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Efficient reduction of power losses in modern systems can be achieved by selectively connecting tie lines and disconnecting radial branches in distribution networks with scattered energy generation sources. The influence of load power is pivotal in this optimization process. Consequently, it becomes crucial to accurately model the demand side, portraying the behavior of electricity consumers more effectively. Given that loads are not constant and exhibit variations with changes in voltage magnitude, contingent on the consumer type, it becomes imperative to represent each load through its load model's constant-impedance, constant-power, and constant-current components. Establishing the relationship between these components and load types is essential for proposing more flexible reconfiguration models. Therefore, this study employs mathematical formulations to express the correlation of load components with consumer types, aiming to provide a more flexible reconfiguration formulation compared to conventional models. The results obtained from well-known distribution systems demonstrate that the proposed framework is sufficiently efficient, offering optimal solutions within a shorter computing time compared to other existing reconfiguration models.

The reconfiguration of distribution networks results in modifications to the voltage magnitudes of buses, subsequently influencing the power demands of connected loads. This variable load demand plays a pivotal role in determining the optimal system topology. While a few studies have recognized this crucial aspect, they often present non-linear models or simplified formulations. Non-linear models, though solvable with commercial solvers, demand extensive computational time, particularly when accommodating the time variability of loads in the formulation. Alternatively, solving these models may require meta-heuristic methods, introducing the challenge of obtaining precise solutions. In contrast, existing linear models have been subject to approximation through piecewise linearization, introducing a level of uncertainty. Achieving accurate solutions for reconfiguration problems within a short computational timeframe is imperative for practical operational applications. As a result, present study addresses this challenge by introducing four effective reconfiguration methods that explicitly consider the voltage dependence of time-varying loads. These techniques leverage linear solvers, enabling the computation of precise solutions within a condensed computational timeframe. By adopting these proposed methods, the study aims to enhance the efficiency and accuracy of solving distribution network reconfiguration problems, thereby facilitating practical applications in real-time operational scenarios. The linear mathematical models introduced in the current study can find exact solutions in a short computational time, unlike meta-heuristic algorithms and non-linear formulations. Additionally, the proposed time and voltage variability formulation causes the presented reconfiguration frameworks to solve the problem in very low computational time compared to models with exact load variations inclusion.

Optimizing the reduction of power losses together with the investment and operational costs in electrical distribution systems via optimizing network reconfiguration (NR), placement of capacitor (CP), and distributed generation (DG) allocation simultaneously amidst uncertain fluctuating load conditions is considered an effective strategy. Meanwhile, this operation poses a formidable challenge of stressful computation due to the numerous iterations that the model goes through to find the best solution, particularly in the case of large-scale systems, demanding significant computational resources. In many of the previous studies within this domain, the dynamic nature of load variations was often overlooked in order to reduce the computation burden and time of the optimization problem. Albeit, such simplification has often compromised precision and led to suboptimal solutions. In this regard, the current research has introduced an innovative and streamlined approach that can incorporate load variations and uncertainty into the complex coordination of NR, CP, and DG optimization within large-scale distribution systems for the reduction of losses and costs at a relatively shorter time. We showcase the effectiveness of our modeling approach through its application to various test networks, showcasing its precision, reduced computational time, and straightforward implementation in contrast to traditional approaches which have only relied on certain peak demand considerations.

Optimal power flow (OPF) problem solution is a crucial task for the operators and decision makers to assign the best setting of the system components to obtain the most economic, environmental, and technical suitable state. Artificial Hummingbird Algorithm is a recent optimization algorithm that has been applied to solving several optimization problems. In this paper, a Modified Artificial Hummingbird Algorithm (MAHA) is proposed for improving the performance of the orignal Artificial Hummingbird Algorithm as well as effectivelly solve the OPF problem. The proposed MAHA is based on improving the searching capability by boosting the exploitation using the bandwidth motion around the best solution, while the exploration process is improved using the Levy flight distribution motion and the fitness-distance balance selection. This modified version helps overcome issues such as stagnation, premature convergence, and a propensity for local optima when tackling complex, nonlinear, and non-convex optimization problems like OPF. In order to confirm the effectiveness of the proposed algorithm, a series of tests are conducted on 23 standard benchmark functions, including CEC2020. The resulting outcomes are then compared to those obtained using other algorithms such as fitness-distance balance selection-based stochastic fractal search (FDBSFS), antlion optimizer (ALO), whale optimization algorithm (WOA), sine-cosine algorithm (SCA), fitness-distance balance and learning based artificial bee colony (FDB-TLABC), and traditional artificial hummingbird algorithm (AHA).The proposed algorithm is evaluated by solving the OPF problem with multiple objective functions on the IEEE 30-bus system. These objectives include fuel cost, fuel cost with valve loading effects, power losses, emissions, and voltage profile. Additionally, the algorithm's effectiveness is further assessed by testing it on single objective functions using medium and large-scale IEEE 57 and 118-bus networks.The results obtained by the proposed MAHA demonstrate its power and superiority for solving the OPF problem as well as the standard benchmark functions , surpassing the performance of other reported techniques.

