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This research focuses on the development and simulation analysis of heat-dissipating fins made of copper, integrated into photovoltaic panels, with the aim of mitigating temperature increases during operation. This initiative arises from evidence that solar panels experience a reduction in energy efficiency when operating at temperatures higher than standard test conditions. The photovoltaic panel was simulated both without fins and with fins under standard test conditions and extreme conditions. The simulation consists of the following steps: design, meshing, selection of physical models and materials, assignment of boundary conditions, validation of the simulation, and interpretation of the results. During validation, results obtained via simulation were compared experimentally, yielding a mean absolute percentage error of 0.28%. It was concluded that the fins with the greatest heat dissipation relative to their area are those of 40 mm height; with this height, the temperature of the photovoltaic panel is reduced by 2.64 K, which represents an efficiency increase of 1.32%. Furthermore, it was concluded from the analyzed data that the efficiency of the fins increases at high temperatures. [ABSTRACT FROM AUTHOR]

In this study, an improved bald eagle search optimization algorithm (IBES) is utilized to develop home energy management systems for smart homes. This research is crucial for energy field researchers who are interested in optimizing energy consumption. The primary objective is to optimally manage load demand, reduce the average peak ratio, lower electricity bills, and enhance user comfort. To accomplish this goal, the load conversion strategy is used to coordinate household appliances and manage the home power system effectively. This approach aims to minimize peak–average ratios and electricity costs while ensuring consumer convenience. To minimize electricity bills, the study schedules the consumer's daily activities based on actual time and next day's energy demand. Furthermore, a fitness criterion is used to balance the load between off-peak and on-peak hours. The scheduler is designed to achieve an optimal device on/off state that minimizes device waiting time by coordinating household appliances in real time. To address the background problem of real-time rescheduling, dynamic programming is employed. The study evaluates the modified algorithm's performance using three pricing strategies: critical peak pricing, real-time pricing, and time of use. The modified IBES technique is utilized to achieve the specified objectives of minimizing the electricity bill, reducing the peak–average ratio, and enhancing user convenience. [ABSTRACT FROM AUTHOR]

The concept of interconnected multi-microgrids (MMGs) is presented as a promising solution for the improvement in the operation, control, and economic performance of the distribution networks. The energy management of the MMGs is a strenuous and challenging task, especially with the integration of renewable energy resources (RERs) and variation in the loading due to the intermittency of these resources and the stochastic nature of the load demand. In this regard, the energy management of the MMGs is optimized with optimal inclusion of a hybrid system consisting of a photovoltaic (PV) and a wind turbine (WT)-based distributed generation (DGs) under uncertainties of the generated powers and the load variation. A modified Capuchin Search Algorithm (MCapSA) is presented and applied for the energy management of the MMGs. The MCapSA is based on enhancing the searching abilities of the standard Capuchin Search Algorithm (CapSA) using three improvement strategies including the quasi-oppositional-based learning (QOBL), the random movement-based Levy flight distribution, and the exploitation mechanism of the prairie dogs in the prairie dog optimization (PDO). The optimized function is a multi-objective function that comprises of the cost and the voltage deviation reduction along with stability enhancement. The effectiveness of the proposed technique is verified on standard benchmark functions and the obtained results. Then, the proposed method is used for energy management of IEEE 33-bus and 69-bus MMGs at uncertainties conation. The results depict that the energy management with inclusion of WTs and PVs using the proposed technique can reduce the cost and summation of the VD by 46.41% and 62.54%, and the VSI is enhanced by 15.1406% for the first MMG. Likewise, for the second MMG, the cost and summation of the VD are reduced by 44.19% and 39.70%, and the VSI is enhanced by 4.49%. [ABSTRACT FROM AUTHOR]

