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The pulp and paper industry is highly energy-intensive. In mills that use chemical pulping, roughly half of the higher heating value of the cellulosic material used to manufacture the product typically is incinerated to generate steam and electricity that is needed to run the processes. Additional energy, much of it non-renewable, needs to be purchased. This review considers publications describing steps that pulp and paper facilities can take to operate more efficiently. Savings can be achieved, for instance, by minimizing unnecessary losses in exergy, which can be defined as the energy content relative to a standard ambient condition. Throughout the long series of unit operations comprising the conversion of wood material to sheets of paper, there are large opportunities to more closely approach a hypothetical ideal performance by following established best-practices.

Droplet-based microfluidics is a modular platform in high-throughput single cell and small sample analyses. However, this droplet microfluidic system was widely fabricated using soft lithography or glass capillaries, which is expensive and technically demanding for various applications, limiting use in resource-poor settings. Besides, the variation in droplet size is also restricted due to the limitations on the operating forces that the paper-based platform is able to withstand. Herein we develop a fully integrated paper-based droplet microfluidic platform for conducting droplet generation and cell encapsulation in independent aqueous droplets dispersed in a carrier oil by incorporating electric fields. Through imposing an electric field, the droplet size would decrease with increasing the electric filed and smaller droplets can be produced at high applied voltage. The droplet diameter can be adjusted by the ratio of inner and outer flow velocities as well as the applied electric field. We also demonstrated the proof of concept encapsulation application of our paper device by encapsulating yeast cells under an electric field. Using a simple wax printing method, carbon electrodes can be integrated on the paper. The integrated paper-based microfluidic platform can be fabricated easily and conducted outside of centralized laboratories. This microfluidic system shows great potential in drug and cell investigations by encapsulating cells in resource-limited environments. This article is protected by copyright. All rights reserved.

Triboelectric nanogenerators (TENGs) have been investigated as a promising device to harvest mechanical energy into electricity. At present, paper-based triboelectric nanogenerators (P-TENGs) have been widely researched because paper features with lightweight, low cost, abundance, renewability and biodegradability. In this paper, the operating principles and operation modes of TENGs, the development progress and performance enhancement routes of P-TENGs are comprehensively reviewed. Additionally, the overview, challenges and opportunities of P-TENGs applications are also presented.

New electricity demand from e-mobility can incentivize investment in new clean generation and grid infrastructure, thus improving power system services. As electric vehicle charging is flexible, this demand can be shifted to follow supply from low-cost variable renewables, like wind and solar. In this working paper, we identify nine key barriers to effective e-mobility/ renewable energy integration, which revolve around upfront cost, locked-in governance and technology uptake. We present a framework of enablers for effective e-mobility and renewable energy integration. The framework divides enablers into four categories and subdivides them into ten subcategories. We identify enablers according to this framework, which will be used as a base from which to develop practical solutions in the Asia-Pacific context. 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The industry of disposables has undergone an astronomically immense revolution in this century owing to the diligent lifestyles and technological advancements in its engenderment and manufacturing process. Paper cups are one of the widely used products in disposable industry. It has multi utility options that is it can be acclimated to imbibe water as well as beverages, soups, liquids etc. This paper entirely fixates on incrementing the quality factor of engenderment and manufacturing process of these paper cups by integrating adscititious circuitry to the subsisting machinery to truncate waste of raw materials as well as the per unit cost involved in wastage of electricity. There is a necessity to improvise the quality of overall production parameters using automation techniques. Replacing the whole machinery is a very high costing implementation hence economic feasibility of automation can be achieved by adding an additional circuitry and improving the overall perfoemance. This research work is predicated on utilizing proximity sensors to sense the materials and molded paper cups in front of machinery and minimize the electric wastage through a relay Mechanism assembly. This is a PLC predicated live project example of efficaciously incrementing the overall efficiency of the manufacturing plant

The present scenario in the world largely demands affordable, fast, recyclable, and flexible electronic devices for bio sensing. Varieties of paper-based devices such as microfluidics paper electrodes, paper diodes, and paper-based transistors etc. have been developed and validated. Most of the fabrication techniques published so far have focused on economic, environment-friendly straightforward methods to develop paper-based field-effect transistors (PFET) sensors, additionally, explored their applications. The synthetic-free, mechanically flexible, biocompatible, and signal amplification capability render PFET based sensors for wearable device makers. Modified organic/inorganic PFETs identify target analytes based on the electrical signal and endow them as perfect transducers. In the field of PFET bio sensing technology, numerous challenges are needed to be discussed to proceed forward in biomedical and other analytical applications. Realizing biologically or chemically modified PFET having an exceptional signal to noise ratio, specificity, with rapid detection ability is challenging. This review recapitulates the fabrication techniques, performances of various PFET sensors, and summarizes the report by concluding remarks including the limitations of the existing PFET based sensors and the future holds in regards to the sustainable nature of PFET.

Microbial fuel cell (MFC) is an emerging technology which has been immensely investigated for wastewater treatment along with electricity generation. In the present study, the treatment efficiency of MFC was investigated for hydrocarbon containing wastewater by optimizing various parameters of MFC. Mediator-less MFC (1·2 l) was constructed, and its performance was compared with mediated MFC with Escherichia coli as a biocatalyst. MFC with electrode having biofilm proved to be better compared with MFC inoculated with suspended cells. Analysis of increasing surface area of electrode by increasing their numbers indicated increase in COD reduction from 55 to 75%. Catholyte volume was optimized to be 750 ml. Sodium benzoate (0·721 g l–1) and actual common effluent treatment plant (CETP) wastewater as anolyte produced 0·8 and 0·6 V voltage and 89 and 50% COD reduction, respectively, when a novel consortium of four bacterial strains were used. Twenty MFC systems with the developed consortium when electrically connected in series-parallel connection were able to generate 2·3 V and 0·5 mA current. This is the first report demonstrating the application of CETP wastewater in the MFC system, which shows potential of the system towards degradation of complex organic components present in industrial wastewater.

