Large Electricity Consumers Research Articles

A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil

The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.

Comparative analysis of battery energy storage systems’ operation strategies for peak shaving in industries with or without installed photovoltaic capacity

Battery energy storage systems can address energy security and stability challenges during peak loads. This study examines the integration of such systems for peak shaving in industries, whether or not they have photovoltaic capacity. The battery-sizing problem has been analyzed extensively. However, most analyses assume a specific battery operation strategy and ignore the impact of battery-charging schemes on system behavior. In this paper, the authors compare three different operation strategies for charging batteries in an industrial peak-shaving application based on historical demand data from a large electricity consumer in El Salvador. The three strategies are fast charging, time-based charging, and low-power threshold charging. The study analyzes the possible integration of a photovoltaic system with two different sizes for a range of battery sizes (from 250 to 1,500 kWh capacity), examining optimal peak shaving levels, economic savings, and battery degradation. Results indicate that fast-charging reduces monthly billing but degrades batteries faster. The estimated lithium-ion battery lifespan is 10-10.5 years, with a maximum difference of five months. These differences could affect the life cycle economics due to the high costs of battery replacement. The economic savings achieved by the peak shaving operation of the storage system are not enough to compensate the battery investment in this study. However, other case studies with different load profiles or other locations with more expensive electricity tariffs could make the adoption of these systems economically viable.

Smart life and sustainable development: a comparative analysis on energy and water efficiency in China and the EU

Smart life relies on huge volumes of data analysis, which makes data centers become important infrastructure in smart city areas. Nowadays, data centers are constructed in many countries and have become increasingly large electricity consumers. The consumption of electricity is caused not only by the servers of data centers but also by the cooling systems. Meanwhile, data centers also demand water - cooling methods, which result in water consumption. As for sustainable development, water and energy consumption are huge challenges. Laws and rules are necessary for utilizing efficiently relevant sources. This paper will analyze China and the European Union (EU) as two examples of jurisdictions to compare how the governments regulate the construction of data centers to implement the thrift of electricity and water. In China, provinces and cities established diverse regulations based on their different natural and climate conditions, thus there is not a uniform model for the implementation in the country. In the EU, the amendment of Energy Efficiency Directive seeks to establish common policies. The difference of rules in these two jurisdictions reflects that China and the EU have their own principles and objectives: China attempts to balance the demand of developing smart city infrastructures and the consumption on natural resources, but the EU has strong ambition on low carbon emission in the whole Union. However, the two jurisdictions still have a common goal to maximize the efficiency of water and electricity consumption which is a fundamental issue for accelerating the transition towards sustainability.

Analysis of patterns of electricity consumption in mining and processing enterprises

Mining and processing enterprises are large consumers of electricity, with a large share of energy costs in the cost of production. Thus, the price of electricity in the cost of production takes higher values for Hydrometallurgical Plant 1. In this regard, the development of energy consumption models that can be used as a basis for managing the energy consumption process is an important component of learning. It is important to analyze the energy consumption to estimate the electricity consumption under different operating conditions of the mining industry. In order to study and plan electricity consumption and develop methods for regulating electricity consumption, it is necessary to determine the place of each technological stage in the energy consumption balance [1]. This article examines the laws of electricity consumption in mining and processing enterprises. The study of such laws helps to determine the level of energy consumption management for the mining and processing plant.

Minimal Energy Efficiency Indicators for Poultry Industries

This paper discusses the behaviour of energy efficiency in thermal processes of chicken slaughterhouses in Brazil, as they are large consumers of electricity and have few indicators to system monitoring. The work addresses some indicators that are monitored by the slaughterhouse, the main factors affecting the cooling circuit and the technologies that are available in the market for equipment manufacturers. The article proposes measurements in the refrigeration circuit to implement indicator that monitors the power consumption expended to generate the system's cooling capacity and also check and eliminate factors that affect the cooling system to reduce the power consumption. Energy consumption between two slaughterhouses indicates a 49.27% difference. The disclosure of efficiency comparison information may help the national industry to become more efficient and competitive.

Electricity and natural gas tariffs at United States wastewater treatment plants

Wastewater treatment plants (WWTPs) are large electricity and natural gas consumers with untapped potential to recover carbon-neutral biogas and provide energy services for the grid. Techno-economic analysis of emerging energy recovery and management technologies is critical to understanding their commercial viability, but quantifying their energy cost savings potential is stymied by a lack of well curated, nationally representative electricity and natural gas tariff data. We present a dataset of electricity tariffs for the 100 largest WWTPs in the Clean Watershed Needs Survey (CWNS) and natural gas tariffs for the 54 of 100 WWTPs with on-site cogeneration. We manually collected tariffs from each utility’s website and implemented data checks to ensure their validity. The dataset includes facility metadata, electricity tariffs, and natural gas tariffs (where cogeneration is present). Tariffs are current as of November 2021. We provide code for technical validation along with a sample simulation.

