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Secure, Clean, and Efficient Energy is one of the great societal challenges of our time. Electricity as a sustainable energy carrier plays a central role in the most effective transition scenarios toward sustainability. To harness this potential, the current electricity infrastructure needs to be rigorously re-engineered into an integrated and intelligent electricity system: the smart grid. Key elements of the smart grid vision are the coordination mechanisms. In such a system, vast numbers of devices, currently just passively connected to the grid, will become actively involved in system-wide and local coordination tasks. In this light, transactive energy (TE ) is emerging as a strong contender for orchestrating the coordinated operation of so many devices.

Due to rapid movement of rural population to new regions and countries in order to find work and for better living conditions, population growth and economic development, there is gradual increase in production of waste and demand of electricity worldwide. According to World Bank statistics, the global Municipal Solid Waste generated was 1.3 billion tons in year 2012 and this is expected to increase above 2.2 billion tons per year by 2025. Due to lack of sites for waste disposal, it has become a serious environmental as well as health issue. Now various methods have been adopted for waste disposal. Among this, energy accessed from municipal solid waste is the most common practice adopted by developing countries. In addition, Waste to Energy and Energy from Waste technologies are used which include thermal and biological technologies. Gasification, pyrolysis and incineration are thermal technologies used in conversion of waste to energy. During these processes, syngas is produced which is used in combustion engine or turbines to generate electricity. Apart from this, biomethanation is also used in which biogas is produced. In this review article, we have discussed all the environmental and green methods used for the generation of energy from Municipal Solid Waste their advantages and drawbacks.

The advanced metering infrastructure (AMI) in modern networked smart homes brings various advantages. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of ML algorithms. In this paper, we present a covert electricity theft strategy through mimicking normal consumption patterns. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we propose a feature extraction method and develop a novel detection model based on deep learning. Extensive experiments show that the presented attack could evade existing mainstream detectors and the proposed countermeasure outperforms existing leading methods.

International audience; Optimizing nuclear unit outages is of significant economic importance for the French electricity company EDF, as these outages induce a substitute production by other more expensive means to fulfill electricity demand. This problem is quite challenging given the specific operating constraints of nuclear units, the stochasticity of both the demand and non-nuclear units availability, and the scale of the instances. To tackle these difficulties we use a combined decomposition approach. The operating constraints of the nuclear units are built into a Dantzig-Wolfe pricing subproblem whose solutions define the columns of a demand covering formulation. The scenarios of demand and non-nuclear units availability are handled in a Benders decomposition. Our approach is shown to scale up to the real-life instances of the French nuclear fleet.

The coordinated operation of interconnected but locally controlled electricity markets is generally referred to as a coupling. In this paper we propose a new decentralized market mechanism for efficient coupling of independent electricity markets. The mechanism operates after each individual market has settled (e.g. hour-ahead). Based upon the reported supply and demand functions for internal market optimization (clearing), each market operator is asked to iteratively quote the terms of energy trade (on behalf of the agents participating in its market) across the transmission lines connecting to other markets. We show the mechanisms outcome converges to the optimal flows between markets given the reported supply and demand functions from each individual market clearing. In light of incentive compatibility issues that result from pricing power flows across interconnection lines with locational marginal prices, the mechanism features incentive transfers (updated at each iteration) that compensate each given market with its marginal contribution, i.e. the cost reduction to all other participating markets. We show that these transfers imply truthful participation in the mechanism is a Nash equilibrium. The proposed decentralized mechanism is implemented on the three-area IEEE Reliability Test System where the simulation results showcase the performance guarantees of the proposed mechanism.

The demand for electricity increases rapidly along with the advancement of the industrial age. To ensure efficient distribution of the electricity, maintain low losses and high level of quality, and the security of electricity supply, the smart grid concept was proposed. The concept enables a small, individual scale to generate electricity and sell it to the grid. However, the concept adds complexity to the existing system, such as how a transaction between these generators and consumers are conducted, verified and recorded. This paper proposes the blockchain as a tool to manage transactions in the smart grid. Transactions are performed with smart contracts, and the network acts as a transaction verifier. The blockchain provides immutability of the transactions, which ensure every transaction between generators and consumers will always be executed. It also provides immutability to transaction history, which can be used for audit or solving a transaction dispute.

For the integrated energy system (IES) formed by a cluster of energy hubs (EHs), the outputs of EHs and system parameters, which greatly influence the security performance, should be properly adjusted. In this article, an event-triggered distributed hybrid control scheme is proposed to achieve security and economic operation for the IES. First, the EH output control is designed based on the features of energy networks as well as the containment and consensus algorithms. According to the control of outputs, the electricity and heat load power can be accurately shared without knowing the network parameters. Second, the pressure is bounded within an acceptable range, and the frequency is reverted to the reference value by implementing the proposed control method. Third, an event-triggered communication strategy is employed to design the corresponding protocols, resulting in reduced communication cost. Finally, the control of devices based on the equal incremental principle is proposed to achieve the minimal economic cost for each EH with considering energy prices. The results of numerical case studies are presented to validate the performance of the proposed control method.

Electric vehicle (EV) are the future of the automobile industry in terms of reducing the greenhouse gas emissions, air pollution and the better life comfort level all over the world. This paper compares the results obtained by various authors in terms of life cycle assessment (LCA) of EV and conventional vehicles powered by fossil fuels, and the life cycle cost (LCC) analysis of the both types of vehicles, as every new technology comes with an additional cost which need to be feasible with respect to the present trends. And also discuss some of the software used for both type of LCA and LCC analysis. The finding of review concludes that with the adoption of EV there is a reduction in greenhouse gas emissions (GHG) but there is an increase in the human toxicity level due to the larger use of metals, chemicals and energy for the production of powertrain, and high voltage batteries. And in terms of cost it has flexible pricing as there is uncertainty in pricing of future gasoline and electricity mix, higher initial cost at the time of purchasing due to higher pricing of battery.

