Search

In Vietnam, there are about 17,000 pig farms with over 500 pigs, and less than 0.3% of them have a biogas facility. Biogas production from this size is about 50,000 m3/year. This biogas is methane-rich, based on a typical composition of 60–70% methane. However, trace amounts of H2S are also present, which hinder their use as it is very toxic and corrodes the equipment. In this study, an adsorption system using iron-based adsorbent (FeOOH) was developed to remove hydrogen sulfide from biogas at the Thanh Hung pig farm (Thanh Oai District, Hanoi, Vietnam). The initial biogas contained high CH4 concentration of 72%, and the H2S was at the rage of 1995 to about 4000 ppm. Based on the results obtained from the study, it could be seen that FeOOH has high capacity for H2S purification from biogas. The adsorption capacity of the FeOOH was estimated to be up to 0.18 g H2S/g FeOOH. The H2S concentration in the effluent was almost lower than 5 ppm which was suitable for electric generators. In this study, the purified biogas was used for a small electricity generator of 3.5 KVA. As a result, the biogas consumed was about 0.64 m3 per 1 kWh of electricity produced. This amount could fully meet the demand of electricity of a farm. In conclusion, the potential of market for biogas production and consumption in Vietnam is huge. Biogas purification technology for electricity generation, therefore, plays an important role in the biogas market of Vietnam.

In recent years, the use of photovoltaic systems in traffic has become more frequent. The field of application within the field of transport refers to signaling such as high poles, navigation, railway signals, traffic signs, information boards, as well as in terms of public lighting and safety lighting. The paper will provide a detailed review of the literature regarding the applied renewable solar energy and all applicable technologies for highway corridors. Also in this paper, the installation of a solar photovoltaic power plant for meeting the energy demand of highway corridors and tunnels is analysed. The production of electricity from these PV power plants is used to power highway corridors own consumption, such as tunnels and lighting today, and tomorrow for mass use to charge electric vehicles. In this paper, a techno-economic analysis of a solar photovoltaic power plant with an installed capacity of 1 MW in the village Tarcin, next to the A1 highway, is performed. From the obtained results, it can be concluded that the investment in solar power plant represents a cost-effective techno-economic solution for reducing electricity costs. The other benefits of using solar photovoltaic power plant along national highway corridors are in emission reduction and better transport planning. Integrating PV system into national grids can reduce transmission and distribution line losses, increase grid resilience, lower generation costs, and reduce requirements to invest in new utility generation capacity.

This paper presents a novel feed-forward neural network for wind speed forecasting. The electricity sector accounts for a quarter of the world CO2 emissions. To reduce these emissions, several national, regional and global agreements have been signed, setting ambitious goals to increase the penetration of renewable energy sources (RES). Although achieving those goals is essential for the sector decarbonization and, therefore, to mitigate the global climate crisis, renewable-based generation can depend on highly variable and uncertain resources, such as the wind. Hence, having access to reliable forecasts of those resources availability is essential for the operation of several actors in the power and energy sector, and for the effectiveness of the whole system. This paper contributes to surpass this problem by introducing a new forecasting model based on a feed-forward neural network to forecast wind speed. The proposed model is applied to real data from a wind farm in the south of South America. Results show that the proposed model can achieve lower forecasting errors than the baseline models, which consist of Numerical Weather Predictions.

The electric power grid undergoes a transformation, with many consumers becoming both producers and consumers of electricity. This transformation poses challenges to the existing grid as it was not designed to have reverse power flows. Local energy communities are effective in addressing those issues and engaging grid users to play an active role in the energy transition. Such communities encourage the consumption of the excess of renewable energy locally, which reduces the stress on the grid and the costs for the users. In this paper, we present a multiagent system developed to implement an intelligent local energy community. The multiagent system models the energy grid as a network of computational agents that solve energy flow problems in a coordinated way and use the solutions for controlling flexible loads. The model effectively distributes the tasks among the agents considering the flows of electricity and heat. The Alternative Direction Method of Multipliers determines the agent interaction protocol. The obtained results demonstrate the ability of the multiagent system to automate an intelligent operation of the community while reducing the energy costs and ensuring the grid stability.

Upon identifying the troubled system of public transportation in the Northern-border City of Juarez, Mexico, this research adopts an exploratory approach to address such problem and provide a plausible path to solve it. Urban mobility has become one of the main challenges for the Smart City. The latter concept is a global tendency to achieve competitive edge via adopting technological development regarding public mobility. Juarez system of public transportation experiences lacks of modernity infrastructure, which results in pollution due to obsolete vehicles, delays, passenger saturation during heavy hours, traffic accidents and users’ inconveniences. Although many proposals arose in the past to deal with the prevailing situation, to date the city experiences no improvements whatsoever. This paper examines what the scenario would be if the City embarked in the endeavor of adopting high-tech vehicles for public transportation. Particularly, the paper transits through the sate-of-the-art literature to provide in-sights regarding modern transportation alternatives that not only are environmental-friendly, but also financially viable, promoting productive chaining and local benefits. The proposal centers in buses powered by electricity. The paper discusses financial figures and possible scenarios; it also highlights the need for future urban infrastructure improvement and reconfigurations, especially pavement and transportation routes redesign to allocate charging stations.

