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The stochastic fractal search (SFS) algorithm, among population-based metaheuristic automation algorithms, is a robust optimization algorithm for solving optimization problems in different fields of science, inspired by the diffusion feature and natural growth phenomenon seen regularly in random fractals. However, as in population-based optimization algorithms, it is a great challenge to effectively design the selection process in the SFS method. To imitate the selection process in nature effectively and accurately, the dynamic-fitness-distance-balance (dFDB) selection method has been used in the SFS algorithm in six different versions. In this way, the dFDB-SFS algorithm has been developed, which more effectively mimics nature with exploitation, exploration, and balanced search capabilities. Firstly, the performance of the proposed dFDB-SFS algorithm was investigated in CEC 2020 benchmark test functions. Wilcoxon and Friedman statistical analyses of the results obtained from the test functions were made and according to these analysis results, the best version of the proposed approach was determined. Secondly, the performance of the proposed algorithm was investigated in determining photovoltaic module parameters, which is one of the real-world engineering problems. In this article, the dFDB-SFS algorithm uses the root mean square error (RMSE) as the objective function to estimate the unknown parameters of the single diode model (SDM), double diode model (DDM), and PV module models. In terms of a quantitative and qualitative performance evaluation, it reveals that the proposed algorithm provides better results than other proposed algorithms in terms of accuracy and robustness when obtaining PV parameters. [ABSTRACT FROM AUTHOR]

Summary: In this paper, several methods are proposed to find the load flow (LF) solution of large‐scale ill‐conditioned systems. These methods are based on the Adams‐Bashforth formulation. With the use of the proposed methods, the convergence problems of the most popular LF methods are addressed especially when the flat initial guess point is considered. The proposed methods are validated using three ill‐conditioned systems: the modified IEEE 300‐bus system, the 3375‐bus Polish system, and a 13 659‐bus portion of the European transmission system. The obtained results prove that the proposed methods constitute a family of robust and efficient methods for the LF problem. [ABSTRACT FROM AUTHOR]

The reliability and effectiveness of today's electrical grids rely heavily on optimal reactive power dispatch (ORPD). The ORPD problem gets more challenging to resolve in the setting of ever-increasingly dynamic and complex systems. To handle the ORPD while also considering the existence of electric vehicles, this research introduces a novel technique: the Hybrid Particle Swarm Optimization and Sea Horse Optimization (PSOSHO) algorithm. Byreducing both active power loss and voltage variation, the proposed PSOSHO approach aims at improving the efficiency and reliability of the power grid. Simulation studies on reference power grids, including the IEEE 30-bus and IEEE 57-bus networks, verified its efficacy. Existing metaheuristic optimization techniques are compared using the same restrictions, governing parameters, and data. The results show that the PSOSHO method is trustworthy and effective in resolving the ORPD problem. One of the most pressing issues facing today's electricity grids is how to accommodate the growing number of electric vehicles. Real data on electric vehicles are incorporated in the analyses to obtain a realistic study. The suggested PSOSHO algorithm is a significant step forward in this area, providing a reliable and effective answer to the problem of optimum reactive power dispatch and helping to ensure the long-term viability of power systems in the age of electromobility. • This paper presents a novel optimal energy management of smart grids. • The model includes electric vehicles variability and uncertainties. • A practical study is presented including real data of electric vehicles. • A novel hybrid optimization algorithm is proposed to solve the problem. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

The principal contribution of this article focusing on obtaining a precise model of the lithium-ion battery (LiB). This in fact affects the simulation analyses and dynamics of such batteries in several applications including electric vehicles, microgrids, distribution systems, and smart grids. The main challenge here is the heavy nonlinearity of the optimization problem. The proposed gazelle optimization algorithm (GOA) is utilized in minimizing the fitness function, which depends on the integral squared error approach. The error is considered between the identified and practical battery voltage. The validity of the proposed approach is checked considering various operating conditions such as the loading effect, fading impact, and other dynamic responses. The effectiveness of introduced approach is validated by comparing the simulation results with practical results on a real Panasonic LiB of 2.6 Ahr capacity. These results are performed by MATLAB software. Furthermore, GOA-based LiB model is compared with various heuristic and conventional algorithms-based models. With the proposed GOA-based LiB model, an efficient and accurate battery model can be built. • This paper presents a novel optimal lithium-ion battery model in smart grids. • The model can be used with electric vehicles applications. • Practical measurements are implemented to verify the battery model. • The GOA methodology is proposed to solve the optimization problem. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

