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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.

Non admixed Natural gas-fuelled diesel engines are usually associated with harsh engine emissions as well as low performances at moderate to high engine loads. However, the use of Moringa biodiesel as additive in hydrogen-compressed natural gas (HCNG) is a viable novel strategy for reducing toxic emissions such as hydrocarbons and nitrogen oxides in CI engines. In this study, five hybrid HCNG–Moringa biodiesel (MB) oil samples labelled A–E were analysed for their abilities to improve the overall performance of a CI engine (Petter PH1W diesel engine). The fuels had a fixed volume by volume hydrogen : carbon ratio of 87:13% respectively, and the HCNG–MB hybrid samples consisted of 0, 5, 7, 10 and 13 %v/v MB for samples A, B, C, D and E, respectively. At higher engine loads (i.e. 58%–100%), the engine performance improved thus giving higher BTEs in the range of 19–33.9% for all the fuel blends relative to the BTE (15.1%–19%) of the unblended HCNG fuel; this confirms the suitability of MB as an additive for improving the brake thermal efficiency of CI engines; there were also reductions in HC, O2, CO2, CO and NOx emissions for all the MB–HCNG fuels relative to the case of the unblended HCNG fuel. At optimum condition, the fuel that gave the best results in terms of the aforementioned engine characteristics is the MB–HCNG fuel blend having 10% MB.

Researchers have turned to alternative fuels in diesel engines due to the depletion of fossil fuel reserves, global warming and energy consumption. Among alternative fuels, biodiesel is preferred because of its high oxygen content, better combustion, reliability and no need for any modification in the fuel line. In this study, the combustion characteristics and engine performance processes of biodiesel fuel produced from fig seed oil with different blend ratios were experimentally examined. Up till today, fig seed oil has not been produced as biodiesel and has not been tested in any diesel engine. The biodiesel fuel was produced from fig seed oil for the first time and used in diesel engines. Diesel engine was operated at engine speeds of 1550, 1700, 1850, 2000 and 2150 rpm and full load. 5%, 10% and 20% fig seed oil methyl ester by volume was added to the diesel fuel. The effects of fig seed oil methyl ester with different ratios on in-cylinder pressure, pressure increase rate, net heat release rate, brake specific fuel consumption, engine torque and exhaust gas temperature were evaluated. The combustion characteristics were numerically verified by using AVL Fire. In-cylinder pressure and net heat release rate of diesel and biodiesel fuels were compared numerically and experimentally, and each other close to results were achieved. Experiments show that the fig seed oil methyl ester contributes to the combustion properties of the engine. At 2000 rpm, the in-cylinder pressure’s of diesel, B5, B10 and B20 fuels were 79.29, 81.21, 83.23 and 82.25 bar, respectively. The brake specific fuel consumptions of diesel, B5, B10 and B20 fuels were 274, 279, 281 and 285 g/kWh, respectively. As a result, better combustion has been achieved with %10 biodiesel fuel blend because of the excess oxygen in the biodiesel itself, shorter ignition delay and fast burning.

The supply of sustainable and distributed energy becomes a concern. In this study, physico-chemical characterizations of coffee grounds, raw wood, composting sewage sludge, and raw comparable coal, as well as their chars were performed using carbonization/combustion kinetics and electrochemistry in order to promote the supply of sustainable and distributed energy. The carbonization kinetics of coffee grounds were faster than those of raw wood, composting sewage sludge, and raw coal because of the higher content of volatiles and potassium. The specific surface area of the wood char with steam activation was larger than those of the other chars, and that of the coffee grounds char with steam activation was also relatively larger. The graphitization degree of the wood char was similar to that of the coal char. The average potential of a direct carbon fuel cell (DCFC) using the composting sewage sludge char was higher than that of the other chars. Thus, the coffee grounds, wood, and composting sewage sludge chars may be better candidates for use as an adsorbent, heat source, and DCFC fuel, respectively.

