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Maintaining a continuous supply of power with a sustainable microgrid is still a difficult task for the off-grid area in a developing country. Using HOMER software, the techno-economic potentiality of various system configurations is demonstrated, and a PV/WT/battery/DG-based optimal system is chosen for the selected off-grid area based on Net Present Cost (NPC). The result of this optimal system is compared with the result obtained by Mixed Integer Linear Programming (MILP). The NPC ($1087520) and Renewable Fraction (RF) (90.7%) of MILP-based systems are 3.7% lower and 29.1% higher than HOMER-based systems. The amount of carbon emissions in the HOMER-based system is 79.7% larger than the MILP-based system, even though the difference in NPC is not excessive. Due to lower NPC and carbon emissions, MILP-based systems are more cost-efficient and eco-friendly. A sensitivity analysis of uncertain parameters, including annual load, discount rate, fuel price, and solar radiation, has been added to evaluate the effect of the variation of these parameters.Additionally, a few linguistic factors, such as land usage, future of technology, employment, comfortable living, and biological diversity, are also examined by normalization to demonstrate the efficiency of the optimal system. The optimal system achieved top ranking for its outstanding preference values in all criteria except land usage and is recommended in the environmental, technological, and social categories. This evaluation approach can aid researchers, decision-makers, and financiers in better planning microgrids focusing on sustainable features.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

This paper compares the differences in power generation and resident experience between S-shape and M-shape photovoltaic (PV) systems at the same location with the same number of modules. First, the sky anisotropy model (Hay-Davies-Klucher-Reindl, HDKR) is used to simulate solar irradiance (direct, diffuse, and reflection radiation) received by solar modules based on the laws of movement between the Earth and the Sun in the solar system. Next, the annual output power of two different PV system arrangements is calculated based on the solar irradiation received. Furthermore, because the solar module receives three types of solar radiation, interference is added to each of the three parts one by one to simulate shadows cast by moving objects such as birds, leaves, or clouds. Subsequently, off-grid and grid-connected power consumption models are established, and two different photovoltaic power generation systems are used at the power generation end. Lastly, the photovoltaic systems were tested based on residents’ electricity consumption habits, considering both off-grid and grid-connected households. Among them, the grid-connected model adopts the Particle Swarm Optimization (PSO) algorithm to generate an intelligent and flexible power consumption strategy. The results, based on data from Naha, Japan, show that with the same number of modules, the M-shape arrangement generates less annual power than the S-shape. In summer, though, the M-shape can generate more power, and the two structures have similar immunity to disturbances. The S-shape and M-shape have little difference in the power consumption experience. However, the general applicability of these results to other regions remains uncertain, and further testing is needed to account for environmental variations.

Objective This study aimed to determine the factors associated with minimum dietary diversity (MDD) and estimate the socioeconomic inequalities in MDD among children from five South Asian countries.Design Cross-sectional.Setting The study used the most recent round of secondary databases of Demographic Health Survey data of Bangladesh (2017–2018), India (2019–2021), Maldives (2016–2017), Nepal (2018) and Pakistan (2017–2018).Participants This study used information on MDD and other explanatory variables from a total of 136 980 (weighted) children aged 6–23 months.Methods Multivariable logistic regression was employed to identify the factors associated with MDD and concentration index (CIX) and Lorenz curve were used to measure the socioeconomic inequalities in MDD.Results The overall weighted prevalence of MDD in South Asia was 23.37%. The highest prevalence of MDD was found among children from Maldives (70.7%), while the lowest was in Pakistan (14.2%). Living in affluent versus poor households, having a mother who is employed versus a mother who is unemployed, exposure to various forms of media (newspapers and magazines), seeking antenatal care (ANC) more than four times compared with those who sought ANC less than four times and having children older than 4 years old are the most common significant factors associated with MDD deficiency. This study found the value of the CIX for MDD (MDD: CI=0.0352; p

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility.

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.

Background In many low- and middle-income countries (LMICs), including Bangladesh, socioeconomic inequalities in access to maternity care remain a substantial public health concern. Due to the paucity of research, we attempted to determine the factors affecting the facility delivery, quantify wealth-related inequality, and identify potential components that could explain the inequality. Methods We used the latest Bangladesh Demographic and Health Survey (BDHS 2017–18) data in this study. We utilized logistic regression to investigate the associated factors of facility delivery. The concentration curves (CC), concentration index (CIX) and decomposition of CIX techniques were used to analyze the inequality in-facility delivery. Results Women living in the urban areas, age at first birth after (18–24 years ≥25 years), being overweight/obese, having secondary and higher-level education of the women and their husband, seeking four or more ANC, coming from more affluent households, and women with high enlightenment were significant determinants of facility delivery. The concentration curve was below the line of equality, and the relative concentration index (CIX) was 0.205 (p Conclusion The study demonstrates a clear disparity in the use of facility delivery among Bangladeshi women; hence, immediate action is required to lower the inequalities, with a special emphasis on the contributing factors.