Small-scale PV generation has become popular with residential customers in several jurisdictions with high solar radiation, as an alternative to improve their carbon footprint and reduce their electricity bills. However, massive deployment of such distributed generation is creating a particular and undesirable shape in the net demand, which deepens at hours of peak solar PV injections at noon and suddenly rises towards the evening, known as the “duck curve". Hence, this paper investigates the use of pre-cooling strategies in residential households to mitigate the duck-curve effects. To this aim, appropriate thermal models and simulations of houses are first developed and carried out to demonstrate the technical feasibility of pre-cooling in a house with a typical configuration, based on the Smart Residential Load Simulator (SRLS) developed at the University of Waterloo. Then, an aggregation technique is proposed to evaluate the effects on a large grid of different penetration levels of PV, and pre-cooling approaches to manage the duck-curve in California and Texas, concluding that such techniques are capable of substantially flattening the system net demand curve.

It is significant that the forecasting models give the closest result to the true value. Forecasting models are widespread in the literature. The grey model gives successful results with limited data. The existing Triangular Fuzzy Grey Model (TFGM (1,1)) in the literature is very useful in that it gives the maximum, minimum and average value directly in the data. A novel combined forecasting model named, Moth Flame Optimization Algorithm optimization of Triangular Fuzzy Grey Model, MFO-TFGM (1,1), is presented in this study. The existing TFGM (1,1) model parameters are optimized by a new nature- inspired heuristic algorithm named Moth-Flame Optimization algorithm which is inspired by the moths flying path. Unlike the studies in the literature, in order to improve the forecasting accuracy, six parameters (λL, λM, λR, α, β and γ) were optimized. After the steps of the model is presented, a forecasting implementation has been made with the proposed model. Turkey’s hourly electricity consumption data is utilized to show the success of the prediction model. Prediction results of proposed model is compared with TFGM (1,1). MFO-TFGM (1,1) performs higher forecasting accuracy.

Many cities across the world are committing to deep decarbonisation efforts. While solar photovoltaics (PV) will play a critical role in this pursuit, the role of rooftop and facade-integrated PVs within the urban landscape is yet to be fully understood. This work presents an analysis into the solar energy harvesting potential of PVs integrated as building rooftops, walls, and windows at various spatial resolutions that range from city to building scale within the City of Melbourne, Australia, as a contemporary case study. It further investigates the relationship between calculated electricity production from such PVs with the urban morphology, seasonal variation and the measured electricity consumption by the local distribution network service providers. The results indicate that PV rooftops are responsible for the largest share of the city’s solar energy potential. However, for individual blocks with high densities of high-rise and glazed buildings, it is shown that the PV potential from windows becomes more prominent. The technical workflow presented here will enable different cities to facilitate decision-making on the PV implementation in urban environments.

Façade is accepted as a determinant component on energy performance of a building, forming the boundaries between inner and outer conditions. With an intention to improve the building energy performance of an existing office building, façade integrated shading devices are examined through the cooling energy consumptions. OpenStudio simulation software is used for calculating heating and cooling electricity consumptions. The ilding simulation model is validated by comparing the simulation results with monthly electricity consumption bills. Shading device requirements are determined by using the building model without shading devices and simulation results are studied together with the sun path diagram analysis results. Hourly and seasonal solar movements are considered as the main parameters affecting the ‘transparency’ and ‘elevation angles’ of the shading devices. As a result of the shading device requirement analysis, climate adaptive shading device (CASD) scenarios are presented for the case building. Consequently, existing shading devices and proposed CASD scenarios are compared and discussed in terms of electricity consumptions and window solar radiation energy parameters. As a result of the comparisons, shading devices that are adaptable to both hourly and seasonal solar movements gave the highest improvement results in terms of decreasing cooling energy consumptions. Also, suggestions are given for developing the best performing façade for further studies.

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

Coal can be considered as the main fuel for electricity generation in Afghanistan. However, there is a quite limited data available about the overall quality, distribution, and character of coals in Afghanistan. Specific gravity (S.G) of coal as a key factor can be used for the estimation of potential tonnage production and be a fundamental parameter for the selection of coal washery process method. However, there is no investigation which comprehensively explores relationships between S.G and coal properties. In this investigation, the potential of S.G prediction based on conventional properties for Afghan coal samples was explored by stepwise regression and random forest. Pearson correlation (r) and variable importance measurement (VIM) of random forest (RF) were applied to select the most effective variables among conventional parameters for the S.G prediction. Results of VIM indicated that ash and carbon content of coal samples had the highest importance for the S.G prediction. Stepwise regression and RF models were developed based on these two coal variables. Testing the generated models indicated that S.G of Afghan coals can quite accurately predict by these models (R2 > 0.90). Modeling outcomes showed that the highest S.G (S.G > 2) for Afghan coal occurred when ash was higher than 40% and carbon was lower than 30%. Validerad;2023;Nivå 2;2023-06-30 (joosat);Licens fulltext: CC BY-NC-ND License

Cost degression in photovoltaics, wind-power and battery storage has been faster than previously anticipated. In the future, climate policy to limit global warming to 1.5–2 °C will make carbon-based fuels increasingly scarce and expensive. Here we show that further progress in solar- and wind-power technology along with carbon pricing to reach the Paris Climate targets could make electricity cheaper than carbon-based fuels. In combination with demand-side innovation, for instance in e-mobility and heat pumps, this is likely to induce a fundamental transformation of energy systems towards a dominance of electricity-based end uses. In a 1.5 °C scenario with limited availability of bioenergy and carbon dioxide removal, electricity could account for 66% of final energy by mid-century, three times the current levels and substantially higher than in previous climate policy scenarios assessed by the Intergovernmental Panel on Climate Change. The lower production of bioenergy in our high-electrification scenarios markedly reduces energy-related land and water requirements. The impact of rapidly falling costs of renewable energy and battery technology on long-term climate stabilization pathways is not well understood. Luderer et al. show that reduced renewable costs and climate policies will make electricity the cheapest energy carrier and can lead to electricity accounting for nearly two-thirds of global energy use by mid-century.

Short-term electricity load forecasting (STLF) plays a key role in operating the power system of a nation. A challenging problem in STLF is to deal with real-time data. This paper aims to address the problem using a hybrid online model. Online learning methods are becoming essential in STLF because load data often show complex seasonality (daily, weekly, annual) and changing patterns. Online models such as Online AutoRegressive Integrated Moving Average (Online ARIMA) and Online Recurrent neural network (Online RNN) can modify their parameters on the fly to adapt to the changes of real-time data. However, Online RNN alone cannot handle seasonality directly and ARIMA can only handle a single seasonal pattern (Seasonal ARIMA). In this study, we propose a hybrid online model that combines Online ARIMA, Online RNN, and Multi-seasonal decomposition to forecast real-time time series with multiple seasonal patterns. First, we decompose the original time series into three components: trend, seasonality, and residual. The seasonal patterns are modeled using Fourier series. This approach is flexible, allowing us to incorporate multiple periods. For trend and residual components, we employ Online ARIMA and Online RNN respectively to obtain the predictions. We use hourly load data of Vietnam and daily load data of Australia as case studies to verify our proposed model. The experimental results show that our model has better performance than single online models. The proposed model is robust and can be applied in many other fields with real-time time series.