The power grid faults study is crucial for maintaining grid reliability and stability. Understanding these faults enables rapid detection, prevention, and mitigation, ensuring uninterrupted electricity supply, safeguarding equipment, and preventing potential cascading failures, ultimately supporting the efficient functioning of modern society. This paper delves into the intricate challenge of ensuring the robust operation of wind turbines (WTs) in the face of fault conditions, a matter of substantial concern for power system experts. To navigate this challenge effectively, the implementation of symmetrical fault ride-through (SFRT) and asymmetrical fault ride-through (AFRT) control techniques becomes imperative, as these techniques play a pivotal role in upholding the stability and dependability of the power system during adverse scenarios. This study addresses this formidable challenge by introducing an innovative SFRT–AFRT control methodology based on rotor components optimization called RCO tailored for the rotor side converter (RSC) within a doubly-fed induction generator (DFIG) utilized in wind turbine systems. The proposed control strategy encompasses a two-fold approach: firstly, the attenuation of both positive and negative components is achieved through the strategic application of boundary constraints and the establishment of reference values. Subsequently, the optimization of the control characteristic ' β ' is accomplished through the utilization of a particle swarm optimization (PSO) algorithm integrated within an optimization loop. This intricate interplay of mechanisms aims to optimize the performance of the RSC under fault conditions. To measure the efficacy of the proposed control technique, a comparative analysis is conducted. Fractional-order (FO) proportional–integral–derivative (PID) controllers are employed as an additional method to complement the novel approach. By systematically juxtaposing the performance of the proposed SFRT–AFRT control technique with the FO-PID controllers, a comprehensive evaluation of the proposed approach's effectiveness is attained. This comparative assessment lends valuable insights into the potential advantages and limitations of the novel control technique, thereby contributing to the advancement of fault mitigation strategies in WT systems. Finally, the paper highlights the economic viability of the proposed control method, suggesting its suitability for addressing broader power network issues, such as power quality, in future wind farm research. [ABSTRACT FROM AUTHOR]

In recent years, more efforts have been exerted to increase the level of renewable energy sources (RESs) in the energy mix in many countries to mitigate the dangerous effects of greenhouse gases emissions. However, because of their stochastic nature, most RESs pose some operational and planning challenges to power systems. One of these challenges is the complexity of solving the optimal power flow (OPF) problem in existing RESs. This study proposes an OPF model that has three different sources of renewable energy: wind, solar, and combined solar and small-hydro sources in addition to the conventional thermal power. Three probability density functions (PDF), namely lognormal, Weibull, and Gumbel, are employed to determine available solar, wind, and small-hydro output powers, respectively. Many meta-heuristic optimization algorithms have been applied for solving OPF problem in the presence of RESs. In this work, a new meta-heuristic algorithm, weighted mean of vectors (INFO), is employed for solving the OPF problem in two adjusted standard IEEE power systems (30 and 57 buses). It is simulated by MATLAB software in different theoretical and practical cases to test its validity in solving the OPF problem of the adjusted power systems. The results of the applied simulation cases in this work show that INFO has better performance results in minimizing total generation cost and reducing convergence time among other algorithms. [ABSTRACT FROM AUTHOR]

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the Osprey Optimization Algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by ±5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions. • This paper presents novel Osprey optimization algorithm-based optimal lithium-ion battery model. • The model can be used with electric vehicles and power grid applications. • Practical measurements are implemented to verify the battery model and sensitivity analyses. • The OOA approach is proposed to solve the optimization problem. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

The integration of photovoltaic generation systems and variable demand can cause instability in the distribution network, due to power fluctuations and the increase in reactants, particularly in the industrial sector. In response, photovoltaic units have been equipped with local storage systems, which eventually absorb power fluctuations and improve installation performance. However, during this procedure other functionalities that energy storage could provide are neglected. Consequently, this study provides a multi-mode energy monitoring and management model that enables voltage regulation, frequency regulation and reactive power compensation through the optimal operation of energy storage systems. With this objective, a smoothing control algorithm is developed that interacts with parameters of the electrical grid at the common connection point and also allows the compensation of reactive power based on an industrial demand profile. This strategy uses the Long short-term memory neural network of historical demand data prior to energy consumption with a relatively low RMSE of 1.2e-09. The results are previously validated in a development environment using a real-time OPAL-RT simulator and tests in the electrical Microgrid laboratory at the University of Cuenca. This configuration allows establishing a demand forecasting model that improves the supervision, automation and analysis of daily energy production. A series of results are provided and analyzed that demonstrate that the new tool allows taking advantage of the provision of multimode functionalities, achieving optimal voltage regulation and improving power quality by reducing the total harmonic distortion THD (V) and THD (I) indices by 0.5. % and 2 % respectively. [ABSTRACT FROM AUTHOR]