A high proportion of new energy sources are connected to the grid, which not only alleviates the energy shortage, but also brings hidden dangers to the safe and stable operation of the grid. Aiming at the inadequate consideration of uncertainty caused by new energy sources such as wind power being connected to the grid, a method for identifying voltage-dominant cascading fault types is proposed. A voltage-dominant fault analysis model in the sense of probability is established. Based on the fault data, the nonlinear mapping relationship between the initial fault and the fault propagation characteristics is established through the neural network. Finally, the effectiveness of the method proposed in this paper is verified in the IEEE39-bus system, and the simulation results show that the method proposed in this paper can effectively identify the fault types of high-proportion new energy systems.

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.

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 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.

System integration for process automation promoted the technologies that were accessed to functions and data of field device, controller, and SCADA system. In addition, the data from filed devices can apply to solve device diagnostic and preventive maintenance. This paper describes the practical technique to configure and implement of control and SCADA system that focus on integration of field device, PLC controller, and SCADA system. The paper presents case study of Allen-Bradley (AB) PLC by using WirelessHART device to temperature control and device diagnostic through SCADA system. PLC’s input/output (I/O) modules are installed that consists of Modbus module, digital I/O module, analog I/O module. In addition, Prosoft configuration builder and Studio 5000 software are new software to build add on Modbus instruction and ladder diagrams, respectively. The experiments perform in case study of temperature control by using Proportional–Integral–Derivative (PID) function in the AB PLC. Wonderware InTouch SCADA software is implemented to monitor the process mimic, trend, and alarm. Moreover, the technique can support the operators and engineers to make decision for process control and devices diagnostic.

Aiming at the problems of extensive management and energy waste in the operation of shopping malls, this paper proposed a shopping mall building energy-saving diagnosis model based on an improved PSO-SVM neural network. Firstly, the operation data with good energy-saving characteristics were selected from the collected historical data of energy consumption by clustering algorithm as the data basis for the establishment of the model. Secondly, the Adaboost algorithm was used to optimize the PSO-SVM neural network to complete the model construction, and the energy-saving data was used to complete the training of the model. Finally, the judgment criteria for abnormal energy consumption were established, and the energy consumption situation at the current moment was diagnosed by using the trained energy-saving diagnosis model. The abnormal judgment criteria of energy consumption were used to analyze the diagnostic results, and the energy-saving diagnosis process was completed, which was verified by case analysis. Through case analysis and verification, the energy-saving rate of the energy-saving diagnosis model established in this paper has been increased from -1.1% to 11.9%, and the problem moments generated during the operation of air conditioning in shopping malls have been successfully diagnosed. The model established in this paper can not only find out the unreasonable conditions in the operation of the air-conditioning system, but also provide a reference for building energy-saving management and operation.

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.

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.

The opening and closing speeds of GIS switches are relatively fast, so it is difficult to judge the possible defects of the mechanism through manual observation. In order to quickly and accurately identify the switch state or defect type, this paper designs a GIS switch state detection platform based on image recognition technology and wireless power transfer (WPT) technology. The wireless charging capability creates the conditions for the platform’s versatility and portability to some extent. This paper mainly analyzes the stability and anti-offset of the WPT system. According to the theoretical analysis, the efficiency of the wireless charging system is more stable than that of the traditional LCC WPT system under the same output power. Within the offset distance of 0.11 m, the efficiency can be maintained at about 92%, with almost no change. The results show that the proposed method is effective.