Latin America and the Caribbean is a region that has grown enormously in public policies and especially in the implementation of renewable energy generation plants, which is why it deserves to study its growth to provide long-term deep decarbonization routes. It has approximately 634 million inhabitants, and it is expected that by 2050 it will reach 784 million inhabitants, which worries researchers to establish a new model of energy supply in accordance with future demand. The concern does not end here, the new energy supply must be friendly to the environment and renewable energies are the appropriate option to avoid the increase in global temperature. The challenge of this research is to provide an adequate option for the decarbonization of Latin America and the Caribbean to achieve 100 % renewable energy by 2050. For this purpose, the energyPLAN tool is used, a flexible tool, very useful to transform large energy systems based on fossil fuels and achieve comprehensive 100 % renewable markets. Power systems are considered a challenging task and are widely studied around the world to avoid the great negative effects of altering the climate. Modeling the zero carbon energy system was implemented using the concept of smart energy. The combination shows that it is necessary to use a wide variety of energy sources and that no energy source more than 35 % stake, to increase energy security. The historic hydroelectric technology will continue to be an essential part of the Latin American energy mix. However, in 2050, the technologies that are destined to have the greatest participation will be wind turbines and solar photovoltaic with 32.7 % and 31 % respectively. Technologies with less participation such as renewable thermal and geothermal are also identified. Achieving a 100 % renewable energy mix by 2050 will prove to be economically beneficial. Based on the results obtained, it will be effective to achieve a 100 % renewable scenario when Latin America and the Caribbean form a solid regional alliance exclusive to fight for the environment and legislate favorably so that its member countries contemplate important budgets in a conscious, responsible and supportive way.

The energy management (EM) solution of the microgrids (MGs) is a crucial task to attain most economic, reliable and sustainable operation state of the MGs. This paper aims to solve the optimal scheduling and stochastic EM problem of a smart MG without and with demand side response (DSR) including MT, FC, PV, WT, and a battery storage system (BSS). A study case in Wenzhou city of China is conducted to reduce the operation cost and maximize the utilization of renewable energy. The system uncertainties like loading, temperature, solar irradiance and wind speed are considered which were obtained from real meteorological data. The normal, lognormal, Weibull PDFs as well as Monte-Carlo and RBS methods are used for uncertainty modelling. A modified artificial rabbit optimization (MARO) is proposed for EM solution based on three strategies including fitness-distance balance, exploitation mechanism of PDO and quasi-opposite-based learning (QOBL) to boost exploration and exploitation phases of traditional ARO. The statistical and non-parametric tests are applied via benchmark functions to validate the performance of MARO. As per the obtained results, the MARO is superior for EM solution compared to other techniques and the operation cost is reduced from 252.0721€ct/day to 184.8435€ct/day with saving of 26.86 % considerably with application of DSR.

The operational cost of distribution systems is significantly affected by the energy losses of the system. Distribution network losses are known to be higher than transmission system losses, constituting over 70% of all the losses that occur in the power system. For the achievement of loss minimization, operation cost mitigation, and generation expense reduction, the efficient allocation of distributed generation (DG) in distribution networks is considered an effective solution. The penetration of renewable DG units in distribution systems has seen an increasing trend in recent times due to the numerous benefits that come with the use of renewable sources for power generation, especially for the environment. Power demand variations and the variability of load levels play a vital role in the determination of the amount of losses that occur on the network and subsequently the optimal generation of DGs. Meanwhile, taking into consideration the variability of load level in the DG allocation problem worsens the computational stress and time significantly. Therefore, this paper has formulated an effective DG allocation model for energy loss minimization considering power demand variation. The developed model was simulated for different distribution systems using A Mathematical Programming Language (AMPL) to validate the performance of the proposed formulation by comparing the proposed model results with two different models (exact and peak load model). Parameters tested in the study are network losses, cost, and computational time. The evaluation of numerical results indicates the proposed model is very effective for DG allocation in terms of losses, cost, and computational time.

Enhancing the efficiency of distribution systems can be effectively achieved through hybrid reconfiguration and the coordinated operation of distributed generators (DGs). This integrated approach proves to be highly effective in minimizing power losses within distribution networks, with a particular emphasis on the substantial impact of load power and grid voltage on distribution losses and the output of DGs. Load and DG powers are dynamic, varying with fluctuations in voltage levels. Furthermore, the correlation between power demand and renewable sources generation with voltage is contingent upon the specific type of load and DG. Nevertheless, scant attention has been given to these crucial factors in the research pertaining to network reconfiguration and the planning of DG. A limited number of papers have taken into account the type and voltage dependency of DG or load within their respective formulations. However, the reconfiguration models they proposed introduce significant nonlinearity without considering the simultaneous effects of grid voltage on load and DG power, necessitating computation through nonlinear solvers or metaheuristic algorithms. Alternatively, the implementation may require intensive linearization techniques for compatibility with linear solvers. Nonlinear solvers demand extensive computational time, while metaheuristic algorithms cannot ensure the attainment of optimal solutions. Hence, it is crucial to accurately model load behavior when undertaking the reconfiguration of active distribution systems. This paper presents a streamlined reconfiguration model, designed to be easily implemented with conventional optimization tools while ensuring accuracy in identifying appropriate solutions for the reconfiguration problem.

Voltage collapse, blackout and voltage instability are considered main problems faced by the power systems which are due to the lack of reactive power in the system. One of the most important sources for injecting reactive power to the system is the flexible AC transmission systems (FACTS) devices. Thyristor-controlled series capacitor (TCSC) is one of the modern FACTS devices that can be used for reducing the overloading in line flow, allowing power system to operate in secure state and improving power system stability. In this paper, optimal steady-state load shedding and TCSC allocation in power system problems are comprehensively solved using a new robust and effective technique, called moth swarm algorithm. This algorithm is based on the moth’s orientation toward moonlight considering a new adaptive crossover and Lévy flight mutation. Optimal load shedding and TCSC allocation are achieved simultaneously to remove or mitigate congestion and emergency situations. To prove the capability of the proposed algorithm for minimizing the amount of load shedding and active power loss, improving voltage profile and voltage stability, standard IEEE 30-bus test system is used under normal and contingency operations at different single- and multiobjective functions. The results obtained by the proposed algorithm are compared with those obtained by other well-known optimization techniques: TLBO, PSO, GWO, WOA, MFO. These results demonstrate the effectiveness and robustness of the proposed algorithm.