Storage systems and demand-response programs will play a vital role in future energy systems. Batteries, hydrogen or pumped hydro storage systems can be combined to form hybrid storage facilities to not only manage the intermittent behavior of renewable sources, but also to store surplus renewable energy in a practice known as 'green' storage. On the other hand, demand-response programs are devoted to encouraging a more active participation of consumers by pursuing a more efficient operation of the system. In this context, proper scheduling tools able to coordinate different storage systems and demand-response programs are essential. This paper presents a stochastic mixed-integer-lineal-logical framework for optimal scheduling of isolated microgrids. In contrast to other works, the present model includes a logical-based formulation to explicitly coordinate batteries and pumped hydro storage units. A case study on a benchmark isolated microgrid serves to validate the developed optimization model and analyze the effect of applying demand-response premises in microgrid operation. The results demonstrate the usefulness of the developed method, and it is found that operation cost and fuel consumption can be reduced by ~38% and ~82% by applying demand-response initiatives. [ABSTRACT FROM AUTHOR]

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system's demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study's main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators' operation, due to the penetration of renewable energy sources into the studied networks. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, microgrids are emerging as an invaluable framework for the integration of renewable energy sources and demand response programs. In such systems, energy storage facilities are also frequently deployed to properly manage surplus energy from renewable sources on pursuing more efficient management of the system. Hybrid storage systems in which various storage facilities are combined may result in a more effective solution than only considering one storage technology. This way, the good features of the different technologies may be jointly exploited while their drawbacks are minimized. Due to the large‐scale integration of renewable energies in this kind of grid, coping with uncertainties becomes a critical issue. Moreover, the operation of microgrids frequently deals with other kinds of uncertainties related to energy pricing from the upscale grid (in the case of grid‐connected mode) or local demand. This way, proper modeling of uncertainties is essential for adequately operating these systems. This paper contributes to this pool by developing a novel interval‐based formulation, for optimal scheduling of microgrids considering battery and pumped‐hydro storage systems. To achieve this goal, the optimal scheduling of a microgrid with pumped‐hydro and battery energy storage considering demand response is modeled, firstly. Then, the new interval‐based formulation is used to cope with the uncertainties. Finally, the suggested model is verified using simulations in various cases, and the results confirm the effectiveness of the novel interval‐based formulation for the optimal scheduling of microgrid with pumped‐hydro and battery energy storage under uncertainty. [ABSTRACT FROM AUTHOR]

The forward method for modeling electric vehicles is one of the most suitable for estimating energy consumption in different imposed driving cycles. However, a detailed description of the methodology used for the development of electric vehicle models is necessary and is scarce in the current literature. To fill this gap, this study focuses on highlighting the intrinsic characteristics through a theoretical study with a mathematical model, complemented by demonstrative simulations in Matlab/Simulink. The results show that the forward method can be estimated more accurately based on the energy consumption of the electric vehicle. Moreover, this paper aims to be explicitly descriptive for the development of more complex electric vehicle models to incorporate real driving cycles, being able to size the drivetrain of the vehicle itself or develop ecological routes. [ABSTRACT FROM AUTHOR]

Bioenergy is one of the most important renewable-energy sources worldwide, accounting for more than two-thirds of the renewable-energy mix. Biomass accounted for 13–14% of the primary energy consumption in 2018, and by 2050, it is expected to account for 50% of the global primary energy consumption. This article studies the biomass potential in Iraq. The potential of this country to be one of the leading producers of bioenergy is discussed, remarking on the importance of agricultural crop waste. Nowadays, Iraq generates a great quantity of biomass every year. Unfortunately, instead of contributing to the energy industry and economic progress, these wastes are burned directly, potentially causing a slew of environmental issues. Based on earlier studies, the theoretical energy potential of Iraq agricultural wastes is assessed. It is concluded that 10 million tons of dry agricultural leftovers can create 115 PJ of energy per year. According to the findings of this study, 10 million heads of cattle in Iraq could generate 72 million m3 of biogas per day, with a total potential power of 946 TJ per year from animal wastes, mainly cattle dung. On the other hand, bioenergy potential is heavily reliant on the geographical distribution, availability, and accessibility of real waste. Wasit, Qadisiyah, and Mosul are the most feasible locations for this agricultural waste potential. This might lead to the development of a long-term economic plan for the successful and sustainable utilization of important accessible waste for bioenergy generation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Featured Application: Agrifood industries may benefit from investing in energy efficiency and diversifying into new business models (distributed cogeneration and biochar production) through waste gasification, while promoting circular economy and environmental sustainability. This research work presents a techno-economic analysis of a biomass gasification plant fueled with residues from the olive oil and almond industries for combined heat and power generation in the agrifood sector. The experimental plant consists of a downdraft fixed bed gasifier, a producer gas cleaning and cooling system and a spark-ignition engine–generator set as a power generation unit, which generates about 10–12 kW of rated electric power. With an average consumption between 13–14 kg/h of exhausted olive pomace pellets as feedstock, the producer gas volumetric flow rate was 31 Nm3/h (vol. %: 19.2 H2, 12.9 CO, 1.9 CH4, 19.2 CO2, 46.7 N2). The average cold gas efficiency was nearly 63%. This work also addresses the characterization and potential application of the carbonaceous solid residue (biochar), discharged from the gasifier at 1.7 kg/h. Finally, an economic feasibility analysis was developed, wherein the payback period ranges between 5–9 years. [ABSTRACT FROM AUTHOR]

