Major Consumers Of Electricity Research Articles

Nuclear microreactors: Status review and potential applications; the Mexican case

Microreactors are a class of small modular reactor with a typical power output up to 10 MWe. Their factory fabricated, transportable, and self-regulating design makes the microreactors suitable for supplying electricity to off-grid communities, emergency situations, and space and naval applications. Likewise, microreactors are appropriate for non-power applications such as hydrogen and synthetic fuels production, high and low temperature process heat, various industries (mining, cement, etc.), district heating, and water desalination. Many microreactor concepts are under development around the world with different nuclear technologies, such as the Micro Modular Reactor, a high temperature gas-cooled reactor; Aurora, a liquid metal-cooled fast reactor; and eVinci, a heat pipe-cooled reactor, among others. A recent market assessment of microreactors, conducted by the Idaho National Laboratory, identifies five potential markets for microreactors: Isolated Operations, Distributed Energy, Resilient Urban, Disaster Relief, and Marine Propulsion. In the case of Mexico, which remains dependent on fossil fuels for its energy supply (oil covers 44% of the energy source, while natural gas covers 38% and coal 3%), all potential markets are attractive to consider deploying microreactors, particularly Isolated Operations and Distributed Energy, due to its industrial sector and remote/isolated communities. Mexico’s mining cement, and iron and steel industries are the major consumers of electricity, which is generated primarily with fossil fuels. Similarly, oil accounts for 99.7% of the energy mix of the national transport sector. Furthermore, isolated regions in Mexico, such as the state of Baja California Sur, which generates its own electricity mainly with natural gas, fuel oil, and diesel, could implement microreactors, not only as a potential solution for their electricity supply, but also to implement cogeneration application as water desalination.

First of its kind in Hong Kong - innovative reuse of treated effluent and enhanced energy efficiency for air-conditioning systems

In Hong Kong, air-conditioning (A/C) systems are major consumers of electricity. Although water-cooled air-conditioning systems (WACSs) are more energy efficient, the need for water towers, water treatment and other operation and maintenance (O&M) demands restrain their application in most buildings. This paper reviews a pilot application of treated effluent from sewage treatment works as a cooling medium for the Direct Expansion type of A/C system within the treatment Plant. The Drainage Services Department (DSD) installed a WACS with a self-assembled effluent-cooled condenser to study the energy efficiency, cooling performance and actual O&M requirements of the new system over a one-year pilot. It revealed that an energy saving of up to 27.6% per year could be achieved when compared with a conventional air-cooled air-conditioning system (AACS). This innovative effluent cooling A/C system achieves innovative reuse of treated effluent as well as reduction in carbon footprint as a result of higher energy efficiency for sustainable development.

REVIEW OF ENERGY AUDIT AND BENCHMARKING TOOLS TO STUDY ENERGY EFFICIENCY THROUGH REDUCING CONSUMPTION IN WASTEWATER TREATMENT SYSTEMS

Wastewater treatment systems are major consumers of electricity being responsible for 3 to 5% of global energy consumption, and 56% of greenhouse gas emissions into the atmosphere in the water treatment sector. Climate change currently imposes the definition of a new pattern of human behavior in the defense and sharing of a common space that is the planet, so the optimization of water treatment models plays a crucial role in the definition of sustainability strategies as part of the challenges for decarbonization by 2050. The physical-chemical characteristics of the influent, the treatment techniques and associated technologies and the unpredictability of external phenomena of inefficiency transform wastewater treatment plants (WWTPs) into complex systems, sometimes difficult to understand. The study of energy efficiency plays an important role in the emergence of a standard behavior model, which allows the correction of unbalanced situations in the expected energy consumption. Given the importance of the topic, the present review aims to study energy auditing techniques and benchmarking tools developed for the wastewater treatment sector to reduce the current electricity consumption, which could represent up to 90% of total energy consumption. The result of the research was organized according to the criteria defined for the characterization of auditing techniques and benchmarking tools. A review was conducted from 51 scientific papers from different reference research platforms published in the last 20 years according to the keywords. This literature review has shown that there are, in the classification of consumption reduction, energy auditing and benchmarking tools; energy management techniques and methods directed to the energy efficiency of the treatment stages and specific equipment; and, finally, decision support tools. According to the methodology followed, it was possible to conclude that although the concern is not recent, there are techniques and tools for assessing energy performance more suitable for the wastewater sector. However, the authors recognize that associated with the complexity of wastewater treatment systems, inefficiency phenomena still strongly impact energy efficiency assessment, so the contributions for their identification and quantification may represent an added value for data analysis, systematization, and optimization methodologies.