Electricity generation from biomass energy resources is compared with wind and solar power considering an uncertain electric load and a variable generation in the present study. The comparison results can give a good perspective to readers for judgment about energy utilization method in places which all of these renewable energy sources are available and the electricity consumption as well as wind and solar energy are not certain and constant. The hybrid operation of biomass, wind, and solar is unavoidable due to limited availability to energy sources in each region under load uncertainty and generation variability. For examples, some areas have a great potential for wind power generation, while they are cloudy most of the times as solar irradiation efficiency is low in such places. Whereas in some sunny areas, often wind is absent and in some other places like seashores both wind blow and sunshine is good. Important question is that if these three energy generation sources are available enough in the same place, which of them is better and more efficient to satisfy electricity demand of consumers with uncertain load patterns? To address this concern, a village in Morocco with great potentials of wind, solar, and biomass energy generation is economically studied to find out better renewable electricity generation method in uncertain environments.

The distribution losses significantly affect the system's operational costs and voltage profile that can be reduced by network reconfiguration. However, load power is not constant and can be changed by voltage fluctuation of network buses, in which reconfiguring the topology of distribution systems impacts the magnitude of bus voltages. Accordingly, alteration of nodal voltages affects load amount and subsequent power losses. Still, most of the research works associated with distribution systems reconfiguration have not included load power dependency to nodal voltages in their proposed formulations. Only a few articles have considered voltage-dependent loads in their reconfiguration models but introduced nonlinear formulations or highly approximated linear approaches. While nonlinear models can be solved by metaheuristic algorithms without guaranteeing the optimality of solutions or may be implemented by commercial nonlinear solvers, they require intensive and time-consuming computations. Moreover, linearized models are highly approximated by piecewise linear functions with many unknown parameters. Thus, this paper presents the effective design of the reconfiguration problems, including voltage dependency of loads, which can be solved using commercial linear solvers. The proposed models are precise enough to find accurate results for the reconfiguration problem and are adequately fast to converge to optimal solutions. The results show that the proposed solutions not only reduce active power losses significantly but increase the minimum voltage of the system effectively. Nevertheless, the second strategy presents a better efficiency over smaller networks and provides higher voltage levels.

Distribution network losses constitute over 70% of all the losses that occur in power systems, resulting in the increment of operational cost, voltage profile distortion, and power supply instability. Therefore, addressing power losses has become a major concern for power system owners and operators, leading to the development of several mechanisms for network losses minimization. Among the proposed methods for loss minimization in power systems, the placement of distributed generation (DG) in reconfigurable power networks is considered as one of the most efficient mechanisms to reduce network losses, operation costs, and generation expenses. However, load variability consideration in the DG allocation and reconfiguration problem raises the burden and computational time considerably. Consequently, the current paper proposes an effective reconfiguration and DG allocation approach for loss reduction in the presence of variable loads. In the proposed method, an equivalent load factor was appropriately calculated from an actual load profile and embedded into the constraints of DG and reconfiguration problem. The proposed model is compared with exact and conventional models in several IEEE busbar systems in a mix-integer liner programming model using A Mathematical Programming Language (AMPL) in CPLEX. Numerical results evaluation of the proposed model and the exact model shows that the proposed model is able to reduce the computational time of the exact load profile model by 9 to 1000 times with power loss error margin of between 7 and 18%. While comparing the proposed model with conventional one indicates the proposed formulation is able to reduce the systems power loss of the conventional model by up to 50%. From the results, it was concluded that the proposed model is very efficient in terms of computational time reduction as well as reducing the active losses of a distribution network.

Urban borders are expanding in cities, solar photovoltaic and wind energy are being used and decentralized more and more, while the electrification of transport systems is in permanent progress. Users trust more in the modernization of electrical systems giving rise to various applications. The efforts made by both the public and private sectors are isolated and are not framed within comprehensive planning. For this reason, cities must be fully planned and contemplated in their land use plans. This article presents a long-term roadmap for the comprehensive electrification of mobility. To achieve a proper approach, it is based on the EnergyPLAN tool that uses the concept of smart energy and determines the long-term scenarios, the case of study is for the City of Cuenca in Ecuador. It seeks to take advantage of the potential of renewable energies available in the territory, which are evaluated and provide the necessary energy to feed future decentralized transport systems with a view to 2050. The long-term results show that the energy mix would be composed of wind with 37.3%, followed by solar photovoltaic with 33.9% and hydroelectric with 25.4%. There are others technologies such as biomass that do not exceed 3.4%.

Ecuador promotes an energy matrix with zero net emissions by 2050, knowing that hydroelectric power from a reservoir has been fundamental in the electrical system. The reservoirs comprise large unused areas, in many of these sites there are interesting wind speeds thanks to the wind tunnels that are formed between hill and hill and can be used by installing floating wind turbines. This research presents an alternative to increase the driving actions of Ecuador to structure its 100% renewable energy system in a diversified way. For this reason, the resulting impact is analyzed by including Floating Wind Power (FWP) systems and four points of interest are analyzed in this study: Mazar, Coca Codo Sinclair, Manduriacu and Delsitanisagua. The energy mix is evaluated using EnergyPLAN software, a specialized tool to evaluate diversified smart systems of completely renewable electricity in the long term. This study is novel, breaks the traditional schemes in Ecuador and provides a different vision for decision makers, such as investors, legislators and researchers to discuss before committing economic resources. The results show that in 2050 floating wind energy would be contributing 11.13% of the total electricity in Ecuador and 16.27% of the wind component. Interpreting these values, the floating wind component may be significant and would further diversify energy production in this South American country.