Greece is one of the first European countries that was committed to decarbonize its energy sector by 2028 in the framework of the European Green Deal. However, this rapid and abrupt phase-out of coal-powered electricity raises concerns over the distributional consequences on the Greek households. The transition to clean technology is associated with higher costs, expected to primarily burden the local economy of the coal-producing areas, while the financial support provided by the Just Transition Mechanism is considered insufficient to deal with the negative socio-economic impacts of the lignite phase-out policy. The environmental policies and economic instruments that have been proposed to mitigate the expected side effects on income distribution are considered to be vague, inadequate, and insufficient. The paper aims to shed light on the-neglected-potential impacts of the delignitization policies on income distribution, at both the trans-European, inter-regional, and intra-regional levels, and suggests that governments and policy makers should not neglect the distributional impacts of the policies they apply.

Although humic acid (HA) is a complex natural organic matter, it can potentially harm the environment and human health. In this study, aluminum–air fuel cell electrocoagulation (AAFCEC) was used to remove HAs from water while generating electricity. Initial pH, electrolyte concentration, HA concentration electrode distance and external resistance were investigated to determine the power generation and removal efficiency. The results showed that the better performance of power generation has been acquired in the alkaline solution and larger electrolyte concentration and short electrode distance. Further, Al–Ferron complexation timed spectrophotometry was used to determine the Al speciation distribution in the solution under different parameters. The power density of the cell reached 313.47 mW/cm2 for the following conditions: 1 g/L NaCl concentration, 3 cm electrode distance, 20 Ω external resistor, and pH 9. After about an hour of electrolysis, the optimum removal rate of HA was above 99%. The results demonstrated that the AAFCEC is an efficient and eco-friendly water treatment process, and it could be further developed and disseminated in the rural areas and households. HIGHLIGHTS This Al-air fuel cell electrocoagulation can be used to treatment wastewater without external power source.; The output power density can reach up to 427.59 mW/cm2.; The form of Al in the solution have been studied by Al-Ferron complexation timed spectrophotometry.; The removal rate of HA was above 99% after about an hour electrolysis.

Although the treatment of municipal wastewater using microbial fuel cells (MFCs) has been extensively studied, scaling the systems up for practical use remains challenging. In this study, a 226 L sewage treatment reactor was equipped with 27 MFC units, and its chemical oxygen demand (COD) removal and electricity production were evaluated. The MFC units were tubular air cores with a diameter of 5 cm and length of 100 cm, which were wrapped with a carbon-based cathode, anion exchange membrane (AEM), and nonwoven graphite fabric. The air-cathode–AEM MFC generated 0.12–0.30 A/m2, 0.072–0.51 W/m3, and 1.7–4.6 Wh/m3 in a chemostat reactor with a COD of 140–36 mg/L and hydraulic retention time (HRT) of 9–42 h throughout a year. The decrease in the COD was represented as the first-order rate constant of 0.038. The rate constant was comparable to that of other air-cathode MFCs with cation exchange membranes, indicating the necessity of a posttreatment to meet the discharge standard. It has been estimated that the MFC operation for 24 h before post-aeration can reduce the energy required to meet the discharge standard by 70%, suggesting the potential applicability of MFC in long HRT-treatments such as oxidation ditch. The resistances of the anode, cathode, and AEM were 0.60, 0.28, and 0.51 mΩ m2, respectively, and surface dirt rather than deterioration primarily increased the AEM resistance. A current exceeding 0.2 A/m2 significantly increases the anode potential, indicating that the current was limited by low COD. Increasing the anode-specific surface area can improve air–AEM MFCs used for practical applications.

Pairing the electrocatalytic hydrogenation reaction with different anodic reactions driven by renewable electricity offers a greener way for producing value-added chemicals and fuels. In particular, replacing the sluggish water oxidation with a biomass-based upgrading reaction can reduce the overall energy cost, thus allowing for the simultaneous generation of high-value products at both electrodes. This mini-review summarized the recent progress in paired electrolysis of biomass-derived compounds, particularly the furanic chemicals. Some perspectives and outlooks were proposed for further improvements in this research area.

The aim of this work was to compare the performance of the two types of photosynthetic microbial fuel cells (MFCs) fed with real wastewater- one having plant Canna indica (PMFC) and the other having alga Chlorella vulgaris (AMFC) at the cathode. The chemical oxygen demand (COD), phosphate, and nitrate removal stood at 57.16% 88.81%, 59.82% for PMFC and 65.27%, 95.59%, 66.61% for the AMFC. While AMFC was slightly superior in water treatment, the power output was 6 times higher in PMFC (22.76 mW m−2) than the AMFC (3.64 mW m−2). The biomass growth was good in both systems, with biomass productivity of 0.031 Kg m−3 day−1 in AMFC and a leaf area index of 0.006 in PMFC. The study's findings suggest that PMFCs are equally good or even better than AMFCs when the goal is simultaneous water treatment and power generation.