Influence of Geodemographic Factors on Electricity Consumption and Forecasting Models

The residential sector is a major consumer of electricity, and its demand will rise by 65 percent by the end of 2050. The electricity consumption of a household is determined by various factors, e.g. house size, socio-economic status of the family, size of the family, etc. These factors play a critical role in analyzing the energy consumption causalities in the residential sector for better energy prediction models, effective price policy implementations, and improved customer engagement in energy efficiency programs. However, determining the effect of demographic factors on energy consumption is a challenging prospect. First, it is not trivial to study the causes of energy consumption variation, even for similar size residential houses, without analyzing the impact of interdependencies between demographic factors on energy consumption behavior. Second, to achieve higher accuracy of energy prediction models, it is necessary to identify key geodemographic factors that influence these models. Previous studies have only identified a limited number of socio-economic and dwelling factors. In this paper, we study the significance of 826 <i>geodemographic</i> factors on electricity consumption for 4917 homes in the City of London. <i>Geodemographic factors</i> cover a wide array of categories e.g. social, economic, dwelling, family structure, health, education, finance, occupation, and transport. Using Spearman correlation, we have identified 354 factors that are strongly correlated with electricity consumption. We also examine the impact of using <i>geodemographic</i> factors in designing forecasting models. In particular, we develop an encoder-decoder LSTM model which shows improved accuracy with geodemographic factors. We believe that our study will help energy companies design better energy management strategies.

Cooling seasonal performance of inverter air conditioner using model prediction control for demand response

The supply-demand imbalance of electricity increases the operating burden on smart grids, decreases the average efficiency of power generation equipment, and threatens the safe operation of power grids. Residential air conditioning is a flexible load and a major consumer of electricity. Therefore, demand response control can be applied to air conditioners (ACs) to shift their peak energy consumption and save energy. Model predictive control (MPC) is an effective demand response control method. In this study, we analyze the cooling seasonal performance of an inverter AC with MPC. A time-varying MPC was designed and evaluated using a simulation testbed that was constructed using MATLAB. Subsequently, the energy, cost, and temperature control performances of the MPC were analyzed in detail from electricity pricing model, weather conditions and fluctuation of real-time price. The results show that compared to the proportional–integral–derivative (PID) control method, MPC can shift the peak-hour energy consumption by 6.34%–21.60% and reduce the total electricity costs by 13.44%–27.43%, while maintaining indoor thermal comfort during the whole cooling season, and Demand response with MPC control is very suited to hot weather conditions with highly fluctuating RTP. By applying MPC hybrid demand response under real-time price, there are better performances on peak shifting and cost saving.

Behavioural interventions yield electricity savings among high-income households in Johannesburg, South Africa

The societal benefits of addressing wasteful electricity use practices through behavioural interventions are now well-established. Surprisingly, in South Africa, where the economy is highly dependent on fossil fuel (coal) for electricity generation, this subject remains little studied and understood. The residential sector is a major electricity consumer, and high-income households, in particular, use a substantial proportion of total electricity with serious adverse impacts on grid stability and the environment, which can disproportionately affect the poor. Using a field-based experiment, this study examines the impact of behavioural interventions on household electricity savings and the determinants of success among high-income households in Johannesburg, South Africa. Over the intervention period, households exposed to a combination of electricity-saving information, frequent reminders and feedback on monthly electricity-saving performance showed mean electricity savings of about 1.5%, ranging from 2% to 4% of electricity, while households in the control group showed increased electricity consumption by approximately 11%. Out of all the demographic and personal value factors considered, age, achievement and benevolence promoted electricity savings, while household size, number of rooms, baseline electricity consumption and security inhibited savings. The findings empirically validate the impact of behavioural interventions on, and the positive influence of, personal values in promoting participation in electricity-saving actions within households.

Operational flexibility of active distribution networks with the potential from data centers

With the development of information technology, the scale and quantity of internet data centers (IDCs) are expanding rapidly. IDCs have emerged as the major electricity consumers in active distribution networks (ADNs), which dramatically increase the electricity load and have a significant impact on the operational flexibility of ADNs. Geographically distributed IDCs can participate in the operation of ADNs with the potential for spatio-temporal load regulation. This paper proposes flexible dispatch strategies of data centers to improve the operational flexibility of ADNs. First, a data-power model of IT equipment is proposed based on piecewise linearization to describe the power consumption characteristics of data centers. The flexible dispatch strategies for the delay-tolerant workload are further proposed from two aspects of temporal transfer and spatial allocation. Then, considering the potential for spatio-temporal load regulation, the operational flexibility analysis model with data centers is formulated to adapt to the operational requirements of ADNs in complex environments. Case studies show that through the spatio-temporal regulation of workload, the energy efficiency of IDCs can be effectively improved. The flexible dispatch of IDCs can also reduce the voltage violation and feeder load imbalance of ADNs, which can facilitate providing the high-quality power supply for IDCs.