In modern power systems, distributed generation (DG) plays an important role in energy losses reduction, but DG penetration in such networks is restricted by technical and investment limitations. Therefore, distribution network reconfiguration (DNR) and DG operation reduce power losses more efficiently than when only DG is utilized. Also, network reconfiguration mitigates improper DG placement in such systems which are subjected to high penetration of variable loads and DG. On the other hand, electrical energy demand is estimated by load forecasting methods and therefore is a time-variant and uncertain parameter. Moreover, DG power generation is uncertain and depends on variable load power. Consequently, models presented for the reconfiguration problem should be adequately robust against load and generation uncertainties. Thus, the present paper proposes an effective robust model for reconfiguring distribution networks with load and generation uncertainties. The proposed model is sufficiently simple for implementation and is accurate enough to find the optimal solutions in acceptable computational times. The main features of the proposed model are its high robustness against uncertain parameters, in which proposed configurations are not changed easily by small variations in DG power and load amounts, and its effectiveness, which means that uncertain parameters may change the obtained solutions.

The decentralization of power systems and networks calls up for a more active participation of end users. In this context, new market and power trading models are arisen. Catalyzed by the evolution of communication infrastructures under the Smart Grid concept, new paradigms such as peer-to-peer (P2P) trading are becoming more common nowadays. This paper develops a P2P platform model, involving the participation of distributed generators (dispatchable and renewable), storage facilities and energy communities. Economic-oriented models are presented for each peer, considering arbitrage capability from storage, generation and flexibility provision. An original market structure is proposed seeking for equilibrium among agents. Moreover, risk-aware operating strategies are developed, which consider adaptive interval formulation of uncertainties. The new approach allows adopting risk-averse or risk-seeker strategies, thus allowing to consider the impact of uncertainties in a flexible fashion. The new platform is tested on a 5-peers case. The impact of demand and renewable penetration on local prices is assessed, concluding that cheap generation contributes to reducing prices and thus improving the economy of users, which can trade energy locally under low prices. Moreover, the impact of uncertainties is also analyzed, observing that the uncertainty level and the risk strategy adopted impact notably on the expected realization of uncertainties. It is also shown that the developed tool effectively seeks for improving the economy of users, even when pessimistic conditions of uncertainties are assumed. Results demonstrate that energy communities are more severely impacted for uncertainties, due to their reduced regulation capability. Finally, the developed tool is further validated in fifty P2P instances from an economic and computational point of view.

Currently, there is highly concentrated attention given to microgrid energy management, particularly due to the significant increase in the adoption of renewable energy resources (RERs) such as photovoltaic units and wind turbines, as well as the growing reliance on electric vehicles (EVs) to reduce environmental pollution. Considering uncertainties, which are associated with solar irradiance and wind speed, can increase the ability to achieve proper energy management. In addition to that, considering the smart grid's role in the electricity market as a price maker and the unpredictable behavior of EVs, these considerations aim to minimize costs and maximize profits for microgrids. The main contribution of this paper is studying the impact of increasing the charging tariff of EVs in the presence of different types of RERs on charging behavior, maximizing the profit of the microgrid, and reducing the total energy purchased from the wholesale market. For this purpose, a metaheuristic-based optimization problem called Gorilla Troops optimizer (GTO) is employed to optimally allocate the RERs and the EV charging stations. The results demonstrate that increasing the charging tariff without RERs or with only one source of RERs results in the charging of EVs following an uncontrollable strategy. Hence, the cost of purchasing energy from the utility increases. On the other hand, compared to the base case, integrating EV charging stations with multi- type RERs resulted in the profit increasing by 61.92 % and the total energy purchased from the wholesale market decreasing by 24.98 %. Moreover, the waiting time at the charging station will be reduced shortly; the annual installment of multi- type RERs decreased by 31.25 % compared with considering only one source of RERs, and the charging strategy of EVs followed the controllable charging strategy.

In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 $/kWh, the net present cost (NPC) with 6,170,134 $, the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y.

Nowadays, load serving entities require more active participation from consumers. In this context, demand response programs and home energy management systems play a crucial role in achieving multiple goals such as peak clipping. However, the adoption of demand response initiatives typically has a negative impact on the monetary expenditures of the users. This way, a demand response program should be as effective as possible to make the different goals more easily achievable without compromising the financial requirements of the users. This paper develops a home energy management system that incorporates three novel effective demand response strategies. The effectiveness of the adopted demand response strategies is checked through extensive simulations in a benchmark prosumer environment. To this end, a novel scenario-based approach is developed in order to manage uncertainties. The introduced strategies are compared with other well-known demand response mechanisms. To that end, a novel comparative index, which serves to evaluate the compromise between demand response achievements and energy bills, is introduced. Results obtained demonstrate that the developed strategies are more effective than other approaches. In fact, through the use of the proposed mechanisms, different indicators can be improved until ∼70%, while the electricity bill is only scarcely increased (∼0.11€). Other relevant aspects like the influence of the storage capacity and computational performance of the introduced optimization framework are also analysed.

Load frequency control (LFC) plays a prominent role in electrical power grids. Its importance is evident in controlling the frequency as well as the tie-line power exchange. Recently, frequency deviations and tie-line power exchange have been enhanced when implementing renewable energy sources (RESs) and energy storage (ES) equipment in modern power systems. This research proposes a novel optimization algorithm, the Coati Optimization Algorithm (COA), to fine-tune the gains of proportional-integral-derivative (PID) controllers. These gains are used in the LFC loop in the presented multi-area power system model. This power system model incorporates conventional generating units, RES, electric vehicles (EVs), and ES units, which presents a significant challenge to the PID controller in handling the effects of RES uncertainties. Two types of RESs are considered: wind and photovoltaic systems. Additionally, different elements of ES equipment have been incorporated: superconducting magnetic energy storage and capacitor energy storage and EVs integrated with the grid. The efficiency of the COA-based PID controller is compared to that tuned by alternative methods used in PID controllers based on other algorithms indicating a reduction in the transient response specifications such as maximum percentage undershoot, maximum percentage overshoot, and settling time of frequency and tie-line power deviations by more than 50 % than other several algorithms. The system's response has been assessed through three scenarios: without the effect of RESs, including RESs uncertainty, and finally, including the effect of RESs, EVs and ES units. Notably, the proposed methodology can enhance the transient response of frequency and tie-line power deviations with ES devices by more than 10 % compared with the case of without storage units.