• Presenting a multi-stage mechanism for managing energy interactions in renewable-based electricity grids. • Executing AS and regulation markets in day-ahead and intraday horizons, respectively, for more accurate resource allocation. • Assessing the technical and economic effects of the DSs participation in the SR market. • Evaluating the impact of ESSs, DR aggregators and V2G services on reducing regulation market costs. • Reducing system LMP and improving voltage profiles during peak period through the participation of DR aggregators, EV users and ESSs in the regulation market. The growing uncertainties in power operations due to the integration of renewable generations (RGs) and electric vehicles (EVs) into electricity grids have amplified the significance of ancillary services (AS). These services have become essential to ensure the sustainable functioning of the grid. In light of this, we introduce a two-stage optimization framework to manage competitive energy and AS markets at the interface of the transmission system (TS) and distribution system (DS). Our approach takes into account a comprehensive set of economic, technical, and security factors. This mechanism is structured in two stages: the first stage encompasses the energy market, while the second stage encompasses AS markets. Spinning reserve (SR) is supplied by conventional thermal units (TUs), whereas regulation capacity is provided by energy storage systems (ESSs), fast-response generators, electric vehicles (EVs), and demand response (DR) aggregators. We applied this mechanism to a 30-bus transmission network connected to four 10-node DSs and utilized the GUROBI solver in GAMS for solving. The simulation results demonstrate that the engagement of DSs in the SR market reduces the reliance on costly TUs, thereby decreasing system costs by approximately 10%. Furthermore, involving ESSs, EVs, and DR aggregators in the regulation market enhances technical performance and results in a 6.91% reduction in total system costs. This approach provides a robust solution to the evolving challenges posed by RGs and EVs in modern electricity grids. [ABSTRACT FROM AUTHOR]

• This paper presents a novel application of AHA to tune the controllers parameters. • The super twisting sliding mode controller is applied to microgrids. • The results of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. It is essential to improve optimal control for an autonomous microgrid operation. This paper presents a novel application of an artificial hummingbird algorithm for achieving optimal control of an autonomous microgrid. It also suggests a comparative analysis for the optimal super-twisting sliding mode control with both optimal fuzzy logic control and optimal proportional-integral control. The proposed super-twisting sliding mode controller parameters are adequately designed by using the AHA optimization method; meanwhile, the FLC and PI controllers are designed by using the African vultures and the Gorilla troops optimization algorithms, respectively. A MATLAB/Simulink environment is used to verify the effectiveness of the suggested microgrid control methods. The overall system detailed approaches for modeling, designing, and controlling are introduced. The investigation of the ST-SMC approach is also tested in a real-time setting. OPAL-RT 4510 rapid control prototyping and OPAL-RT 8660 data acquisition are used to implement the suggested controller. The verification results prove the superiority of the ST-SMC based on the AHA optimization technique compared with other optimal control techniques. [ABSTRACT FROM AUTHOR]

Enhancing the reliability of energy networks and minimizing downtime is crucial, making self-healing smart grids indispensable for ensuring a continuous power supply and fortifying resilience. As smart grids increasingly incorporate decentralized prosumers, innovative coordination strategies are essential to fully exploit their potential and improve system self-healing capabilities. To address this need, this paper presents a novel bi-level strategy for managing the self-healing process within a smart grid influenced by Hydrogen Refueling Stations (HRSs), Electric Vehicle Charging Stations (EVCSs), and energy hubs. This approach taps into the combined potential of these prosumers to boost system self-healing speed and reliability. In the initial stage, the Smart Grid Operator (SGO) conducts self-healing planning during emergencies, communicating required nodal capacities to prevent forced load shedding and outlining incentives for smart prosumers. Subsequently, prosumers schedule their activities and contribute flexible capacities to the SGO. Bridging the first and second stages, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm ensures convergence between the SGO and prosumer schedules within a decentralized framework. This strategy underwent implementation on a 118-node distribution system using GAMS. Results demonstrate that the proposed concept reduces Forced Load Shedding (FLS) by 32.04% and self-healing costs by 17.48% through effective utilization of smart prosumers' flexible capacities. Furthermore, outcomes indicate that the SGO reduces FLS by 6.69% by deploying Mobile Electrical Energy Storages (MEESs) and Mobile Fuel Cell Trucks (MFCTs) to critical nodes. • Introducing a bi-level self-healing strategy for smart grids with smart prosumers • Developing an adaptive ADMM for decentralized coordination among SGO and prosumers • Utilizing the flexible capacities of energy hubs, EVCSs and HRSs to reduce FLS • Embedding a robust strategy into the bi-level methodology to address uncertainties [ABSTRACT FROM AUTHOR]

