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Energy management is the most prominent means of improving energy efficiency, and improved energy efficiency constitutes the cornerstone in decarbonization. For successful industrial energy management, defining accurate energy performance indicators (EnPIs) is essential. Energy-intensive industries have previously been found to have an improvement potential regarding the current monitoring of EnPIs, especially at process level. While general models for developing and implementing EnPIs exist, manufacturing industries are diverse in terms of their production processes, which is why industry-tailored models for EnPI development are needed. One major outcome of this paper is a unique model specifically tailored for kraft pulp mills. The model derives from a practice-based approach for EnPI development, building on real-life experiences from a Swedish group of companies. This paper’s developed model, and the validation of the EnPIs, further increase the understanding of the kraft pulp industry’s processes and how to apply descriptive and explanatory indicators. The developed model can potentially be generalized to other sectors. Funding: Swedish Environmental Protection Agency; Swedish Agency for Marine and Water Management [802-0082-17]

The number of households experiencing fuel poverty is thought to have risen by at least 600,000 in the UK because of the ongoing Covid-19 pandemic. The concentration of fuel poor households in poor quality, energy inefficient accommodation that they have little power to improve means they are particularly negatively affected by the retreat into the home brought about by successive lockdowns and restrictions. For many such households, the home is not the place of sanctuary that it needs to be at a time like this. However, our empirical research into the lived experiences of fuel poverty reveals additional consequences for fuel poor households, chiefly associated with restricted access to third spaces and other disruptions to their usual coping strategies. Based on our evidence, we highlight three key considerations for policy on fuel poverty in the era of Covid-19: the need to rapidly upgrade the energy performance of the existing housing stock; the need to address the additional financial hardship faced by fuel poor households; and the need to prioritise access to third spaces and high-quality public spaces while restrictions last. This paper develops the concept of energy poverty by considering the role of spaces outside the home as part of the overall experience of energy poverty and the range of ways in which policy makers can mitigate its impacts.

In recent years renewable and cleaner fuel for diesel engines are compulsory due to depletion of fossil fuel. Various types of bio-based fuels are investigated by the researchers. Biodiesel is anticipated as potential contenders of diesel fuel. Though it is possible to utilize pure biodiesel in diesel engines, some burdens like higher density, lower cetane number and lesser calorific value hinder it from replacing conventional diesel completely. Therefore, using blends with biofuels in diesel engines has a preference. Thus, this paper reviews two different approaches on the role of nanoparticles on biofuel production and effects of nanoparticles in biodiesel–diesel fuel blends on performance, combustion analysis and emission characteristics of diesel engines. Wide range of results from previous research studies with potential and application of nanoparticles in bioethanol production, the effect of the addition of nanoparticles into diesel fuel with different biofuels ratios are collected in this review study. There are different engine performances enhancing methods surveyed. Nanoparticles can be utilized in the production of biofuels from feedstock pre-treatment to chemical reaction as catalysts. It was observed from the overall results that by adding nanoparticles, there was a significant reduction in the brake specific fuel consumption about 20% to 23% as compared with biodiesel–diesel blends with and without alcohol as additives. Besides as nanoparticles possess high thermal conductivity, the addition of nanoparticles enhanced the process of combustion and increases the brake power about 2.5% to 4%. Emission results showed that in most reviews, NO x emission is increased by up to 55%, while HC, CO and PM are decreased significantly. It was concluded from the study that a diesel engine could be effectively run and give better performance and effective regulated emissions on the application of added nanoparticles with biodiesel and their blends as fuel in a CI engine.

Shearer is an indispensable and important equipment for coal mining and the mining efficiency of shearer depends on the performance of pick to a great extent. In this paper, we conducted a series experiments of single pick cutting on the coal and rock cutting testbed. Simultaneously, we built the single pick cutting coal seam model, based on which, considering the strength condition of coal seam and coal–rock interface, the rotary cutting mechanical model of pick cutting coal seam with coal–rock interface is established. According to the crushing theory of coal and rock interface, this paper analyzed stress strain and strength condition of the area surround the interface, which ensured the basics for the further research. The data of picking force in the cutting process under different experimental conditions was collected by the sensor installed on the drum, a phenomenon appeared in the amplitude domain analysis of the data that the bigger force arising along with the greater compressive strength difference between coal seam and coal–rock interface, which means the force is positively correlated with the compressive strength. The results of time domain analysis shown that some basic properties of the cutting load changes over times.

The DC transformer based on the Modular Multilevel Converter (MMC) structure has the advantages of robust scalability, low output voltage harmonic content, easy realization of back-to-back connection, and high power transmission efficiency. It is widely used in DC distribution networks. However, traditional control methods hardly provide inertia for the DC distribution network, thereby reducing the stability of the distribution network when new energy output fluctuations, DC distribution network failures, or other disturbances occur. Aiming at the problem that the DC bus voltage is easily affected by power fluctuations, the paper proposes a virtual inertia control method applied to the DC transformer with MMC structure. Similar to the introduction of virtual synchronous generators in the AC distribution network to enhance the system inertia, the paper designs a control strategy for dynamically adjusting the virtual inertia with a virtual capacitor added, which is based on the traditional two-phase shift control. The paper also presents the calculation method of the virtual capacitance. When the power in the DC microgrid fluctuates significantly, the strategy can effectively enhance the inertia of the DC distribution network and suppress the voltage fluctuation of the DC bus. The paper finally establishes a DC distribution network model with distributed power sources in the PLECS simulation software, and verifies the effectiveness of the control strategy compared with the traditional strategy.

This paper presents the PLC-Based system for controlling the SCR power module which is AC–DC converter proposes to linearity output voltage with trigger signal. The technique converted the trigger signal by arccosine function and analog module in the PLC. Converted signal from PLC system is fed to SCR power module instant the traditional signal. Proposed of this paper describes the PLC hardware configuration, compute/math and trigonometric functions with Studio 5000 software. In addition, the interfacing, parameters configuration, and monitoring are considered for testing. The technique with PLC-based, devices configuration and experimental results have been presented will be useful for electric power supply in the industry with more and more demand to apply the PLC system to develop the traditional industry to semi or automatic control in the industry.