Based on an iconographic analysis of the covers of six Dutch editions of the Attic Nights published between 1644 and 1706 (objects, fauna and characters), as well as the possible relationships of some of the elements studied with the work itself, we have established several guidelines to better understand the cultural history of the Attic Nights during the Dutch Golden Age. In a particular way, the scientific interest, as well as some elements of everyday life, already present in the Dutch art of the moment, allow us to better appreciate the approach that is made of the Latin work to a new cultural and historical context.

This article proposes a novel power flow (PF) solution methodology. Unlike to conventional techniques, the new approach is based on recursive formulas rather than conventional iterative methods. Stability of the novel methodology is proved and some relevant computational aspects are discussed. A wide variety of well and ill-conditioned benchmark systems shows the performance of the developed methodology. The results demonstrate that the proposed recursive formula is computationally competitive with other traditional PF methods, besides it is scalable and reliable.

With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.

The reduction of power fluctuations from intermittent renewable sources is one of the most pressing challenges today. Recent research has shown that prediction and control mechanisms, when combined with energy storage systems, significantly contribute to improving these techniques. However, substantial research gaps still exist regarding the optimization of energy storage system operability. This article introduces an innovative power smoothing method based on neural network predictive control, in conjunction with the exponential moving average method. The proposed approach encompasses the ability to substantially reduce energy fluctuations, optimize battery state of charge, and mitigate ramp rates, thereby preventing deep discharges that shorten battery lifespan. Furthermore, the control system's primary objective is to optimize energy exchange with the grid, surpassing the performance offered by other conventional power smoothing methods. The control system excels in optimizing energy exchange within the network, surpassing conventional methods. Extensive testing on the University of Cuenca microgrid reveals a consistently more stable and higher battery charge compared to conventional methods. Numerical results for underscore the method's effectiveness with a fluctuation suppression rate of 30.78 % compared to 34.85 % (low pass filter) and 36.22 % (ramp rate) methods respectively. The enhanced voltage profiles at the common coupling point ensure the delivery of high-quality and stable power.

Currently, renewable energies and electric vehicle charging stations are essential for energy sustainability. However, the variable generation from renewable sources, such as photovoltaic systems, can lead to power peaks that impact the stability of the grid. This challenge is exacerbated by the increasing demand in fast-charging stations. Addressing these demand peaks is crucial to ensure the stability of the electrical grid. This paper introduces the predictive-flex smoother, an innovative method designed to mitigate power fluctuations in grid-connected photovoltaic systems while optimizing energy management in electric vehicle charging stations. The predictive-flex smoother method incorporates a hybrid energy storage system comprising supercapacitors and vanadium redox flow batteries to respond rapidly to electric vehicle charging stations demands, enhance grid electricity purchase optimization, and improve energy quality delivery. The proposed method integrates two control strategies: photovoltaic fluctuation reduction strategy and peak demand reduction strategy for electric vehicle charging stations. By leveraging prediction algorithms and machine learning techniques, the predictive-flex smoother method achieves precise power fluctuation forecasts, allowing efficient utilization of supercapacitors and vanadium redox flow batteries to smooth photovoltaic power fluctuations and reduce electrical vehicles peak demand. Comprehensive experimental investigations and simulations validate the method's performance under various operational conditions. The results demonstrate the effectiveness of the predictive-flex smoother method, significantly improving the quality of power delivered to the grid while reducing costs. The experimental platform, validates the real-time response of the proposed method, with response times under 500 ms. The experimental results further confirm the efficiency of the method in power smoothing and charging strategies with varying electrical vehicles models and connection coefficients.

Increasing penetration of electric vehicles may pose dramatic challenges for existing infrastructures. Indeed, current fast (and upcoming ultra-fast) charging technologies allow charging at high power levels which, together with the unpredictable and high volatile pattern of charging demand, may lead to serious operational problems if charging infrastructures are not properly planned and operated. In this context, planning and operational tools for modern charging stations become a valuable contribution for station owners and network operators. This paper focuses on day-ahead and real-time energy management of multi-mode (fast and semi-fast) charging stations, equipped with photovoltaic generators and eventually large-scale stationary battery systems. The developed management model casts as a two-stage model in which day-ahead decisions are taken in the first level, where uncertainties in charging demand and local generation are modelled using robust optimization. This way, day-ahead operational decisions are taken under worst-case realization of uncertainties. Secondly, a fast and always-feasible optimal routine is proposed for real-time management in order to correct possible imbalances caused by deviations of actual realization of uncertainties. To properly model charging demand into the proposed robust framework, a novel methodology is proposed based on synthetic demand scenarios, from which the expected and maximum demand can be derived for different charging modes. The day-ahead robust model is posed as a bi-level optimization framework, which is further reduced to a master-slave structure solved using the well-known Column & Constraint Generation Algorithm. The resulting mixed-integer-linear programming model is validated on a benchmark charging infrastructure for different battery capacities, showing their capabilities and usefulness. The results show that uncertainty in demand notably impacts the total operational cost, incrementing it by 85 %, while the uncertainty in renewable generation has a more limited impact. On the other hand, it is shown that the performance of batteries mainly depends on the tariff scheme adopted. Moreover, it is highlighted that although importing energy supposes the main expenditure of the station (>80 % of total cost), exporting surplus energy may improve the economy of the system, reducing by 16 % the total energy costs. Finally, a scalability analysis is conducted showing that the proposed methodology scales well with the system size.