The rise in distributed renewable generation and active prosumers has heightened power exchanges in Distribution Systems (DS), causing congestion issues. This paper presents a four-layer optimization model for Distribution System Operators (DSOs) to efficiently manage network congestion through smart prosumers. The first two layers involve an iterative bidding process in the energy market to maximize social welfare during settlement. The third and fourth layers address congestion in the market, employing an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm for decentralized coordination between DSOs and smart prosumers, including Microgrids (MGs), Energy Hubs (EHs), and Electric Vehicle Parking lots (EVPLs). Layer 1 optimizes market player bids, layer 2 maximizes social welfare, layer 3 handles congestion using various strategies, and layer 4 reschedules smart prosumers based on DSO's requests. Implemented on a 118-bus DS in GAMS, the model demonstrates the efficacy of the proposed adaptive ADMM in ensuring player privacy and market efficiency. Additionally, integrating the market-based mechanism with Distribution Feeder Reconfiguration (DFR) optimally utilizes smart prosumers, resulting in a 46.85% reduction in net DSO costs. This practical implication underscores the model's effectiveness in addressing network congestion challenges and providing economic benefits for DSOs. [ABSTRACT FROM AUTHOR]

In recent years, batteries have evolved increasingly overall in numerous applications. Among batteries, LIBs are the most advantageous technology because of their raised power and energy densities. This study proposes a hybrid method, combining a war strategy optimization (WSO) algorithm and a hierarchical deep learning neural network (HDLNN) named WSO-HDLNN, to identify the parameters of lithium-ion batteries (LIBs) used in hybrid and electric vehicles (HEVs and EVs). The hybrid approach utilizes the WSO technique to generate parameters and predicts the components using the HDLNN approach. The proposed method significantly reduces the estimated voltage and measured voltage error while effectively identifying the battery parameters. The MATLAB/SIMULINK platform is employed for implementation and comparison with other existing methods such as differential evolution (DE), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO). Simulation results demonstrate the efficiency of the proposed WSO-HDLNN strategy in reducing battery voltage errors by accurately identifying parameters and improving voltage estimation accuracy. Further, notable novelty in this work is the integration of the WSO algorithm with the HDLNN in the WSO-HDLNN protocol for LIB parameter identification. This fusion is distinct as it synergizes the strengths of optimization and deep learning, enhancing efficiency and accuracy in LIB parameter estimation. The WSO algorithm introduces a novel war strategy element, leading to faster convergence to optimal solutions, significantly reducing computational time. Moreover, the WSO-HDLNN approach showcases robustness in handling noisy data, a unique feature ensuring accurate parameter estimates amidst real-world uncertainties, setting it apart from conventional LIB modeling methods. • Hybrid WSO-HDLNN technique for identifying LIB parameters in electric vehicles. • WSO-HDLNN minimizes voltage errors, surpassing existing methods. • WSO's inclusion facilitates faster convergence and reduced computational time. [ABSTRACT FROM AUTHOR]