The choice of condenser cooling option for concentrating solar power (CSP) plants is likely to affect their techno-economic feasibility. In view of this, an attempt has been made to assess relative techno-economics and net life cycle CO2-eq emissions mitigation (LCCM) potential for 50 MW nominal capacity wet-cooled and dry-cooled parabolic trough solar collector (PTSC) and dry-cooled solar power tower (SPT) based CSP plants with 6.0 h of thermal energy storage for two potential locations in India. It was observed that though dry cooling is likely to save significant amount of water (∼92%) in PTSC based plants, the same shall result in higher capital cost, higher performance penalty and higher parasitic power requirements leading to around 20% higher levelized cost of electricity (LCOE) as compared to wet-cooled PTSC based plants. It was also observed that the dry-cooled SPT based plants shall be able to deliver up to 4.5% higher annual electricity output and LCOE is also likely to be lowered by 13% than wet-cooled PTSC based plants. Considering emissions embodied and emissions associated with water transport/extraction from the source and water treatment, the estimation of LCCM from the PTSC and SPT based CSP plants have also been undertaken.

In the last few decades, the energy demand is increasing globally which, given the present dependency of the energy generation on fossil fuels, results in a continuous increase in CO2 emissions. In an isolated electric grid system like in the SIDS, the majority of the electricity is produced by fossil fuels, therefore most of the SIDS nations are now focusing on Variable Renewable Energy sources (VREs). VREs such as wind and solar are hardly predictable and bring instabilities in the electric power system if not buffered by a storage system. Here we investigate the possibility of using Seawater Pump Storage Hydropower Systems (S-PSHS) as a renewable energy storage solution in an isolated electric grid. For this, the island of Curacao (one of the SIDS nations) is used as proof of the concept. For detecting potential locations for the S-PSHS sites on the island, GIS application was developed. The application of this conceptual proposed solution in similar systems is straightforward and it can be easily upscaled in other geographies. The concept of using seawater for the pumped hydro project is not common in practice and it is anticipated to have technical, environmental and financial challenges which are discussed in this paper.

Utilizing the heat energy of exhaust gases is a promising application of thermoelectric generator (TEG), which can convert low grade thermal energy into electricity. The annular thermoelectric generator (ATEG) is thought to be much more feasible for cylindrical heat source compared to the flat-plate thermoelectric generator (FTEG) in reference to the compatibility. Nevertheless, the quantitative comparison is lacking to clarify the prevalent geometry of TEG for cylindrical heat source. In this work, the performances of ATEG and FTEG are compared in detail with varying inlet temperature, velocity and the convective heat transfer coefficient when cylindrical heat source is applied. The turbulent heat source is described by the standard κ - e functions together with the two-equation heat transfer model, while the output power and conversion efficiency of the ATEG and FTEG are calculated by solving the coupled thermo-electric equations. Our results showed that the output powers and the conversion efficiencies of ATEG and FTEG both increase with the increase of the inlet velocity, temperature, and the convective heat transfer coefficient. The conversion efficiency of ATEG is always higher than that of FTEG. The conversion efficiency of ATEG becomes even larger than that of FTEG when inlet temperature and/or convective heat transfer coefficient are relatively larger. In contrast, the output power of ATEG has no obvious difference with that of FTEG. This study addressed the condition when the ATEG has obvious advantage compared to the FTEG with cylindrical heat source applied. Our results suggest the annular TEG is better choice for cylindrical hot source especially when the inlet temperature and/or convective heat transfer coefficient are relatively larger. It could be a helpful guide for choosing suitable geometry of TEGs for energy harvesting in complex condition.

Industrial development, increased demand for energy, limited fossil fuels, and the prevention of environmental damage have led to the use of renewable energy to produce power. The combined system includes renewable wind and solar energy systems or other renewable energy. The hybrid system has been widely used in recent decades to provide energy. In this research, an optimal integrated system has been used to generate electricity. The proposed system includes wind, hydropower, and photovoltaic systems. to estimate the maximum power generation and reduce fluctuations in electricity generation, three proposed algorithms have been used and three common solutions have been applied to solve the optimization problem. The performance of the combined system in different weather conditions has also been assessed. The results showed that among the three proposed algorithms, the model improved the Barnacles Mating Optimizer Algorithm (IBMO) has the best distribution and convergence. Among the three common solutions, a common solution has the most logical solution to the optimization problem. And among the different hydrological conditions, the highest electricity generation is related to the wet season. the most important role in the hybrid system related the hydropower so that despite the reduction of electricity produced in the wind and photovoltaic power plants, the hydropower plant compensates for the drop in production of these systems and causes the production flow to continue.

A vast number of studies have considered economic aspects of industrial energy consumption, whereas, to the best of our knowledge, very few studies have considered demographic aspects, and none of them provided a theoretical foundation for the demographic variables employed. Studying the effect of demographic factors on industrial energy consumption is also important because of the reasons found in the literature. This motivated us to develop a theoretical framework using features of production function and the labor market, in which industrial energy consumption is linked to both economic and demographic factors. We then empirically tested the reliability of the developed framework. To produce robust results, various unit root and cointegration tests, long-run estimation methods, small sample bias corrections, as well as two functional forms and two measures of the demographics were employed in the empirical analysis. The results are theoretically and empirically coherent: demographic and economic indicators have statistically significant long- and short-run impacts on industrial electricity consumption. Further analysis showed that the demographic indicators also exert statistically significant and theoretically coherent impacts on other energy types used in industry within this new framework. We concluded that the suggested framework can be used to examine the role of both demographic and economic factors in the industrial consumption of electricity and other energy types.