•Utilization of six chaotic maps enhanced the performance of standard Rao algorithms for accurate PEMFC parameters estimation.•Algorithm validation shows superior accuracy and robustness in PEMFC analysis, outperforming standard methods.•Statistical validation of algorithms' improved PEMFC estimation accuracy underscores their optimization effectiveness.•Dynamic PEMFC model shows algorithms' adaptability to real-world loads, enhancing electrical performance analysis.

Energy communities are recognised as a valuable framework to promote penetration of renewable sources at the residential level, as well as increment the efficiency and self-sufficiency of domestic users. In such paradigms, energy storage may bring notable benefits due to its energy arbitrage and capacity to efficiently manage renewable sources. However, at the residential level, storage systems are not widely deployed and, in this regard, collective storage options may suppose an attractive alternative. From a market perspective, energy communities can be cooperative or competitive. This paper focuses on the second case when internal community markets are launched to promote competition among prosumers. In such a framework, two possible collective storage schemes are analysed. On the one hand, the so-called competitive storage paradigm, in which storage assets partake in internal markets as an independent agent. On the other hand, the so-called cooperative storage scheme, by which storage assets are centrally managed to pursue collective welfare. To this end, the day-ahead scheduling tool for the community is extended to include both collective storage arrangements, thus resulting in different complementarity models that can be efficiently solved using off-the-shelf solvers. Such complementarity models are applied to a benchmark three-prosumers community, extracting various relevant results and conclusions. Generally, it is concluded that the cooperative storage scheme is more attractive for prosumers, allowing them to reduce their bill notably. However, this scheme seems very sensitive to the storage and community size and can discourage installing high-power photovoltaic units. In this sense, the so-called competitive storage scheme, although less beneficial for prosumers, seems a more easily implementable option in communities with internal market structure.

Nowadays, the intermittent nature of renewable energy systems represents one of the most significant challenges in isolated systems, where power fluctuations can cause instability and compromise energy quality. Although hydrogen systems and supercapacitors have been widely studied in the literature, they have been less investigated as participating agents, and further research is needed in this area. This paper presents a novel power smoothing method for an off-grid system that consist of photovoltaic panels, hydrokinetic turbines, fuel cells and a hybrid storage system (hydrogen and supercapacitors). Two well-known power smoothing methods were used to generate the power signals for the new method. The main novelty is based on controlling the state of charge of the supercapacitor using the fuel cell, for the reduction of power fluctuations and efficiently hydrogen produce. First, the capacity of the renewable system is optimized using the HOMER Pro software. Then, the optimized system was used to simulate the new method proposed in Matlab-Simulink. Finally, to validate the results obtained, extensive experiments were conducted in a laboratory test bench. The results showed that the power fluctuations index was reduced by up to 50 % in the electrolyzer and 20 % in the fuel cell, with a levelized cost of electricity of 0.19 USD/kWh. Therefore, the application of the new proposed energy smoothing method significantly improves hydrogen production.

One of the most important aspects of power quality for a distribution network's operation is the voltage sag issue. Simultaneous starting of irrigation motors fed from a distribution network leads to a voltage drop, which degrades the network's power quality. Mitigation of the voltage sag was carried out before by using superconducting magnetic energy storage (SMES) with a pre-defined capacity. The innovation of the present research work is optimal design of SMES including optimal sizing of SMES and its controller parameters with the consideration of its optimal cost for mitigating voltage sag resulting from simultaneous starting of irrigation motors in a real Egyptian distribution network. This is made by minimizing a multi-objective function formulated by a weighted-sum voltage sag and SMES cost. A new optimization technique called Mountain Gazelle Optimizer (MGO) is used to optimize the sizing of fuzzy logic controller (FLC)-SMES as well as the weight factors of multi-objective function. These factors are not free for the decision maker to choose. An actual Egyptian electric distribution network named Karot feeding 16 induction-driven irrigation motors and residential loads, is implemented as a test case study to exhibit the performance of the suggested optimal FLC-SMES strategy in mitigating the voltage sag. For validating the performance of the MGO technique, the obtained results are compared with those determined by particle swarm optimization (PSO). The present results show that the MGO-optimized SMES unit with a capacity of 0.135 MJ and actual cost of 0.2483 M$ successfully mitigated the voltage-sag in the investigated network due to simultaneous starting of the irrigation motors where the voltage never drops below 0.9 p.u. against 0.625 MJ capacity and 0.6934 M$ actual cost for the non-optimized SMES. This corresponds to about 78 % and 64 % reduction in capacity and actual cost of the SMES unit.

Hydrogen fuel stations (HFSs) may be connected to power systems; to absorb electricity, produce hydrogen and supply hydrogen demands. To the best of the authors' knowledge, the resilience of HFS-integrated power systems has not been addressed in the literature. In this research, a novel strategy is proposed for resilience enhancement of PV-rich HFS-integrated power systems, considering the uncertainties. In the proposed strategy, fuel cells (FCs) and hydrogen tanks are added to HFSs and mobile batteries are added to the power system. A two-stage stochastic model is developed in which the location of mobile batteries are the first-stage here-and-now decision variables and other variables are second-stage wait-and-see decision variables. The model is formulated as a multi-objective problem as the ability of the system to supply both electricity and hydrogen demands are considered. Expected load not served (ELNS) is used as resilience metric. Case study is a modified IEEE 24-bus power system with 5 HFSs. The results confirm that with the proposed resilience enhancement strategy, hurricane does not result in any shed for hydrogen demands. As per results, mobile batteries are the most efficient tools in resilience enhancement of the studied system; they cause 84 % improvement in ELNS of electricity and 43 % improvement in ELNS of hydrogen. The results also show that besides mobile batteries, hydrogen storage tanks and FCs are the most efficient components in resilience enhancement of the system. Hydrogen tanks improve ELNS of electricity by 1.8 % and remove the need of system operator to shed hydrogen demands. A sensitivity analysis is done to see how the results of the developed model are sensitive to its parameters.