Concise overview of BIPV systems and its future scope

Buildings are considered as a major consumer of electricity. It is said because 40% of the total energy produced is utilized by the buildings. Harnessing the solar energy by means of photovoltaics is seen as sensible option to meet out the buildings demand entirely or partially. Recent trends has seen BIPV systems as a viable option that not only enhances the aesthetics of the building but at the same time meets out the buildings energy demand. Apart from the aesthetics and electricity production, BIPV, which acts as an integral component of the buildings, reduces solar heat gain at the same time allowing daylight to enter into the building. This paper discusses the different cell technologies used in solar BIPV systems, different types of PV modules used for building integration, orientation and tilt angle optimization of panels, performance analysis with different case studies, cooling methods adopted in this technology and the main barriers that slows down the development of this system. Payback periods is also studied and discussed in this paper.

An Artificial Intelligence Solution for Electricity Procurement in Forward Markets

Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems.

Android-based energy saving control system integrated with islamic value on lamp switching power

Lighting is one of the major consumers of electricity and it is often found to be inefficient in its use. Therefore, this study aimed to provide solutions to these problems through energy-saving behavior that is integrated with Islamic values on lamp switching power. This study use system design build methods. This design results in the form of hardware and software assembly. The results of the performance test of the integration of hardware and software devices provides informations as follow: first, control system of application has not been able to operate on all mobile platforms. Second, the connection range of the control system is able to operate according to its standard frequency range. Third, the connection access of control system is able to pass through the barrier which has a small inhibitory. Fourth, the success of the control system in executing input is high.

Demand-side management in the Smart City concept

In the context of the widespread adoption of information and communication technologies, the key objectives are to enhance the effectiveness of infrastructure management in certain agglomerations and cities, as well as to optimize the use of municipal energy resources. The paper deals with the opportunities and avenues for the use of price-dependent electricity demand management in today’s wholesale and retail electricity markets in Russia within the framework of the Smart City concept. Methodologically, the paper rests on the principles of macroeconomics and the Smart City concept. In the study, the methods of analysis, synthesis, systematization, statistical observation, etc. are applied. The information base of the research includes empirical data on the parameters of uneven electricity consumption by various municipal facilities. The research results show that city electric vehicles, city lighting, pumping stations, electric heating systems, etc. are major electricity consumers, which allow managing the costs incurred in purchasing electric resources at municipal level. The authors develop a model and an algorithm for price-dependent electricity consumption management of municipal facilities in the Smart City concept and test them using the case of the trolleybus network of the city of Chelyabinsk. The study proves the expediency of performing price-dependent electricity consumption management of municipal infrastructure in the Smart City concept.

Reducing the web's carbon footprint: Does improved electrical efficiency reduce webserver electricity use?

Webservers are major consumers of electricity and, therefore, offer important opportunities for energy conservation. Server electrical efficiency has increased dramatically in recent years, suggesting that technological innovation can curtail electricity consumption. However, over a century ago, Jevons noted reasons to expect that technologies that increase the efficiency of resource use, paradoxically, can increase consumption of those resources. Here, we investigate the extent to which recent gains in server efficiency have translated into lower electricity use. We use the Standard Performance Evaluation Corporation's dataset on the electrical consumption and efficiency of over 600 server models tested between 2007 and 2019 to identify the extent to which improvements in electrical efficiency reduce server electricity use (watts) or increase server performance (operations per second). Our analysis estimates that server design innovation tends to favor the latter over the former. Electricity reductions typically equal one-quarter to one-third of a given improvement in electrical efficiency, suggesting a conservation-offsetting “rebound” but not one large enough to constitute a Jevons paradox in which efficiency actually increases resource use.

Analyzing factors necessitating conservation of energy in residential buildings of Indian subcontinent: A DEMATEL approach

India is experiencing challenges in meeting the national energy demand due to various reasons. The demand from Residential Buildings is one among the highest due to various factors. These buildings are one of the, major consumers of electricity in India. Rapid urbanization and development has led the way for construction of new buildings in urban and rural India. The building energy demand is also on rise and cause burden on the present energy generation system. These residential Buildings are consuming energy at a faster rate than it is generated. This consumption trend is continuously growing every day. Therefore, to conserve energy in residential buildings the factors responsible should be analyzed. This research is one such attempt to analyze the factors that necessitate the conservation of energy in residential buildings. The results show that, among the fifteen factors, Population explosion and industrialization are top two major factors that necessitate the cause for conservation of energy in residential buildings. Among the most effected factors are Government regulation and utility bills. For better analysis and to study interrelation of factors, Multiple Criteria Decision Making MCDM based DEMATEL methodology is applied to arrive at results. The results of this research could be applied in designing energy systems for residential buildings and can serve as a guide to energy managers and facility administrators in residential towers.