Water is a very important resource in the daily life activities of people. It is one of the most significant resources that individuals need every day. Water can be used for individuals, industry, or farms. Unfortunately, many regions of the world do not have sufficient water to use. Some areas like Jazan, Saudi Arabia, do not have enough water, and the necessary water is generated by desalination from sea water. This water can be supplied to cities and farms. However, this desalination plant should be located near the seashore to provide water to the inland, and there would be transportation costs for that water. In this paper, water from air, a new water generation method, is proposed. The Jazan area has a lack of rainfall but it has relatively high humidity and constant wind. Using this wind and sun light, electricity will be generated, and water will be generated from the electricity. The goal of this paper is to check the viability of generation of water from air.

Solar radiation and wind data play an important role in renewable energy projects to produce electricity. In Ecuador, these data are not always available for locations of interest due to absences of meteorological stations. In the scope of this paper, a low-cost automatic meteorological station prototype based on Raspberry technology was developed to measure the aforementioned variables. The objective of this paper is twofold: a) to present a proposal for the design of a low-cost automatic weather station using the Raspberry Pi microcomputer, showing the feasibility of this technology as an alternative for the construction of automatic meteorological station and; b) to use Forecasting with neural networks to predict solar radiation in Manta, Ecuador, based on the historical data collected: solar radiation, wind speed and wind direction. We proved that both technology feasibility and Machine learning has a high potential as a tool to use in this field of study.

Nowadays, with the popularization of electricity, the status of grid business office in people’s lives has become more and more important. Grid business office often have a large flow of people, so it becomes very necessary for abnormal detection of grid business offices. Traditional video surveillance has a lot of problems. This paper uses various computer vision technologies to improve traditional video surveillance. First, the YOLO v3 algorithm is introduced to detect the number of people in the business office, and then the motion foreground extraction algorithm is used to calculate the contour of the human body. The contour judges whether there is an abnormal situation currently, and finally introduces the concept of image entropy to judge whether there is intense movement currently. Finally, in the end of the paper, the current work is summarized and the future word is prospected.

Climate-related risks pose inevitable threats to investors and the assets in which they invest; therefore, institutional investors have become increasingly concerned about climate-related risks, but physical risks are difficult to measure and manage because it requires physical hazard data, asset exposure, and vulnerability. Water-related risks are prominent physical risks that affect a wide range of industries, including the thermal power sector. Using traditional financial analysis and econometric techniques, this paper introduces a new methodology to quantify the financial impacts of water shortages on India’s thermal power sector. The methodology leverages asset-level data from the World Resources Institute’s 2018 working paper, Parched Power: Water Demands, Risks, and Opportunities for India’s Power Sector, and combines it with geospatial physical risk and corporate financial data to analyze the financial impacts of water-shortage-induced electricity generation outages at five publicly listed Indian thermal power companies from FY 2014–2017. The paper uses India power sector as an example because its electricity regulator publishes daily generation and outages data, which is essential to apply this methodology. It also uses outputs from Intergovernmental Panel on Climate Change (IPCC) climate models to project the impact of future water shortages on electricity production at the five companies. This paper serves as a tool for investors to understand, analyze, and engage more effectively with thermal power companies on water-shortage risks.

Energy market models are generally focused on energy balancing using the optimum energy mix. In countries where the energy markets are not fully liberalised, the State Regulators reflect any cost of being off-balance on the utility companies and this affects the consumers as well. The right short term prediction of the market trends is beneficial both to optimise the physical energy flow and commercial revenue balance for suppliers and utility companies. This study is aimed to predict the sign trends in the power market by selecting the influencing factors adaptive to the conditions of the day ahead, 10 h, 5 h, 2 h and 1 h before the electricity balance is active. There are numerous factors consisting of weather conditions, resource costs, operation costs, renewable energy conditions, regulations, etc. with a considerable impact on the predictions. The contribution of this paper is to choose the factors with the highest impacts using the Genetic Algorithm (GA) with Akaike Information Criteria (AIC), which are then used as input of a Recursive Neural Network (RNN) model for forecasting the deviation trends. The proposed hybrid method does not only reduce the prediction errors but also avoid dependency on expert knowledge. Hence this paper will allow both the market regulator and the suppliers to take precautions based on a confident prediction.

Machine learning can perform electricity load prediction on the demand side. This paper compared the electricity prediction errors between two machine learning algorithms: Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) architecture. LSTM can solve the regression problem in time-series. Due to that, this paper applied LSTM. The traditional machine learning approach, ANN, was used to compare the effectiveness of LSTM in performing the time-series prediction. A dataset that consisted of historical electricity consumption data with independent variables was used in this study. The mean squared error (MSE) and mean absolute error (MAE) evaluation metrics were used to evaluate the models. The model generated using LSTM showed the lowest error with MSE value of 0.1238 and MAE value of 0.0388. These results indicated that choosing a suitable machine learning algorithm for the time-series problem could improve the model generated from the training session.

With the rapid development of new energy, new energy has the characteristics of high volatility and anti-peak regulation, and high penetration of new energy has brought new challenges to the power system. The range of peak and valley period of new energy such as wind and solar energy and overall load in a region can be known. The power system guides users to use electricity in low valley through sectional electricity price, so as to help the power grid cut peak and fill valley and make efficient use of resources. In this paper, a load classification method for period of power consumption based on typical daily load curve is proposed. So it can effectively distinguish the low and peak period of power consumption of users, so as to better understand users load characteristics, and lay the foundation for the source-load interaction. In this paper, the k-means algorithm is used to obtain the typical daily load curve of users, and the curve symbol aggregation approximation based on the typical daily load curve is used to classify the load periods. This method is very fast and needs less historical data. In this paper, the historical data of electric boiler with heat reservoir in Jilin Province are used to test the effectiveness of this method, which verifies that this method can effectively analyze the load operation characteristics, classify load users in different power consumption periods.