A significant progress has been made to increase electricity generation capacity in the country over the last decade. The electricity supply for growing demand is the foremost challenge of the government, as the country move towards the vision of middle-income status by 2021 and high-income by 2041. The energy security is the concern due the running out of gas supply soon. Despite significant progress on energy in recent years, the goal of access to electricity for all would not be attainable without implementation of a consistent national energy policy. Well-coordinated national energy policy is required to achieve sustainable development goals of the country. Priorities for action and regulatory framework of energy need to identify to overcome energy crisis. Long-term energy planning and private financing policy are also crucial to extend the pace of progress in the energy sector. This review provides insights into the energy policies at different sectors, notably coal fired electricity in Bangladesh. The results revealed that the effective and efficient CO2 emission could be reduced from power generating sector by introducing advanced fuel technologies and management system. Besides, higher thermal and mechanical efficiency can be obtained by initiating the combined power cycle systems in energy sector. The achievement in energy sector as well as challenges facing in ensuring sustainable energy is highlighted. The key challenges in the existing legal framework and substantial progress improving energy efficiency are also discussed in this review.

The main drawback of heating biogas digestion systems based on solar energy is its unavailability overnight and at different times (days and months). To circumvent this problem, a hybrid system(solar and electricity) powering the heat digester could provide the required mesophilic conditions. The present study aimed to evaluate the technical and design feasibility of using solar energy to assist in heating the digestion units. Also, measured and determined the contribution of solar energy to heat the system. This study was conducted in the central area of Coastal Delta, Egypt (30.5 ∘ N, 30.6 ∘ E; 8.5 m a.s.l.). Results show that the contribution of solar energy to biogas production was 75.21%, 60%, and 53.58% when using three settings temperature of (37, 40, and 45 °C) of the energy consumed via cattle dung solution heating. Furthermore, increasing the set temperature inside the horizontal and vertical digesters from 37 °C to 45 °C augmented their daily average volumetric biogas production by 87.12% and 59.45%, respectively. Finally, the current study says that solar energy has reduced energy consumption by 61.28%. Also, the economic analysis indicates that the estimated return profit was $ 177.41 (USD), which represents 45.15% of the total income per operation.

Increasing temperatures will make space cooling a necessity for maintain comfort and protecting human health, and rising income levels will allow more people to purchase and run air conditioners. Here we show that, in Brazil, India, Indonesia, and Mexico income and humidity-adjusted temperature are common determinants for adopting air-conditioning, but their relative contribution varies in relation to household characteristics. Adoption rates are higher among households living in higher quality dwellings in urban areas, and among those with higher levels of education. Air-conditioning is unevenly distributed across income levels, making evident the existence of a disparity in access to cooling devices. Although the adoption of air-conditioning could increase between twofold and sixteen-fold by 2040, from 64 to 100 million families with access to electricity will not be able to adequately satisfy their demand for thermal comfort. The need to sustain electricity expenditure in response to higher temperatures can also create unequal opportunities to adapt., Adaptation to heat stress through the use of air conditioners has received increasing attention. Here the authors show that income and humidity adjusted temperature are common determinants for adopting air conditioning, but their relative contribution varies in relation to household characteristics.

The past decade has not only witnessed a great shift in the energy mix, with electricity gaining a more and more prominent role, but has also seen electricity coming increasingly from renewable energy sources, whose proportion is forecasted to reach 50% by 2030. As the integration of photovoltaic energy cannot be deemed successful without the electricity supply being both sustainable and secure, such far-reaching developments prompt legislations and policy makers, including those of the European Union, to make changes to accommodate not only ever-changing technologies, including energy storage solutions, but also new players in the market. Currently, battery energy storage systems are not used for enhancing the precision of photovoltaic power generation schedules, so actors in the market find it difficult to make well-grounded decisions on the viability of utilizing batteries for such a purpose. The innovative novelty of the procedure presented in this paper is that it is suitable for the planning, analysis and evaluation of the schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion-based battery energy storage systems of various capacities. By the examples of two European Union countries, this article studied the deviations of day-ahead and intraday photovoltaic power generation forecasts from the actual electricity generation of 1000 MWp photovoltaic systems. The two main objectives of the project were to investigate the amount of the photovoltaic balancing needs, based on actual data compared to the day-ahead and intraday forecasts, and to establish to what extent sodium-sulfur and lithium-ion-based battery storage systems can decrease those requirements. Furthermore, by using a model for simulation, the potentials of a number of different energy storage capacities (nominal net storage capacities from 10 MWh to 10,000 MWh) for reducing grid balancing needs were assessed. The practical use of the model is that its application makes it possible to create a dynamic design, analysis and assessment system mechanism that can allow one to establish the multilevel schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion energy storage systems linked to solar power plant systems. Real photovoltaic data from Belgium and Hungary were used to find out how the accuracy of PV power generation forecasts influence the level of the annual utilization of energy storage systems.

The viability of the store-on grid (SoG) scheme model across 13 selected Sub-Saharan African (SSA) countries was investigated. The individual country's datasets applicable to the SoG scheme and a typical industrial building as a baseline prosumer were used in the evaluation. Two SoG scheme scenarios (A and B) based on solar photovoltaic (PV) module prices were used in this analysis. The electricity tariffs under the SoG scheme were compared with the time of use rates for the selected SSA countries. The analysis results revealed that the scheme could be viable in only nine and four out of 13 countries under scenario A and scenario B, respectively, with high lending interest rate in some countries being the key hindrance. Under the SoG scheme model, solar PV systems in all the selected countries could feed about 15.65–39.25% of the generated energy to the grid. The government tax in the range of 1.95–3.38 and 2.48–4.31 c$/kWh could be collected from the energy fed to the grid under SoG scheme scenario A and scenario B, respectively. Overall, the SoG scheme exhibits greater potential as an energy business model to foster rooftop solar PV technology adoption in developing countries.

This paper examines the gap between the design and in-situ performance of solar streetlight interventions in two humanitarian settings. Displaced settlements often lack street lighting and electricity. Given that off-grid solar streetlights produce surplus energy, we hypothesized that this energy could be made available for daily usage, to improve system performance and provide further energy access to displaced populations. We recognize, however, that solar streetlight performance and longevity have typically been poor in remote and refugee settings. Eleven solar streetlights were fitted with ground-level sockets and their performance monitored, in two displaced settlements: a refugee camp in Rwanda and an internally displaced population settlement in Nepal. Considerable performance gaps were found across all eleven systems. Inefficient lights and mismatching system components were major issues at both sites, reducing targeted designed performance ratios by 33% and 53% on average in Rwanda and Nepal, respectively. The challenges of deploying these types of systems in temporary settlements are outlined and a number of suggestions are made to guide future developments in the design and implementation of sustainable solar streetlight interventions.