Waste management has always been one of the main challenges of developing countries. So far, several methods have been proposed to manage municipal solid waste (MSW) and reduce its environmental impact. The purpose of this paper is to determine the amount of MSW generated in two different cities in Asia and to investigate the performance of both landfill gas and anaerobic digestion methods for electric power generation. Both Tehran and Beijing have a high rate of daily waste production and can be a good case study to examine the performance of both energy generations technologies from MSW. However, the use of such technologies requires more and more detailed studies in order to encourage private and government sector investors to use them. In addition, the use of alternative energy sources such as electricity from waste is inevitable due to the current problems in the field of energy. The assessment presented in this paper includes the amount of electricity generated in both technologies, economic feasibility based on Levelized Cost of Energy (LCOE) and Payback Time (PBT) calculations approaches, and environmental evaluation according to the global warming potential (GWP) under different three Scenarios. It was found that after a 20-year period, the amount of waste generated in regions of Beijing is 1.96% lower than in Tehran. The total electricity generated by anaerobic digestion technology is 45.2% and 41.9% higher than that of landfill gas project in Tehran and Beijing, respectively. In addition, on the average MSW management under scenarios 2 and 3 in the Tehran and Beijing could reduce GWP by 79.16 and 92.65%, respectively. The average PBT value for a 20-year period in Tehran is 1.1 and 0.67 years higher than in Beijing under landfill gas and anaerobic digestion technologies, respectively.

The concern for environmental devastation due to high dependency on fossil fuel sources is shifting focus towards efficient utilization of energy by integrating renewable energy and integrated energy system. Efficient energy deployment can be attained by alleviating the actual losses, costs and environmental impact of the energy sources. This paper aims to address the above issues by reviewing the 4E analysis known as energy, exergy, exergoeconomic and exergoenvironment analysis of thermal power plants, intermittent renewable energy and integrated energy system. Amongst other noteworthy aspects outlined in this paper, it has been found out that extensive work has been done on TPP energy and exergy analysis but exergoeconomic and exergoenvironment analysis are still considered as emerging fields. Moreover, most of the analysis conducted for intermittent renewable energy and integrated energy system is based on energy analysis. Some vital conclusions have been drawn from literature survey like boiler, PV surface module and wind rotor are the major sources of exergy destruction/losses in thermal power plants and intermittent renewable energy system. Decreasing the above components losses will significantly increase the energy quality, thereby will achieve a cost-efficient, sustainable and eco-friendly energy system.

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.

Wind Turbine (WT)-based Doubly-Fed Induction Generator (DFIG) is a nonlinear system, in which the wind has variable behavior, and the local reactive power depends on the random demand of the AC grid; all these make conventional controls insufficient for an adequate power output. Thus, a new direct power control (DPC) strategy is proposed in this paper, to force the system to track the desired dynamics with higher performance and less drawbacks. This new approach is based on machine learning, Neural-Network- and Neuro-Fuzzy-DPC (NN- and NF-DPC), both are designed to overcome the problems related to power control, wind and local reactive power variations. All operating modes (sub-synchronous, super-synchronous and synchronous modes), with the possibility of local reactive power compensation are considered in this paper. Both NN and NF networks have been trained under the MATLAB interface. The trained NF showed better efficiency than the NN; the learning time is reduced to a few seconds with less computational effort, and less complexity. The effectiveness of both controls has been confirmed through simulation tests using MATLAB software. The obtained results have shown that the NF-DPC strategy presents better performances than NN-DPC and conventional control, with fast response, robustness, less power ripples and reduced Total Harmonic Distortion (THD) in the generated currents.

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.

This paper focuses on a novel model named multi-station fusion (MSF). The proposed model integrates transformer substation, data center, energy storage system (ESS), photovoltaic (PV), electric vehicle charging station (EVCS), connection information base station and other systems. It aims at improving the utilization rate of the spare space in existing substation, reducing operation and maintenance cost, and increasing the revenue of power grid enterprises. This paper designs three types of AC/DC system based on reliability priority, economy priority and retrofit flexibility priority. Markov process Monte Carlo method is introduced to verify data center reliability in MSF. The business model of MSF is analyzed. Finally, the construction scheme of MSF model, the contents of the planning considerations and cost–benefit study demonstrate the economic efficiency of the proposed model.

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.