The electricity situation in Nigeria has always been a mystery. With the rich deposit of resources in Nigeria, energy deprivation still hovers around the country, which mostly affects rural areas. Nigeria’s energy is dependent on hydro-power and fossil (gas) thermal power. With about 195.9 million populace in the country up to 60% are effectively not supplied with electricity, 94% do not have access to it. Due to lack of access to electricity in the rural areas, it is essential to introduce renewable energy to the community, which will, in turn, help in the development of the rural community, also leading to the development of Nigeria and an increase in access to electricity. This paper aims to study a review of the relationship of renewable energy to rural economic development and how renewable energy impact the development of remote areas; we looked at a brief background relating to renewable energy. This paper concludes that although clean energy has a high prospective for economic development in Nigeria’s rural community but due to factors like environment, management, also infrastructure will slow down the development. However, with good policy making, all these factors are addressed.

The aim of this paper is to examine the impact of post merger and acquisitions on value creation. The paper also analyses the impact of lagged synergy (a proxy of sales growth) on the post merger and acquisition (MA moreover, this paper takes into consideration different sectors viz. Manufacturing, Mining, Electricity, Construction, Service and Real Estate which gives a holistic view of Indian sectors.

A smart city represents a complex approach to the development of all systems, directions and spheres aimed at providing comfortable living conditions for citizens and qualitative management and reliable infrastructure for the city. The paper answers questions about how to estimate the ability of the implementation approach within a smart city, and how the stakeholders, such as energy policy makers, governance, utilities and municipalities, can choose a model that would promote maximum efficiency by the implementation in the energy sector of the city. The paper has three main sections, the first of which provides a literature review with a classification of different approaches to the implementation of a smart city depending on the scope and tasks. The main emphasis is placed on the analysis of the energy component of smart city decisions. The second section proposes a series of criteria for evaluating the existing approaches to a smart city. The ability to flexibly react to the changes in consumption, unpredicted situations, changes in the needs, sustainability in generation and the supply of electricity and heat, as well as considering alterations in the legislative base, has been taken into account when analysing smart city infrastructure. Finally, the third section presents a decision-making scheme related to choosing the development methodology in a smart city that enables accounting of multifaceted nature of urban environment development, as well as describing a scheme of the block-integrated approach to the methodology of smart city development. In the authors’ opinion, it can have the largest potential for flexible, sustainable and stable development and will produce the greatest qualitative impact on smart city development. As a result, convolving the approaches to the development of a smart city according to various criteria makes possible highlighting the most optimal decision-making process.

Because the current meter does not have the function of detecting electromagnetic fields, there is nothing to do when the meter is disturbed by the high-power electromagnetic field. In order to avoid this kind of electricity stealing behavior, this paper studies how to quickly detect the electromagnetic interference outside the meter based on the characteristics of the meter itself, and timely implement the alarm to prevent electricity theft. In this paper, a feasible and effective electromagnetic field detection technology scheme is proposed, which can detect the electromagnetic field strength around the electric energy meter in real time. When the electromagnetic field intensity reaches the threshold value that affects the metering function of the electric function meter, the electromagnetic field detection module will perform alarm behavior.

Constant decrease of photovoltaic and battery system prices imposes the need for cost–benefit analysis of using combined photovoltaic and battery system for own consumption of generated and stored electric energy. Furthermore, European Union promotes increasing self-consumption by reducing feed-in tariffs for electric energy feed into the power grid. The objective of this paper is to provide a cost–benefit analysis of combined photovoltaic and battery system for certain household based on household annual load profile and annual irradiation profile considering the price of equipment, electricity feed-in tariff and retail price. The paper describes dependency of self-consumption indicators on installed photovoltaic power and battery capacity based on simple battery dispatch algorithm. Provided analysis show that investment in improving self-consumption is becoming attractive economical solution.

Historically, high voltage electric transmission facilities in the USA have been owned and operated primarily by incumbent utilities subject to state and federal certification, siting, and cost-of-service regulation. Federal and state regulation of transmission as a natural geographic monopoly continued after vertical and horizontal restructuring, the creation of competitive wholesale electricity markets, and the formation of independent system operators (ISO/RTO) (There is little difference between an RTO and an ISO. I will simply refer to both as an ISO in this paper. https://www.ferc.gov/industries/electric/indus-act/rto.asp.) to operate these markets and the associated transmission networks. The possibility of extending competition into the development, ownership, and operation of high voltage transmission projects has been a subject of interest to economists and other students of electricity sector restructuring for many years. A theoretical merchant transmission model where transmission investments are supported by tradeable transmission rights whose value reflects the difference in locational prices, attracted a lot of attention and excitement as restructured wholesale electricity markets matured in the 1990s. While this merchant transmission model is elegant, and in some ways, remarkable, when more realistic assumptions are introduced, the attractive attributes of the merchant model are seriously undermined. More importantly, this model has attracted very little interest in practice, especially in the US FERC Order 1000 issued in 2011 has stimulated increased interest in and use of competitive transmission procurement programs in the USA. This paper discusses the provisions of Order 1000, its application by ISOs, and examines the evidence to date regarding the use and attributes of the competitive transmission procurement model in the USA. The limited evidence to date is promising, though the diffusion of transparent head-to-head competitive procurement programs has been slow. Refinements can help to secure more of the potential benefits of competition in the future.