Phosphorous (P) recovery from industrial wastewaters solves both P deficiency and P pollution problems. A sequencing batch iron-air fuel cell was set up to recover P from synthetic wastewater containing 0.6 g-P/L Na2HPO4. In the cell, ferrous iron goes into the liquor from iron-anode to precipitate soluble P and form vivianite. Electrons travel from iron-anode to air-cathode through external circuit thus to generate energy. During 3 months' continuous operation, the P removal efficiency stably achieved at around 97.6%, and the average output voltage of cell was 404 mV. After long time operation, performance degradation of iron-air fuel cell was observed due to the electrode passivation caused by the accumulation of P precipitate on the iron-anode surface. The precipitate layer on the iron-anode impeded, but it did not block the mass transfer of ferrous iron to the anode liquor. The cell still worked with 25% decrease of output voltage, 86% decrease of current density, 87% decrease of power density and 9 times increase of internal resistance. Further analyses by XRD, FITR and Mossbauer illustrated that vivianite was the main component in both precipitates on the iron-anode surface and at the bottom of anode chamber with respective content of 66% and 30%. Vivianite on the iron-anode surface was a preferable choice due to higher content for P recovery. The iron-air fuel cell system could be a feasible option for achieving the multiple goals of P pollution control, resource recovery as vivianite, and energy generation, thereby contributing to the sustainable development of wastewater treatment.

This first-attempt study illustrated the microbial cooperative interactions related to bioelectricity generation from the mixture of sludge fermentation liquid (SFL) and fruit waste extracts (FWEs) via microbial fuel cells (MFCs). The optimal output voltages of 0.65 V for SFL-MFCs, 0.51 V for FWEs-MFCs and 0.75 V for mixture-MFCs associated with bioelectricity conversion efficiencies of 1.061, 0.718 and 1.391 kWh/kg COD were reached, respectively. FWEs addition for substrates C/N ratio optimization contributed considerably to increase SFL-fed MFCs performance via triggering a higher microbial diversity, larger relatively abundance of functional genes and microbial synergistic interactions with genera enrichment of Clostridium, Alicycliphilus, Thermomonas, Geobacter, Paludibaculum, Pseudomonas, Taibaiella and Comamonas. Furthermore, a conceptual illustration of co-locating scenario of wastewater treatment plant(s), waste sludge in situ acidogenic fermentation, fruit waste collection/crushing station and MFC plant was proposed for the first time, which provided new thinking for future waste sludge treatment toward maximizing solid reduction and power recovery.

This paper presents a techno-economic approach to readily assess the profitability or otherwise of combined cycle power plants (CCPPs) for increased electricity production in Nigeria. As a case study for this analysis, a combined cycle gas turbine plant with 650MW installed capacity at Afam VI power station is used to evaluate the installation of a 1000MW and 1500MW CCPP economically. The results and analysis determined a Levelized cost of electricity of N41.57k/KWh and N34.09k/KWh for the 1500MW and 1000MW CCPP, respectively. It signifies an increase of 33.33% and 66.67% in the cost of electricity per kWh between the 1000MW and 1500MW plant capacities respectively, relative to the 650MW CCPP. Therefore, the low LCOE makes it economically viable to install the 1000MW CCPP for electricity production in the country. The paper also proposes upgrading existing gas-fired power plants in the country into combined cycle power plants for improved electricity supply.

Decarbonizing the energy sector in order to reduce the consequences of climate change is possible through a comprehensive set of new measures in this sector: integration of a higher share of electricity from renewables and reduction in the share of fossil fuels in the energy mix, improvements in energy efficiency across the board, but also the development of a new, functioning model of the electricity market. As the electricity market is a complex and dynamic environment, this paper highlights that the new vision for the sector outlined here requires a holistic approach with multiple perspectives. The paper focuses on possible challenges in achieving these goals. Having in mind a common vision, it has been up to each country to reshape its domestic energy sector, as well as to use the opportunity to benefit from such a transition to the suggested framework. The contribution of this paper are detailed suggestions for that transition process in the case of the Republic of Serbia. To the best of our knowledge, this is the first paper to systematically explore the evolution of the electricity market design in the Republic of Serbia to ensure a feasible decarbonization process.

DC circuit breakers (DCCBs) play a significant role in obtaining the reliability and stability of DC power systems. Selective and fast isolating of faults minimizes the power supply outages to end electricity users and protects the power converters implemented in the DC system. During the last decades, DCCBs have been deeply investigated in industry and academia. There has been impressive progress in terms of efficiency, response time, power-density, lifetime, and electrical operating ratings. Introducing wide bandgap (WBG) devices further has motivated researchers to propose more efficient and novel topologies. This paper provides a point of reference and a background review for DCCBs from novel aspects, fulfilling absent parts in the literature. The elaborated subjects include current commutation circuitry, DCCBs trip-curves developments, newly added functions to DCCBs, as well as marketing analysis. The advancements of DCCBs are surveyed and development needs are recognized. This paper aims to identify the future research needs of DCCBs for emerging DC power systems.

This paper proposes a new mathematical formulation for the optimal operation scheduling of a remote PV farm and a set of distributed electric vehicle charging stations (D-EVCSs), which are owned by a private entity. The proposed model is formulated to maximize the profit of the private investor through optimal electric vehicles (EVs) charging coordination and pricing mechanism. The proposed pricing mechanism aims to achieve an expected revenue by the private investor, while guaranteeing a low EVs charging price to ensure the EV owners satisfaction. The D-EVCS integrates a battery storage system (BSS) and rooftop PV for the EVs supply application. However, the rooftop PV existence is constrained to the limited footprint of the EVCS. For this reason, the investment in a remote PV farm and its impact on the expected revenue and the EVs charging price is introduced in this paper. The power generated by the remote PV farm can be transacted to the D-EVCSs using a power purchase agreement through the utility grid infrastructure. In such a case, the electricity service and distribution fees are paid to the utility grid for overseeing the PV farm power transaction. Also, the opportunity of the D-EVCSs to participate in the provision of operating reserve and demand response ancillary services is considered in the proposed model. Different case studies are evaluated to validate the effectiveness of the proposed model.