Recently, fast uptake of renewable energy sources (RES) in the world has introduced new difficulties and challenges; one of the most important challenges is providing economic energy with high efficiency and good quality. To reach this goal, many traditional and smart algorithms have been proposed and demonstrated their feasibility in obtaining the optimal solution. Therefore, this paper introduces an improved version of Bonobo Optimizer (BO) based on a quasi-oppositional method to solve the problem of designing a hybrid microgrid system including RES (photovoltaic (PV) panels, wind turbines (WT), and batteries) with diesel generators. A comparison between traditional BO, the Quasi-Oppositional BO (QOBO), and other optimization techniques called Harris Hawks Optimization (HHO), Artificial Electric Field Algorithm (AEFA) and Invasive Weed Optimization (IWO) is carried out to check the efficiency of the proposed QOBO. The QOBO is applied to a stand-alone hybrid microgrid system located in Aswan, Egypt. The results show the effectiveness of the QOBO algorithm to solve the optimal economic design problem for hybrid microgrid power systems. [ABSTRACT FROM AUTHOR]

The integration of several renewable energy sources (RESs), such as photovoltaic generation (PVG) and wind turbine generation (WTG), introduces a significant amount of uncertainty for the optimal planning of the electrical power systems. The optimal power flow (OPF) problem became more difficult to solve when RESs uncertainties were taken into account. In this research, the stochastic OPF problem is solved using an Adaptive Lightning Attachment Procedure Optimizer (ALAPO) with the integration of RESs. The ALAPO is proposed in this study to improve both the exploration and exploitation process of the conventional Lightning Attachment Procedure Optimizer (LAPO). In order to create a number of scenarios that account for the uncertainties associated with load demand, wind speed, and solar irradiation, the scenario based reduction method is utilized. Further, to determine the optimal setting of active power of generators, tap setting of the transformers, reactive power of the capacitor banks, and the voltage profile at the generator buses for each scenario of RESs the objective function considered in this study is to minimize the power loss, voltage deviation, and voltage stability index of the power system. The effectiveness of the proposed algorithm is tested on IEEE 57-bus system. The results in this study show that the proposed ALAPO is superior for solving the OPF with the integration of RESs compared to the conventional methods like LAPO, Particle Swarm Optimization (PSO), Sand Cat Swarm Optimizer (SCSO), Gray Wolf Optimizer (GOW), Whale Optimization Algorithm (WOA), Black Widow Optimization Algorithm (BWO), and Capuchin Search Algorithm (CapSA). [Display omitted] • Integration of several renewable energy. • Optimal power flow (OPF) problem. • Adaptive Lightning Attachment Procedure Optimizer (ALAPO). • Voltage profile at the generator buses is the objective function considered. [ABSTRACT FROM AUTHOR]

The increase of renewable energy generation to change the productivity of a country and electrify isolated sectors are some of the priorities that several governments have imposed in the medium term. Research centers are looking for new technologies to optimize the use of renewable energies and incorporate them into hybrid generation systems. In the present work, the modeling of a solar thermal energy generation plant is being carried out. The climatic data used belong to two coastal cities and one island of Ecuador. The contribution of this work is to simulate a complete model of SCF and PCS, in which the variables of outlet temperature and oil flow are involved at the same time. Previously investigations use only outlet temperature for evaluating power plants. The model of the solar thermal plant is composed of a field of solar collectors, a storage tank, and an energy conversion system. As a result, we obtain a model of a thermosolar plant that will allow us to make decisions when considering the incorporation of micronetworks in systems isolated from the electrical network. The use of thermosolar technology allows the reduction in the risk of spills by the transport of fossil fuels in ships. The study of the CO2 emission factor in Ecuador from 2011 to 2018 is also carried out. [ABSTRACT FROM AUTHOR]