The trend on electricity grids digitalization is gradually leading to the shift of business value towards more sustainable and efficient electricity services. Sustainability and efficiency are challenged by the increasing demand for electricity which is followed by a dramatic transformation of energy systems. While smart grids seem to be crucial in this process, there is a discrepancy in understanding the costs and benefits for the multiple actors involved. In addition, there are benefits of smart grids that cannot be measured directly in terms of money, such as higher energy system reliability or commitment to carbon reduction. Despite the rise of interest to the managerial aspects of smart grids implementation and development, many aspects remain out of the scope. This paper contributes to the research of smart grids by providing a conceptualized business model that would allow for value co-creation, delivery and capture. A Russian energy sector perspective is primarily considered throughout the paper and the results are supported by evidence from interviews with of industrial experts.

In this paper, we analyze the interdependence between the structure of transmission infrastructure and the electricity mix, applied to the case of Germany today and into the future. In particular, we are interested in how an energy system based on a high share of distributed renewable sources operates under different transmission regimes, for example, copper plate or more constrained network topologies. We develop a stylized model of optimal generation and storage investment and operation, under different transmission expansion scenarios (current state, 2035 with and without HVDC lines, and copper plate). We take real data of the German electricity system, characterized by a particularly high share of distributed renewables, and select an extreme value for the future, that is, 100% renewable. Results suggest that the system can accommodate the high share of renewables, by installing a large amount of short-term and long-term storage capacities. Renewable electricity capacity investment is inversely related to the state of transmission expansion, and so is storage investment. The high level of granularity of the model also allows for a spatial allocation of renewable capacities, where wind is placed mainly in the north and solar PV rather in the south. The few cases of transmission congestion suggest that by expanding the grid modestly, a high share of renewable can be accommodated. The paper ends with a discussion of model constraints and further research ideas.

In this paper, a comparison is made between two bilevel programming models to design time-of-use tariffs in the electricity retail market. The upper-level objective function consists of the maximization of the retailer’s profit and the lower-level problem relates to the minimization of the consumer’s cost. In the first model, the periods in which prices apply are pre-defined and the aim is to determine the price values. In the second model, which is developed for the first time in this paper, both the periods and prices are decision variables, thus leading to a very large search space for the upper-level problem due to the number of combinations periods-prices. For the model with variable periods, a hybrid approach combining a genetic algorithm for the upper-level search with a mixed-integer linear programming solver to obtain optimal solutions to the lower-level problem is herein developed. Computational results comparing the two models are presented.

Building energy plants generate, consume and transfer a large amount of energy to deliver various services, such as heating, cooling, lighting and electricity. In large buildings, these energy plants can be complex to manage optimally. Decision support systems are useful tools for aiding managers, however, they too can also become overly complex. Participatory approaches for decision support system development and application are suggested in literature for overcoming this issue. This paper presents the foundations of a wider research project that is applying participatory techniques in the development of a decision support system at a hospital energy plant. A generic integrated building energy plant optimisation model is formulated and expressed using problem domain language with the aim of promoting participation from stakeholders, such as facility managers or maintenance personnel, that do not necessarily have modelling expertise. While the formulation is targeted towards modelling the plant’s operational behaviour and decisions, the paper describes how it can be used in a decision support system for aiding short-, medium and long-term decisions of facility managers – highlighting again the model’s flexibility to meet stakeholder requirements elicited using participatory techniques. The project’s case study site, Lady Cilento Children’s Hospital in Queensland, Australia, is introduced along with the planned methodology for participatory development of the decision support system. Finally, future directions are proposed for both further research and practical applications of the contributions.

This paper extends the service quality model by using the service blueprint theory. Through combing traditional perceived service quality indicators with the characteristics of service informationization, this paper adds two dimensions of “running once at most” and “information quality”, then, constructs 58 evaluation index systems. Based on data from the random questionnaires of all kinds of electricity users, the empirical results of the service quality model show that: Consumers pay more attention to “running once at most” and information quality. Based on this, author puts forward suggestion on improving service items such as “running once at most” and information quality as well as how to using this extending model.

This paper addresses the scenario reduction for stochastic optimization applied to short-term trading of photovoltaic (PV) power. Stochastic optimization becomes a useful technique when leading with problems involving uncertainty. Short-term trading of PV power in electricity markets is an example of a problem involving a high level of uncertainty, namely uncertain parameters as PV power and market prices. As the level of uncertainty raises and the optimization problem becomes more complex, the prerequisite of scenario reduction becomes crucial without losing the representativeness of the original scenarios. Thus, in this paper is proposed an effective scenario reduction algorithm based on backward method in order to obtain a profitable trading of PV power in electricity markets. The scenario reduction method is applied to a two-period scenario tree, i.e., a scenario fan including uncertainty on day-ahead market (DAM) prices, on imbalance prices and on PV power. Through a case study is analyzed the performance of the scenario reduction algorithm and the comparison with the original set of scenarios. The results show that the reduced set of scenarios still has a very high level of accuracy.

This paper presents a conceptual design of hybrid air-conditioning system which uses solar energy to heat/reheat water and generate electricity (solar collectors and photovoltaic panels), wind energy to produce electricity (small horizontal or vertical wind generator), and geothermal energy of water or soil in the building environment. The paper presents a pilot plant for hybrid air-conditioning system at the building of Faculty of Mechanical Engineering in Mostar and analyzes the efficiency and cost-effectiveness of system and its individual components.