With the continuous deepening of power market reforms, studying the interaction of multi-stakeholder in multi-microgrid (MMG) energy trading is of great significance to improve the economic performance of the distribution network. In order to analyze the economic optimization method of multiple stakeholders in the distributed energy system, this paper proposes a multi-stakeholder benefit optimization method based on Stackelberg game theory (SGT), and establishes a local market energy transaction model. The model is composed of external distribution network, leader-intermediary agent (IA) and follower-microgrids (MGs), and uses the master–slave game method to solve the optimization strategy. Under this mode, the profit of each stakeholder in MMG increased by 2.64%, 4.24%, 1.38% respectively. The case results show that based on the method in this paper, multi-stakeholder in MMG can improve economic performance and can increase the level of energy autonomy of MMG.

In its contribution towards fighting climate change, Tunisia aims at reducing greenhouse gas emissions across all sectors through reducing carbon intensity in the country by 41 per cent in 2030, relative to the base year 2010 The Tunisia energy sector mitigation scenario is proactive and incorporates ambitious programs for improving energyefficiency and developing renewable energy resources. This paper reviews the prospects of one of the major renewable energy sources in the country: wind energy. It presents the state of wind energy sector in the world and tracks in particular, the evolution of wind power development in Tunisia since its initiation in 2000 till the present. The paper outlines the prevailing climatic conditions, the regulatory and legal instruments on renewables as well as national policies and strategies on the energy transition. The pathways to sustainable wind energy sector development is analyzed and discussed. Additionally, the wind energy research status in Tunisia is presented and the gaps in research are explored.

A comprehensive review about machine learning application in power system especially in smart grid, renewable energy sector etc. is summarized in this paper. In the power sector, the power consumption is increased day by day very tremendously. So, it is very important that we have to generate the more power without disturbing the environment and whatever the generated power must be utilize effectively with minimum losses and higher efficiency. This will be possible with effective way of using the modern technology like machine learning (ML), artificial intelligence etc. This paper also describes the different types of machine learning techniques with diagram which will be very useful for many researchers who want to understand the basic fundamentals of machine learnings.

In 2019, natural gas accounted for 25.4% of gross inland consumption in the European Union (EU), making it one of the most important energy sources in the EU. The importance of natural gas, together with the ongoing liberalization of the gas market, has made the natural gas sector significantly commercially sensitive. To reduce the risk of financial losses, balance group managers often need to have an accurate forecast of natural gas consumption. An accurate forecast will ensure small deviations between actual gas consumption and reserved gas volumes and transmission system capacity resulting in less balancing energy required, which is sold at a higher price in the final balancing process. This paper researches the optimal number of smart meters and best fitted consumption data distribution in order to achieve satisfactory results in terms of the accuracy by using simple forecasting methods. Beside mentioned, this paper provides accuracy overview of various already available forecasting methods, as well as the selection of input parameters for forecasting short term natural gas consumption. Using the calculated linear temperature dependence together with the lognormal distribution, the consumption of natural gas was simulated for 12 different cases. The simulation showed that, if more than 10 000 smart meters were installed, deviation between average estimated natural gas consumption and the real data would be less than ± 2.96 %. In case of 100 000 smart meters installed, deviation would be less than ± 1.20 %, but the “large” partly temperature independent consumers must be disregarded.

Smart Grid 2.0 is envisaged to automate the operations of the intelligent electricity grid. Blockchain and smart contracts are integrated to facilitate the transformation from DSO-centric operations to consumer-oriented, distributed electricity grid management. The envisaged smart grids, integrated with blockchain would provoke challenges, which would hinder the maximum utilization of Distribute Energy Resources (DERs). This comprehensive review aims at analyzing the applicability of blockchain technology in Smart Grid 2.0, which would facilitate a seamless decentralization process. Further, the paper elaborates the blockchain-based applications of future smart grid operations and the role of blockchain in each scenario. The paper further provides a concise analysis on the blockchain integration challenges, thereby ensure secure and scalable, decentralized operations of future, autonomous electricity networks.

Energy coming from large hydroelectric plants is recognized worldwide due to the great environmental impact caused by the deviation of the rivers during the construction period and the dams while they are operating. This paper shows the design and construction of a micro hydroelectric power plant (MHPP) that was carried out in Cundinamarca Department, in Colombia, in order to analyze the feasibility of generating electric power with micro-power plant, which can be dispatched to the National Interconnected System, taking into account technical and economic aspects for the recovery of the investment. On the other hand, different scenarios are considered, such as self-consumption or the sale of energy to users of the rural sector that are close to the project, performing the financial analysis with economic indicators such as Internal Rate of Return, Uniform Annual Equivalent Cost and Net Present Value. These types of initiatives take special relevance in Colombia because the country issued Law 1715 of 2014, whose purpose is to improve energy reliability, it supports these kinds of projects with tax incentives such as: Discount of Value Added Tax (19%), 50% deduction of the income tax for up to five (5) years and elimination of the tariff levies for the equipment that make up the project. Based on the considerations above and because of the testing of the 24 kVA micro hydroelectric power plant, a paper that deals with the entire construction process of the micro-plant has been prepared, which includes: Flow capacity, turbine selection, selection of the generator, design of the electrical installations and commissioning to verify the effective value of the energy generated and the feasibility of being dispatched to the National Interconnected System.

Daylight Saving Time is argued to be effective in saving energy. Turkey is one of the few countries that annulled the clock changes and remained in the summertime zone in 2016. Therefore, the country provides a unique natural experiment to test and confirm this policy change. This paper studies the impact of clock changes on electric energy consumption and load shift. We use historical electrical energy consumption, electricity prices, and relevant atmospheric essential climate variables data in Turkey between 2012-2020. We adopt Multiple Linear Regression, Difference in Differences and Interrupted Time Series methodologies to analyse the historical data. This paper shows that the Daylight Saving Time policy does not lead to a measurable amount of electrical energy savings. Furthermore, it does not cause a noticeable continuous daily load shift throughout the year. We also claim that our findings should apply to countries such as the United States, India, Japan, Australia or China, and continents of Africa and South America, whose latitudes are between 42.0 degrees north and south of the equator. (C) 2021 The Authors. Published by Elsevier Ltd.