This paper proposes a price-maker bidding and offering model for networked microgrids (NMG) in a pool-based day-ahead electricity market. The objective of this model is to maximize the net revenue of NMG by coordinating the joined individual microgrids to submit aggregated offers/bids to the market operator. A hybrid stochastic-robust optimization framework is developed to offset multiple associated uncertainties. The bidding and offering model is first formulated as a hard-to-solve mixed-integer nonlinear programming (MINLP) problem, which is later converted to its easy-to-solve mixed-integer linear programming (MILP) counterpart. To resolve privacy concerns of each microgrid and improve the scalability of the proposed bidding and offering model, a coordinated scheduling framework for NMG based on the Dantzig-Wolfe decomposition (DWD) method is proposed to obtain the global optimum. Numerical simulations with real-world measured data validate the effectiveness of the proposed price-maker bidding model, which is shown to outperform existing price-taker models.

Drying of materials is an important and essential mechanism during the production process in industries. Drying fruits and vegetables is an ancient method used for food preservation. Apart from the agricultural sector, there are many industries such as automobile, rubber, paper and pulp, sugarcane, wastewater treatment, lignite/coal, etc. require heat energy for drying during production processes. Conventional energy sources such as coal, natural gas, and electricity are used for the heat energy required for drying. Due to the increase in cost and pollution involved in conventional sources, solar energy-based drying systems can be encouraged. This review work provides a detailed analysis of solar-based dryers used in various industries namely agricultural, marine, tea, sugarcane, automobile, rubber, pulp, and paper industries. In addition, the utilization of solar energy for sewage drying, industrial waste drying, and lignite coal drying for power generation are reviewed. Different types of dryers presently available in the market are also discussed in the review work. Various parameters used for the performance of the dryers such as drying rate, amount of energy required for drying, collector efficiency, drying efficiency, specific energy consumption have been discussed. Economic, environmental, and social aspects of solar dryers are also presented, and recommendations are given in the paper.

Nowadays, because of the population growth, the electricity supplying has faced different several types of issues. An example of these issues is about telecommunication stations (BTS), which are sometimes built at high altitudes with difficult access. Due to the abundance of these stations around the world, it is certain that choosing the optimal method to supply them with energy will save significant electricity costs. A new optimized and clean system by the proton exchange membrane fuel cell (PEMFC) is proposed in this study to achieve a consistent supply system for the telecommunication towers. The main purpose is to present an optimal regulated output voltage from PEMFC to the BTS to provide a safe power for the Telecom system. To do so, an optimal PI controller based on a new designed metaheuristic, called Courtship Learning-based Water Strider Algorithm (clWSA) is introduced. A comparison of the final achievements of the regulator with some other techniques are carried out to show the proposed system superiority against them.

Thermoelectric generators (TEGs) can convert heat into electricity directly. However, the thermal boundary conditions are intermittent and high temperature in most TEG systems. Therefore, thermal management of TEG systems is of great significance. It is an efficient technology to use phase change material (PCM) for thermal management of the TEG systems. PCM is applied to hot side of TEG in this study, and the phase change material-thermoelectric generation (PCM-TEG) system is set up. For the purpose of exploring the effect of the PCM on the output performance of TEG, the common TEG system is established to compare with PCM-TEG system. Furthermore, the effect of the heat transfer characteristics of PCM on TEG output performance is investigated and discussed in detail under different pulse transient thermal boundary conditions. Experimental results show that PCM can reduce the temperature fluctuation to protect TEG and extend the temperature difference operation time. The PCM of solid state, solid-liquid coexistence state and liquid state have different effects on reducing the temperature fluctuation. Besides, the PCM-TEG system cannot provide the peak output power provided by the common TEG system, but it is still beneficial to provide a stable output power.

A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper’s novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterisation of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector. This work emanated from research conducted with the fi-nancial support of the European Commission through the H2020project BIGG , grant agreement 957047, and the JRC Expert Con-tractCT-EX2017D306558-102.D.ChemisanathanksICREAfortheICREA Acadèmia. Dr J. Cipriano also thanks the Ministerio deCiencia e Innovación of the Spanish Government for the Juan dela Cierva Incorporación grant

Global electricity consumption has raised in the last century due to many reasons such as the increase in human population and technological development. To keep up with this increasing trend, the use of fossil resources has increased. But these resources are not environmentally friendly, and for this reason, many countries and governments are encouraging the use of green sources. Among these sources, PV technology is widely promoted and used due to its improved efficiency and lower prices for photovoltaic panels. Therefore, the importance of forecasting power production for these plants is necessary. In this work, two hybrid models were proposed (CNN-LSTM and ConvLSTM) to effectively predict the power production of a self-consumption PV plant. To confirm the efficiency of the proposed models, the LSTM model was used as a baseline for comparison. The three models were trained on two datasets, a univariate dataset containing only the power output of the previous days, while the multivariate dataset contains more features (weather features) that affect the production of the PV plant. The time frames for the forecast ranged from one day to one week ahead of time. The results show that the proposed methods are more accurate than a normal LSTM model.

This paper primarily aimed to assess the impacts of regional trade integration on the prospects of undergoing renewable energy transition in selected South Asian economies between 1992 and 2015. The overall results from the econometric analyses, controlling for the cross-sectional dependency and slope heterogeneity issues, highlight the importance of promoting intra-regional trade among the South Asian economies to boost the renewable energy consumption shares and renewable electricity output shares in the total final energy consumption and aggregate electricity output figures, respectively. Besides, the non-linearity of the nexuses between intra-regional trade shares and renewable energy consumption shares and between intra-regional trade shares and renewable electricity output shares are also ascertained. The threshold intra-regional trade shares concerning the renewable energy consumption and renewable electricity output shares are predicted at 20.53% and 17.50%, respectively. However, the predicted threholds are significantly higher than the current average intra-regional trade share in South Asia. Moreover, the panel causality analysis reveals unidirectional causal relationships stemming from intra-regional trade shares to renewable energy consumption and renewable electricity output shares. Besides, greater FDI inflows are found to reduce the overall use of renewable energy while higher levels of economic growth and CO2 emissions are found to catalyze renewable energy use in South Asia. Furthermore, the results also implicate a non-linear U-shaped association between positive crude oil price shocks and renewable energy consumption and renewable electricity output shares. Therefore, these findings impose critically important policy implications for liberalizing the intra-regional trade barriers, reducing dirty foreign direct investment inflows, expediting economic growth, reducing fossil fuel dependency and abating carbon dioxide emissions to facilitate renewable energy transition in South Asia.