Electricity bidding plays a key role in a restructured electricity market. This paper proposed an optimal method to minimize the system generation cost of active power produced by thermal generators in a photovoltaic (PV) integrated deregulated power market with considering demand side bidding. The proposed problem has been solved in two folds: initially PV power generator is optimally placed in the system based on the objective of minimum fuel cost and minimum system losses. Secondly one load bus has been selected for demand side bidding based on the demand of the buses and system requirements. To solve and validate this problem, Whale Optimization Algorithm (WOA) has been used in this work. To validate and compare the obtained results, Elephant Herding Optimization (EHO) Algorithm and Artificial Bee Colony (ABC) algorithm are used in this paper. The usefulness of these techniques is applied in IEEE 30 bus system for the analysis purpose.

A secure and reliable supply of electricity is a necessary requirement of a well-functioning economy. Reducing the likelihood of system disturbances or, in the extreme, load (demand) shedding is a fundamental objective of Transmission System Operators during planning and operation of electricity grids. The integration of new forms of generation - diverse in location and operation profile – as well as rapidly changing demand from new consumer technologies powered by breakthroughs in technology, poses a different set of challenges in their daily business. System flexibility, like many other aspects of the bulk electric system, can be examined as a supply and demand problem. A holistic assessment of flexibility examines the resources available to supply flexibility and the factors driving the demand or requirements for flexibility. This paper aims to highlight the importance of flexibility assessment studies alongside capacity adequacy studies being carried out during system planning. Moreover, the paper will present the challenges facing the electricity networks for integrating high amount of renewable energy which are well expressed by the views of the energy stakeholders regarding priorities for innovation technologies and this is also very well reflected in the research and development projects. The FLEXITRANSTORE project, which is aiming to implement innovative technologies of battery storage solutions, smart grid technologies and market platforms in the SEE region, is briefly presented. Furthermore, a wide review of flexibility-related research and innovation projects in Europe and United States has been carried out during the project and their contribution is presented, analysed and mapped against specific power system challenges. A detailed survey conducted during FLEXITRANSTORE on identifying stakeholders’ opinion on electricity networks’ challenges is presented and commented. Stakeholders’ are stressing that highest priorities for the electricity networks in their country are renewable energy integration, cross border interconnections, grid stability in short- and long-term horizon. These renewables integration and cross border interconnections answers could be also linked with the European targets: To sum up, the paper is identifying significant opportunities for future research and innovation in storage and market models, regarding the inherent flexibility of the electricity networks to host the electrification ‘wave’ of the following decades.

The FLEXITRANSTORE project inter alia aimed at improving the flexibility of the organized, wholesale electricity markets of Bulgaria and Cyprus fitted into the European electricity market framework. The paper introduces the market settings of these countries and defines the intraday time horizon as a focus for the research. The authors take into account the needs of the different market participants in order to increase the flexibility of the electricity market. After a review and evaluation of the currently available spot market products and orders, the authors propose the introduction of new orders and products on the intraday market so that the trading capabilities of storage and DSM-based technologies are facilitated. The suggestions would help the integration of variable renewable generation as the participation of new flexibility providers such as storage owners and aggregators are facilitated. The most significant proposals of the authors are the volume constrained and the cumulative volume constrained order types that are realized by proposing a new set of execution constraints for existing basket orders. The paper also highlights the advantage of the novel orders compared to the recent product developments of power exchanges.

Credit management system is an important part of electricity market construction. Firstly, this paper expounds the necessity and basic requirements of credit management in electricity market. Then it comprehensively compares and analyses the credit management system of PJM, ERCOT and Guangdong electricity market. On the basis of the above research, the paper takes full account of the factors such as market risk management system, types of electricity transactions, market settlement cycle and so on. The key factors and the general rules to be followed in the design of electricity market credit system are analyzed for four aspects: credit limit requirements, calculation of unsecured credit limit, calculation of guaranteed credit limit and credit risk assessment. Finally, regarding to China’s national conditions, the paper puts forward some opinions and suggestions on the construction of credit management system in China’s electricity market for four parts: the construction of credit rating system, the establishment of risk coverage period and risk calculation index, the acquisition method of guarantee credit line and the object of credit management.

Conventional grid moves towards Smart Grid (SG). In conventional grids, electricity is wasted in generation-transmissions-distribution, and communication is in one direction only. SG is introduced to solve prior issues. In SG, there are no restrictions, and communication is bi-directional. Electricity forecasting plays a significant role in SG to enhance operational cost and efficient management. Load and price forecasting gives future trends. In literature many data-driven methods have been discussed for price and load forecasting. The objective of this paper is to focus on literature related to price and load forecasting in last four years. The author classifies each paper in terms of its problems and solutions. Additionally, the comparison of each proposed technique regarding performance are presented in this paper. Lastly, papers limitations and future challenges are discussed.

Hospitals are one of the most energy-consuming commercial buildings in many countries as a highly complex organization because of a continuous energy utilization and great variability of usage characteristic. With the development of machine learning techniques, it can offer opportunities for predicting the energy consumptions in hospital. With a case hospital building in Norway, through analyzing the characteristic of this building, this paper focused on the prediction of energy consumption through machine learning methods (ML), based on the historical weather data and monitored energy use data within the last four consecutive years. A deep framework of machine learning was proposed in six steps: including data collecting, preprocessing, splitting, fitting, optimizing and estimating. It results that, in Norwegian hospital, Electricity was the most highly demand in main building by consuming 55% of total energy use, higher than district heating and cooling. By means of optimizing the hyper-parameters, this paper selected the specific parameters of model to predict the electricity with high accuracy. It concludes that Random forest and AdaBoost method were much better than decision tree and bagging, especially in predicting the lower energy consumption.