Accurate electric energy metering in micro-grid is one of the most urgent problems in the field of electric energy metering. In order to solve the problem that the lackness of electric energy metering can't be accurately calculated with the existing electric energy compensation algorithm when the electric energy metering device is completely faulty, this paper proposes an electric energy compensation algorithm that is limited in micro-grid and is based on standard particle swarm to optimize the deep belief network. Taking the historical electric energy data of each micro source metering point in micro-grid as an example, it is divided into the training set and the test set, the standard particle swarm optimization algorithm is used to determine the optimal learning layers and learning rate of deep belief network, and the electric energy algorithm model is established. The model verification results show that the proposed algorithm is nearly five times lower than the algorithm based on deep belief network solely. Further engineering case comparison shows that lackness of electric energy metering compensated by the algorithm studied in the paper is closer to the theoretical value, and the error is also less than the traditional average electricity algorithm. The real-time power curve is closer to the theoretical value, and has the higher accuracy.

This paper takes an industrial extraction heating unit as the research object, introduces a heat storage device into the steam bypass and proposes two schemes for discharging, one for electricity users and the other for heat users. The unit’s original peak shaving plan, bypass peak shaving plan, and bypass heat storage combined peak shaving plan were analyzed in this paper by building a unit analysis model in Ebsilon software. The results show that the bypass heat storage combined peak shaving scheme for power users has the largest peak shaving range, it has increased by 163.87MW compared with the original unit when the heat supply get to the largest. In one storage and discharge cycle, the scheme of bypass heat storage combined peak shaving discharging to electricity users has the highest cycle thermal efficiency and the highest operating profit, compared with the bypass peak shaving scheme, the operating revenue of the scheme has increased by 16.85%.

Masses of user-side smart meter data provide a foundation for utilities to acquire users’ electricity consumption patterns and promote refined energy management. Therefore, the charging load of electric vehicles can be identified non-intrusively through the smart meter data of residential users, which provides flexible resources for the demand side response of power grid enterprises and is beneficial to the power dispatching decision making. Meanwhile, revealing residential energy consumption behavior from smart meter data is treated as energy privacy leakage. Extensive data collection from smart meters has raised privacy risk, which could lead to the reduction of trust in utilities from households. Aiming at this problem, the paper proposes a non-intrusive method to extract residential electric vehicles, and on this basis, discusses the privacy protection. In this paper, DWT method is used to decompose the total power of smart meter to extract the charging load of electric vehicle, and similar charging load patterns are added to smart meter data to preserve the residential EV charging behavior privacy. Practical households with EVs are used to test the proposed method and the result shows its feasibility for the performance of privacy-preserving.

The difference between load demand in the off-peak and peak period is usually high in the context of Nepal, and the high peak load for a short time is the main reason for insufficiency in electricity, frequent electricity outage, and poor power quality. This paper investigates the effectiveness of the time of use (TOU) strategy to reduce peak demand and to improve power quality. TOU electricity price is one of the electric demand response (DR) strategies, which may motivate the customers to reduce their consumption in peak periods and shift load in the off-peak period, which propel the electricity industry towards a higher efficiency compared to that of common flat prices. In this paper, a process based on an equal step length iteration algorithm is used to obtain an optimal period partition. Using the particle swarm optimization (PSO) algorithm, a TOU-based optimization model is proposed to find the optimal electricity price. Then, its impact on peak demand reduction and voltage fluctuation is analyzed. The impact of TOU price on peak demand reduction is investigated in the hourly load profile of Nepal, which indicates the significant reduction in peak demand. Similarly, the impact on voltage fluctuation is analyzed through a case study of the IEEE 33-bus radial distribution test system, which shows voltage profile is improved.

The virtual power plant (VPP) is a cloud-based aggregator that combines heterogeneous distributed generations (DG) to give a reliable and friendly power supply. In this paper, a VPP which consists of natural gas network, P2G equipment, flexible load and other resources is introduced in electricity market and natural gas market considering the uncertainties of renewable energy outputs and demand responses. Moreover, the optimal scheduling strategy of VPP includes power and gas dispatch considering bidirectional flows of power and gas, and the optimization model considering dual energy markets which include electricity market and natural gas market is investigated by developing different scenarios to maximize the VPPs profit. Case studies have been carried out to analyze the economy and scheduling strategy of the VPP. Simulation results show that the VPP studied in this paper can effectively reduce the proportion of renewable curtailment by selling electricity or P2G equipment. Besides, it is beneficial to maintain pressure stability and security operation of natural gas network, and the VPP could decrease unbalanced penalty cost and reduce the impact from uncertainties in dual energy markets.

The echelon utilization of retired batteries has attracted more attention as the market share of electric vehicles (EVs) increases steadily year by year. The current battery recycling methods cannot utilize the remaining value of these retired batteries effectively. Studies have shown that the Second-Life Batteries (SLBs) have a much lower cost compared to fresh batteries but retain a certain capacity simultaneously, which means they have a high secondary utilization value. Therefore, this paper proposes a novel multi-stage system planning model including Battery Energy Storage System (BESS). BESS designed in this paper involves not only the use of fresh batteries but also the echelon utilization of SLBs and considers the multiple lifespan cycles based on its State of Health (SOH). Moreover, the proposed planning strategy integrates the electricity and transportation networks in the first stage and evaluates the flexibility of the planning system by comparing the adaptation costs of each system in the second stage. The proposed planning model is verified on the IEEE 33-bus electricity system and the 20-node transportation system. According to the simulation results, the differences in system cost and system operation situations caused by fresh batteries and SLBs are analyzed, verifying the effectiveness and flexibility of echelon utilization of SLBs.