A CCHP is modeled here, which is operated based on thermal energy provision because the target consumer demands more heat and cooling than electricity. The main components of the CCHP system are a PEMFC as the main generator, absorption chiller, electric chiller, and auxiliary boiler. The fuel consumption, cost, and carbon release improvements compared to a separated generation system (SGS) are maximized in this system using the proposed modified mayfly algorithm. The system design and operation are optimized in two different stages. A hotel building has been chosen as the case study to demonstrate the effectiveness of the proposed CCHP system and optimization in comparison to the traditional mayfly algorithm (MA) and genetic algorithm (GA). The proposed optimization algorithm has performed 8.15% and 10.06% better compared to MA and GA, respectively. It also reached its solution in far shorter time. The optimum size of PEMDC and share of auxiliary chiller (AC) in cooling provision is determined to be 548 kW and 43.1%, respectively. It is shown that improvement achieved by using the AC in the CCHP is increased by 23.21% compared to a CCHP without one. Furthermore, the AC make the improvement of CCHP more constant and robust to load variation. Moreover, the sensitivity of the optimum operation condition of the CCHP against the electricity and fuel price changes and share of the AC in supplying the cooling demand are studied to provide the system operators with the necessary tools needed to optimally handle price changes.

This study presents a methodological framework for auditing, optimization as well as the integration of hybridized solar energy in Ghana’s agro-industrial sector. The Functional Analysis System Technique was introduced for systematically translating plant-wide energy requirements into detailed functional and performance design criteria for energy efficiency, optimization, and solar energy integration. The results of the case study indicated that insulating the fermenter and distiller as well as using energy-efficient rasper, pumps and cables reduced energy consumption by 41.8%. The cost of integrating solar thermal energy was 31.8% higher than solar photovoltaic power due to a lack of subsidies in the sector. A 250 kW solar photovoltaic model was designed with a performance ratio of 75% and a solar hot water system with a thermal fraction of 91% for electricity and process water respectively. The framework, therefore, presents an invaluable tool for optimizing energy efficiency and integrating solar thermal and solar photovoltaic energy sources for the agro-industrial sector.

Utility voluntary green power programs that allow energy consumers to pay a premium for electricity generated from renewable energy sources have gained great momentum worldwide in recent years. While there is growing literature examining customer behaviors related to green power purchases, limited work has been done to systematically evaluate the efficacy of voluntary green power programs implemented in conjunction with government-led renewable energy policies. In this research, we construct an innovative panel dataset for 49 U.S. states and Washington, D.C. from 2001 to 2013, with the goal to understand factors influencing total sales and customers of voluntary green power programs. Hybrid random effects regression models are estimated to investigate the impact of state renewable portfolio standards (RPSs) on the performance of voluntary green power markets. We find that for a given state, an increase in RPS policy stringency by one unit would lead to a reduction of green power program customer enrollment by 12.37%, and a reduction in green power program sales by 12.98%. Our results suggest that the competing effect is dominant between the voluntary green power market and the renewable energy regulatory regime: state RPSs tend to crowd out cheap renewable-sourced electricity that is available for sale through utility green power programs.

A techno-economic study has been proposed for an off-grid combined renewable energy system (HRES) by photovoltaic (PV) and fuel cell (FC) systems. The proposed HRES has been studied to provide electricity for a remote area in Jiaju Tibetan Village, Danba, Sichuan Province China The main idea is formulated according to the Total Annual Cost (TAC). The study uses a new improved metaheuristic, called Amended Water Strider Algorithm (AWSA) to optimal estimation of the photovoltaic panels’ number, electrolyzer, fuel cells, and hydrogen storage tanks to get the minimum value of the overall net present value (ONPV). For increasing the system efficiency, the loss of power supply probability (LPSP) has been considered. Sensitivity analysis is used on the changing of the proposed HRES according to Levelized cost of energy (LCOE). Also, a comparison of the algorithm with several techniques from the literature is performed to illustrate its effectiveness. The results showed that the proposed method has further proper results than the other algorithms.

This work proposes a methodology for optimizing Solar Chimney Power Plant (SCPP) geometric parameters (chimney height, collector area and chimney radius for fixed values of canopy height) and also presents a framework for a combined techno-economic analysis of an optimized 1 MW SCPP to be implemented in Tozeur, located in the south of Tunisia. A CFD model was used to investigate the influence of the chimney height and radius and the collector radius on the plant's electricity production and these were used in a cost estimation method to obtain an optimized design of the 1 MW SCPP. Subsequently, an economic analysis, based on the Levelized Cost Of Electricity (LCOE) was carried out for the optimized 1 MW SCPP configuration. The optimization procedure yielded an optimum collector radius of 452 m and a corresponding chimney height of 462 m and radius of 5 m to produce 2.1 GWh of electricity per year. For an interest rate of 3%, an inflation rate of 6% and a lifetime of 40 years, the price of electrical energy produced and fed into the national grid is 0.105 TND/kWh. The SCPP would avoid the emission of 480.9 tons/year of CO2 during its 40 year lifetime.

Background Norway is currently in the process of replacing internal combustion engine (ICE) vehicles with electric vehicles (EVs). A steadily increasing number of EVs being charged in the evening when the demand peaks are already high has added to the strain on local electricity grids. Smart charging is proposed as a solution to peak-load challenges of this kind, as it can delay charging until evenings and nights when demand peaks are generally low. However, current knowledge of the preferences and motivations of EV owners regarding the installation and use of smart chargers is lacking. Accordingly, the study aims to contribute an improved understanding in this respect. Methods The authors conducted a qualitative study based on semi-structured interviews, in which they gained information on householder’s experiences following their participation in a pilot project to test smart charging of EVs. The interviews explored the interviewees’ motivation for participating in the project, their experiences of using and charging EVs, and their thoughts on flexibility in the timing of EV charging. Interviews were recorded and transcribed verbatim, after which a domestication theory approach was employed to perform an in-depth analysis of the interview data. Results The analysis of the interviews revealed that the study participants had four motivations for participating in the smart charging demonstration project and engaging in the further use of smart charging: (1) the fire-safety aspect and speed of charging devices for EVs was better than charging from a socket; (2) they derived a broader interest in and more joy from using smart home technology; (3) attractive practical and economic benefits; and (4) the flexibility offered by smart charging had the potential to have a positive impact on physical comfort. Conclusions The study revealed key domestication processes unfolding in the Norwegian energy transition with the increasing numbers of EVs and the corresponding grid peaks. The findings clearly indicate that users’ motivations go much further than simple economic rationalization, which should be of interest to policymakers, smart home developers, and all others that work with end-user flexibility in the grid.