In order to effectively solve the problem that the offshore island energy supply system can not be applied to the near land island, the micro energy network optimization configuration model under the typical scenes of the near land island is constructed, and the specific load data is used to configure the capacity of the main equipment in the scheme. The model satisfies the problems of electricity load, heat load and desalination. The objective function of the model is the minimum operating cost and the maximum customer satisfaction. Considering the constraints of various types of power generation and energy storage, thermal power balance and desalination, particle swarm optimization algorithm is used to solve the problem. Taking a micro energy network of an island as an example, this paper simulates the micro energy network of cogeneration type, and makes a comparative analysis with the common micro energy network of the island. The results show that the optimized configuration model of the micro energy network of the near land island studied in this paper can effectively provide the power load and heat load for the users, improve the satisfaction of the users, and thus improve the economy of the micro energy network.

Now days electricity has become the most demandable thing for the human beings. Due to continuous depletion of the conventional energy resources the demand for the use of nonconventional energy resources has increased. In the present paper we have used non-conventional energy resources i.e. solar energy and wind energy for generating uninterrupted power supply for the consumers. This paper comprises of combination of two sources of energy that will provide uninterrupted power supply to the system. Solar panels and wind turbines together have been used for converting the respective energies to the electrical energy. In this paper generation of electrical energy by combining two non-renewable sources at minimal cost and by not affecting the natural environment.

This paper brings into the picture the perspective of the energy regulator. It casts light on the challenges associatsed with the task of guaranteeing a number of medium-to-long-term objectives such as an adequate flow of investment in infrastructure, development of well-functioning markets, and a balance between private profit and the general interest. In particular, the paper addresses the concern of fostering long-lasting, “future-proof” investment in a sector that has been characterised by intense technological change and fast-changing market conditions—both sources of major uncertainty as to the effects that regulatory decisions will have on future developments within the sector. Drawing on the author’s personal former experience as president of the Italian regulatory agency from 2011 to 2018, the paper illustrates and assesses past regulatory action with reference to (i) reform of the gas market, (ii) adoption of a new generation of smart meters, (iii) reform of the electricity retail market, (iv) and integration of RES into the electricity market. The review illuminates the multifaceted circumstances that have fostered—or hindered—successful forward-looking regulation.

In view of the applicability of accident causation models to different types of accidents, this paper comprehensively considers various kinds of accident causation theories, compares, and analyses seven kinds of accident causation models, and points out the limitations of existing models. Then, according to the mechanism of electricity falling accident, combining the theory of Bode accident causality chain with the theory of man–machine–environment system, this paper presents an accident causation model suitable for the analysis of electricity falling accident, which has a strong guiding significance for the prevention of electricity falling accident. Finally, the management improvement measures are put forward from the aspects of safety operation standard, safety production responsibility system, safety education, and training, in order to prevent the recurrence of electricity falling accident.

Current electricity transmission and distribution networks can classified as conventional electricity networks because they have not been able to provide excellent services and present real-time data. This network has not been able to provide reliability, safety and efficiency in supplying electrical energy even not yet have the flexibility to be integrated with the generation of renewable energy or micro-grid. In this paper, a comprehensive review has been conducted to map the literature studies to a coherent taxonomy of Smart Energy grid. The fundamentals of smart grid presented in this paper can benefit readers who wish to embark in smart energy grid research and applications development.

This paper presents an alpha stable regression model to estimate prices in the Mexican Electric Market. This market began operations in February 2016. The observed prices show great fluctuations in the observed data due to diverse aspects, a seasonality of the demand, the availability of fuel and the problems of congestion in the electrical network. It is relevant in a market context to have a price estimation as accurate as possible for the decision making of supply and demand. This paper proposes a methodology of the price estimation through the application of stable alpha regressions, since the behavior of the electric market has shown the presence of heavy tails in its price distribution.

Being a tropical country, India has huge potential of sustainable energy tanks like solar, wind and micro-hydro energy to deliver electricity to the remote areas in a stand-alone mode. The nonlinear nature of renewable energy sources poses severe issues to the regulation of outcome capacity in order to meet the load demand. To mitigate these reliability issues, Diesel generators (DGs) along with devices capable of storing electrical energy may be utilized to compensate the fluctuations in renewable energy supply. This paper elaborates the analysis of optimized hybrid sources (combination of solar powered energy system, wind energy system, micro-hydro power, DG and battery) to enable the remote areas without the provision of grid power supply to enjoy the benefits of electricity. In this paper “Interior point” algorithm is employed in optimization solver “fmincon” as it is able to resolve complex programming problems with huge amount of data with speed by considering all the constraints.

Global demand for electrical energy is in constant growth because of the constant increase in the number of inhabitants and the economy growth. This paper presents the way the increasing demand for energy and the constant increase in the capacity of power plant production systems exacerbates the working conditions of the system parts and leads to the remaining working life reduction. Efficient operation of thermal power plants is the basis of economical production of electricity based on combustion of lignite. Ventilation mill as such is one of the basic equipment in thermal power plants which work has a significant impact on the level of energy efficiency. This paper presents the complexity of the optimization problem of the different thermal power plants and the system demands to use multidisciplinary research. However, any optimization of an energy plant, such as a ventilation mill, in this case, represents a unique approach to the problem of operating efficiency of the plant, and as such it asks for many new requirements and solutions. This paper also presents the way for solving this complex problem by using detail analyses of work of system of ventilation mill and dust channel by analysing the period of failures. This paper describes the following: the usages of CFD numerical simulation in FLUENT software for determining the working conditions in ventilation mill and dust channel; possibilities for use the process of coating damaged parts with wear resistance material and procedure for selection and coating on real working parts; analyses of damaged coated working parts of ventilation mill after testing in exploitation condition; data analysis of working parts before and after coating, time and time and resource saving. One of the main goals is to analyse the damaged coated working parts of ventilation mill after testing in exploitation condition.