Similar to the carbon transfer among regions and countries, the embodied carbon emissions among industries also have serious carbon leakage problems. It is important to determine the embodied carbon flow relationships among industrial sectors, to clarify the responsibilities of each sector, and to adjust the sectoral structure accordingly. Given that, an embodied carbon emission flow relationships network among industrial sectors (ECEFRN) is developed in this paper. In detail, an environmentally extended input-output (EEIO) model is applied to quantitatively estimate the embodied carbon emission flow relationships among industrial sectors in China. The roles and functions of each industrial sector in the network and the evolution characteristics of the embodied carbon emission flow relationships are determined by the social network analysis (SNA) method. Results show that ECEFRN has the small-world characteristics, and key industrial sectors in the network are identified based on the analysis of degree centrality and betweenness centrality. During the study period, in the ECEFRN, “Processing of petroleum, coking, and nuclear fuel”, “Smelting and pressing of metals”, “Production and supply of electricity and heat” are important “suppliers”. “Construction”, “Other service” and “Manufacture of transport equipment” are important “importers”. “Manufacture of foods and tobacco”, “Manufacture of transport equipment”, “Manufacture of computers, communication and other electronic equipment” are important “intermediaries”. Based on the aforementioned research results, this paper proposes corresponding policy suggestions. Network-based research has contributed more insights to the understanding of carbon emission reduction responsibilities among industrial sectors. These findings will provide certain theoretical support and policy references for policymakers when formulating policies for carbon emission reduction and industrial structure adjustment.

The demand and supply gap of energy is large and the people are pressured to bear increasing hours of load losing, unnecessary intake of energy makes subjects even worse. So, the importance and requirement for energy conservation is increased exponentially. This paper outlines a step towards the conservation of electricity in standard and electricity especially with the aid of employing efficient constructing system with automation and installation of renewable based Micro Distributed Generator (MDG). The Smart Grid (SG) is a technological transformation from conventional electric grid, electro-mechanically controlled system, to smart, intelligent, and electronically controlled system. There are about 20–30% losses present in the conventional electric grid due to substandard operations at generation, transmission and distribution side. MDG play important role to build an efficient and autonomous micro-grid. In this paper importance of microgrid and some renewable energy sources utilized in formation of microgrid are discussed.

The integration of renewable energy (RE) sources is increasing day by day because of their permanent existence and the limited quantities of fossil fuels. One of the most promising RE sources is photovoltaic (PV) technology, which is developing quickly in many countries worldwide. PV cells generate electricity by converting the sunlight to DC voltage. PV arrays are installed in outdoor areas and on the rooftops of homes to be directly subjected to the sun. Consequently, they are frequently subjected to lightning strikes, which may cause damage to PV arrays, service interruption, and additional cost for PV replacement. Therefore, an adequate lightning protection system (LPS) must be installed to protect the PV panels. In addition, the transient performance of PV panels during lightning strikes must be analyzed well. This paper presents a comprehensive review of the superior modeling methods of PV systems during lightning strikes. In addition, the paper displays the different platforms to simulate the transient effects of lightning strikes on PV systems. The lightning transient effects on PV arrays are studied based on the system modeling to assess the recommended LPS designs studied in the literature. The paper also gives some recommendations about the modeling methods and protection of PV systems during lightning strike.

Electricity theft has been a growing concern for the smart grid. It can be defined as illegal customers use energy from electric utilities without a contract or manipulate their meter readings to pay less or not pay electricity bill. Over the past decade, significant researches have been done to prevent and combat theft. This paper aims to provide a general overview of the progress of electricity theft detection, including threat models, datasets and input features adopted, methodologies and techniques, and evaluation metrics. We also make a performance comparison for each detection method. Finally, the challenges and future research directions are presented in this paper.

Residential electricity consumption in China experienced a boom in recent years and may increase faster in the future. To reduce residential electricity consumption, China has adopted Increasing Block Tariffs (IBT) since 2012. The previous work mainly used regional level aggregated data to assess the impact. Few studies using household level data did not overcome the endogeneity problem. Based on the instrumental variable method, this paper found that commonly used OLS estimation gave unreasonable results and instrumental variable method is appropriate and effective in this study. The IBT was found to significantly reduce residential electricity consumption in high-income group, which achieving the initial goal of China's IBT. Lastly, this paper proposed to generate more publicity of the IBT policy and increase the electricity price of second or third tier.

Renewable energy production is one of the most important policy instruments to fight climate change. However, despite global benefits, renewable energy production entails some local challenges, such as requiring more space per unit production capacity. In this paper, we study the external effects of large-scale conventional and renewable electric power generation facilities on local house prices. We combine information of all coal, gas, and biomass plants, as well as all wind turbines in the Netherlands, with 1.5 million housing transactions over a period of 30 years. Using a difference-in-difference as well as a repeated sales model, we study the effects of facility openings and closings. Our results show negative external price effects for gas plants and wind turbines, but positive effects for biomass plants, conditionally upon ex-ante lower priced locations. The external effects of power generating facilities on local housing markets are important to consider, especially with the current focus of public policies on the expansion of renewable energy generation. Our paper is one of the first to present a large-scale study, using detailed information, and comparing several different energy sources in one framework.