The growing reliance on hydroelectric power and the risk of future droughts pose significant challenges for power systems, especially in developing countries. To address these challenges, comprehensive long-term energy planning is essential. This paper proposes an optimized electrical system for 2050, using Ecuador as a case study. For forecasting electricity demand, a Neuro-Fuzzy Adaptive Inference System is employed, utilizing real historical data. Subsequently, the EnergyPlan software constructs a long-term energy consumption model, exploring three scenarios based on Ecuador's energy potential. The first scenario represents a 'business as usual' approach, mirroring the current trend in the Ecuadorian electricity system. In contrast to the second scenario, it encompasses a broader range of renewable sources, including offshore wind, pumped storage, biomass, and geothermal energy. The third scenario extends the second one by incorporating demand response systems, such as vehicle-to-grid and hydrogen-to-grid technologies. In terms of novelty, this study highlights the innovative use of the Neuro-Fuzzy Adaptive Inference System for demand forecasting, along with a comprehensive exploration of multiple scenarios to optimize the electrical system. Research findings indicate that the integration of these new renewable energy sources not only reduces electricity import costs but also ensures surplus electricity production. Consequently, it is anticipated that the 2050 electricity system will reduce its dependence on hydroelectric energy while adopting photovoltaic and wind energy with penetration rates of 65 %, 11.2 %, and 9 %, respectively. This transition will be facilitated by a pumped storage system with a 28 % penetration rate and enhanced connectivity with neighboring countries, enabling the seamless integration of electric and hydrogen vehicles. [Display omitted] • Introducing a long-term electricity demand forecast with adaptive neuro-fuzzy inference. • An energy schedule for Ecuador in 2050 is derived from the demand forecast. • Renewable sources reduce costs, surplus electricity, and imports, enabling a 100 % renewable system. • By 2050, Ecuador's electrical system will rely on hydro, photovoltaic, wind, and pumped hydro power. • The optimized Ecuadorian electrical system will enable V2G and H2G integration in 2050. [ABSTRACT FROM AUTHOR]

In this paper, a technical-economic study of the 100% renewable energy sources in the Galapagos islands is done. Historical consumption data for 2011–2020 have been considered to forecast the load curve. To achieve this goal, the load is forecasting by Nonlinear autoregressive exogenous neuronal network model for 2030 and 2050. The study focuses on supplying the energy demand of the islands with renewable sources, analyzing possible scenarios based on the current electricity system. The methodology studies the capacity of renewable sources to balance supply and demand through dispatch simulations using the EnergyPLAN software. The results show energy flows, costs and long-term energy balances (2050), with 100% renewable energy from several wind and photovoltaic combinations. Moreover, The precision of the demand forecast was 98.12% with a mean square error of 0.013%. The total annual cost decreases while the capacities of the renewable sources increase to a certain point of equilibrium. As salient features of the developed approach, various sensitivity analyzes are presented that allow understanding the uncertainties, scope and limitations of the proposed models. [Display omitted] • This study performs a long-term demand forecast using artificial neural networks for the year 2030 and 2050. • To determine the feasibility of the long-term renewable system, the EnergyPLAN software has been used. • Several sensitivity analyzes are presented regarding the capacities of the sources, storage system and the total annual cost. • Diesel generator capacity is gradually reduced until completely mitigated. • Long-term 100% renewable system reduces surplus electricity and greenhouse gas emissions. [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]