Application and analysis of a model based controller for cooling towers in compression chiller plants

Cooling towers or recoolers are one of the major consumers of electricity in a HVAC plant. The implementation and analysis of advanced control methods in a practical application and its comparison with conventional controllers is necessary to establish a framework for their feasibility especially in the field of decentralised energy systems. A standard industrial controller, a PID and a model based controller were developed and tested in an experimental set-up using market-ready components. The characteristics of these controllers such as settling time, control difference, and frequency of control actions are compared based on the monitoring data. Modern controllers demonstrated clear advantages in terms of energy savings and higher accuracy and a model based controller was easier to set-up than a PID.

Determining key drivers of efficient electricity management practices in public universities in Southwestern Nigeria

PurposeUniversity campuses are one of the major consumers of electricity. Therefore, it is important to investigate factors related to electricity saving. This study aims to examine the key drivers in achieving efficient electricity management (EEM) practices in public universities.Design/methodology/approachTo achieve the objective, 23 drivers of EEM practices were identified through a comprehensive literature review and an empirical questionnaire survey was performed with 1,386 electricity end-users of three public universities having staff and students’ halls of residences in Nigeria. The collected data were analyzed using the statistical package for social sciences (SPSS version 21) to identify the number of components that could represent the 23 identified drivers.FindingsThe relative importance index ranking results indicated that 18 drivers were critical. The top five most critical drivers were understanding of the issues, understanding the vision and goal of an energy management programme, knowledge and skill, risk identification and good and effective communication among relevant stakeholders. An exploratory factor analysis revealed that the underlying grouped drivers were raising awareness, top management support and robust energy management team, risk management and stakeholders’ participation. This study also indicates that the most dominant of the four underlying groups was raising awareness, which highlights the role of increasing awareness and public consciousness as a significant catalyst in promoting EEM practices in public universities.Research limitations/implicationsGeographically, this study is limited to the opinion of respondents in public university campuses in Nigeria. Although this study could form the basis for future studies, its limitation must be considered carefully when interpreting and generalizing the results.Practical implicationsThis paper has highlighted a few drivers of EEM practices in public universities. The results of this study present scientific evidence that can be used as a basis for formulating public policies that could be incorporated into the energy management regulations of university buildings. It is most important for policymakers to pay adequate attention to the most critical drivers especially those that are related to the “raising awareness” factor to promote sustainable campuses.Originality/valueThis study provides practical knowledge for university management to develop effective methods to implement the identified drivers of efficient and sustainable electricity management on the campus. This study also contributes to the body of knowledge in the field of energy management.

Simulation-Based Optimization of Building Orientation for Optimum Electricity Consumption of Indian Residential Buildings

The demand for electrical energy had grown tremendously over the last few decades. The 2019 annual report by electricity sector of India shows that India is the third largest consumer of electricity in the world. Studies revealed that buildings are major consumers of electricity, in which residential buildings consumes the most. Tremendous increase in electricity demand are anticipated in Indian residential sector in the coming years due to development plans such as Power for all policy. Studies depicted that residential buildings have an energy saving potential of about 30-50% and most of the previous researchers had focused only to replace the electrical appliances in the constructed facility and had ignored the design features of the building to minimize electricity consumption. The present study therefore focused on reducing the energy use in residential sector of India by simulation-based optimization of buildings at its design stage. Design Builder software version 6.1.0.006 with Energy Plus simulation engine 8.9 was used for the study. A two storied residential building in Kochi, Kerala was considered for the study. The building model was validated using previous electricity bills and was then retrofitted using Energy Conservation Building Code (ECBC). The model was simulated for eight different orientations for different cases in five different climatic zones in India. East orientation was found to be the best orientation for all cases except cold climatic zone. The percentage saving in annual electricity consumption and cost of electricity bills that could be saved by opting the best orientation were also found.