The electricity use is a significant part of human’s environmental footprint. Fossil energy is still used as the major energy resource for power generation. Policy makers seek a remedy to mitigate carbon emissions of fossil fuels. A virtual carbon emission tracing method is discussed in this paper. This paper facilitates allotting carbon obligation in power systems.

It has become a major point of interest and possible bone of contention that in a future near-decarbonised electricity network, i.e. approaching 100% reduction in emissions by 2050, a low carbon space heating market will be enabled through the deployment of electrically driven heat pumps. It is recognised that such a vision will place a significant extra burden on the existing electricity network. This will be especially true of the low-voltage network at Distribution System Operator level, e.g. 11kV or below, but as such systems do not operate in isolation, challenges will also be seen at the Transmission Operator level as well. This may be further complicated by the perceived rapid deployment of electric vehicles well before 2050. Building insulation programmes will reduce heating loads, but experiences have illustrated that current UK best practice lags somewhat behind our European counterparts. Energy demands for heat pumps would therefore reduce and efficiencies would increase depending on the depth of retrofit. The rates of retrofit may require 2050 targets to be met by active energy systems rather than the passive roles of insulation. Demand-side management in its many shapes and forms becomes part of the solution. The thermal mass of buildings can be deployed to understand duration of off-times after a period of heating while maintaining thermal comfort. Understanding end-user behaviour is critically important in this process as it is believed that the smart learning of the behaviour of the building can be subtly tailored to end-user needs. Selective fabric retrofits and selective occupancy controls can adjust these parameters, further giving an element of control to the end-user while satisfying electricity network operator needs. The deployment of energy storage is also an option. Heat pumps and water storage (or sensible heat) trials and experiments reveal both noticeable heat losses from the tanks and large sizes of tanks for significant scales of time management. Finding the space for larger tanks in typical UK housing types would be challenging. However, aggregation of large numbers of these units that are electricity network controlled (at the distribution operator level for example) has the potential to reduce individual storage volumes, thus potentially realising greater social acceptance. Novel materials, e.g. phase change or thermochemical, will deliver further reductions in size, and low-temperature thermal networks may upgrade heat pump performance and increase energy efficiency. Hybrid heat pumps or gas-driven types (adsorption or absorption) will utilise existing gas networks that may be increasingly decarbonised with biogas or hydrogen deployments. Domestic and local photovoltaic (PV) system deployment would provide electricity network relief when accompanied by energy storage. Thermal storage will satisfy thermal comfort needs when operating with heat pumps while battery deployment may operate with electric vehicles and/or electric heat pumps at times of local grid congestion. Such batteries and electric vehicles will also become demand-side response units in their own right, the latter whether you are at home or not, with vehicle-to-grid technology. This paper will link these aspects and others into viable pathways to the decarbonisation of domestic space heating in the UK. It will highlight current progress and research gaps and offers some recent comparisons with Ireland where biogas may offer an alternative scenario.

Solar energy has been accepted as one of the sources of energy of today. One of the technologies is photovoltaic-thermal (PV/T) hybrid solar technology. It is one of solar energy technologies which has the potential for future energy applications. New technology developments in solar energy utilization are expected to result in the improvement of both electrical and thermal performances of the collectors at lower production cost. The current popular technology converts solar energy into electricity and heat separately. The effort has been made to generate electrical and thermal energy simultaneously by using photovoltaic-thermal (PV/T) hybrid system. The synchronization of PV/T system led to the development of PV/T air-based and PV/T water-based systems and PV/T with nano-fluid-based systems. This paper presents an overview of PV/T systems, including the photovoltaic-thermal combination system (PV/T Combi), with a combination of photovoltaic panel with air- and water-based systems as one unit. This paper also describes our experience in introducing the system for cottage industry in Malaysia. The research activities on the PV/T systems were carried out at the Solar Energy Research Institute (SERI), Universiti Kebangsaan Malaysia, since the early 1980s.

Large reserves and worldwide availability of coal indicate that this energy will play an important role for electricity generation in the future even if there is a significant increase in production from renewables. Besides that, the electricity market is open and the result is competition between the market actors. The goals of advanced thermal power plants are using as less as possible input energy to generate electricity as much as possible and reducing the CO2 emission. In thermal power plants, 7–15% of the generated energy on the generator does not reach the power plant’s threshold because it is geared back to pumps, fans and other auxiliary power systems. Given the fact that each MWh is important today, it is clear that auxiliary power systems of advanced thermal power plants must be energy efficient. In this paper contemporary regulated auxiliary power systems of advanced thermal power plant “Stanari” are presented. After an introduction, fresh air fan, flue gas fan and feed water pump analysis are presented. In addition, the paper analyzes the total power change of auxiliary power systems, depending on the change of the generator power. The most important findings are summarized at the end of the paper.