Multi-energy communities (MEC) integrated with renewable resources are known as a cost-effective and highly efficient solution to meet the diverse energy needs of subscribers. The increasing integration of MECs with electricity and natural gas networks has made it necessary to design new frameworks to optimize their energy management and then facilitate their participation in competitive energy markets. Hence, this article presents a bi-level optimization strategy for the decentralized coordination of MECs in competitive electricity and gas markets, in which the system operator adopts a robust technique to deal with operational uncertainties. The daily planning of MECs is performed in the upper level, while in the lower level, the planning of electricity and natural gas networks takes place. An adaptive alternating direction method of multipliers (ADMM) algorithm has also been introduced to settle the electricity and natural gas markets in a decentralized space while considering the CO 2 footprint tax. The proposed strategy is implemented on a system containing a modified 69-bus IEEE distribution electricity network (EN) and a 65-node natural gas network (NGN). The results obtained from the case studies show that the proposed adaptive ADMM algorithm reached the optimal point in 113 iterations less than the original version, reducing the solution time by 48.01 %. The results prove that the proposed strategy has been able to coordinate the decentralized MECs with the least data sharing in the competitive electricity and gas markets. Additionally, it effectively utilizes the capabilities of renewable-based assets, storage systems, and smart EV charging to reduce the CO 2 footprint, alleviate congestion, and improve the voltage and gas pressure profiles, while leading to reduced market clearing prices. • Providing an adaptive ADMM for MECs' coordination with electricity and gas markets. • Addressing emission reduction by embedding CO 2 tax in the market clearing process. • Reducing CO 2 footprint through coordinating storage systems and daily scheduling. • Reducing settlement prices and mitigating network congestion by smart EV charging. • Embedding a robust method into the proposed strategy to handle uncertainties. [ABSTRACT FROM AUTHOR]

Ocean thermal and power energy systems are promising driving forces for seashore coastal communities to achieve net-zero energy/emission target, whereas energy planning and management on ocean thermal/power and distributed building integrated photovoltaic (BIPV) systems are critical, in terms of serving scale sizing and planning on geographical locations of district building community, and cycling aging of battery storages. However, the current literature provides insufficient studies on this topic. This study aims to address this research gap by transforming towards zero-energy coastal communities from the district level in subtropical regions, including centralised seawater-based chiller systems, distributed BIPVs and coastal oscillating water column technologies, as well as multi-directional Vehicle-to-Building energy interaction paradigms. Advanced energy management strategies were explored to enhance renewable penetration, import cost-saving, and deceleration of battery cycling aging, in response to relative renewable-to-demand difference, off-peak grid information with low price, and real-time battery cycling aging. Furthermore, in accordance with the power generation characteristic of two wave stations (i.e., Kau Yi Chau (KYC) and West Lamma Channel (WLC)) in Hong Kong, energy system planning and structural configurations of the coastal community were proposed and comparatively studied for the multi-criteria performance improvement. Research results showed that, compared to an air-cooled chiller, the water-cooled chiller with a much higher Coefficient of Performance (COP) will reduce the energy consumption of cooling systems, leading to a decrease in total electric demand from 134 to 126.5 kWh/m2·a. The scale for the net-zero energy district community with distributed BIPVs and oscillating water column was identified as 5 high-rise office buildings, 5 high-rise hotel buildings, 150 private cars and 120 public shuttle buses. Furthermore, the geographical location planning scheme on the Case 1 (office buildings close to KYC, and hotel buildings close to WLC) was identified as the most economically and environmentally feasible scheme, whereas the Case 3 (only office buildings are planned close to all power supply with oscillating water column) showed the highest flexibility in grid electricity shifting, together with the highest value of equivalent battery relative capacity. This study demonstrates techno-economic performances and energy flexibility of frontier ocean energy technologies in a coastal community under advanced energy management strategies, together with technical guidance for serving scale sizing and planning on geographical locations. The research results highlight the prospects and promote frontier ocean energy techniques in subtropical coastal regions. • Ocean energy for sustainability in buildings and integrated electric vehicles. • Zero-energy coastal community transformation with ocean and solar energy. • Energy planning and geographical location on districts for high efficiency. • Grid responsive strategies for cost-saving and energy flexibility. • Frontier ocean energy technologies in a coastal community with technical guidance. [ABSTRACT FROM AUTHOR]

• A novel stochastic-robust approach is developed for energy communities. • Individual and collective assets are considered and analysed. • The problem is solved using a novel three-stage procedure. • Operating cost is incremented 77$ when the degree of robustness grows. • Collective storage systems help to reduce unserved demand and wasted generation. This paper presents an extended model for aggregation of operational settings of droop-controlled Distributed Energy Resources (DERs) in Microgrid (MG) planning, thus deriving in a novel problem named Optimal Siting, Sizing and Setting (OSSS) of DERs. The proposed approach precisely deals with the power flow formulation for autonomous MG as an essential constraint for accurate results. Furthermore, the proposed OSSS model considers the frequency and voltage dependence characteristic of loads. The problem casts in multi-objective formulation to consider different technical, power quality and controlling aspects. This will help the designer to provide efficient decision for allocation of DERs. The effects of realistic load model as well as power sharing error index are elaborately investigated in simulation results. The model is thus further validated and its usefulness demonstrated. Finally, the performance of multi-objective OSSS model is verified by comparing the results with the previous simplified models presented in the recently published literatures. [ABSTRACT FROM AUTHOR]