The challenge of global warming requires deep decarbonization of the electricity system, which can be achieved with a higher share of renewables in the electricity mix. However, the driving mechanism for renewable penetration in electricity production has not been clearly revealed so far. Using the data of the European Union (EU) during 2001–2017, this paper aims to investigate the spatial-temporal heterogeneity and dynamic evolution mechanism of renewable electricity penetration. To this end, from the investment-production-consumption perspective, renewable electricity generation is decomposed into seven factors through temporal and spatial factor decomposition methods. Then, this paper integrates the factor decomposition results into a coupling elasticity analysis to study the dynamic evolution mechanism of renewable penetration in electricity production. The results are as follows. (1) Investment structure contributes the most to increasing renewable electricity generation, while the energy intensity effect and renewable generation-to-capacity ratio effect are the main reasons for the reduction in renewable electricity generation. The effect of technological structure is minor. (2) The economic scale effect and the investment structure effect are dominating factors accounting for the regional difference of renewable power generation between the EU countries and the benchmark region. (3) The progress of renewable electricity penetration accelerates over time for the EU, western EU, and eastern EU. The Western EU has realized remarkable progress in facilitating renewable electricity penetration during 2009–2017. (4) On the whole, the investment effect makes the largest contribution to promoting renewable electricity penetration, while the production effect is the major inhibiting factor, showing that there is considerable potential in renewable generation efficiency improvement in the EU. This paper provides insightful implications for energy policies aimed at the shift towards a low-carbon electricity system in the EU, which also gives a reference for other economies.

Nowadays, researchers have focused on manufacturing processes that extensively consume energy. Low-carbon production, purposing to reduce carbon emission, is a hot topic in the literature. This paper presents an integrated Taguchi goal programming (GP) model of quantifying carbon emissions of a conventional turning process. The study also investigates the breakdown of the turning machine tool sub-systems that contribute to the overall carbon emissions of a turning process, such as cutting tools, electricity, workpiece, cutting tool material specifications, and chip disposal. We developed a model for quantifying the carbon emissions from individual sub-systems. Also, we integrate the Taguchi model and GP to determine the values of the factors that achieved a sustainable turning process. In this paper, a different perspective from the literature-based carbon emission optimization studies is developed. Hence, a sub-cutting factor (parameter) level-based optimization study that searches the more sensitive parameter ranges for the turning process is presented. So, Taguchi’s principle-based GP model is an appropriate way to reach this objective.

Purpose The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters. Design/methodology/approach A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy. Findings The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time. Originality/value This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

Due to the harmful effects of greenhouse gas emissions emitted by petroleum-based vehicles, the transition to alternative fuel vehicles, running on cleaner sources of energy such as electricity, is an important trend. At the beginning of the transition period to alternative-fuel vehicles, due to the lack of comprehensive recharging infrastructure, it is essential to optimally locate recharging stations so that the network is covered as much as possible. Most existing studies on this topic generally assume that the driving range of vehicles and the flow volume are known, however, in practice, they are highly stochastic. The first aim of this paper is to incorporate this type of uncertainty into the problem and formulate it as a robust scenario-based model in which the expected number of drivers who can complete their trip without running out of charge is maximized. The development of a solution algorithm, capable to solve large instances of the model, is another aim followed in this paper. Computational results over different instances taken from the literature or generated randomly confirm the efficiency of the proposed model and algorithms.

In-depth mining and analysis of electricity data in low-voltage area are essential for the further intelligent development of power grids. However, in the actual data collection and measurement of low-voltage area, there will be missing data, and complete electricity data cannot be obtained. To obtain complete power data, this paper proposes a low-voltage station area missing data complement model based on joint matrix decomposition. First, we analyse the characteristics of the low-pressure station data. Then, a model that comprehensively considers the characteristics of the low-voltage station area data is proposed, which includes three parts: the construction of a low-voltage station area data tensor, the joint matrix decomposition, and the completion of the missing data, and it is named LPZ. After that, the CIM learning algorithm proposed in this paper is used to iteratively solve the model to obtain the completed data. Finally, the method proposed in this paper is used to complement the two situations of random loss and all-day loss of real current data in a low-voltage station area and compared with the traditional complement method. The experimental results show that this method is not only effective but also that the completion effect is better than that of other completion methods.

The aim of this paper is to develop a new Time of Use (TOU) tariff scheme for Demand Side Management (DSM) of residential consumers in Bangladesh. In this advanced era of technology, the requirement of electrical energy is continuously increasing. However, the overall growth of electricity generation is comparatively slower due to depletion of fuel sources. In this situation, a strategic plan needs to be implemented at consumer end to mitigate the energy crisis. As such, the concept of DSM is introduced where consumers can save electricity bills by changing their consumption pattern. It subsequently reduces the generation requirement during peak hours, which offers operational flexibility and financial benefits to utility. In the literature, DSM has been used in residential, commercial, and industrial sectors. However, the existing techniques do not explicitly consider the low-income consumers when TOU scheme is utilised. It may result in financial repercussion for these consumers. To overcome this challenge, a TOU scheme is proposed in this paper by ensuring financial benefits for all types of residential consumers and utility. To this end, an optimisation model is formulated by taking into account the various consumer groups such as low-income, middle-income and high-income. Two different meters (meter 1 and meter 2) are prudently allocated to these consumers. Using genetic algorithm technique, optimisation model is solved to provide electricity tariff and block sizes for various consumers under two meters. The proposed algorithm is applied to the residential inhabitants of a practical distribution system in Bangladesh. The results suggest that significant amount of annual savings can be achieved simultaneously by consumers and utility. The low-income consumers remain financially least affected while the proposed TOU tariff scheme is executed. In addition, the proposed TOU approach is compared to existing methods and found that optimisation based TOU outperforms the conventional ones.