This paper presents renewable energy systems based on micro-hydro and solar photovoltaic for rural areas, with a case study in Yogyakarta, Indonesia. The Special Region of Yogyakarta, located on the island of Java, Indonesia, has a high potential for the development of renewable energy resources, especially hydropower and solar power. Many rural areas in Yogyakarta lack a supply of electricity. In this study, data on the potential for hydropower and solar power in rural regions of Yogyakarta are processed to determine the best capacity of hydroelectric and solar power plants. The extended particle swarm optimization (PSO) technique has been used to ensure optimal capacity optimization of this hybrid systems. The final result of this study is the most optimal of hydropower and solar power generation capacity based on the calculation of cost of capital, grid sales, cost of energy, and net present value.

Bioeconomy, as a key strategic innovation pillar, supports energy transition and involves biomass resource assessment and its application. Biomass has an enormous potential to fulfil the domestic energy demand in Brazil. Hence, this study aims to quantify the theoretical, technical and techno-economic potential of agricultural and forestry residues to generate electricity via thermochemical processes in Brazil for the 2018–2050 timeframe. The analysis was carried out via an integrated approach, according to statistical reports and literature data. Conversion technologies for the estimation of technical potential consist of organic rankine cycle, combustion with conventional rankine cycle and gasification with an internal combustion engine. The results showed that Brazil’s theoretical potential is 7185.39 PJ and 16,708.12 PJ in 2018 and 2050. The technical potential in 2018 is 1795.16 PJ, generating 71,236.47 MWe, while the potential in 2050 is projected as 5771.49.4 PJ providing 229,027.31 MWe. The main restrictions are dispersed distribution of biomass over a large area, supply costs, and low efficiencies for energy conversion. Therefore, the techno-economic potential is around 19,704.01 MWe and 63,348.95 MWe for 2018 and 2050. The north region has the lowest electricity generation potential, while the southeast region takes a prominent position to generate electricity mainly from sugarcane residues. Levelized costs of electricity values varied from 10.532 to 15.507 US$/MWh for 2018 and from 7.320 to 13.002 US$/MWh for 2050, which highly influenced by the biomass price. The results of this study can support policy-making towards future planning on Brazilian bioenergy.

Providing clean water for energy generation is of particular importance. Due to limited water resources, hydropower generation is facing problems in today’s world societies. Due to this issue, the purpose of this study is to investigate the optimal management of water resources and its impact on optimal hydropower generation. The innovation of this research is the use of a new version of Developed Wildebeest Herd Optimization (DWHO) to forecast hydropower generation. This proposed algorithm solves optimization problems and increases the accuracy of the results obtained for reservoir operation to generate power. The results of this study showed that the developed algorithm has the highest convergence speed and utilizes minimum time-consuming mathematical processes to reach the global solution and prevents trapping in local solutions. The results related to the estimation of power showed that the DWHO method produces about 17% more electricity than other compared algorithms. Also, the highest reliability index 89.7% and resilience index 68.1% and the lowest vulnerability index 12.8% belong to the DWHO method.

This paper addresses the financial and economic feasibility of biogas production to meet energy demands at a household or village scale. To this effect, the potential cost and benefit of biogas digesters were explored. The biogas digesters were characterised under controlled conditions before being deployed to the KrwaKrwa and Upper Ncera villages in the Eastern Cape, South Africa. At the control site, meteorological weather parameters that influence biogas production were monitored continuously. Based on typical rural household electrical usage profile from 2-years of data observed at the sub-station level, assumptions about the cooking times and electrical demands were made. It was found that the bio-digesters are capable of supplying the energy required for cooking, provided the digesters are continuously fed at least every 5th day. Also, 1.0 m3 of CH4 is equivalent to 4.70 kWh of electricity. With reloading after every 5th day, the total biogas produced per day over 31 days is 2.93 m3. This is equivalent to demand-side avoidance of 13.77 kWh/day and monetary savings of R26,88/day or R833,43/month. A financial cost of approximately R 17 000,00 per digester, Net Present Value of R22 000,00 and a payback period of 22 months was obtained for the feasibility of the project. Based on the findings of the study, it can be said that biogas digesters are financially sound investments and economically feasible at both household and societal levels. These digesters can also ensure that the household remains above the poverty threshold.

Excessive consumption of fossil fuels has led to depletion of reserves and environmental crises. Therefore, turning to clean energy sources is essential. However, these energy sources are intermittent in nature and have problems meeting long-term energy demand. The option suggested by the researchers is to use hybrid energy systems. The aim of this paper is provide the conceptual configuration of a novel energy cycle based on clean energy resources. The novel energy cycle is composed of a wind turbine, solar photovoltaic field (PV), an alkaline fuel cell (AFC), a Stirling engine and an electrolyzer. Solar PV and wind turbine convert solar light energy and wind kinetic energy into electricity, respectively. Then, the generated electricity is fed to water electrolyzer. The electrolyzer decomposes water into oxygen and hydrogen gases by receiving electrical power. So the fuel cell inlets are provided. Next, the AFC converts the chemical energy contained in hydrogen into electricity during electrochemical reactions with by-product (heat). The purpose of the introduced cycle is to generate electricity and hydrogen fuel. The relationships defined for the components of the proposed cycle are novel and is examined for the first time. Results showed that the output of the introduced cycle is 10.5 kW of electricity and its electrical efficiency is 56.9%. In addition, the electrolyzer uses 9.9 kW of electricity to produce 221.3 grams per hour of hydrogen fuel. The share of the Stirling engine in the output power of the cycle is 9.85% (1033.7 W) which is obtained from the dissipated heat of the fuel cell. In addition, wind turbine is capable of generating an average of 4.1 kW of electricity. However, 238.6 kW of cycle exergy is destroyed. Two different scenarios are presented for solar field design.