Increasing the connection of renewable energy sources and electric vehicles (EVs) to modern cities with smart homes (SHs) makes the need for new mechanisms to coordinate them with the operation of transmission (TN) and distribution networks (DNs) inevitable since the uncertain behavior of the above-mentioned resources can create various technical and economic challenges for the operators. Hence, this article introduces a tri-stage mechanism for the joint management of the energy and ancillary services markets in coordinated TN and DN, taking into account wind and solar renewable resources, SHs and EVs. In the first stage of this mechanism, SHs do their daily planning with the possibility of participating in the regulation market and send it to the DN operator. In the second stage, DN operator determines its strategy for participation in the energy and ancillary services markets according to the programs received from SHs and sends it to the TN operator. Finally, in the third stage, the TN operator settles the markets according to the plans received from the DN operators. This model is designed in the mixed-integer linear programming (MILP) format and simulated using CPLEX solver in GAMS. The simulation outputs illustrate that SHs by controlling their internet-of-things (IoT)-based appliances and EVs have provided cheap services in the regulation market and subsequently reduced the costs of this market by 10.4%. Also, the results reveal that the participation of large-scale industrial units in the demand response program (DRP) not only reduces the costs of the energy and ancillary services markets but also improves the voltage profile during the peak period. • Co-managing of energy, responsive reserve and regulation markets in TN-DN interface. • Leveraging SHs, EVs, ESDs and private generators to reduce ancillary service costs. • Improving technical and economic factors of TN by applying DRP on industrial loads. • Settling wholesale markets considering the optimal programs of DNs and SHs. [ABSTRACT FROM AUTHOR]

• A novel optimal FLC is presented for performance enhancement of microgrids. • A detailed model of microgrids and its real-time implementation is introduced. • The optimal FLC is based on the AVOA strategy. • The proposed model is compared with other recent models. • The effectiveness of the proposed model is verified under different disturbances. Distributed power grid systems require more intelligent and adaptable control management and optimization to preserve the best performance. These balance the generated power source and load demand even during severe interruptions. This research proposes a comparative analysis of an autonomous microgrid system optimized by fuzzy logic control and proportional-integral controller. The microgrid fuzzy logic control parameters are designed using the African vulture and Particle swarm optimization algorithms. At the same time, the proportional-integral controller is designed by using the Gorilla troops optimization algorithm. The suggested microgrid control techniques are verified using a MATLAB/Simulink environment. The overall system's detailed modeling and control methodologies are also presented. The investigation of the fuzzy logic control approach is also tested in a real-time setting. It is implemented by using OPAL real-time 4510 rapid control prototyping. The attained results prove the superiority of the fuzzy logic controller based on the African vulture optimization technique compared with other optimal control techniques. It is applied to give an ideal design for the fuzzy logic control parameters to preserve adequate power to the different loads. The proposed optimal controller is able to bring a robust and effective control with an accuracy of approximately 4%, 5%, 28%, and 26% than other techniques under different scenarios. Index Terms: African Vultures Optimization algorithm (AVOA), Autonomous Microgrid, Decentralized energy generation, Fuzzy Logic Control, Opal RT-LAB, Proportional plus integral controller. [ABSTRACT FROM AUTHOR]