Due to globalization and industrial development, the need of power is increasing day by day. In addition, due to the high demand for clean energy sources, the use of renewable systems has expanded in recent years. Wind systems can take advantage of the copious and free energy provided by the wind, which is a significant benefit. However, changing incident energy from renewables generate harmonics, which may result in poor system performance. The power quality has therefore become an important issue for both the power suppliers and consumers. Power electronic devices and nonlinear loads have been used frequently in industries hence introducing harmonic distortions in the industrial power distribution network. Harmonics have adverse effects on the power quality like degradation of power generation efficiency, higher operational costs, overheating of equipment, saturation of transformer, electromagnetic interference etc. In order to solve these problems, the implementation of suitable filtration techniques and the evaluation of efficiency with harmonic reduction are crucial. Since, utilities are always aiming to improve the power quality with minimum filtering units, this paper compares the performance of two available filter implementation plan, namely the shunt active power filter (SAPF) and the single tuned filter (STF) in a renewable penetrated industrial distribution network. Based on the outcome of the comparison, a genetic algorithm (GA) based approach is proposed in this paper to determine the optimum size and position of the minimum required filtering units that improves the network’s existing harmonic scenarios. Simulation results to validate the proposed methodology are presented. According to the results, the proposed method of incorporating filtering units significantly outperformed in lowering total harmonic distortion (THD) in an industrial power distribution network with a higher renewable penetration.

Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy. (C) 2021 The Authors. Published by Elsevier Ltd.

To increase the energy utilization efficiency, it becomes fairly promising to convert the surplus electricity from renewable generation to other forms of energy for multi-dimensional consumption. In this paper, we propose a hybrid energy scheduling model for a multi-energy microgrid with the integration of the hydrogen energy storage system (HESS) and the heat storage system (HSS). In our study, the operational uncertainties induced by renewables and loads (including electrical, hydrogen, and heat demand) are comprehensively considered. We investigate such an operating regime that HESS stores the surplus electricity in case of abundant renewable generation and generates electricity through hydrogen fuel cells otherwise. Further, heat units including HESS, combined heat and power (CHP), and external heat suppliers are modeled in this paper. We split the decision-makings of energy scheduling for both the day-ahead stage and real-time stage to tackle the power balancing issues. To effectively solve the aforementioned optimization model, a flexible weighted Model Predictive Control (weighted-MPC) strategy is proposed, in which the receding horizon can be suitably adjusted according to the forecasting accuracy of system uncertainties. The effectiveness of the proposed hybrid model for microgrid energy scheduling is comprehensively verified through extensive case studies.

The aim of this paper is to measure the impact of certain factors on the formation of the price of electricity, which is obtained from conventional and renewable energy sources, in the deregulated markets of developing countries, such as Montenegro. Another goal is to identify and analyze the criteria that can lead to a change in the electricity market structure. This study was especially inspired by the fact that no similar research has been conducted in this domain, according to the authors’ knowledge. A regression model is applied in the paper, which can be used to display the fluctuation of electricity prices based on the analyzed factors. In addition to price as a dependent variable, the model also examined eight factors that are independent variables. Apart from the basic model, another model has been formulated which, in addition to a price as dependent variable and the examined factors, also takes into consideration the production of electricity from renewable sources. The results of the research showed that an increase in the amount of electricity produced will lead to a slight increase in price, which is to be expected considering there is still one large producer servicing the market, which, despite the deregulation of the market, still has significant monopoly power. The generation of electricity by small hydropower plants will not significantly affect price change, and these entities will behave as if they were in a market of perfect competition. Further opening up the market to new competitors would lead to a fall in the price of electricity. Based on the predicted capacity of electricity generation from small hydropower plants, the electricity deficit could be reduced; in another words it could lead to the reduction of imports. Based on the results of the study, energy policy makers will be able to adopt informed strategies and make decisions related to developments in the energy market, most notably the creation of a common EU energy market. Also, the results of the conducted research expand the theoretical body of work dealing with the determination of the price of electricity. Future studies could address the issue of using natural potential for the generation of electricity from renewable sources, such as solar energy, wind energy and other sources.

Large-scale atmospheric circulation patterns are the primary drivers of wind power variability on power networks at timescales of hours to days. This paper proposes a methodology that allows power system operators and planners working on networks with high levels of wind generation, to conduct probabilistic power flow (PPF) analyses by defining network ‘operating scenarios’ – i.e. the probability density functions of generators, and correlations between generators representative of a future system state – based on concurrent classified atmospheric states. The most significant contribution made by this paper is in illustrating how PPF operating scenarios derived from clustering historic generation data as a function of a set of classified atmospheric states reduces simulation uncertainty within a PPF analysis. It is anticipated that the proposed methodology may provide network planners with more appropriate operating scenarios for PPF analyses when compared to an unclustered base state, and may assist network operators in converting wind power point-forecasts into probabilistic forecasts whereby the spatial correlations between generators are incorporated. This methodology is illustrated through a case study considering 11 geographically disperse wind generators on the South African transmission network.

Proton exchange membrane fuel cell system has been proposed as an alternative to the internal combustion engine due to its clean and high efficiency. Analysis of the coupling effects among components is critical to improve the design of the fuel cell system and shorten the development cycle. In this paper, the steady-state modeling of a fuel cell system is developed, focusing on the coupling effects between various components and the influence of operating conditions on the internal parameters. Firstly, the model of each component is established based on the mechanism or experience, and the boundary conditions of each component model are defined. Then, the component models are integrated into a system model, and the operation parameters of the system are solved by an iterative method. The operation conditions at different ambient temperatures are simulated and the results are discussed. It is indicated that a higher ambient temperature will significantly increase auxiliary power consumption and decrease system efficiency. The steady-state model proposed in this paper predicts the operation parameters of the system, which is helpful to reduce the development cost.