Understanding the main factors influencing energy intensity in China is important to reduce total energy consumption and protect the environment. Based on the OECD–WTO Database of Trade in Value-Added, we subdivide the secondary sector and employ the LMDI method to analyze how much changes in industrial structure and intra-industry energy intensity contribute to the overall change in energy intensity. The results prove that the change in intra-industry energy intensity is the main factor that influences the overall change in energy intensity in recent years. Furthermore, we use firm-level energy consumption data that cover various regions, scales and kinds of energy consumption, and employ firm fixed effects regression to analyze intra-industry influencing factors and find that the scale effect exists for coal, oil and electricity efficiency; foreign-invested firms have lower energy intensity; and firms in coal-rich areas have higher coal intensity. Our results show that if firms’ energy intensity decreases to the level of firms with lower energy intensity within the industry, total energy consumption will decrease considerably without changing the output or industry structure. This paper will give implications for developing countries, where it is difficult to make industrial structural changes to reduce energy consumption.

There is a common misconception that at least 95% of Egypt’s potential hydropower has already been used, which does not reflect reality. Currently, only five hydropower plants operate along the river Nile, from Aswan high dam to Assiut barrage. The total installed capacity is around 2840 MW. However, Egypt has many main canals and Rayahat and an irrigation network system, including barrages, head regulators, and navigation locks, which enable 120 MW of clean electricity (equivalent to 4 times of Assiut power station generation) if used to generate electricity. This paper aims to assess mini and micro hydropower’s potential in Egypt for the main grid areas. Eight sites have been selected and ranked as an application sample in this work based on their hydraulic data availability. The rank of the most suitable energy generation sites is determined with spatial maps’ aid based on the flow duration curves, capacity factor, and multi-criteria analysis. The decision matrix developed to rank the selected locations, and the turbine efficiency curve is proposed in terms of the available flow to decide the suitable turbines. Also, the spatial maps give a clear vision to decision-makers of the possibility of exploiting different resources and the priority of implementation. The results obtained show a promising prospect of mini and micro hydropower in Egypt. The annual energy from the eight studied sites is estimated by 200 GWh according to the daily hydraulic data of the year 2017. Damietta barrage takes priority as the weight value of criteria is the highest value by 2.85, and its annual energy is estimated by 78.5 GWh. The eight sites are more suitable for peak load plants, but Damietta and Zefta are appropriate by less priority for baseload plants.

Due to environmental concerns, renewable energy sources (RESs) gradually replace conventional power generations. In the last decade, wind and solar electricity generation have increased from 55 million MWh to 275 million MWh and from 2 million MWh to 96 Million MWh, respectively, in the US. Recently, FERC Order 2222 allows distributed energy resources, including distributed renewable generation and energy storage, to participate in the electricity wholesale market through aggregations. Limited rotating inertia of renewable generation resources results in reduced system inertia, which will increase the rate of change of frequency (ROCOF) due to sudden mismatches of generation and demand. During the high visibility national social event, the brightness of LED TV display put simultaneous demand change in power systems. With the development of slim, tightly packaged, and yearly update features of Smart TV, the penetration of different LED displays climbed rapidly from 9% to 60% in the US during 2012–2019. In this paper, the impact of societal events on frequency stability caused by large viewership on LED TV s is discussed. Increasing RESs penetration and FERC order 2222 are considered in this study. The power demand data of a 55-inch LED TV is collected and analyzed to address the problem. This study discusses the frequency stability issue caused by LED TV s in a low inertia power system. It can help system operators to develop frequency control practices accordingly.

Globally, demand for electricity has seen a steady increase, with nations striving to meet the demand. However, Nigeria is abysmally struggling to meet the increasing demand with the meagre per capita electricity consumption of 151 kWh, which is lower than the average of per capita electricity in Africa. Presently, the grid-tied electricity installed capacity in Nigeria, estimated at 13,435 MW is primarily generated by fossil and hydro-power plants at approximate distribution of 82.3% and 17.7%, respectively. This work presents single capsule that shows a holistic narrative of the energy access sector in view of energy penetration, sustainability, technology advancement and energy policy evolution. From available records, renewable energy sources in Nigeria are equitably distributed throughout the country, unlike the fossil reserves that are mostly concentrated in the south-south geopolitical zone. Also, there are huge potentials buried within untapped renewable energy in the country, namely wind, small-scale hydropower, geothermal, tidal and biomass energy. Furthermore, cutting edge power generation technologies that incorporate fuel cell, gas-, steam-, and low grade-turbine with carbon capture and fired by dual fuel of natural gas and biomass is promising for both energy access and climate action. The Federal Government of Nigeria's friendly energy policies, coupled with political will to excite implementation are panacea to significantly drive energy access for sustainable socio-economic development.

Lack of electricity networks in remote areas and also the high cost of connecting these areas to national networks due to the unfavorable geographical situation of the region leads to the use of other energy sources independent of the network in these areas. Today, the use of distributed generation sources is increasing due to the reduction of fossil fuel sources and heat problems. The output power of distributed generation sources, especially renewable energy sources, fluctuates under the influence of atmospheric conditions. The main purpose of the present study is to present a new optimal configuration for a hybrid photovoltaic/diesel generator/battery system to refine the load demand of a rural area in Hoxtolgay, China. The idea is to minimize the CO2 emissions value, the annualized cost, and the loss of load probability of the system. To simplify this system, e -constraint method is performed. The problem has been solved by an adaptive version of Marine Predators Algorithm (AMPA) to improve the optimization results. The total ACS for the optimal system is achieved 8224.15 $. Also, an optimal value of 451.30 kW for diesel generator, 4266$ for battery storage, and 2670.53 $ for PV generator are achieved. The initial capital cost of the system is achieved 47940 $ that is less than the NPC. In this case, the maximum cost of the system with 43.12% belongs to the PV system of total cost of the system. Finally, CO2 emission by the suggested AMPA with 1624 kg/year shows the minimum value with cleanest result among the others. Final results of the proposed technique are compared with PSO method and HOMER-based optimization to indicate its effectiveness. Sensitivity analysis has been also performed to determine the system efficiency.