In the context of global emission reduction, as a type of clean and eco-friendly energy, natural gas has attracted wide attention. Accurate prediction of natural gas consumption can provide reference for relevant departments to formulate the corresponding development plans and strategies. At present, there are many forecasting tools for processing and analysing natural gas consumption, but most of them have the disadvantages of complex modelling or low precision. In order to solve this problem, inspired by the polynomial regression prediction model and the discrete grey prediction model, a novel polynomial discrete grey prediction model with fractional order accumulation (abbreviated as FPDGM(1,1) model) is established in this paper. In order to find the optimal parameters of the FPDGM(1,1) model, three most popular intelligent algorithms are used to solve the parameters of the model, and then the optimal parameters from the three intelligent algorithms are selected in this paper. In addition, the advantages of the FPDGM(1,1) model over other four forecasting models are confirmed through two parts in this paper. First, the natural gas consumptions of four municipalities in China are used as cases to verify the effectiveness of the FPDGM(1,1) model in this paper. Subsequently, the FPDGM(1,1) model and several kinds of frequently used forecasting models are applied to analyse and predict China’s natural gas consumption, and the FPDGM(1,1) model is used to predict China’s natural gas consumption in the next 5 years. The results of these two parts indicate that the FPDGM(1,1) model shows the best predictive performance in all cases. Lastly, based on the predicted results of the FPDGM(1,1) model, some reasonable suggestions are put forward for China’s natural gas development plan.

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.

With the prevalence of Aerospace Technologies, the regulations of cybersecurity are becoming smarter, assured, and long-lasting. Modern communication network technologies have enormous growth in the cyber threats and masquerading attacks to steal data. Hence concepts and mechanisms are built and made into regulations for a safer environment. Unmanned aerial vehicles (UAVs), often known as drones, are becoming increasingly common, posing new problems in areas such as monitoring, agriculture, weather prediction, surveillance and other fields. This includes a large number of devices that, owing to a lack of energy or a system shutdown, might occasionally send incorrect signals and must be monitored autonomously by drones in remote regions. In this paper, we propose a energy intensive blockchain-based platform for controlling drone operations while ensuring trust and security for all parties involved. The goal of this paper is to explore the extent of Unmanned Aerial Vehicle (UAV) vulnerability to deceptive (Global Navigation Satellite System) GNSS signals by establishing the necessary conditions for UAV via GPS (Global Positioning System) spoofing. The existing algorithms used to mitigate spoofing attacks have unbounded long-term errors, which increase in time during its performance. An innovative idea is necessitating to eliminate those errors, thereby in the proposed work, Ethereum Blockchain has been implemented to create a blockchain network to mitigate the spoofing attacks. Blockchains are incredibly popular nowadays and is the basic technology for cryptocurrencies. Blockchain technology greatly impacts the applications in UAVs. The proposed methodology uses the network that has to be registered in the aerospace components through the ledger associated with relevant data communication in the Blockchain. When an intruder gets acquired with the data in the network with a single block, it cannot affect the entire network due to the data integrity in the ledgers that has been cryptographically assigned. The blockchain network intermittently verifies the geolocation data so that any outlying data would be detected and eliminated quickly. The data that has been verified is made available for the view of aviation and spacecraft operations through the distributed network. The proposed methodology outperforms the existing methods in intense drift error and, in the case of confidentiality and integrity, it has very low risk when compared to existing methods.

This paper proposes a method for selecting the parameters of the main components of an autonomous photovoltaic (PV) stations to ensure the most efficient conversion and use of solar energy. The main energy characteristics are analyzed and the mathematical models of the components of an autonomous PV system are developed to study the modes of tracking the maximum power point (MPP). The necessary conditions for matching the parameters of the PV and the DC–DC buck converter are evaluated to track the MPP. This paper aims to create an original method and algorithm for calculating and selecting parameters of the main elements of a PV stations. The optimal value of the sampling time of the MPP controller is obtained for different schemes of the PV system . A practical example is considered for selecting the parameters of a DC–DC buck converter and a digital MPP of an autonomous PV station. The simulation of dynamic modes of an autonomous photovoltaic (PV) station is performed in the MATLAB/SIMULINK software package. The simulation results of dynamic operations of the PV system show that the voltage converter and the MPP controller with the selected parameters according to the proposed method provide reliable and effective tracking of the MPP in all different connections of the PV.