Remote electrification remains a critical issue for the Gilgit Batistan region of Pakistan, where most of the population lives without access to the national grid and mostly relies on traditional energy resources, which undermines the huge potential of renewable energy in the region. This paper addresses the energy access issue of 1.8 million people of Gilgit-Baltistan (GB), of which 86% reside in the rural areas. To resolve the problem of energy access in the region, this paper analyzes cost-effective solutions based on renewable energy resources and proposes suitable hybrid systems for 14 independent sites. For this purpose, available energy resources and regional demands have been assessed to present the optimized hybrid energy systems for the identified sites. Hybrid optimization model for multiple energy resources is designed to carry out the techno-economic feasibility analysis. Moreover, equivalent forest absorbing CO2, glacier mass loss, and social cost have been evaluated. Results show that the combination of wind and hydro with battery backup gives the most optimum cost of energy (COE) ranging between 0.0470–0.0968 $/kWh. Similarly, 8349 acres of forest are needed to absorb the annual CO 2 emissions. As 0.58 million tons of glacier, and 0.882 million $/year social cost of carbon (SCC) can be lost due to emissions of fossil fuel-based generation. To ameliorate from such adverse effects, a community-based hybrid system may provide clean and green energy to increase energy access across Gilgit-Baltistan.

DC high current measurement is one of the important supporting technologies of the power system. As an emerging current sensing technology, tunnel magnetoresistance (TMR) has attracted more and more attention due to its advantages of high sensitivity and good linearity. But the TMR sensor is not suitable for high current measurement, and its accuracy can be further improved. Aiming at these problems, this paper proposes a TMR array with a circular skeleton coil for high current measurement. Eight TMR chips are evenly installed on the circular skeleton coil to measure the magnetic field generated by the primary current carrying conductor, and the output of the feedback amplifier generates a compensation current to the feedback winding to generate a compensation magnetic field, so that the magnetic flux in the sensor is almost zero. The circular structure can reduce measurement errors caused by wire offset and external interference. Since there is no iron core, the nonlinear errors are also avoided. Simultaneously, the feedback loop designed based on zero magnetic flux principle can reduce the hysteresis error and expand the measurement range, making the sensor suitable for DC high current measurement. This paper proposes a new TMR array current sensor model, builds a prototype and compares it with a closed-loop TMR current sensor with an iron core. Experimental results proved that the TMR array sensor’s DC measurement error could be lower than 0.01%, compared to 0.32% of the closed-loop current sensor. Moreover, the proposed current sensor consistently has higher measurement accuracy in the full range of primary current.

A light water reactor (LWR) is the most common reactor type in the world today, and will likely continue to be the main technology of nuclear power towards the mid-21st century. This paper analyzes the factors that made large LWRs the global mainstream and points out the root causes why only a few of other types of reactors, such as small modular reactors (SMRs) and fast neutron reactors (FRs), have been put into practical use so far. It is a universal principle common to all engineering that characteristics and weaknesses sometimes cannot be detected on paper, and it is crucially important to learn lessons from actual experiences of construction, operation and maintenance. LWRs have been selected by their customers because they use common materials such as water, concrete and stainless steel, which enabled utilities and vendors to promote the improvement of materials and operating conditions by globally sharing the experiences of many reactors including some severe accidents. If SMRs, FRs and other innovative reactors are to follow suit, not only the developer’s perspective but also the customer’s perspective, such as economics and stability of supply, is essential.

According with the new European Regulations the supply chain sustainability became more and more challenging for the shipping companies. They must adapt their ships to respect the new regulations, but this means economic investment for environmental and social scopes? Or replacement of traditional fuel with a green one, will make the companies more competitive, will reduce costs and will improve the operational performance? It can be difficult for the top management to take and assume a decision like this. In this paper we will use the analytic hierarchy process (AHP), like a management instrument, based on Multi-Criteria Decision Making (MCDM) to see what solution brings more advantages for the ship owners and for the community. An illustrative case with two alternative marine fuels, liquified natural gas (LNG) and oil gas, was presented and analyzed in this paper using the proposed method. The most sustainable scenario being the one in which is used natural liquefied gas like marine fuel. The results obtained demonstrate that the method used to prioritize alternative marine fuels is feasible and helps decision makers.

Particle Impact Drilling (PID) is a new technology to effectively improve the rate of penetration (ROP) for oil and gas drilling in hard and strongly abrasive formations. In this paper, numerical simulation method is used to analyse the motion characteristics and the modulation method of particle swarm in high-pressure tank for the particle injection system based on differential pressure ejection in PID. The numerical simulation results show that: when there is no modulation elements, the motion of particle swarm in the high-pressure tank follows an asymmetric funnel flow with pulsating state, which could be divided into vertical flow domain, fast flow domain, slow flow domain and stagnation domain. The unstable dynamic arching effect of the funnel flow, the viscous effect of the liquid bridge force and the collapsing effect of the particle swarm could probably lead to the blockage of the discharge port of the high-pressure tank. When the semiapex angles of the high-pressure tank decreases, the volume flow rate of particles increases and the stagnation domain becomes smaller, but it becomes easier to form arching and blockage. The modelling results indicate that the pulsation of the funnel flow is minimum when the semiapex angle is 45° without the mutilation element, which means the funnel flow of the particle swarm is relatively stable. By introducing a conical modulating element above the discharge port, the unstable funnel flow of the particle swarm could be transformed to an overall uniform flow. The modelling results indicate that the installation height of the modulation element has the greatest influence on the pulsation degree. The optimized parameters for the conical modulation element based on numerical modelling tests are 70° for the vertex angle, 35 mm for the length of the flank and 70 mm for the installation heigh.

Hydropower contributes a lot to electricity generation. Myanmar has enormous potential for water resources which can guarantee domestic electricity supply and export surplus power to neighbouring countries. This paper analyses the status-quo of water resources and obstacles of Myanmar’s hydropower development, and concludes that: (1) Myanmar harbours great potential for hydropower development and utilization; (2) the spatial distribution of hydropower stations and the supply of electricity are unbalanced; (3) main obstacles for hydropower development and utilization include weak governance over water resources, lack of fund and technology, natural disasters and environmentalism resistance. To achieve full electrification in 2030, it is essential for Myanmar to promote the construction of both small-scale and responsible large-scale hydropower stations.