On-board batteries from electric vehicles can be used in domestic installations as an auxiliary source of energy through their vehicle-to-home features. To properly manage this kind of assets, Home Energy Management systems are expected to be essential tools in future smart grids, enabling a more efficient use of energy in residential applications. However, these tools normally perform under severe uncertain environments caused by intermittent renewable generation, uncertain prices, uncertain demand as well as uncertain behaviour of electric vehicles. In this regard, further research should be conducted in order to develop advanced robust energy management tools that account for the effect of uncertainties. So far, most of the existing literature focused on simple stochastic approaches, that may pose various issues, while other more sophisticated uncertainty models have not been sufficiently studied. In this regard, this paper proposes a fully robust Home Energy Management model, which accounts for all the inherent uncertainties that may arise in domestic installations. In contrast to previous researches, robust optimization is applied to all the uncertainties, resulting in a tractable, but robust yet paradigm model which allows to adapt the degree of robustness. A case study is conducted on a benchmark prosumer paradigm to validate the new proposal as well as highlight the benefits of flexible appliances. Results reveal the usefulness of the new tool, which allows to handle very unfavourable uncertain profiles, especially in case of the electric vehicles, for which a very low initial state-of-charge and early departure time can be assumed, leading to more conservative results that suppose incrementing the electricity bill by 38 %.

This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series–parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints. [ABSTRACT FROM AUTHOR]

As already happens with the electric vehicles (EVs) expansion, technology associated with their charge also must be improved. This paper presents a novel decentralized control method (DCM) for charging stations (CSs) based on a medium-voltage direct current (MVDC) bus. This kind of CSs is integrated in a microgrid with a photovoltaic system, a battery energy storage system (ESS), a local grid connection, and two units of fast charge. The main contribution of this paper resides in the cited DCM based on fuzzy logic that includes the state of charge (SOC) of the battery ESS as a control variable. This control contains two independent fuzzy logic systems (one for the battery ESS and other for the grid), whose function is to maintain the MVDC voltage and the battery ESS SOC within proper thresholds and to keep the power balance stable among the units of fast charge and the rest of the CS components. The new control method was tested in a considerable number of operating situations (two hundred cases studied) under different conditions of sun irradiance, initial SOC of battery ESS, and number of EVs connected to the CS with the objective of showing its correct performance in all the considered scenarios.

Because of their level of abstraction, Domain-Specific Languages (DSLs) enable building applications that ease software implementation. In the context of web applications, we can find a lot of technologies and programming languages for server-side applications that provide fast, robust, and flexible solutions, whereas those for client-side applications are limited, and mostly restricted to directly use JavaScript, HTML5, CSS3, JSON and XML. This article presents a novel approach to creating DSL-based web applications using JSON grammar (JSON-DSL) for both, the server and client side. The approach includes an evaluation engine, a programming model and an integrated web development environment that support it. The evaluation engine allows the execution of the elements created with the programming model. For its part, the programming model allows the definition and specification of JSON-DSLs, the implementation of JavaScript components, the use of JavaScript templates provided by the engine, the use of link connectors to heterogeneous information sources, and the integration with other widgets, web components and JavaScript frameworks. To validate the strength and capacity of our approach, we have developed four case studies that use the integrated web development environment to apply the programming model and check the results within the evaluation engine.

This paper proposes a novel two-stage frequency-regulating control design for modern power systems considering high renewable energy sources (RESs) penetration and electric vehicles (EVs). The proposed controller structure is based on a tilt fractional-order integral derivative (TFOID) in the first stage and a proportional derivative with filter (PDN) in the second stage, referred to as a TFOID-PDN controller. Moreover, this paper proposes a virtual synchronous generator (VSG) based on EVs' batteries that emulate the characteristics of synchronous generators and provide inertia and damping properties, thus restraining the frequency instability problem of future power systems when operating in low inertia. Furthermore, the parameters of both the proposed VSG and the TFOID-PDN controller are fine-tuned using a new reliable metaheuristic optimization algorithm called the artificial hummingbird algorithm (AHA). The efficacy of the proposed TFOID-PDN controller design is examined and investigated through a real large multi-source power system (e.g., Egyptian power system) considering the future scenario in 2035 (i.e., in the presence of high penetration levels of RESs, EVs, and different operating conditions). The superiority of the proposed AHA is validated by comparing it with other powerful optimization techniques such as the marine predators algorithm, grey wolf optimizer, and artificial bee colony optimization through designing the load frequency control based on the PID controller of a well-known two-area interconnected power system. Additionally, the proficiency of the proposed controller is verified over other controllers used in the literature, e.g., fractional-order proportional integral derivative (FOPID), tilt integral derivative (TID), proportional integral derivative (PID), and proportional-integral (PI) controllers, under load/RESs fluctuations. The simulation results carried out by the MATLAB software proved the superior performance of the proposed TFOID-PDN controller compared to other controllers. Moreover, the proposed VSG design prevents modern power systems from reaching instability when operating with a high share of RESs and low inertia.

La cirugía mínimamente invasiva ha presentado una expansión muy importante en la última década. Con el objetivo de aportar un lenguaje común tras cirugía transoral de la orofaringe, se ha creado un sistema de clasificación de las resecciones en esta zona, independientemente de la instrumentalización utilizada.