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.

A hybrid system is the most suitable option for supplying the network demand in a renewable resource power plant. In this paper, a hybrid system of fuel cell-wind turbine was assessed based on energy. For a country region with a sustainable wind speed, the system was studied, and its benefits and limitations were evaluated. In this work, based on regional wind data, the specifications of the elements were determined, and a model was simulated, which can assess the power production of a power plant. In the case study region, Kohak Qazvin, by applying the model, wind turbine capacity increased about 2%, and wind turbines and fuel cells contributed 39% and 61% in demand supplying, respectively. Some undesirable environmental effects of renewable energy were evaluated in this paper, including changes in regional ecosystem, thermal pollution production, chemical pollution, and biological pollution due to the use of fuel cell systems and digestive power plants. This paper investigates the environmental effects of using a hybrid wind turbine system, electrolysis system, solid oxide fuel cell and gasifier. The results showed that the hybrid system reduced 34.62% of carbon dioxide production, compared to the single system. Also the price of the electrical energy produced by the wind farm was 20.6 cent/kWh but by applying the hybrid system, the cost reduced to 15.47 cent/kWh.

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.

Exploring the relationship between economic growth, energy consumption and CO2 emissions is an increasingly important research issue. The paper examines the association between gross​ domestic product per capita, CO2 emissions and energy consumption from renewable sources for the 27 countries of the European Union and the Western Balkans for the period 2008–2018. The panel vector autoregressive approach (PVAR) was used in the analysis, and the panel VAR model with three variables was evaluated based on the generalized method of moments (GMM). The main findings of the paper are that there is a relationship between the investigated variables: (1) two-way relationship between gross domestic product and CO2 emissions, (2) two-way negative relationships between CO2 emissions and energy consumption from renewable sources, (3) no causality has been established between gross domestic product and energy consumption from renewable sources (4) the sign of the relationship in the direction from gross domestic product to CO2 emissions is negative, while in the opposite direction is positive. The results indicate that most of the variations in CO2 emissions are determined by variations in GDP, so reducing CO2 emissions in the long run can be achieved by continuously increasing GDP. Increasing consumption from renewable sources will lead to a direct drop in CO2 emissions which will have desirable environmental effects. Findings obtained in this research can be important for decision makers in managing energy policy, environmental policy and economic growth and development.

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.

The International Marine Organization (IMO) regulations forcing shipbuilders to use electric ships to reducing pollution emitted from ship engines. Renewable energy resources are the perfect solution to solve this issue. The ship’s hybrid energy power system consists of several non-homogeneous energy resources diesel generator, renewable energy source or more, energy-storing system, and may hydrogen source as fuel cells. The EMS manages and controls the balance between the different types of sources and loads demand to ensure system stability and dependability. In this paper, energy management strategy (EMS) for fuel cell and battery hybrid systems is an essential property of controlling power flow between sources. A crucial ingredient of an intelligent control strategy is to manage the flow of a hybrid system corresponding to the changing of the load demand and battery state of charge (SoC) using the ANFIS/Simulink toolbox implements a case study architecture. This paper proposes a hybrid energy source for use in a naval ship’s silent mode of operation while looking for submarines with a low acoustic signature based on adaptive neuro-fuzzy procedures. The main objective of this analysis is to investigate the efficiency of the proposed system preserving compared with practical results obtained from previous work. These studies provide valuable understandings into hybrid power systems’ flow of EMS on the marine field.

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.