In recent years, power grid enterprises have continuously promoted the construction of intelligent operation inspection system. Pilot applications of various equipment status sensors, online monitoring devices, intelligent wearable devices, mobile terminals and Beidou navigation satellite system have been gradually carried out. The configuration range of intelligent equipment such as UAV and robot has been continuously expanded, and big data analysis technology, artificial intelligence diagnosis technology have also been preliminarily applied. With the continuous development and breakthrough of key technologies such as edge computing and wireless transmission, it provides favorable conditions for the intelligent, diversified and ecological development of the construction and application of the IOT for power transmission and transformation equipment. This paper mainly introduces the overall architecture and functional positioning of the construction of power transmission and transformation IOT, and combined with the business needs of some typical application scenarios in the operation of power grid equipment, to promote the integrated application of IOT technology and power grid equipment operation and inspection business, and also establish an open, shared and symbiotic IOT ecosystem of power transmission and transformation equipment, so as to lay the foundation for intelligent equipment management.

Along with the deepening of the power system reform, the input and output evaluation and analysis of asset became more and more important. Arounding the goal of comprehensive optimization of safety, efficiency and cost throughout the life cycle of assets, this paper constructed a quantitative evaluation model for asset input–output analysis, and put forward an input–output analysis method combining the boston matrix method and the equivalent analysis method. Actual substations’ input cost and output efficiency analysis verified the method feasibility, which could provide decision-making support for optimizing power grid asset management strategies and enhancing asset value operation benefits.

A hybrid solar lighting/ thermal system utilized a parabolic solar dish as a solar collector illustrated in this paper. A two-axis tracking system was developed and employed to improve the energy output of a concentrating solar system (CSP) that produces hot water and light. During the daytime, sunlight is transmitted by fiber optics to an indoor photovoltaic PV device to generate electricity, or it can be used directly as daylighting. The heat generated is used to heat water. The findings show that the proposed system is a low-cost, high-performance approach for solar energy use in building energy efficiency, and readily available tracking components and tools. The experimental work shows that the maximum efficiency of the hybrid solar lighting/ thermal system controlled with a dual-axis solar tracking system was 32.2%.

Currently, there is a beginning of liquefied natural gas (LNG) market transformation, which is deepening global supply chains through the growth of the number of small-scale projects. The main driver of this transformation was the creation of large-scale LNG hubs, which did not meet expectations for improving the efficiency of gas distribution systems (like in Europe). This has led to an understanding of the need of increasing flexibility and decentralization of gas supply projects, which is almost impossible to implement with traditional large and medium scale facilities. Increasing decentralization leads to a change in the role and composition of stakeholders, and, consequently, determines new algorithms and schemes for their interaction, which are poorly described in the scientific literature. This paper systematizes information about the specifics of such project implementation, defines a list of key stakeholders, their area of responsibility and area of interests. Proposed conceptual model of interaction between stakeholders has shown a significant dependence of small-scale projects on regional economic and infrastructure conditions, as well as on the availability of multiple distributed points of gas consumption.

To handle the multiple-objective problem including carbon emission reduction and economic operation of integrated energy system (IES), this paper proposes an optimal scheduling model for IES considering combined weights for low-carbon and economic operation. In the dispatch model of IES, we mainly use the combined cold heat and power (CCHP) to optimize the energy distribution, and use the charge and discharge power of electric vehicles (EVs) to enhance the regulation capability of CCHP. Furthermore, aiming at the lowest carbon dioxide emission and economic cost, and the highest renewable energy accommodation, a multi-objective function considering combined weights is proposed, which combines subjective weights with objective weights. The balance constraints of electricity, heat and cold for the IES are established. Finally, the particle swarm optimization algorithm is used to optimize the multi-objective model based on the energy balance and unit constraints of IES. The proposed method is verified by the case studies. The results show that the proposed model can improve the accommodation of renewable energy and reduce carbon dioxide emissions.

In this paper, the steam production of the power plant from four biomass types has been investigated via the combination of the neural network (NN) model and constrained particle swarm optimization (CPSO) methods. First, the power plant modeling was accurately developed using NN simulation. Then, the model was applied to the optimization step, where the CPSO (the modified algorithm from the classical PSO) was introduced. The proposed method included real operation constraints to the algorithm, such as biomass feed availability and minimum steam production rate. In the optimization step, the COP has been defined as the cost function to check the present operation’s economic viability. To obtain the minimum production cost (COP), the CPSO suggested feeding wood bark from sources C and D with the amount of 104.1 and 463.8 tons/day, respectively. The result has been extensively validated by the real operation. The calculation framework in this study then applies to various production systems. Besides, by integrating the cost function in the optimization model with other indicators such as environmental impact indices, the output would provide an additional holistic picture of economic and environmental aspects.

The solar radiation is a critical metric for design and optimal operation of the solar energy system. Most of the locations available solar radiation records are not available due to high cost for buying and maintaining the measuring instruments. Aim of this paper is building an efficient neural network model that can reliably estimate solar radiation. In this work, different ANN models with three popular algorithms admired from the literature are investigated. The models were trained using meteorological data collected over a year from six different places from the hot area locations of Tamil Nadu, India. Based on minimal Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the optimum ANN algorithm and model are determined (R). Furthermore, the research illustrates that the ANN model’s prediction performance is dependent on the entire set of data utilised to train the network. Also, this article aims at finding out the accurate number of hidden layer neurons for the developed model. The proposed ANN model offers improved accuracy and applicability for estimating hourly average global radiation for the purpose of designing or evaluating photovoltaic (PV) installations in areas without meteorological data collection facilities.