The proliferation of charging stations entails multiple challenges for power systems. In this regard, the installation of photovoltaic-battery systems may help to mitigate the negative effects of charging points. However, such assets should be carefully planned, paying attention to economic aspects, principally. Most of existing works optimize the photovoltaic-battery system in charging infrastructures taking a representative-space of the involved variables (e.g. photovoltaic potential, charging demand or energy prices). However, this approach tends to ignore low-probable scenarios. Thus, the best-case scenario for charging demand (i.e. that for which the highest charging profit is accessible) may not be included in the analysis and therefore such demand could be not attended properly, thus losing this monetary opportunity. This paper focuses on this issue and questions if considering the best-case scenario into planning photovoltaic-battery systems for charging stations is worthwhile or not. To this end, a novel best-case-aware planning tool is developed, including the best-case scenario through a novel chance-constrained formulation. The overall problem is then decomposed into a master-slave structure by which the economy of the system is optimized together with the number of scenarios for which the best-case profile can be attended. A case study serves to validate the developed tool and shed light on the questions arisen in this work. In particular, it is checked that considering the best-case scenario into planning tools is questionable from a monetary point of view. Nevertheless, its inclusion unlocks some collateral advantages such as incrementing the users' satisfaction or reducing the grid-dependency. [ABSTRACT FROM AUTHOR]

• A new price setting mechanism for microgrid clusters is developed. • The new approach only requires sharing boundary information. • Uncertainties are incorporated using interval notation and equivalent scenarios. • The novel proposal encourages the participation in energy trading. • The strategy adopted by agents notably impact on the energy transacted. With the advance of communication infrastructures and the necessity of increasing the efficiency of energy systems, electricity networks are evolved towards more decentralized architectures and operational schemes. In this context, microgrid clusters are emerging as a valuable framework for optimal integrating renewable sources, demand response initiatives, and storage systems. When the cluster is competitive, the different agents partaking in the cluster compete for trading energy with others, for which an upstream agent (coordinator) sets nodal prices in a similar way to conventional energy markets. This paper is focused on this aspect by developing a novel price setting mechanism for islanded microgrid clusters based on an original equilibrium problem with an equilibrium constraints structure. The new proposal concerns about privacy issues, for which an original diagonalization algorithm is proposed by which only boundary information is transferred from microgrids to the coordinator. Moreover, uncertainties from renewable generation and demand are accommodated using interval notation and equivalent scenarios. The overall problem is raised as a bi-level model, which is further linearized and transformed into a single-level framework tractable by off-the-shelf solvers. A 4-microgrid cluster integrated into a 9-bus network serves as an illustrative case study. The impact of uncertainties is profusely studied, showing that total energy exchanged among microgrids can decrease by 61 % when the influence of uncertain parameters is notable. Other relevant aspects are discussed, and the practicability of the new proposal is demonstrated by analysing its computational performance. Moreover, the developed tool is further validated on a medium-scale network involving a large number of microgrids. [ABSTRACT FROM AUTHOR]

• A novel hybrid stochastic-IGDT model is developed for HEM problems in nZEBs. • Real energy price and vehicle-to-home capability are properly modelled. • Unpredictable behavior of the electric vehicle is formulated via IGDT. • A bi-objective structure is presented to jointly optimize the energy consumption and cost using lexicographic optimization. • The problem is kept linear being solvable by standard solvers. Residential sector is being promoted to evolve towards nearly-Zero Energy Buildings (nZEBs), which draw a yearly net energy consumption near zero. This target can be attained through on-site renewable generation, achieving high efficiency in consumption. In this context, Home Energy Management (HEM) systems become an indispensable tool for obtaining optimally coordinating smart appliances, renewable generation, and on-site storage facilities. Due to the high unpredictability of renewable generation and some emerging appliances like electric vehicles, these tools must be able to deal with different uncertainties properly. At the same time, a variety of objectives are jointly considered. The existing approaches commonly fail to deal jointly with these two premises. This paper aims to fill this gap by developing a novel solution for HEM systems in nZEBs. The proposed procedure uses lexicographic optimization to find a compromise solution among objectives. At the same time, the variety of uncertainties caused by unpredictable weather, demand, energy pricing, and electric vehicle behavior is adequately modeled using a hybrid stochastic-Information Gap Decision Theory (IGDT) approach. The mathematical modeling is sufficiently comprehensive (comprising various energy sources and vehicle-to-home capability) and tractable due to its integer-linear structure. A case study on a benchmark nearly zero energy home is considered to validate the developed approach, which its results reveal its effectiveness in terms of minimizing various objective functions while the degree of robustness is preserved and the whole procedure is efficient yet. [ABSTRACT FROM AUTHOR]