Electricity end-users have been increasingly generating their own electricity via rooftop solar panels. Our paper studies the implications of such “distributed renewable energy” for utility profits and social welfare under net metering that has sparked heated debates in practice. The common belief is that such type of generation significantly decreases utility profits because (i) distributed generation reduces utility’s market size, and (ii) under net metering, utilities must buy back the excess generation of their customers at a rate typically larger than their procurement cost. Based on this understanding, there have been various lobbying campaigns against the net-metered distributed solar energy. A distinctive feature of our paper is that it considers supply-side effects of distributed renewable energy on utilities by explicitly modeling the wholesale market dynamics, while also accounting for (i) and (ii). Our analysis shows that in contrast to the common belief, the presence of net-metered distributed renewable energy can strictly increase the expected profit for utilities (compared to the case with no such energy) when wholesale market dynamics are factored in. Specifically, we prove that the net-metered distributed renewable energy strictly improves the utility’s expected profit if and only if the equilibrium wholesale electricity price is sufficiently sensitive to the changes in the wholesale demand and the utility’s expected market-reliance level is above a threshold. Our numerical study uses data on distributed solar energy in California and the CAISO’s wholesale electricity market, and demonstrates that our results continue to be valid under realistic parameters.

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.

Increasing penetration of renewable electricity in the power systems coupled with reduction in its cost has resulted in increased interest in green hydrogen globally. Industry has been using fossil fuel-based hydrogen as an input for several decades. This paper makes an assessment of existing hydrogen production capacities in petroleum refineries and ammonia synthesis units in India along with estimating the potential for installing solar photovoltaic (SPV) powered alkaline electrolysers for producing green hydrogen and SPV capacity required for this purpose. Levelised cost of hydrogen production in these industries in India has been analysed and found to be competitive. The paper also discusses about water requirement, land requirement for SPV power plants, CO2 emissions avoided and likely investment to be made for establishing infrastructure for green hydrogen production. With launching of national hydrogen mission in India, a transition to green hydrogen by the industry appears to be a near term possibility.

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises.

This paper is being developed in Blazer on industrial engineering. In the last few decades, the global economy, in particular in international commerce and manufacturing organizations, has evolved substantially. The technical framework of today is thus distinguished by productivity-oriented, which can be accomplished by industrial engineering. This paper presents the different principles & approaches used in industrial engineering, such as time analysis, work studies, line balancing and the outcome of organizational disintegration tests in Blazer. This research has focused on several kinds of machinery to produce a Blazer. The traditional manufacturing industry has issues such as low efficiency, longer lead time, high rework & re-work, poor line balance, low style shift flexibility, etc. The implementation of Industrial Engineering correctly resolved these issues. This study looks at how to develop Blazer's processes by using Industrial Engineering, wasting less resources, time, raw materials, human capital and electricity.

Hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) are indispensable tools in reducing greenhouse gas emissions to fight the twin evils of pollution and climate change. In these vehicles, battery replacement and fuel costs are the major recurring costs over a lifetime. Hence, there is a growing attempt to develop strategies that reduce the long-run expenditure in these vehicles without compromising on performance levels. Further, an increase in the fuel economy is also required for the effective penetration of these vehicles in society. Here, the authors attempt to identify the optimal operating values for battery state of charge (SoC), power ratings of motor, and fuel converter to increase the battery life and fuel economy without degrading the vehicle performance. The simulations have been carried out on Ford C-Max Energi (2016) as a representative for PHEVs based on the Urban Dynamometer Driving Schedule (UDDS) and Highway (HWY) driving cycles. The software used for these simulations is the future automotive systems technology simulator (FASTSim), developed by the National Renewable Energy Laboratory (NREL). In this paper, firstly, the effect of important parameters like battery SoC, fuel converter power, and motor power on HEVs' driving range, battery life, fuel economy, cost, and charge-depleting range has been analyzed. Based on this analysis, the optimal values of the parameters have been estimated. These parameters have resulted in improvements of driving range by 4.3% and battery life by 18% at a minute cost of a 1% decrease in the charge-sustaining battery life and a 0.4-s increase in the time the car takes to hit 60 mph from the rest. This paper presents a simple, effective, and new approach that explores the effect of altering the existing design parameters on vehicle performance, without manipulating, adding, or deleting any component or controller. This can further be extended to study the impact of various other parameters in the proposed work and opens a way to explore other parameters that exist in various other components of XEVs (where X can be H/PH//F). This study will help in achieving optimal cost reduction in these vehicles.

This research paper attempts to investigate both the long-run and causality relationship among electric power consumption (EPC), technological transfer, financial development (FD), and environmental quality for the Saudi Arabia (KSA) economy from 1980 to 2019. In doing so, we propose a carbon emission function tested by incorporating multi-steps techniques such as autoregressive distributed lag (ARDL) has been exploited to determine the existence of cointegration or no; while vector error correction model (VECM) has been applied to decide the direction of causality. In this paper we have proposed two proxies of technological transfer, namely imported technology (MT) and Technical cooperation grants (TCG). The results indicate the existence of cointegration between the concerned series. Besides, the existence of a feed-back effect among variables (except TCG) in the long-run. However, in the short run feed-back effect exists among EPC and EnvQ; MT and EnvQ; EPC and MT. Thus, the paper provides original visions for policy makers to encourage the technological transfer by financing and supporting the electric energy sector which constitutes the main locomotive to improve the environment quality for the KSA.JEL classification : C32, C52, O11, Q43

The use of schedules to disconnect consumers from electricity to reduce the load on the UES of Ukraine is an outdated model and may not meet the standards and requirements of the new model of the electricity market in Ukraine. The study of consumer desires and needs is a very promising area in improving the efficiency of the balancing mechanism in the electricity market and is an important factor in calculating interruption tariffs. In this paper, to minimize costs due to power outages, the consumer considers the creation of a hybrid econometric approach that combines the advantages and minimizes the disadvantages of two models: the popular consumer survey model and the econometric model. An econometric model that uses the added value created by a group of consumers for the year is a practical way to estimate the cost of planned outages. The method of consumer surveys is the most popular tool for assessing reliability in the electricity market. The paper proposes to use publicly available consumer data collected through a simple survey of consumers about their actions in case of undesirable power outages, taking into account the specifics of their work. It is worth noting that this study focuses on the interruption scenario that will lead to the highest shutdown costs, it covers only winter outages in the afternoon and eliminates the effect of the difference between time of day and season.