Natural-gas fired units (NGFU) occupy an increasingly important position in the power system, leading to a closer connection between the power system and natural gas system, especially in the distribution level. To utilize the residual differential pressure energy and reduce operation costs, the conversion model of the generator using residual differential pressure energy is formulated, and then the cooperation model of the power system and natural gas system is built in this paper. Due to the original optimization model of power system and natural gas system in the distribution level is non-convex, the second-order cone programming (SOCP) based on a modified convex concave procedure (CCP) algorithm is used to handle the NP-hard problem. The simulation results show operation costs are reduced via the proposed strategy, and the SOCP based on the modified CCP algorithm achieves good performance in fast solution with the constraint of Weymouth equation.

Because the gas insulated switchgear (GIS) is closed, it is impossible to directly see the actual position of internal contacts. The image recognition technology can be used to collect, analyze and process the images of the switches in different states, and finally obtain the actual opening and closing state. In addition, some potential flaws can also be identified in advance. Considering the particularity of the GIS internal environment, wireless charging system can be used to supply power for internal video sensor. In this paper, a new type of coupling coil structure, DC coil, is proposed, which supplies power for video sensor through GIS observation window by wireless power transfer (WPT) technology. The proposed DC coil is analyzed theoretically, optimized by genetic algorithm (GA), and verified by finite element method (FEM). The experimental results indicate that the proposed structure can achieve the same transfer performance in lateral and rotation misalignment, while the conductor and space is saved by 8.49% and 22.22% compared with the traditional DD coil. The proposed DC coil is feasible to be applied in the actual GIS scene.

This paper presents a stochastic model to quantify the impact of the electric vehicle (EV) on China’s electricity load profiles. Most of the existing literature utilized travel data to model EV charging behavior and ignored the influence of people’s social attributes that are significant for the accuracy of EV charging behavior. Based on the dataset of the national household travel survey, the most significant influencing factors, e.g. age, location, and weekday/weekend, are identified. Markov-chain is used to construct a sequence of destinations of each vehicle trip, depending on EV’s driver, day of the week, and time of day. Vehicle-driven distance, driving time, and parking duration are used to model electricity demand and potential EV charging flexibility. The charging infrastructure accessibility in a certain parking location has an influence on EV charging decisions. The model’s outputs are used to assess the impacts of various EV charging strategies on electricity load profiles on a national scale. It is found that at 60% gasoline vehicle replacement with EVs by 2050, the electricity demand of EV will be 510 TWh, accounting for 4.5% of the national demand in 2050. The national peak loads will further increase by 8.2% under the unmanaged charging strategy of EV. In contrast, implying last-minute charging strategy only increases peak demand by 2.6% relative to the unmanaged charging strategy.

Nowadays, the applications of machine learning techniques have widely been implemented in various domains of the power system and especially to predict three-phase transmission line faults. This paper compares the results obtained from two powerful machine learning techniques such as Concurrent Neuro-Fuzzy and Decision Tree applied to predict faults classification and their location on a nine-phase transmission line system. The results obtained demonstrated that the utilization of these artificial intelligence techniques which have been well applied on various and diversified systems could also been applied on a future nine-phase transmission line system that carry 750 kV over 200 km to classify and locate various fault types and then, to increase the yield of the line.

The present paper aims to develop an advanced tool “SECAP-SSP” to support and facilitate the production of a Sustainable Energy and Climate Action Plan in Municipalities. The methodology will investigate all possible environmental, economic, and territorial potentials through choosing the most optimal list of actions with selected criteria and will ensure its compliance with the Covenant of Mayors launched by the European Commission. First of all, a survey has been conducted to study the various human activities in terms of energy consumption, greenhouse gas (GHG) emissions, and the availability of renewable energy sources for the municipality of Hammana in Lebanon taken as a case study. Then the tool has established the Baseline Emission Inventory and developed the action plan with real and measurable objectives. Measures are mostly related to adopting solar energy for thermal and electrical supply, energy conservation and energy efficiency strategies in buildings, waste and water management, and greening the transportation. Focused analysis show that enhancing buildings’ envelope and adapting solar water heaters have reached a reduction of 2442.9 t CO2-eq/year in the GHG emissions with a payback period of around five years. This study has examined the implementation of two of its proposed actions.

Nanofluids utilized as volumetric absorbers of solar energy have shown significant potential to enhance solar energy absorption and storage. The majority of numerical models currently employed to design and optimize the nanofluid-based solar receiver and storage systems involved assumptions and simplifications that can adversely impact performance prediction. Due to the inherent complexity of nanofluid systems, including interactions between the optical and thermo-physical properties of each phase, the development and optimization of solar energy absorption and storage systems in nanofluid-based receivers require advanced use mathematical–physical models for more effective design and optimization. This study aims to assess the impact of these critical assumptions on predicting thermal absorption and thermal energy storage capacity for such systems and developing accurate predictive modeling to aid the design and optimization of such systems. The results presented in this paper will assist in creating accurate mathematical models of Concentrated Solar Power (CSP) using nanofluids, and therefore enhance its thermal absorptivity and storage capacity.

Cost allocation is a way to promote enterprises to actively implement green supply chain management, and the behavior preference of decision makers will affect the process. This paper considers the situation that manufacturer has fairness preference and retailer is fair neutral, and constructs a secondary supply chain composed of a single manufacturer and a single retailer. We explore two models of cost allocation—based on the Stackelberg model, the impact of retailers’ nonparticipation/participation in cost allocation on supply chain is discussed, while through the Nash bargaining model, the range and optimal solution of the environmental cost allocation ratio are determined. And we show how product wholesale prices, retail prices, green innovation investment, and the optimal ratio of environmental cost allocation are influenced by fairness preference. The results show that retailers’ participation in environmental cost allocation will increase the level of product greenness and make the entire supply chain Pareto optimal. The strong fairness preference of manufacturer will weaken the positive effect of retailers participating in cost allocation on the supply chain. Therefore, the government and enterprises should adopt incentive measures to actively promote the collaboration between channel partners in order to share the environmental cost, and manufacturers should pay less attention to fairness.