Multistage stochastic demand-side management for price-making major consumers of electricity in a co-optimized energy and reserve market

In this paper, we take an optimization-driven heuristic approach, motivated by dynamic programming, to solve a class of non-convex multistage stochastic optimization problems. We apply this to the problem of optimizing the timing of energy consumption for a large manufacturer who is a price-making major consumer of electricity. We introduce a mixed-integer program that co-optimizes consumption bids and interruptible load reserve offers, for such a major consumer over a finite time horizon. By utilizing Lagrangian methods, we decompose our model through approximately pricing the constraints that link the stages together. We construct look-up tables in the form of consumption-utility curves, and use these to determine optimal consumption levels. We also present heuristics, in order to tackle the non-convexities within our model, and improve the accuracy of our policies. In the second part of the paper, we present stochastic solution methods for our model in which, we reduce the size of the scenario tree by utilizing a tailor-made scenario clustering method. Furthermore, we report on a case study that implements our models for a major consumer in the (full) New Zealand Electricity Market and present numerical results.

Wall roughness influence on the NPSH characteristics of centrifugal pumps

Pumps are very important and very widespread energetic machines. They are used in all branches of industry. Pumps are also a major consumer of electricity and therefore their development is even more important. From theoretical through experimental development in the last decades, a quantity of research has focused on numerical analysis. These are used to optimize all kinds of characteristics, from energy and cavitation, to dynamic characteristics. At the beginning of Computational Fluid Dynamics (CFD) analyses were related to the physical model (potential flow, Euler equation, Navier-Stokes equations). Subsequently, a lot of research was carried out in the field of various turbulent models. With the launch of high performance multi-processor computers, more and more time-dependent problems have been analysed. All the above mentioned research was carried out with numerical calculations on smooth surfaces. The fact is that in reality there is always some roughness on wet surfaces, depending on the material, the technology of production of certain parts of machines and finishing surface treatment. In the article, we give preliminary results of numerical analyses of centrifugal pumps, where the roughness of the walls was taken into account. The analyses were completed for different types of pumps, with different roughnesses and for different characteristics. First he effect of the roughness on the efficiency is shown, further the cavitation characteristics were analysed. The basic idea for research comes from the fact that there are still many cases where the matching of the results of the measurements and the results of numerical analyses are not perfect. One of the possible reasons can be the roughness of the walls.

A Compound HVAC-Based Demand Response Method for Urgent Responses of Commercial Buildings Towards Smart Grids

As the major electricity consumers worldwide, buildings can play an important role for power balance of smart grid through demand response (DR). Demand side-based control and supply side-based control are two typical types of DR measures when using centralized building air-conditioning systems for DR. For demand side-based control, the major disadvantage is that the response speed is generally too slow to allow buildings providing an immediate power reduction for the smart grid. For supply side-based control, the response speed is fast enough while it may cause control disorder to the whole system and uneven indoor temperature increase among different zones. In order to overcome above disadvantages, we proposed a novel DR method for building air-conditioning systems, which combines both the demand side-based and supply side-based control simultaneously. It consists of two major steps. First, some running chillers will be shut down to provide an immediate power reduction once urgent power reduction requests from smart grids are received by buildings. Second, the indoor air temperature set-points will be adjusted stepwise based on an “incremental schedule” to achieve a uniformly indoor temperature rise among all concerned zones/rooms. By implementing such two steps, an immediate power reduction is achieved while minimizing the uneven sacrifice of thermal comfort among different occupants. Two new performance indexes are proposed to evaluate the thermal comfort performance of DR methods. The proposed DR method is implemented and tested as case study in a virtual building dynamically simulated by TRNSYS. Five scenarios with different incremental steps for adjusting the temperature set-points are compared to determine the optimum “incremental schedule”. Results show that buildings can provide immediate power reduction and achieve a small and even thermal comfort sacrifice by implementing the proposed compound DR method.

Optimal load dispatch for industrial manufacturing process based on demand response in a smart grid

As a key smart grid technology, demand response (DR) can effectively balance the supply and demand of electricity in the power system, which both reduces the energy consumption costs and improves the stability of the power grid. The industrial sector is one of the major consumers of electricity, and it is of great practical significance and potential to implement DR programs in the industrial sector. In this study, the state-task network is first implemented to model an industrial manufacturing process. Then, a new optimal load dispatch model for the industrial manufacturing process based on DR in a smart grid environment is proposed. In the model, the energy storage system (ESS) and distributed energy resources (DERs) are included. Finally, a stamping process of automobile manufacturing is selected as a case study, in which the effects of DR, ESS, and DERs on the industrial load dispatch are discussed, respectively. The results show that parts of the electricity demand can be shifted from peak to off-peak periods through DR, which can reduce the energy consumption costs for the industrial manufacturing process. Meanwhile, the results reveal that the energy costs can be reduced further by managing the electricity storage of the ESS and through the deployment of DERs.