The Distributed Generator types have different combinations of real and reactive power characteristics, which can affect the total power loss and the voltage support/control of the radial distribution networks (RDNs) in different ways. This paper investigates the impact of DG’s penetration level (PL) on the power loss and voltage profile of RDNs based on different DG types. The DG types are modeled depending on the real and reactive power they inject. The voltage profiles obtained under various circumstances were fairly compared using the voltage profile index (VPI), which assigns a single value to describe how well the voltages match the ideal voltage. Two novel effective power voltage stability indices were developed to select the most sensitive candidate buses for DG penetration. To assess the influence of the DG PL on the power loss and voltage profile, the sizes of the DG types were gradually raised on these candidate buses by 1% of the total load demand of the RDN. The method was applied to the IEEE 33-bus and 69-bus RDNs. A PL of 45–76% is achieved on the IEEE 33-bus and 48–55% penetration on the IEEE 69-bus without an increase in power loss. The VPI was improved with increasing PL of DG compared to the base case scenario.

The main focus of the proposed framework is to examine the importance of electricity interconnections with a high share of intermittent Renewable Energy (RE) sources and attempts to link the gap between planning model and operation with considering realistic operating details. Therefore optimal Unit Commitment (UC) is considered to analyze how operational aspects are appropriately done over the planning period. More specifically, a Mixed Integer Linear Programming (MILP) model is developed to address the specific challenges of the underlying UC problem. For modelling purposes, demand forecast, applicable RE potentials and the cost of RE technologies are estimated. To reduce the expenses and improve system stability, energy storage systems (pump storage hydro and thermal energy storage) are considered as well. For optimal UC, a typical day (24 h) is employed to determine the capacity expansion and daily operational planning. Each selected day expresses a part of the year (e.g., a season). Incorporation of short-term decisions into the long-term planning framework can strengthen the accuracy of the decisions and guaranty the stability of power networks. The proposed approach can provide valuable insights into the appropriate energy strategies followed by the investors and policymakers at a national and regional level.

The absence of electricity is among the gravest problems preventing a nation’s development. Hybrid renewable energy systems (HRES) play a vital role to reducing this issue. The major goal of this study is to use the non-dominated sorting genetic algorithm (NSGA)-II and hybrid optimization of multiple energy resources (HOMER) Pro Software to reduce the net present cost (NPC), cost of energy (COE), and CO2 emissions of proposed power system. Five cases have been considered to understand the optimal HRES system for Kutubdia Island in Bangladesh and analyzed the technical viability and economic potential of this system. To demonstrate the efficacy of the suggested strategy, the best case outcomes from the two approaches are compared. The study’s optimal solution is also subjected to a sensitivity analysis to take into account fluctuations in the annual wind speed, solar radiation, and fuel costs. According to the data, the optimized PV/Wind/Battery/DG system (USD 711,943) has a lower NPC than the other cases. The NPC obtained by the NSGA-II technique is 2.69% lower than that of the HOMER-based system.

This paper presents a smart house-based power system for thermal unit commitment programme. The proposed power system consists of smart houses, renewable energy plants and conventional thermal units. The transmission constraints are considered for the proposed system. The generated power of the large capacity renewable energy plant leads to the violated transmission constraints in the thermal unit commitment programme, therefore, the transmission constraint should be considered. This paper focuses on the optimal operation of the thermal units incorporated with controllable loads such as Electrical Vehicle and Heat Pump water heater of the smart houses. The proposed method is compared with the power flow in thermal units operation without controllable loads and the optimal operation without the transmission constraints. Simulation results show the validation of the proposed method.

This study explores Bangladesh’s present energy condition, renewable energy (RE) possibilities and designs an optimal 100% RE-based off-grid power system for St. Martin’s Island, Bangladesh. The optimal size of a hybrid renewable microgrid based on photovoltaic (PV) cells, a battery energy storage system (BESS), fuel cells (FC), and an electrolysis plant (EP) is proposed. Advanced direct load control (ADLC) and rooftop PV meet the energy demand at the lowest cost, and profits are maximized by selling chemical products produced by seawater electrolysis. Four cases are explored with the mixed-integer linear programming (MILP) optimization technique using MATLAB® software to demonstrate the efficacy of the suggested power system. The system cost in case 1 is lower than in the other cases, but there is no chance of profiting. Cases 2, 3, and 4 have greater installation costs, which may be repaid in 8.17, 7.72, and 8.01 years, respectively, by the profits. Though the revenue in case 3 is 6.23% higher than in case 2 and and 3.85% higher than in case 4, case 4 is considered the most reliable power system, as it can meet the energy demand at the lowest cost while increasing profits and not putting a burden on customers.

Nowadays, the installations of photovoltaics (PVs) in the smart grid have been growing dramatically because the price of PVs is falling drastically. Undoubtedly, this is a great achievement for the recent smart grid technology. However, the colossal penetration of PVs' power at the day-time changes the load demand of thermal generations (TGs) of a smart grid which creates duck shape load curve called duck curve. In a duck curve, peak and off-peak gaps are very large which increase the start-up cost (SUC) of TGs because the units of TGs must be turned ON and turned OFF frequently. Therefore, it is very significant to run TGs units optimally. Only an optimization technique is not enough to bring a good solution. This research considers concentrated solar power and pumped storage hydroelectricity (PSH) as the energy storages. Also, fuel cells are considered as the controllable loads in the demand side's smart houses. In addition, this paper considers the real-time price-based demand response. The optimal unit commitment (UC) of TGs, PSHs, and other generators is introduced for saving the fuel cost and SUC of TGs. The optimal results of the proposed model are determined by using MATLAB® INTLINPROG optimization toolbox. To evaluate the effectiveness of the proposed method, simulation results have been compared with some other methods.