Nowadays, the output of light crude oil is approaching its peak, which has shifted the attentions of petroleum industry to a large quantity of heavy oil and bitumen in order to offset the growing demand of energy and fuels. The heavy oil and bitumen are usually characterized by high viscosity, high density/low API gravity, low yield of low boiling point fuel fraction, and high heteroatom content in comparison with the light oil, requiring in situ upgrading to improve the mobility of heavy oil. before recovery it. One of the methods for upgrading is to add catalysts to the underground reservoir. This paper firstly reviewed various kinds of catalysts, including mineral, water-soluble, oil-soluble, solid acid, ionic liquid (IL) catalysts, and dispersed and super-dispersed catalysts that reported in domestic and foreign literatures, and secondly described their effectiveness in underground catalytic cracking and upgrading of heavy oil. This critical review suggests that it is necessary to develop the catalytic cracking catalysts of low-cost, high activity, high selectivity, and wide adaptability in order to accommodate a wide range of heavy oil with different properties, especially the ultra-dispersed nano-catalysts. The in situ upgrading technology by injecting super-dispersed nano-catalysts is a promising method to improve the quality of the lowest grade heavy oil components and enhance the recovery of heavy oil reservoirs. Moreover, further research is needed to explore the optimal process of catalyst synthesis, catalytic working conditions and the actual mechanisms of heavy oil with catalysts.

Today, some cities around the world have tended to use new technologies and become smart city. New technologies improve the quality of citizens’ life. However, the use of any technology raises new issues and challenges. In a smart city, the vulnerable action of an individual or organization can put the entire city at risk. Due to the reliance of various components of smart cities on information and communication technology, cyber-security challenges (such as information leakage and malicious cyber-attacks) in this field affect smart cities behavior. Therefore, in order to respond to the enthusiastic acceptance of global smart city technologies, cyber security must develop in same direction. The aim of this paper is survey and discus on explanation of cyber security, smart cities, and survey of available relevant literature on security in that technology. For this purpose, the present study focuses on the four main components of a smart city, i.e. smart grid, Smart building, Smart transportation system, and Smart healthcare. In particular, summary of two deep learning method and cyber-security programs as well as technology correlation in smart cities are discussed. Furthermore, effective functional solutions in maintaining cyber-security and user privacy in smart cities are explained. The next progress trends of smart city with cyber security are described. Solutions need to be devised to address each of the security issues. The research in this study showed that meeting these challenges depends on the hard work of governments, developers of equipment and software and companies providing IT security services. In addition, designing flexible systems with high information protection capabilities is essential to prevent serious security incidents as these incidents can lead to disastrous financial, data, credit and loss of public trust.

The modular multilevel converter (MMC) is considered a very promising candidate for medium-voltage and/or high-power grids. One of the major concerns in half-bridge MMCs, especially with large number of levels, is the fault occurs at the converter submodule (SM). Hence, an appropriate control with an SM fault tolerance of the MMC is required to enhance its reliability. This paper addresses the issue and proposes a fault tolerant control-based adaptive carrier phase disposition pulse width modulation (AC-PDPWM) technique for MMCs with superior symmetrical and asymmetrical fault-tolerant capabilities. The proposed control-based AC-PDPWM acts to regulate the flow of energy between arms, rebalance the SM capacitors, and redistribute the pulses to the SM switches. In this study, a single-phase grid connected MMC having both symmetrical and asymmetrical fault conditions is investigated. Compared with other control methods, the proposed control can tolerate the SM fault conditions without requiring redundant SMs or additional computational burden, which decreases the cost and volume of the MMC design and simplifies its control. Simulation and experimental results are included to verify the fault-tolerant capability of the proposed control.

This paper adopted theoretical derivation and experiments to investigate the stress wave propagation characteristics in rock under water jet impact and fracture response of coal. The evolution model of stress and displacement was presented to analyze the formation, propagation and attenuation of stress wave during rock fragmentation. The rock failure criterion for shear and tensile failure under dynamic impact loads, and crack propagation criterion under quasi-static pressure of water jet were established. And the broken pit range formed by shear component of stress wave and the damage range caused by tensile stress were obtained. Based on scanning electron microscopy and binarization method, experiments on water jet impinging coal under various velocities were conducted to study the failure patterns and fracture characteristics of coal. It is indicated that during the water-hammer pressure and its unloading stage, the radial stress and tangential stress decrease first and then increase, accompanying the occurrences of the radial and tangential tensile stresses peaks and the maximum compression displacement. In the subsequent stagnation pressure stage, the radial and tangential stresses trend to be the stable compressive stress and tensile stress, respectively. The peak values of radial stress and tangential stress at different stages all decay exponentially with the propagation distance. Moreover, the broken pit surrounded with radial and annular cracks is formed on coal when the jet velocity exceeds the threshold value, while the split fracture of coal will occur for the high impact velocity. The rock-breaking specific energy consumption decreases first and then increases with the increasing jet velocity. Besides, the theoretical damage scope is verified and close to the experimental value before the splitting fracture of coal. Combined with fracture morphology of coal debris with different sizes, two microscopic damage-failure modes of coal impacted by water jets are revealed, namely shear failure and tensile failure.

This paper proposes a novel virtual engine calibration method for alternative fuels using thermodynamic simulation for in-cylinder pressure prediction. Based on known engine data, including the crank angle of the peak cylinder pressure, the optimization problem is defined for a desired indicated mean effective pressure. The decision variables are the combustion and heat transfer model parameters The method was tested for three different engines of different sizes, operating with ethanol, hydrogen and natural gas, and different equivalence ratios. The Wiebe model and a quasi-dimensional fractal combustion model were compared. The results showed that the method was able to successfully predict the in-cylinder pressure curve, with a coefficient of determination higher than 0.99. Furthermore, the method predicted the peak pressure and the crank angle corresponding to 50% of mass fraction burned with a maximum deviation of 2.5% and 1.5 °CA, respectively.