Building upon work by Mary Hesse (1974), this paper aims to show that a single method of investigation lies behind Maxwell's use of physical analogies in his major scientific works before the Treatise on Electricity and Magnetism. Key to understanding the operation of this method is to recognize that Maxwell's physical analogies are intended to possess an ‘inductive’ function in addition to an ‘illustrative’ one. That is to say, they not only serve to clarify the equations proposed for an unfamiliar domain with a working interpretation drawn from a more familiar science, but can also be sources of defeasible yet relatively strong arguments from features of the more familiar domain to features of the less. Compared with the reconstructions by Achinstein (1991), Siegel (1991), Harman (1998) and others, which postulate a discontinuity in Maxwell's approach to physical analogy, the account defended in this paper i) makes sense of the continuity in Maxwell's remarks on scientific methodology, ii) explains his quest for a “mathematical classification of physical quantities” and iii) offers a new and more plausible interpretation of the debated episode of the introduction of the displacement current in Maxwell's “On Physical Lines of Forces”.

Electric power systems are experiencing an unprecedented technological revolution that poses operational and commercial challenges for today's electric power industry. In this context, low voltage DC microgrid technology is attracting growing interest regarding its energy efficiency and local reliability. These microgrids support the innovation and implementation of new local electricity markets such as the emergent peer-to-peer energy trading paradigm which is perceived as a promising solution for tomorrow's power grid. Although, several pilot projects are under test, research is mainly concerned with the development of hardware and software infrastructure and the design of new business models. This paper deals with the prospects for synergies between low-voltage DC microgrid technology and P2P energy trading markets. After focusing on the fundamentals and the links between both concepts, the paper surveys the most innovative peer-to-peer electricity trading projects. A multi-aspect SWOT (strengths, weaknesses, opportunities and threats) analysis approach is conducted. Finally, the environmental impact is evaluated in a vision of sustainable and collaborative energy governance.

Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil engineering professionals to subdivide complex issues. Based on the assignment’s history, nearby agents, and objective, the agent intended to take the appropriate action to complete the task. MAS models complex systems, smart grids, and computer networks. MAS has problems with agent coordination, security, and work distribution despite its use. This paper reviews MAS definitions, attributes, applications, issues, and communications. For this reason, MASs have drawn interest from computer science and civil engineering experts to solve complex difficulties by subdividing them into smaller assignments. Agents have individual responsibilities. Each agent selects the best action based on its activity history, interactions with neighbors, and purpose. MAS uses the modeling of complex systems, smart grids, and computer networks. Despite their extensive use, MAS still confronts agent coordination, security, and work distribution challenges. This study examines MAS’s definitions, characteristics, applications, issues, communications, and evaluation, as well as the classification of MAS applications and difficulties, plus research references. This paper should be a helpful resource for MAS researchers and practitioners. MAS in controlling smart grids, including energy management, energy marketing, pricing, energy scheduling, reliability, network security, fault handling capability, agent-to-agent communication, SG-electrical cars, SG-building energy systems, and soft grids, have been examined. More than 100 MAS-based smart grid control publications have been reviewed, categorized, and compiled.

(This paper has been presented in BASIC 5 Brawijaya University, 7-8 March 2017 and published in http://basic.ub.ac.id/files/proceeding/PROCEEDINGS-BASIC-2017-VOL1.pdf) Sustainability of water and energy is a major problem that is getting more attention because it is very closely related to the human well-being and quality of life. The next few years, Indonesia will get energy and water resources problems. Plant microbial fuel cells (PMFCs) is one of the alternative green technology that can be used to support the sustainability of other technologies in terms of both energy and clean water production. Bibliometric methods approach used to obtain relevancy and get the latest information of PMFCs. This paper describes the status of energy and water resources, technological developments PMFCs and how PMFCs challenges and development opportunities in Indonesia. This paper reviews the basic understanding of PMFCs development and the way to implement PMFCs in Indonesia.

With the rapid development of electric vehicle (EV) technology under the background of double carbon, a reasonable evaluation of the network planning of EV charging stations is conducive to the long-term stable development of the EV industry. Therefore, this paper proposes a comprehensive evaluation system and method for EV charging networks under the traffic network and power grid coupling. First, an EV travel model is developed based on the travel probability matrix to analyze the spatial and temporal characteristics of EVs. Secondly, the coupling relationship among users, charging network, road network, and the power grid is analyzed, four criteria of user feedback, charging network operation, road network operation impact, and power grid operation impact are proposed, and corresponding evaluation indexes are constructed under each criterion to form a comprehensive evaluation index system. Furthermore, the Analytic Hierarchy Process (AHP) and entropy weighting method are used to assign weights to the index layer and the criterion layer respectively, and the improved TOPSIS evaluation method is used to quantitatively analyze the impact of charging networks on different subjects for comprehensive evaluation. Finally, through the comprehensive evaluation of different charging network planning schemes, the effectiveness of the comprehensive evaluation method and model proposed in this paper is verified, which can provide a reference basis for the planning and improvement of EV charging station networks.