonstrating its good performance. The proposed planning scheme is implemented on the IEEE 33-bus test system and a real DN consisting of 59 buses in Cairo, Egypt. The results show that applying DR alone can increase the average maximum HC value by 29.4%, while combining DR with optimal Volt/VAR control can achieve a 39.6% increase.

Peer-to-peer (P2P) energy trading has emerged as an innovative approach for selling electricity from prosumer to consumer at the distribution level. This paper is the first to conduct a techno-economic assessment of P2P energy trading in Aswan, Egypt. Different scenarios under different electricity tariffs, which consider photovoltaic systems, energy storage systems, and electric vehicles deployment, are analyzed to assess the performance of P2P trading considering different distributed energy resources (DERs) installations. The variety of these scenarios enables a thorough analysis of P2P trading and a clear comprehension of how P2P trading impacts distribution networks. The study offers new perspectives on the impacts of implementing P2P trading on the distribution network since it uses a real demand profiles. Results show that P2P energy trading can reduce community electricity costs, improve self-consumption by reducing exports to distribution system operator, and rise self-sufficiency compared to home energy management system (HEMS). The distribution network operation limits are not violated in any of the studied scenarios and electricity tariffs. The impacts on the distribution network for P2P trading scenarios and equivalent HEMS are very similar for flat tariff. However, for time of use tariff, P2P trading scenarios with flexible devices result in higher impacts on the distribution network than the equivalent HEMS.

The issue of power quality (PQ) has become increasingly critical in modern power systems due to the growing integration of sensitive electronic devices, electric vehicles, and renewable energy sources. Among the various PQ concerns, harmonics have emerged as a significant contributor to the overheating of power system components. This overheating phenomenon can have detrimental effects, including reduced equipment lifespan, increased maintenance costs, and unexpected failures. This comprehensive review paper provides an in-depth examination of the impact of harmonics on power system components, with a focus on the overheating problems they induce. The paper delves into an analysis of PQ indices and standards, underscoring the necessity for efficient mitigation techniques to address harmonic-related overheating and other PQ concerns. The review explores the key PQ devices employed to mitigate harmonics, such as passive power filters, active power filters, and advanced devices like D-STATCOM, unified PQ conditioners, and dynamic voltage restorers. Furthermore, it investigates the various control algorithms and optimization techniques utilized in the design of these power filters, emphasizing their pivotal role in addressing harmonics and preventing overheating. It also discusses the applicability of artificial intelligence techniques in mitigating power harmonics and improving PQ in modern power grid studies.

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.

Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for estimating the unidentified parameters of PV models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer's PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters' identification of PV of solar cells.

Recent hybrid simulators (or co-simulators) of the electric power system are focused on scientific and research features to propose and develop novel and more accurate simulators. The present paper demonstrates one more hybrid modelling approach based on application and combination of three modeling approaches all together: physical, analog and digital. The primary focus of the proposed approach is to develop the simulation tool ensuring such vital characteristics as three-phase simulation and modeling of a single spectrum of processes in electric power system, without separation of the electromagnetic and electromechanical transient stages. Moreover, unlimited scalability of the electric power system model and real-time simulation to ensure the opportunity of data exchange with external devices have been considered. The description of the development of the hybrid model of back-to-back HVDC link based on the proposed approach is discussed and analyzed. To confirm properties of the mentioned hybrid simulation approach and hybrid model of back-to-back HVDC link, the simulation results of the interconnection of non-synchronously operating parts of the electric power system; power flow regulation; dynamic response to external fault and damping of power oscillation in electric power system are presented and examined. Moreover, to confirm the adequacy of the obtained results, the comparison with a detailed voltage source converter HVDC model (Simulink Matlab) and Eurostag software are introduced.

This article presents a novel method for identification of synchronous generator parameters that is based on sudden short-circuit test data and a novel metaheuristic algorithm, called the adaptive black widow optimization algorithm. Unlike traditional methods defined by IEEE and International Electrotechnical Commission (IEC) standards, which rely on the armature current oscillogram, the method proposed in this article uses the field current waveform during the short-circuit test. Moreover, the standard graphical method for extraction of the generator parameters is replaced by an effective metaheuristic algorithm. The proposed algorithm tends to minimize the normalized sum of squared errors (NSSE) between simulation and experimental results. The applicability and accuracy of the proposed optimization technique are verified using experimentally obtained results from a 100-MVA synchronous generator at the Bajina Basta hydropower plant.