Tests of adsorption cooling prototypes with solar energy that use the silica gel–water​ pair have been reported in the literature; however, there is no work that analyzes, compares and integrates them to generate new knowledge from their data and results. In this context, our proposal consists comparing thermodynamic results, determining the characteristics of their parts and functions, and analyzing technical contradictions through the TRIZ methodology, for twelve identified adsorption refrigeration prototypes (including one where a co-author of this paper participated). The results and characteristics of the analyzed prototypes have been systematized, also a diversity of constructive forms was found in the studied prototypes, as well as in the non-standardized results and tests because some aspects were emphasized in each test. The results and simulations are compared using COP and refrigeration capacity as parameters to extrapolate new results consisting of a hybridized conceptual model and projected test results based on those found in the literature.

This paper divided into two items; the first is to assess the technical state of pump stations on the two branches of the Nile Delta for irrigation by solar energy to compensate the shortage of Nile water especially in such intensively cultivated region such as Delta. The second is studying the economics of these pumps when use the solar energy instead of Diesel. Reduction in Nile water resources in Egypt is inevitable due to the Grand Ethiopian Renaissance dam (GERD). Where the major of the expected impact is a reduction in irrigation canals levels, the surface water could be pumped to irrigate these agriculture lands. HEC-RAS model is used to estimate the decline in the Nile level (in the two branches; Damietta and Rosetta). Two cases are of proposed of power system sources for the operation of pump station on Damietta and Rosetta Branches. The time period of the tested system was 25 years. The results showed that; around 28 pump stations will be hurt in the new conditions of the Nile level decline while 6 only can be operate in safe. the capital cost of the solar system was high when compared with the diesel cost, but the net present cost was highly economic in the solar system. The net present cost of the solar system to irrigates 23100 km2 (5.5 Million feddan) which are the cultivated areas in delta, in first and second cases as; about 33.1 (million LE during 25 years), and about 6.76 (milliard LE during 25 years) respectively. The total greenhouse gas emission by diesel system was about 32,469 (kg/year/4200 m2) with considering the daily working hours of pumps is 8 hours. Finally, either solar energy or diesel usages will increase the economic burden of Egypt but using the solar pump is less impact.

Electromagnetic vibration and noise of driving motor affect the comfort for electric vehicles. Considering low harmonic component and pole design flexibility of double-layer interior permanent magnet (IPM) machines, in this paper, an improved analytical method of no-load air gap flux density for double-layer IPM machines is introduced, and the harmonic spectrum of electromagnetic force is analyzed. A commercial double-layer IPM machine is taken as benchmark, the electromagnetic–structure–sound coupling model is established. The main spatial and temporal order of electromagnetic force for benchmark motor is indicated. Based on this analytical model, a method to minimize the electromagnetic force of specific harmonic component is proposed to reduce electromagnetic vibration and noise, by optimizing parameters of double-layer IPM machine rotor pole without impairing the motor performance. The vibration and noise of the optimized motor under working condition and no-load are compared with the results of the benchmark motor by multi-physics simulation, and validate the proposed optimization approach.

Currently electricity markets worldwide encounter a transition phase into cleaner energy. This is specially the case in Greece, where not only the market structure changes to a harmonized EU target model, but also ‘green electricity’ is projected to reach 50% share by 2030 according to the National Energy and Climate Plan of Greece. This paper presents the extension of the Business Strategy Assessment Model (BSAM) into a computationally efficient central-dispatch wholesale market model. BSAM integrates agent-based profit-maximizing policy making with unit commitment scheduling, into one novel tool that can be used to explore the effects of mid-term market design policies in wholesale electricity markets. BSAM is subsequently utilized to model the Greek market’s transitional phase and explore the effects of even higher RES penetration scenarios. Results indicate that the planned short-to-mid-term “de-lignification” and transition of the Greek power system is feasible and will not lead to an excessive RES curtailment situation, but would yield significant CO2 emissions’ reductions and small reductions in the system marginal price, with even more ambitious RES shares also being possible.

Rapid growth and development around the world will lead to a gradual increase in electricity consumption. At present, colleges and universities have become the primary unit of daily electricity consumption. Therefore, accurately predicting the power consumption of colleges and universities is of great significance to the energy conservation and emission reduction of colleges and universities. Taking the actual power consumption of colleges and universities as an example, this article first analyzes its power consumption data characteristics. Based on the analysis and “decomposition and integration” concept, this paper proposes a hybrid network based on the improved complete ensemble empirical mode decomposition with adaptive noise (iCEEMDAN) and long short-term memory (LSTM) to achieve accurate colleges and universities Short-term load forecasting. First, the original power consumption data is decomposed into a series of patterns with noticeable differences by iCEEMDAN. Then use Bayesian-optimized LSTM to predict each mode individually. Finally, the prediction results of each mode are superimposed and reconstructed to form an overall prediction result. In each training, the Bayesian optimization algorithm is used to select the most suitable LSTM hyperparameter values to match the data characteristics of each model. At the same time, the structure of the LSTM prediction large data set is discussed. The results show that, compared with the prediction errors of other models, the proposed hybrid model can accurately predict university power consumption and provide the highest prediction ability among all survey models.

The use of electric vehicles is for reducing carbon emissions, thereby reducing environmental pollution caused by transportation. However, the large-scale production and application of electric vehicle batteries have brought another notable issue, i.e., the production and application of these batteries also cause environmental pollution. Particularly, the precious metal materials used in the batteries are harmful to human health and the surrounding ecological system. Nowadays, many types of batteries are available. It is essential to understand which of them is most suitable for electric vehicles from the perspective of environmental protection. To answer this question, the life cycle environmental impact assessment of LiFePO 4 battery and Li(NiCoMn)O2 battery, which are being popularly used in pure electric passenger vehicles, are conducted in this paper. The research has shown that the two types of batteries show different environmental impact features in different phases. For example, LiFePO 4 batteries are more environmentally friendly in the phase of production, while Li(NiCoMn)O2 batteries are more eco-friendly in the application and transportation phases. Despite this, LiFePO 4 batteries are generally more environmentally friendly than Li(NiCoMn)O2 batteries from the perspective of the entire life cycle. In addition, the research results also suggest that due to the heavier mass, LiFePO 4 batteries can probably gain more benefit when used for energy storage.