The development of distributed energy resources (DER), particularly renewable energy sources (RES) and local energy storage systems (ESS), leads to non-hierarchical and distributed structures. On the other hand, the advent of efficient technologies, such as combined heat and power (CHP) production, leads to the integration of energy infrastructure, such as electricity, natural gas, and district heating networks which create a multi-energy system (MES). Energy hub is a promising option for integrated management of MESs. This paper endeavors to outline a realistic model of future sustainable energy systems by integrating RES and taking into account multiple uncertainties. This paper provides a framework for cooperative performance between integrated energy hubs as multi-agent systems (MAS) for distributed management and energy exchange in the presence of RES, controllable loads, and modeling multiple uncertainties. This is a framework that can be considered for future sustainable energy systems, and the main objective of this study is to realize such cooperation of these systems.

The European Union has been one of the major drivers of the development of renewable energy. In Portugal, renewable generation is subject to specific licensing requirements and benefits from a feed-in-tariff. This paper pays special attention to wind and hydroelectric technologies. Typically, wind farms produce more energy during the night (off-peak periods), when the demand is lower, contributing to a reduction of the market price. Hydroelectric power plants use off-peak periods to pump water, and produce energy in the periods of a 24 hour day where the prices of electricity are higher (peak periods). This paper presents a case study aiming at analyzing the behavior of hydroelectric power producers—that is, in power systems with large renewable generation, producers typically use the periods of the day with lower energy prices for pumping, and the other periods (with higher energy prices) to produce electricity. The simulations are performed using MATREM (for Multi-Agent Trading in Electricity Markets). The results confirm (and rebate) the typical behavior of hydroelectric power producers. info:eu-repo/semantics/publishedVersion

The paper presents a study of the development of regional electric power space. The authors studied the main stages of electric energy infrastructure development in the context of technological advances and features of the territory it is possible to define a place and a role of energy infrastructure in spatial development of a region. The authors define the concept of “infrastructural energy space of a region”. The paper presents a basic features of electric power space of a region influence its spatial development. The structure, the composition of the Unified Energy System of Russia (UES of Russia) has been studied. The authors studied the dynamics of indicators of accessibility of energy infrastructure and the efficiency of the procedures for connecting electricity to Russia that enabled the formulation of the relevant strategic tasks, the tendencies, problems of development of the regional electric power space - asymmetry of spatial distribution, extensive infrastructure, increasing unevenness of development and determined direction for its solution - stimulation of economic development in the regions, equalization of power supply to population, overall optimization of power consumption.

With the widespread use of electricity since the reform and opening up, people have become more and more dependent on electricity in production and life. As a main part of the power grid, the operation and maintenance of high-voltage transmission lines have a direct bearing on the stability of people’s use of electricity and the degree of power supply. It is necessary to set the operation and maintenance management mode of high-voltage transmission lines. Its regular maintenance and operation can ensure the safety and stability of the grid to a large extent, and to ensure the stability and security of electric energy. This paper will analyze the current situation of maintenance and management of high-voltage transmission lines in China to find out the unreasonable phenomena existing in the current operation and maintenance. This paper will also study a set of more practical maintenance and management mode of high-voltage transmission lines, aiming to improve the maintenance of high-voltage transmission lines and to bring people a better life experience.

Sustainability is strongly related to the appropriate use of available resources, being an important cornerstone in any company’s administration due to the direct influence on its efficiency and ability to compete in the global market. Therefore, the intelligent and proper management of these resources is a pressing matter in terms of cost savings. Among the possible alternatives for optimisation, the one regarding electricity consumption stands out due to its strong influence on the expenses account. In general, this type of optimisation can be carried out from two different perspectives: one that concerns the efficient use of energy itself and the other related to the proper adjustment of the electricity contract so that it meets the infrastructure needs while avoiding extra costs derived from poorly sized bills. This paper describes the application of an artificial intelligence based methodology for the optimisation of the parameters contracted in the electricity tariff in the Spanish market. This technique is able to adjust the power term needed so that the global economic cost derived from energy consumption is significantly reduced. The papers discusses the impact that this proposal may have on a demand response scenario associated to load shifting practices within university buildings. Furthermore, the role of human beings, specifically university employees, and their actions towards reducing the overuse of power consumption at the same time is also addressed.

Electricity consumption has been on a rapid increase worldwide and it is a very vital component of human life in this age. Hence, reliable supply of electricity from the utility operators is a necessity. However, the constraints that electricity supplied must be the same as electricity consumed puts the burden on the utility operators to make sure that demand is equal to supply at any point in time in smart and connected communities. Load forecasting techniques, therefore, aim to resolve these challenges for the operators by providing accurate forecasts of electrical load demand. This paper reviews current and mostly used short term forecasting techniques, drawing parallels be-tween them; and highlighting their advantages and disadvantages. This paper concludes by stating that there is no one-size-fits-all technique for load forecasting problems, as appropriate techniques depend on several factors such as data size and variability and environmental variables. Different optimization techniques can be used whether to reduce errors and its variations or to speed up computational time, hence resulting in an improved model. However, it is imperative to consider the tradeoffs between each model and its different variants in the context of smart and connected communities.

Electricity markets are systems for affecting the purchase and sale of energy. Most existing markets are built on well-established principles of competition and transparency. However, their designs are based on centralized power plants with a small participation of end-use customers. During the past years, the share of electricity produced by renewable sources increased significantly. This paper analyses the structure and operation of two European markets and two American markets. The analysis highlights that the design, rules and characteristics of most markets are still not completely adapted to power systems with high levels of variable renewable energy. Accordingly, the paper proposes some recommendations to foster the integration of renewable generation. info:eu-repo/semantics/publishedVersion