A district heating network has generally more than one production unit, and then there are different ways to fulfill the energy demand of the network. This paper deals with the planning problem which is a problem of optimization under constraints. The production units are modeled using MINLP (mixed integer non-linear programming), in particular the following technical aspects are considered: storage capacity (for municipal wastes and biomass), allowable operating range, allowable variation of power over time, partial load performances, minimum working time when a unit runs, and minimum waiting time when it is in stand-by mode. The environmental constraint is the annual share of renewable energies in the global energy mix (for example, at least 50% over a year). The model aims to minimize the operating cost of the network. The problem cannot be solved by a one-shot optimization, so the paper proposes a sequential approach: first, a target of renewable share is calculated for each month of the year; secondly, the planning production is determined each day according to the calculated target. The results of a case study show that the planning strategy that only aims to minimize the operating cost violates the environmental constraint. Using the developed planning model raises a little bit the cost (4.5%) but fulfills the heat demand and environmental constraint at the same time. Considering a reduced VAT eligible in France when the constraint on renewable share is met, the proposed planning is even better in term of the total cost (10% lower).

The renewable energy and alternative power sectors are rising with less global warming, improved public health, inexhaustible electricity, and affordable energy prices day by day. A Sustainable Earth-Battery can be used as an alternative power source as there is no need for an external power to charge the Earth-Battery because it can get charged naturally in the presence of water. This paper aims to show how electricity is generated from the soil and to operate small-scale powered devices. Various soil samples, influential reactors, and electrodes were examined for the entire potential difference and electron flow. After experimentation, it has been shown that Carbon (C6) and Aluminum (Al13) can provide feasible power as an electrode. In a series–parallel relationship between soil cells in the presence of water and energy generation systems through the soil, natural water, filtered and modified soil has been used to achieve maximum efficiency. So, it is also called the water-activated battery, and there is no such thing as a toxic substance that can affect the environment. As a result, it can also be considered a clean energy source. The priority was to use raw materials concerning cost and availability. This paper explains the design, operations, characteristics, and best possible way of soil powered electricity generation system.

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.

In recent years, x-ray tomography has emerged as a powerful analytical tool for the study of lithium ion batteries and the processes occurring within. A region of specific interest is the electrode and, in particular, the heterogeneous and porous structure. The present paper is a review of studies that use x-ray tomography to characterise electrode structure, at both the cell and microstructure scales. At the cell level, x-ray tomography is used to investigate macroscopic design parameters, such as anode and cathode thicknesses, packing density and alignment of assembled cells, as well as to visualise any macroscopic structural defects, such as islanding. At the microstructure level, x-ray tomography allows for quantitative analysis of electrode structures to ascertain parameters such as particle size, tortuosity and volume fraction. The paper also explores different techniques that have been used across the field, from ex-situ, in-situ and operando techniques, to multimodal imaging methods, tomography informed design and results informed imaging.

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.

In the Tokamak device of controllable nuclear fusion, the poloidal field power supply (PFPS) provides the dynamic magnetic field inside the device. The power level of full-controllable devices has been improved with the rapid development of high-power semiconductor technology, which promotes the upgrade of poloidal field power supply. The PFPS adopts an AC–DC–AC circuit structure, which makes the load coil operate in four quadrants. Because the load is a superconducting magnet coil, the power of the load coil will change for a period of time to meet the demand of the internal magnetic field of Tokamak, resulting in the instability of DC bus voltage. This paper proposes an optimized bidirectional buck–boost active storage unit (ASU) with unidirectional inductor current, which can quickly make the bus voltage stable. In the circuit structure, the direction of the inductor current is unidirectional, which avoids the slow response of the inductor current in the reverse direction of the traditional buck–boost converter; And the inductor has a controllable freewheeling circuit, which can reduce the disturbance of inductor current to bus charging. The number of inductors is small, which avoids the large volume of traditional dual-inductor DC–DC converter; Compared with the traditional buck–boost converter, the buck–boost ASU with unidirectional inductor current improves the energy utilization of the bus capacitor. By establishing the mathematical model of the circuit topology, the dual closed-loop controller and modulation strategy are used to control the stable operation of the system. The simulation results show that the circuit structure can balance the bus voltage well and keep stable, when the load power changes suddenly.

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.