This paper describes a novel energy management system for the optimized operation of the energy sources of a grid-connected hybrid renewable energy system (wind turbine and photovoltaic) with battery and hydrogen system (fuel cell and electrolyzer). A multi-objective optimization problem based on the weight aggregation approach is formulated by combining three objective functions (operating costs, efficiency and lifetime of the devices) that can conflict with each. The multi-objective function to be optimized by the energy management system is obtained by solving the problem for all the possible cases. Then, the weights that provide the minimum value of the multi-objective function are selected. As the results demonstrate, the multi-objective function becomes a single-objective function that differs according to the net power (power to be generated by/stored in the energy storage devices) and has to be solved in the energy management system of the hybrid system. It simplifies considerably the multi-objective problem implemented in the energy management system, while taking into account the three control objectives that can conflict with each other, which is the main contribution of this paper. This optimal energy management system is solved using the Particle Swarm Optimization (PSO) method, tested by simulations of the hybrid power generation system throughout 25 years (the expected lifetime of the system), and compared with the results obtained by the energy management systems based on optimizing each single-objective function separately, and by that based on optimizing the multi-objective function combining the three single-objective functions equally weighted. The results demonstrate that this energy management system achieves reasonable operating costs, efficiency and degradation of the devices. [ABSTRACT FROM AUTHOR]

Although electric vehicles (EVs) are experiencing a considerable upsurge, the technology associated with them is still under development. This study focused on the control and operation of a medium-voltage direct-current (MVDC) microgrid with an innovative decentralized control system, which was used as a fast charging station (FCS) for EVs. The FCS was composed of a photovoltaic (PV) system, a Li-ion battery energy storage system (BESS), two 48 kW fast charging units for EVs, and a connection to the local grid. With this configuration and thanks to its decentralized control, the FCS was able to work as a stand-alone system most of the time though with occasional grid support. This paper presents a new decentralized energy management system (EMS) with two options to control the power sources of the FCS. The choice of the power source depends on the MVDC bus voltage, the state-of-charge (SOC) of the BESS, and the control option of the EMS. This control was tested by simulating the FCS, when connected to several EVs and under different sun irradiance conditions. Simulation results showed that the FCS operated smoothly and effectively, which confirms the feasibility of using this technology in EVs. [ABSTRACT FROM AUTHOR]

One of the main barriers for the wide penetration of fuel cell electric vehicles is the lack of proper infrastructures for hydrogen transportation that hinders the implantation of refuelling stations. This barrier could be overcome by deploying onsite hydrogen generators based on mature electrolysis and hydrogen storage technologies. This way, the necessity of hydrogen transportation is avoided. In addition, electrolysers can be onsite supplied by means of renewable generators like photovoltaic panels, while the produced hydrogen can also be destined to generate electricity through fuel cells thus obtaining a monetary revenue. Thereby, the economy of the system may be improved in order to make viable this kind of infrastructures. However, the optimal coordination of the different assets is challenging and requires the use of energy management tools to pursue the optimal performance of the installation. In this kind of infrastructures, the energy management problem is performed under substantial uncertainties; moreover, these unknown parameters have a very different character. Thus, while energy pricing and renewable generation can be forecasted using conventional techniques, refuelling demand is highly unpredictable. To this end, this paper proposes a novel stochastic-interval model for the optimal scheduling of photovoltaic-assisted refuelling stations. The new proposal uses interval notation to model the inherent uncertainty of renewable generation and energy pricing, while the vehicle demand is modelled using a more suitable approach based on scenarios. In this regard, a comprehensive stochastic model for fuel cell electric vehicles is developed, which is based on reported driving behaviour and common characteristics of commercial vehicles. To solve the problem subjected to uncertainties, an iterative solution methodology is developed which allows adopting risk-seeker and risk-averse operational strategies. A case study is analysed to validate the new proposal and discussing the importance of the different economic activities that can be exploited in refuelling stations. Results reveal the importance of selling energy to the grid in order to complement the revenues obtained from refuelling; however, this process is highly impacted by uncertainties and the operational strategy, observing variations up to 50% in the total profit depending on the strategy adopted. [ABSTRACT FROM AUTHOR]