Optimal reactive power dispatch (ORPD) in electrical networks is essential for the secure and stable operation of the entire power system; it also significantly impacts the economic situation. However, the solution of ORPD is highly complex while considering the multiple variables, time-varying characteristics, dynamic loads such as electric vehicles, and operational constraints. The ORPD problem is classified as a non-linear, non-convex complex problem. Thus, a robust optimization technique should be utilized to solve the ORPD. This paper presents a novel Enhanced Transient Search Optimization (ETSO) technique for the optimal solution of the ORPD problem by integrating electric vehicles (EVs). The proposed ETSO implemented for the optimal solution of ORPD consequently minimize the active power loss and voltage deviation at the load buses. The proposed method is tested and verified on the IEEE 30-bus, the IEEE-57 bus, and IEEE 118-bus systems. The simulation results show the robustness and efficiency of the proposed ETSO optimization in solving the ORPD problem by evaluating and comparing it with other well-established metaheuristic optimization methods under the same system data, control variables, and constraints. Statistical features of the proposed ETSO algorithm and the results obtained led to an improvement in the performance of the power systems. • Optimal energy management and reactive power dispatch is presented. • The model is solved using a novel enhanced TSO algorithm. • The impact of electric vehicles on the system performance is studied. • Real data of electric vehicles are implemented for obtaining realistic responses. • The results of the proposed method are compared with other models. [ABSTRACT FROM AUTHOR]

• A hierarchical two-layer distributed control approach is established. • The proposed approach effectively regulates DERs (units) voltage and frequency. • The proposed approach fairly allocates MG reactive load demands among multiple units. • The proposed approach ensures multiple units' economic operation. • Simulation validates the proposed approach for units coordinated operation. Overall, voltage and frequency(V/F) stability are essential in microgrids (MGs) to ensure reliable and high-quality power supply for critical loads and to support the integration of renewable energy sources (RESs) while maintaining the stability of the power system. Therefore, V/F stability is particularly important for causes such as load balancing, quality of power supply, renewable energy integration, and grid stability. This study aims to introduce an interactive control system for hybrid MGs (HMGs) based on distributed energy resources (DERs). Here, the defined control strategy objective is to ensure the four system parameters' stability, including voltage/frequency (V/F) and active/reactive (P/Q) power for the units. In this study, the research innovation consists of two layers. The first layer aims to adjust the unit V/F, using an internal voltage and current controller loop combined in the power droop controller (PDC). The second layer is concerned with the steady-state error minimization, created in the first layer due to the droop controller performance. Therefore, the secondary distributed V/F control strategies are designed based on finite-time consensus theory (FTCT) owing to its resistance and stability against various load perturbations and system disturbances, which makes flexible convergence time possible according to different user preferences and operating conditions. The simulation results prove the efficiency of the proposed method strategy. Furthermore, the values of improving the amplitude of oscillations and fluctuations for V/F components vary from 0.02pu-0.2pu and 0.178pu-0.216pu, respectively. [ABSTRACT FROM AUTHOR]

• Presenting a nested model to coordinate TSO and DSO in energy and flexibility markets with the participation of SBs, EV fleets and DERs. • Presenting a novel DRP design mechanism to build time-varying incentive tariffs based on TSO's flexibility requirements. • Flexibility-oriented scheduling of SBs considering occupants' thermal comfort in a decentralized space with minimal data sharing. • Improving operational security by developing a two-stage interval-based optimization method. • Using KKT conditions, SDT and Big-M method to convert the bi-level NLP problem into a convergent single-level LP problem. Emerging renewable-based transmission and distribution systems, despite many environmental and economic benefits, due to the intermittent nature of their production resources, compared to traditional systems, need more flexibility capacities, which necessitates the need for more suppliers of flexibility. To deal with these challenges, a nested framework is presented to derive the required flexibility of the transmission system operator (TSO) from distributed energy resources (DERs) and active end-users such as smart buildings (SBs) and electric vehicle (EV) fleets at the distribution level. To this end, a novel mechanism to design the demand response program (DRP) is introduced in which tariffs with time-varying rewards are built based on flexibility requirements. The coordination between TSO and distribution system operator (DSO) is initially modeled as a bi-level non-linear programming (NLP) problem, in which the upper-level is day-ahead (DA) operational planning of DS considering the schedules received from SBs, while the lower-level is DA operational planning of the TS. The bi-level NIL problem is transformed into a single-level linear programming (LP) problem by Krush Kuhn Tucker (KKT) conditions, Big-M method and Strong Duality Theory (SDT), which makes it computationally tractable. Finally, a two-stage interval-based algorithm solves the obtained single-level problem to secure the planning against uncertainties where battery energy storage systems (BESSs) are responsible for dealing with extreme conditions. The simulation results testify that the proposed interval-based nested framework has improved the economic, technical and security aspects of the TSO-DSO coordination since it has reduced the daily costs of the energy and flexibility markets, relieved lines congestion and improved voltage characteristics. [ABSTRACT FROM AUTHOR]

