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The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.

This study proposes an optimal sizing methodology for a solar photovoltaic (SPV) system considering lifetime cost requirements. The aim of the design is optimal sizing of SPV system, which is obtained by calculating SPV system output power at certain location, taking into account the calculated optimal number of SPV modules, optimal number of inverters, optimal tilt angle, for a given dimension of land. This design is aimed for minimising the annual cost of grid-integrated SPV system over its life or years of operation. The cost function takes into account the capital cost of installation, operation and maintenance, for each component of the system and the cost of selling energy to the grid. The sizing optimisation has been formulated as a non-linear, multi-variable problem and the particle swarm optimisation algorithm has been tested using MATLAB platform for a particular location to swot up the feasibility of integrated system. The monthly averaged daily and hourly solar radiation data for a given location is calculated using empirical relations on MATLAB platform. Other inputs are specifications of commercially available devices and meteorological details of location.

Paleoceanographic studies often rely on abundance changes in microfossil species, with little consideration for characteristics such as organism size, which may also be related to environmental changes. Using a tropical Indian Ocean (TIO) core‐top data set, we test the Optimum size‐hypothesis (OSH), investigating whether relative abundance or environmental variables are better descriptors of planktonic foraminifera species' optimum conditions. We also investigate the environmental drivers of whole‐assemblage planktonic foraminiferal test size variation in the TIO. We use an automated imaging and sorting system (MiSo) to identify planktonic foraminiferal species, analyze their morphology, and quantify fragmentation rate using machine learning techniques. Machine model accuracy is confirmed by comparison with human classifiers (97% accuracy). Data for 33 environmental parameters were extracted from modern databases and, through exploratory factor analysis and regression models, we explore relationships between planktonic foraminiferal size and oceanographic parameters in the TIO. Results show that the size frequency distribution of most planktonic foraminifera species is unimodal, with some larger species showing multimodal distributions. Assemblage size95/5 (95th percentile size) increases with increasing species diversity, and this is attributed to vertical niche separation induced by thermal stratification. Our test for the OSH reveals that relative abundance is not a good predictor of species' optima and within‐species size95/5 response to environmental parameters is species‐specific, with parameters related to carbonate ion concentration, temperature, and salinity being primary drivers. At the species and assemblage levels, our analyses indicate that carbonate ion concentration and temperature play important roles in determining size trends in TIO planktonic foraminifera. Plain Language Summary In core‐top samples from the tropical Indian Ocean (TIO), we investigate the optimum size‐hypothesis, testing whether species' relative abundance or environmental parameter(s) are better descriptors of planktonic foraminifera species' optimum conditions. Further, we investigate the main environmental drivers of size variations in planktonic foraminifera at the assemblage‐level, given that temperature has been reported to primarily drive assemblage size trends. We use a state‐of‐the‐art machine (MiSo) to automatically identify planktonic foraminiferal species, analyze their size, and quantify fragmentation using machine learning techniques. When compared to identification carried out by human experts across 21 species, the machine classified the species accurately 97% of the time. The MiSo‐generated size data was similar to that by other researchers. The frequency distributions of the species' size spectra show that most species have distributions that form bell‐shaped curves. As species diversity increased, so did the assemblage size (95th percentile size); we attribute this observation to the effect of temperature‐dependent niche separation. We find that, in the TIO, environmental parameters are better descriptors of optimum conditions in planktonic foraminifera than relative abundance. Our results also reveal that size variation at the species and assemblage levels is mostly driven by ambient carbonate chemistry and temperature. Key Points Optimum size‐hypothesis holds true in planktonic foraminifera if one considers the main parameters driving each species' size distribution Size variations in planktonic foraminifera are linked to species' niches and diversity does not increase with productivity Within‐species size is driven by CO32− concentration, temperature, and salinity; assemblage size by CO32− concentration and temperature

In this paper, a novel version of the Optimal Distributed Generation Placement (ODGP) problem regarding the siting and sizing of Renewable Energy Sources (RESs) units is presented, called Optimal RES placement (ORESP). Power losses constitute the objective function to be minimized, subject to operational constraints. The simultaneous installation of a mix of RESs is considered and the Capacity Factor (CF) ratio is used as an aid for taking into account: (a) the geographical characteristics of the area, in which the examined Distribution Network (DN) is placed, (b) the different weather conditions, and (c) the availability of RESs, all of that at the same time, while keeping the problem complexity at minimum. The contribution of this work is that the proposed methodology bypasses the weather uncertainties and, thus, the RESs’ power generation stochasticity and provides an adequate solution with minimum computational burden and time, since the proposed CF use allows solving the problem under a straightforward way. Unified Particle Swarm Optimization (uPSO) is used for solving ODGP and ORESP. Moreover, a sensitivity analysis regarding the CFs variations is performed and finally a comparison of the proposed method with a more realistic one is performed, to consolidate further the claims of this paper. The proposed method is evaluated on RES-region-modified 33- and 118 bus systems.

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.

How parents divide the energy available for reproduction between size and number of offspring has a profound effect on parental reproductive success. Theory indicates that the relationship between offspring size and offspring fitness is of fundamental importance to the evolution of parental reproductive strategies: this relationship predicts the optimal division of resources between size and number of offspring, it describes the fitness consequences for parents that deviate from optimality, and its shape can predict the most viable type of investment strategy in a given environment (e.g., conservative vs. diversified bet-hedging). Many previous attempts to estimate this relationship and the corresponding value of optimal offspring size have been frustrated by a lack of integration between theory and empiricism. In the present study, we draw from C. Smith and S. Fretwell's classic model to explain how a sound estimate of the offspring size-fitness relationship can be derived with empirical data. We evaluate what measures of fitness can be used to model the offspring size-fitness curve and optimal size, as well as which statistical models should and should not be used to estimate offspring size-fitness relationships. To construct the fitness curve, we recommend that offspring fitness be measured as survival up to the age at which the instantaneous rate of offspring mortality becomes random with respect to initial investment. Parental fitness is then expressed in ecologically meaningful, theoretically defensible, and broadly comparable units: the number of offspring surviving to independence . Although logistic and asymptotic regression have been widely used to estimate offspring size-fitness relationships, the former provides relatively unreliable estimates of optimal size when offspring survival and sample sizes are low, and the latter is unreliable under all conditions. We recommend that the Weibull-1 model be used to estimate this curve because it provides modest improvements in prediction accuracy under experimentally relevant conditions.