Abstract: Fuel cell (FC) is an efficient energy conversion technology that is growing rapidly and will have a significant role to play in a number of energy end-use sectors, from small-scale applications to large-scale power plants. In this paper, two new methods for voltage harmonic compensation of a stand-alone single phase inverted-based FC are presented and evaluated. The stand-alone power system under study consists of: 1) Proton-Exchange-Membrane (PEM) FC with unidirectional DC/DC converter, which converts the DC voltage delivered by the FC to the DC bus voltage; 2) single-phase pulse width modulated (PWM) inverter; 3) transformer; 4) passive filter; and 5) linear and non-linear loads. The dynamic model of this system and the non-sinusoidal output-based controls applied to the PWM inverter for voltage regulation and harmonic compensation are detailed in this paper, and evaluated by simulation under different linear and non-linear loads. Simulation results show the effectiveness of the two purposed methods for voltage harmonic compensation to acceptable levels defined in grid codes. [Copyright &y& Elsevier]

Abstract: Problems relating to oil supply, pollution, and green house effects justify the need for developing of new technologies for transportation as a replacement for the actual technology based on internal combustion engines (ICE). Fuel cells (FCs) are seen as the best future replacement for ICE in transportation applications because they operate more efficiently and with lower emissions. This paper presents a comparative study performed in order to select the most suitable control strategy for high-power electric vehicles powered by FC, battery and supercapacitor (SC), in which each energy source uses a DC/DC converter to control the source power and adapt the output voltage to the common DC bus voltage, from where the vehicle loads are supplied. Five different controls are described for this kind of hybrid vehicles: a basic control based on three operation modes of the hybrid vehicle depending on the state of charge (SOC) of the battery (operation mode control); a control strategy based on control loops connected in cascade, whose aim is to control the battery and SC SOC (cascade control); a control based on the technique of equivalent fuel consumption, called equivalent consumption minimization strategy (ECMS); and two based on control techniques very used nowadays, the first one of them is a fuzzy logic control and the second one is a predictive control. These control strategies are tested and compared by applying them to a real urban street railway. The simulation results reflect the optimal performance of the presented control strategies and allow selecting the best option for being used in this type of high-power electric vehicles. [Copyright &y& Elsevier]

• Planning of electrical energy for the Galapagos Islands. • The results show that these islands can achieve 100% renewable penetration. • Study the load forecast for the Baltra-Santa Cruz Island for 2031. • Include a pumped storage hydroelectric (PH) system, more competitive than the batteries for the baltra-santa cruz electricity supply, and supplies turbine water to the desalination plant. • An optimized PV/WT/PH/DG/BAT hybrid system that supplies the electricity demand and energy consumption for drinking water supply for the Baltra-Santa Cruz islands in 2031. • The COE of the hybrid system of Baltra-Santa Cruz is reduced from 0.32 to 0.23 $/kWh. The present study focuses on the planning of electrical energy for the Galapagos islands using different renewable energy technologies for the year 2031 in order to reduce diesel consumption and achieve 100% renewable energy share. To do that, a long-term load demand forecast study on the Santa Cruz and Baltra islands (Galapagos), applying Artificial Neural Networks, is done. Then, to optimize the islands' power system, mostly based on diesel generation, different renewable energy technologies together with energy storage systems, such as batteries and hydraulic pumping, have been analyzed through the application of the HOMER Pro-software. For this purpose, two energy control models have been developed to gradually reduce the operating hours of the diesel power plant. The results show that these islands can achieve 100% renewable penetration with the following configuration: photovoltaic/wind turbines/batteries/pumping hydroelectric storage system/diesel generators (only as back-up). This implies increasing the photovoltaic capacity by 26 MWp and installing a pumped hydraulic storage of 374 MWh. In this way, the electricity generation cost would be reduced from $ 0.32/kWh to $ 0.23/kWh, and the CO 2 emissions would decrease by 16,000 tons of CO 2 into the environment. [Display omitted] [ABSTRACT FROM AUTHOR]