• Presenting a hierarchical framework for managing energy markets of modern MESs. • Presenting a structure for the hydrogen exchange through the transportation system. • Introducing a novel deep learning-based mechanism to design dynamic DR schemes. • Enhancing technical and economic aspects of MES by modifying the behavior of EVs and FCVs. • Managing the scheduling risk using a and a risk-averse IGDT-based strategy. The integrated exploitation of different energy infrastructures in the form of multi-energy systems (MESs) and the transformation of traditional prosumers into smart prosumers are two effective pathways to achieve net-zero emission energy systems in the near future. Managing different energy markets is one of the biggest challenges for the operators of MESs, since different carriers are traded in them simultaneously. Hence, this paper presents a hierarchical decentralized framework for the simultaneous management of electricity, heat and hydrogen markets among multi-energy microgrids (MEMGs) integrated with smart prosumers. The market strategy of MEMGs is deployed using a hierarchical framework and considering the programs requested by smart prosumers. A deep learning-based forecaster is utilized to predict uncertain parameters while a risk-averse information gap decision theory (IGDT)-based strategy controls the scheduling risk. A new prediction-based mechanism for designing dynamic demand response (DR) schemes compatible with smart prosumers' behavior is introduced, and the results illustrate that this mechanism reduces the electricity and heat clearing prices in peak hours by 17.5% and 8.78%, respectively. Moreover, the results reveal that the introduced structure for hydrogen exchange through the transportation system has the ability to be implemented in competitive markets. Overall, the simulation results confirm that the proposed hierarchical model is able to optimally manage the competitive markets of electricity, heat and hydrogen by taking advantage of the potential of smart prosumers. [ABSTRACT FROM AUTHOR]

This research presents a 100% renewable system configured based on its real potential and use of renewable energies for the Ecuadorian Amazon, considered one of the places with the greatest diversity of native plant species on the planet. The transition processes that are born from the central state seek to take a radical turn in the electricity sector but there is still no defined roadmap, this is where this research will allow it to be an important reference for its long-term transformation. The current system is based on fossil fuels and in this study an energy transition process is planned with the support of the EnergyPLAN tool that puts into practice the concept of intelligent energy, progressively and systematically achieving 100% renewable energy for 2050. Finally, after analyzing the results, it is concluded that the energy mix for 2050 can consider 36.47% hydro, 33.04% solar photovoltaic, 29.73% wind energy and other technologies to a lesser extent with 0.74%. Surpluses can be injected into the interconnection sites with Colombia and Peru, also achieving economic income to preserve and expand the infrastructure of the electricity sector. Decision makers can consider this study as a reference before committing economic resources and carrying out modern energy planning. [Display omitted] • A smart Renewable Energy approach is presented. • A convergent method is developed for greater contributions from wind and solar PV. • The proposal launched is to leave the oil underground. [ABSTRACT FROM AUTHOR]

• Power flow Analysis of droop-controlled islanded AC microgrids is proposed. • The full detail of MG network modeling is provided. • The effect of parallel elements is considered in the proposed formulation. • A novel index named Power Sharing Error Index is developed. • Detailed modeling of droop-controlled islanded microgrids. One of the essential studies in the planning and operation of a microgrid is Power Flow (PF). Traditional PF methods are not applicable for droop-controlled islanded microgrids due to the absence of a slack bus in the system. The steady state characteristics of the system such as frequency, bus voltages, Distributed Generators' (DG) output power, and actual loads' demand are obtained based on the droop characteristics of the DGs, and also, the frequency and voltage dependency of the load. Therefore, in this paper, an Enhanced Newton Raphson (ENR) method is proposed for PF in the droop-controlled islanded microgrids. The proposed method is based on the well-known NR method but with a more comprehensive model that considers different droop schemes (resistive, inductive, and complex), load demand dependency on voltage and frequency, π line model, and shunt compensators. Moreover, a new index for selecting proper droop characteristics for any droop-controlled islanded microgrid is proposed. Five test systems with different scales, topologies, droop control strategies, and various load models are considered to evaluate the performance of the proposed method. The results are compared with the recently developed methods and steady-state results of Time-Domain simulations conducted in PLECS software. The results show that the proposed technique has excellent accuracy with low computational time and can be easily integrated into the currently available power system analysis tools. [ABSTRACT FROM AUTHOR]