ANALYSIS OF KEY DIRECTIONS AND PROPOSALS TO MINIMISE THE ECONOMIC IMPACT OF THE GLOBAL ENERGY TRANSITION ON LARGE ENERGY-INTENSIVE INDUSTRIAL CONSUMERS OF ELECTRICITY AND CAPACITY

Over the past decade, the global energy sector has undergone major fundamental and structural changes as part of the global energy transition. The energy industry of the Russian Federation, as a key player in the global energy market and the world economy as a whole, is undergoing similar changes. In this case, in terms of ensuring high competitiveness and long-term energy security of the state, it is crucial to set priorities and build models of sustainable development for each of the industries related to the energy sector. Indeed, the process of replacing carbon-intensive energy sources with a systematic increase in the share of new, renewable energy sources (RES) should be gradual and consistent to avoid imbalances in energy systems and maintain equity for all stakeholders. In this context, the search for advanced, low-carbon energy sources is a priority for the vast majority of countries around the world. In addition, the development of renewable energy is one of the goals of Russia՚s energy strategy until 2035. At the same time, despite the obvious advantages of the Russian power industry such as the absence of dependence on budget funds, the overwhelming majority of private investment in the industry, the availability of effective mechanisms for attracting investment and the basic principle of balancing the interests of all market participants, there are also negative consequences of this approach. The nationwide task of developing the energy system and increasing the availability of electricity on the territory of the Russian Federation in terms of financing is becoming an exclusive burden on electricity consumers themselves; even insignificant risks in their operation can turn into a threat not only to sustainable development, but also to their very existence. In this context, the analysis of key directions and proposals to minimise the economic impact of the global energy transition on large energy-intensive industrial consumers of electricity and capacity is of particular relevance.

Adaptive Illumination: Designing a Smart Street Lighting System for Sustainable Urban Environments

In the age of rapid technological advancement and increasing urbanization, the need for sustainable and efficient infrastructure has never been more pronounced. As cities grow and evolve, so do the challenges related to managing their energy consumption, safety, and environmental footprint. One of the critical components of urban infrastructure that has a direct impact on all these areas is street lighting. Traditional street lighting systems, while essential for safety and visibility, often consume excessive amounts of energy, contribute to light pollution, and require significant maintenance. This is where the concept of adaptive illumination comes into play as shown in Figure 1.
 Adaptive illumination refers to the design and implementation of street lighting systems that can adjust their brightness and intensity based on real-time environmental conditions and human activity. Unlike conventional systems that remain uniformly lit throughout the night, adaptive systems use sensors, data analytics, and connectivity to dynamically modify their output. This not only results in significant energy savings but also reduces light pollution and enhances the overall safety and comfort of urban residents.[1]
 The sustainability aspect of adaptive illumination cannot be overstated. As the world grapples with climate change and the pressing need to reduce carbon emissions, every kilowatt-hour saved has a cascading positive effect on the environment. By ensuring that street lights are only as bright as they need to be, cities can dramatically decrease their energy consumption. This is particularly relevant in the context of urban areas, which are often the largest consumers of electricity within a region.

Analysing the determinants of the Turkish household electricity consumption using gradient boosting regression tree

Residential buildings are the second largest electricity consumer in Turkey. Thus, the goal here is to detect the factors that determine the electricity consumption of the households in Turkey using the Household Budget Survey (HBS). This study applies Decision Tree (DT), Random Forest (RF) and Gradient Boosted Regression Tree (GBRT) methods. Since the GBRT method provides the lowest Root Mean Squared Error (RMSE), the impact of each variable on the electricity consumption is analysed with this method. The most critical determinant is found to be the household size, while income level and heating type are discovered as 2nd and 3rd most prominent determinants for household electricity demand. With the help of the Partial Dependence Plots (PDP) provided by the GBRT method, the impact of each categorical and continuous variable is presented. Using the results of PDPs, the monetary values of both electricity generation and the social cost of CO2 emissions emitted into the atmosphere due to electricity generation are calculated for the most important determinants.

A Tool for Semi-Automated Extraction of Cotton Gin Energy Consumption from Power Data

The gin stand power is measurable using common tools; however, such tools typically do not detect active ginning. Detecting active ginning is important when trying to separate out the energy going to the moving parts of the gin stand (i.e., the baseline energy) versus the active energy doing work to remove the cotton fibers from the seed. Studies have shown that the gin stand is the second largest consumer of electricity in the ginning operation, while electricity accounts for nearly 17% of the average expense per bale. If active energy differences exist between cotton cultivars, there may be room to optimize and lower these expenses. The goal of the current work is to provide a method (and software tool) to analyze typical power logger data, and extract periods of active ginning, along with the energy consumed and ginning times, in a semi-automated way. The new method presented allows multiple periods of active ginning in a single file, and can separate the total energy into the active and baseline components. Other metrics of interest that the software calculates include the ginning time, and average power. Software validation using a simulated test signal showed that a 2%-or-lower error is possible with a noisy signal.