Energy storage will play a vital role in the decarbonization of the electricity sector, especially in domestic installations. In such kind of systems, home energy management applications are becoming essential. These kinds of tools enable active control of domestic appliances and storage systems to pursue a more efficient use of energy in household installations. However, the emergence of renewable generators and electric vehicles makes the operation of residential assets more difficult, as multiple uncertainties should be managed on a whole. These unknowns have a different character, in fact, some of them can be easily predicted while others are subject to a high level of randomness. This paper addresses this issue by developing a novel home energy management tool that accounts for the different levels of the randomness of the uncertainties involved in home operation. To this end, a novel Lexicographic-Interval formulation of the home energy management problem is presented, by which the uncertainties can be easily modelled using interval notation. Unlike conventional tools, the new proposal sorts the uncertainties according to their level of randomness. Then, the energy management mechanism performs the scheduling plan according to the predefined classification so that the more random parameters rule the impact of others, thus giving more importance to those uncertainties that are hardly predictable. A benchmark case study is performed to validate the new proposal and illustrate its capabilities. Different tariffs are compared, showing that the Time-of-Use mechanism is normally more expensive than Real-Time-Pricing tariffs, increasing the electricity bill by 12 % in some cases. However, the level of robustness achieved with Time-of-Use tariffs seems higher, allowing to assume an abruptly unexpected reduction of photovoltaic generation.

Load Frequency Control (LFC) has become a more challenging issue, especially with the increases in generation's unpredictability, inconsistency, and load variations leading to reduced system stability and reliability. This paper presents a novel application of the transient search optimization (TSO) upon Model Predictive Control (MPC) based regulators to solve the LFC problem for multiple zones power networks with hybrid renewable generators and storage devices. An exact model with a governor's dead band, generator's rate constraint, and measurement communication delay are employed. Renewable energy sources (RESs), such as wind and solar systems, in addition to fuel cell generators with different storage elements, such as superconducting magnetic energy storage (SMES) and battery energy storage (BES), are incorporated into the power system investigated in this study. To assess the effectiveness of the TSO-MPC controllers in nonlinear conditions due to renewable energy generation and storage ambiguities, actual wind speed measurements are incorporated into the wind generation model and real solar irradiance and temperature data for the photovoltaic system to achieve an extra credible analysis. Furthermore, the performance of the TSO-MPC regulator is compared with the performance of other optimization techniques based on proportional-integral-derivative (PID) controllers under different conditions, and the results of the proposed controller outperform other controllers by >10 % of the transient specifications. The transient response of the power system is significantly enhanced with the controller proposed in this study.

This article introduces a novel hybrid adaptation algorithm comprising both continuous mixed p-norm (CMPN) strategy and the block-sparse Bayesian (BSB) technology to online adapt all the proportional plus integral (PI) controller gains of the power converters of flywheel energy storage units (FESUs). The FESU is based on the doubly fed induction machine (DFIM). The principal target is to improve the transient stability of grid-connected wind farms. To obtain a realistic study, the two-drive train model is utilized in the wind farm modeling affecting the transient analyses. The cascaded control scheme is implemented on controlling both the generator side converter and the grid side inverter of the FESU using the proposed adaptive CMPN-BSB PI controller. The FESU is tied to the point of common coupling of the wind farm, which is tied to the standard IEEE 39 bus system. The performance of the proposed technology is tested under subject the system to different severe faults. The validity of adaptive CMPN-BSB PI controlled FESU is tested by comparing the numerical results with that achieved by least mean square adaptive controlled FESU. The numerical results are performed by PSCAD software. The proposed controlled FESUs will help in improving the transient stability of wind farms and their power quality concerns will be enhanced.

•This paper presents a novel adaptive algorithm to control the SMES devices.•The goal is to smooth the real power output from grid-tied wind farms.•Detailed models of the system are implemented in this paper.•Real wind speed data and practical SMES units are proposed in the study.•The proposed model is verified by comparing its results with other model results.

•Hydrogen produced from renewable sources (green hydrogen).•Green hydrogen can be effectively stored from surplus renewable energy.•A novel optimal scheduling model for robust optimal scheduling of isolated microgrids.•Logical rules are incorporated into the conventional optimal scheduling tool.•A case study serves to validate the model and highlight the paper.

Since 1787, when F. A. Wolf wrote his Geschichte der Römischen Litteratur, until 1815, when F. Schöll published his Histoire Abrégée de la littérature Romaine, the History of Europe changed completely. Nationalism and Romanticism arose as a new way to understand the literary taste. We can appreciate theses changes in Wolf’s Geschichte and Schöll’s Histoire, where ancient Latinae litterae are regarded as a national literature.

Biomass gasification is one of the several technologies with a very high potential for rural power generation applications. The proposed system is greenhouse gas neutral because the CO2emissions from biomass are considered “carbon neutral”. In this paper a prototype gasification system (70 kWe- 245 kWth) combined with a gas engine is presented while its performance is tested using olive industry wastes (olive kernel) as fuel. The above CHP system represents a decentralized conversion system that functions economically even for small scale, fulfilling the operation conditions for an olive oil cooperative. The open top, downdraft gasifier consists of reactor, cooling and cleaning system. The producer gas exits the reactor at about 500°C, and includes contaminants in form of particulate matter and tar. The hot gas is further purified in the gas cooling and cleaning system in order to reach a state that is acceptable for engine operations. Initial tests in the prototype are carried out in order to achieve a profitable exploitation. Critical parameters such as producer gas composition and pressure drop of gasifier, are examined and assessed in order to enable the complete adaptation of the system to the operational specifications of an olive cooperative. The main objective is the optimization of the system in terms of gasification efficiency, gas composition, gas quality and electrical efficiency. The presented results are repeatable and give information about the steady state of gasifier when working at full load, producing a good quality gas with an electric efficiency of 16.1%.

The aim of this paper is to consider what is a "problem" in Latin from a cognitive perspective: a problem is not but "a thing that falls on us", "a place without exit", or "a thing without movement". We shall study etymologies of Latin words for "problem" and "solution" and usual expressions such as haeret haec res, or vadum safaris, where it is possible to find Source Domains (places, exits...) by which Abstract Concepts can be established as Target Domains to express the complex concept of problem, according to spatial and ontological metaphors.