This paper presents the economic analysis and optimal energy management of a grid-connected MG that comprises renewable energy resources and different battery storage technologies with different characteristics such as initial charge, depth of discharge, and the number of charging/discharging cycles to minimize the total operating cost of the system by maximizing the benefits of BSS, minimizing the investment and replacement cost of BSS, and minimizing the operation and maintenance cost of DGs. Several constraints are considered, such as the output power limits of the distributed generators, the limits of power imported from or exported to the grid, load balance, and other sets of battery storage constraints. The general algebraic modeling system (GAMS) is used to solve the deterministic optimization problem. Second, stochastic optimization is used to solve the deterministic problem with market price uncertainty. Third, robust optimization using the information gap decision theory is presented to model the electric load uncertainty. The validity and effectiveness of the proposed solution are explained by comparing the results obtained by GAMS to the results obtained by other optimization techniques presented in the literature.

This article assesses the energy management of reconfigurable residential smart hybrid AC/DC microgrids considering the combined heat and power (CHP) loads as well as the electric vehicles charging/discharging behaviors. A holistic model is developed for the proton exchange membrane fuel cell to retrieve the unwanted thermal energy generated at the operation time. The proposed model makes use of the unoccupied capacity of the fuel cell for producing/storing hydrogen for the later usage and increasing its efficiency. A stochastic framework is designed using point estimate method (PEM) to capture the uncertainties of the photovoltaic and wind turbine forecast error, power company price, the operating temperature of the proton exchange membrane fuel cell, the price for natural gas, price for selling hydrogen, and the pressure of the H2 and O2 in the fuel cell stack. The PEM approach has shown superior advantages in terms of accuracy and running time. Considering the complex and nonlinear structure of the proposed framework, a proficient optimization technique based on the teacher learning algorithm (TLA) is devised. A two-phase modification method is proposed to increase the algorithm variety and help its convergence characteristics. The performance of the proposed algorithm is compared with the TLA, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). For enhancing the security of the energy and data transaction within the system, a directed acyclic graph (DAG)-based security framework is introduced to guarantee the performance of the system against the subversive accesses. By using this scheme, the essential data of the units are recorded and secured in the form of public, private and transaction blockchains. The economic characteristics of the proposed method are assessed on a residential hybrid AC-DC microgrid test system.

Controlled islanding is known as a last resort to prevent power system blackouts. The most important challenge in this context is the appropriate selection of separation points in a very short time considering the practical constraints. In this regard, several islanding methods have been proposed until now. Among them, the methods that provide the appropriate solutions are usually very complicated and difficult to implement in the real-time application of power system separation. Also, all methods result in one islanding solution, which may not be optimal due to the limitation of the commonly used objective function. To overcome these limitations and reach a comprehensive solution, this paper proposes a straightforward multi-solution approach through a suggested hierarchical spectral clustering algorithm. In this concept, the most desirable islanding scenario could be selected based on secondary criteria to reach more sustainable islands. In the proposed method, the hierarchical clustering algorithm, which has good records in other applications, is improved such that the generator coherency constraint can be considered in the clustering process. Meanwhile, the transmission lines without remote-controllable circuit breakers could be easily excluded from the islanding solutions. The proposed method is tested using the model of the IEEE 39-bus test system. Furthermore, to evaluate the computational effectiveness and accuracy of the method in a large-scale grid, the model of Khorasan Regional Electric Company (KREC) power system (which is the biggest part of Iran power system) is used. The comparative analysis with the state-of-the-art methods verifies the superiority of the proposed approach.

Battery lifetime represents a significant concern for the techno-economical operation of several applications based on energy storage. Moreover, the charging method is considered as one of the main critical elements in defining and influencing the operating lifetime of batteries. Several charging techniques have been addressed in the literature, however almost all of them are suffering from lack of temperature feedback in order to maintain battery lifetime. This paper presents a new high-reliable charging method for battery energy storage systems (ESSs). The proposed temperature compensated multi-step constant current (TC-MSCC) method is developed based upon the modified (MSCC) charging method. It enhances the operating lifetime of batteries by employing a feedback from the battery temperature to control the duration and starting time of each charging current step. Compared with the traditional charging methods addressed in the literature, the proposed TC-MSCC method achieves faster charging than the conventional constant current (CC) and the constant-current constant-voltage (CC-CV) methods. Moreover, the proposed TC-MSCC method possesses longer operating battery lifetime with reduced thermal stresses compared to the traditional MSCC methods. The proposed charging method is verified by simulation and experimental results using 9 Ah lead-acid battery. However, the new proposed TC-MSCC method is generalized and can be applied to various types of batteries. The detailed performance comparisons and results show the superiority of the proposed methods over the most widespread charging methods in the literature.