The topic of microgrids (MGs) is a fast-growing and very promising field of research in terms of energy production quality, pollution reduction and sustainable development. Moreover, MGs are, above all, designed to considerably improve the autonomy, sustainability, and reliability of future electrical distribution grid. At the same time, aspects of MGs energy management, taking into consideration distribution generation systems, energy storage devices, electric vehicles, and consumption components have been widely investigated. Besides, grid architectures including DC, AC, or hybrid power generation systems, energy dispatching problems modelling, operating modes (islanded or grid connected), MGs sizing, simulations and problems solving optimization approaches, and other aspects, have been raised as topics of great interest for both electrical and computer sciences research communities. Furthermore, the United Nations Framework Convention on Climate Change and government policies and incentives have paved the way to massive electric vehicle (EV) deployment. Hence, several research studies have been conducted to investigate the integration of EVs in national power grid and future MGs. Specifically, EV charging stations’ bi-directional power flow control and energy management have been considerably explored. These issues index challenging research topics, which are in most cases still under progress. This paper gives an overview of MGs technology advancement in recent decades, taking into consideration distributed energy generation (DER), energy storage systems (ESS), EVs, and loads. It reviews the main MGs architecture, operating modes, sizing and energy management systems (EMS) and EVs integration.

Continuous urban development and sustainable development goals to develop cities and transform them into smart cities faces many challenges that appear in the transfer of electricity to this place or the difficulty and high price of building a station in this place. And power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration. Distributed generation (DG) can be used in the distribution systems to fight the increase of the load demand and it improve power generation systems and improvement the system efficiency. But it is very strange that if the generators are placed in an ill-considered size and in an inappropriate location, this causes the system to weaken, which has a bad effect on use, and this is not desirable. Not only that, but with the increase in the demand for power to cope with the increasing loads, stages of loss of power appear when it enters the distribution stage through transmission lines to loads. Therefore, the process of selecting the appropriate size and location with the general cost and taking into account environmental factors is a very important process to ensure the stability and reliability of the system .In the event that the generators are placed in an inappropriate size and in an inappropriate location, this leads to many problems in voltage, power and distribution, which causes severe weakness of the system, which causes an increase in the chance that electricity is not connected to the loads. So the appropriate way to choose the right placement and size is very important. In this study, we study the best place in which distribution generators can be added and the best size for them using PSO and its implementation on IEEE14 standard systems

The hybrid photovoltaic (PV) and wind turbine (WT) system combined with a battery (BT) have emerged as an effective renewable energy solution. As the adoption of hybrid systems gain popularity, determining the optimal system size plays a vital role for achieving cost-effectiveness. This study employs a recently developed Black-Winged Kite Algorithm (BKA) to optimize the size of a PV/WT/BT hybrid system to continuously distribute the electrical power to an educational institution sited in Puri, Odisha, India, with the goal of minimizing the total annual cost (TAC) while considering least levelized cost of electricity (LCOE). To ensure the system reliability, the maximum loss of power supply probability (LPSP) is considered 5%. To validate the performance, the BKA algorithm is compared against four prominent intelligence algorithms, including Harris Hawks Optimisation (HHO), Sine Cosine Algorithm (SCA), White Shark Optimisation (WSO), Arithmetic Optimisation Algorithm (AOA), and Snake Optimiser (SO). The examination focused on estimating the optimum size of the suggested off-grid hybrid system in the context of statistical outcome scenarios. The experimental outcomes demonstrated that the suggested BKA approach in optimization of renewable energy system provided the best results compared to HHO, SCA, WSO, and AOA with a TAC of $7105.23 and LCOE of $0.1874 per kWh at LPSP of 5%, achieving an optimal configuration of 60.4722 kW PV, 23.8337 kW WT, and 13.6159 kWh BT. Additionally, the convergence study reveals that BKA has better convergence and convergence than the other four intelligence algorithms by achieving the optimum solution. As a result, the PV/WT/BT combination evolved as a more realistic choice in designing a reliable and costless hybrid system for satisfying load demand in regional areas.

This paper investigates a comparative study for practical optimal sizing of rooftop solar photovoltaic (PV) and battery energy storage systems (BESSs) for grid-connected houses (GCHs) by considering flat and time-of-use (TOU) electricity rate options. Two system configurations, PV only and PV-BESS, were optimally sized by minimizing the net present cost of electricity for four options of electricity rates. A practical model was developed by considering grid constraints, daily supply of charge of electricity, salvation value and degradation of PV and BESS, actual annual data of load and solar, and current market price of components. A rule-based energy management system was examined for GCHs to control the power flow among PV, BESS, load, and grid. Various sensitivity analyses are presented to examine the impacts of grid constraint and electricity rates on the cost of electricity and the sizes of the components. Although the capacity optimization model is generally developed for any case study, a grid-connected house in Australia is considered as the case system in this paper. It is found that the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC compared to other configuration and options. The optimal capacities of rooftop PV and BESS were obtained as 9 kW and 6 kWh, respectively, for the PV-BESS configuration with TOU-Flat according to two performance metrices: net present cost and cost of electricity.