Optimal sizing of the power system can drastically reduce the total cost, which is challenging due to the fluctuation in output power of RE (primarily wind and solar) and pollution from thermal generators. The main purpose of this study is to cope with this output power uncertainty of renewables by considering ADLC, residential PV, and BESS at the lowest cost and with the least amount of carbon emission, while putting less burden on consumers by minimizing the IL. This paper optimizes the cost and carbon emission function of a hybrid energy system comprising PV, WG, BESS, and DG at Aguni Island, Japan, using a multi-objective optimization model. To solve the proposed problem in the presence of ADLC, the ϵ-constraint method and MILP are utilized. After obtaining all possible solutions, the FSM selects the best possible solution among all solutions. The result shows that while case 1 has a lower energy cost than the other cases, the quantity of IL is quite significant, putting customers in a burden. In case 2 and case 3, the total energy cost is 11.23% and 10% higher than case 1, respectively, but the sum of the IL is 99% and 95.96% lower than case 1 as the ADLC is applied only for the consumers who have residential PV and BESS, which can reflect the importance of residential PV and BESS. The total cost of case 3 is 1.72% lower than case 2, but IL is higher because sometimes home PV power will be used to charge the home BESS.

Achieving the goals of distribution systems operation often involves taking vital decisions with adequate consideration for several but often contradictory technical and economic criteria. Hence, this paper presents a modified analytical approach for optimal location and sizing of solar PV-based DG units into radial distribution network (RDN) considering strategic combination of important power system planning criteria. The considered criteria are total planning cost, active power loss and voltage stability, under credible distribution network operation constraints. The optimal DG placement approach is derived from the modification of the analytical approach for DG placement using line-loss sensitivity factor and the multiobjective constriction factor-based particle swarm optimization is adopted for optimal sizing. The effectiveness of the proposed procedure is tested on the IEEE 33-bus system modeled using Matlab considering three scenarios. The results are compared with existing reports presented in the literature and the results obtained from the proposed approach shows credible improvement in the RDN steady-state operation performance for line-loss reduction, voltage profile improvement and voltage stability improvement.

Afghanistan is a key country between energy surplus areas (Central Asian Republics andIran) and energy deficit regions (Pakistan and India). It is in a position that can facilitate and launchregional electricity trade for the benefit of the region also derive significant gains for its own economyfrom energy imports and exports. On the other hand, Afghanistan is endowed with large renewableenergy resources (RERs), which it could exploit not only to satisfy its domestic power demand butalso to earn significant export revenue. This paper firstly explains the methodology and framework forthe power trade and then presents an optimization framework for profit maximization in the short-runtrading and cost minimization in the long-run trading. The proposed methodology is applied to a realcase between Afghanistan and Pakistan. The objective functions, parameters, variables and constraintsare described for both optimization models. System sizing, simulation and optimization are carriedout using genetic algorithm (GA) technique. The results in the short-run model represent optimalityof about 2654 MW electricity export from Afghanistan to Pakistan during summer. Moreover, resultsderived from running long-run model depict that by utilizing its RERs such as solar, wind and hydro,Afghanistan can not only meet its power demand but also can export to Pakistan during its deficitperiods and gain remarkable energy profits.

Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time.

Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

In this study, superfluous jute plants and jute stick were converted into upgraded animal feed by solid state fermentation (SSF) using a cellulolytic fungus, Pleurotus sajor-caju. Prior to fermentation, substrates were subjected to several pretreatments such as soaking with water overnight and alkali or lime pretreatment. SSF was carried out with 20 g of substrate in 100 ml conical flask and was incubated at 30C for 8 weeks. In all treatments, the highest amount of reducing sugar, soluble protein as well as the cellulolytic activities of three enzymes viz. cellobiase, carboxymethyl cellulase and avicelase were obtained at 6th week of fermentation. Compared to raw, unsoaked substrates, soaking treatment alone could produce 10% more soluble protein in both substrates whereas reducing sugar increment was 5% and 6% in jute sticks and jute plants, respectively. From all treatments, combination of soaking and lime treatment in green jute plants yielded higher value than jute sticks in terms of reducing sugar, soluble protein and enzymatic activity. The radiation doses at 20, 30 and 40 kGy appeared to have no effect on sugar and protein accretion. During eight weeks of fermentation, relatively higher cellobiase activity was found compared to that of carboxymethyl cellulase and avicelase. The present investigation indicates that fungal conversion with pretreatment can turn these lignocellulosic agro-wastes to a nutritionally enriched animal feed by increasing the crude protein and reducing sugar content. However, further research is necessary to develop strategies for industrial scale production to overcome the crisis of nutritionally improved animal feed.