Electricity procurement of large consumers considering power-purchase agreements

In an environment with high energy costs worldwide, the adequate determination of electricity procurement strategies by large electricity consumers is vital. This study intends to determine the best procurement strategy for electricity consumers by considering their participation in the pool and the possibility of signing different types of power purchase agreements, which can be physical/financial, on-site/off-site, with conventional/renewable energy suppliers. Additionally, the construction of a photovoltaic self-production unit is considered. This medium-term decision-making problem is formulated using a stochastic programming approach. A realistic case study is solved by considering an existing cement producer, actual pool price and renewable production data pertaining to the Spanish power system. The numerical results indicate that a risk-averse strategy obtained by the proposed model can maintain an increase in the procurement cost below 9% in a situation in which pool prices are 3.3 times higher.

Risk aware decomposition of online scheduling for large flexible consumers considering the age of information

The stability and security of the power system is significantly influenced by large electricity consumers, especially those with flexible demand, whose electricity purchase scheduling has been extensively studied. However, the high penetration of renewable energy and various widespread distributed storage systems have increased uncertainty of the power system, making it difficult to plan effective day-ahead electricity purchase for major electrical consumers. Online scheduling is therefore required so that the surplus can be sold to and the insufficient can be purchased from the real-time market at the lowest electricity cost. To this end, we formulate a multi-stage online scheduling model for large power consumers with flexible demand. To improve the efficiency of online scheduling, we decompose the problem into multiple single-stage sub-problems. Aimed at each single-stage scheduling, an online algorithm is designed to ensure the performance of optimal scheduling strategies maintains at a level comparable to the offline fixed strategies obtained from an oracle who has complete knowledge of the problem’s settings. We prove that our proposed online energy schedule algorithm is able to achieve O(T) regret bound for the single-stage online scheduling problem. To further highlight the significance of data and improve the multi-stage performance of our online scheduling algorithm, we also involve the notion of age of information combined with regret bound to help determine the optimal interval length.

Analysis and prediction of carbon emission in the large green commercial building: A case study in Dalian, China

Reducing carbon emissions from the construction industry has been vital to addressing the growing global environmental change challenge. Building energy consumption data is crucial to many applications, such as carbon emission auditing, energy efficiency improvement, etc. This study aims to mine the energy consumption patterns of commercial buildings and explore the applicability of a data-driven model in building carbon emissions prediction. This research selected Dalian's large-scale green commercial building as a case study. The electrical load data for the past four years were collected, and the indoor and outdoor environmental data were monitored under different seasons. Machine learning was used to develop building carbon emissions forecasting models. The average annual increase rate of building electricity consumption before the pandemic was 5.9%. Miscellaneous electric loads (MELs) is the largest electricity consumer in the target building. On typical days, indoor illuminance and CO2 are highly correlated under different seasons. A forecasting model based on ensemble learning is found to have certain advantages in building carbon emissions prediction.

Effect of localisation and climate change on operation of data centres in Ukraine towards 2050

Data centres (DCs) are large electricity consumers, estimated to account for around 1% of worldwide electricity usage. They release excess heat (EH) during the operation, which can be recovered in district heating (DH) networks. DCs are expected to satisfy the rising demand for digital services.The electricity usage for cooling and available EH from the operation of DCs depends on the air temperature that changes with geographical location. The changes are not uniform and global warming pathways will influence them. Here lays the contribution of the present article. We use an existing thermodynamic model of a hyper-scale DC for different locations in Ukraine to analyse the electricity usage for cooling and EH availability for two climate scenarios (RCP2.6 and RCP 4.5) in 2010–2050.The results show that the electricity consumption for cooling and available EH can vary greatly within Ukraine. The electricity consumption for cooling is over two times higher in the hottest compared to the coldest location in the country, while the emitted EH is around 80% higher on average. The global climate pathway has a minor influence — the electricity use for cooling and EH availability are 4% higher in RCP4.5 than in RCP2.6. Finally, one hyper-scale DC of 150 MW could cover the demand for space heating and domestic hot water for roughly 100 thousand people. This assessment is an important input to consider when developing plans to rebuild Ukraine.

Концептуальное представление энергетического перехода в электроэнергетике региона в новых реалиях

The energy transition is a global problem affecting environmental and energy security, sustainable economic development, social well-being. However, since its implementation differs in certain regions, it is necessary to develop specific approaches that consider local factors, external threats and risks. The article proposes a conceptual vision of energy transition at the regional level in the context of crisis, economic and logistical changes, and socio-economic, scientific and technical transformations. It is hypothesised that in order to implement the energy transition, a compensation method is required to ensure the equivalence of environmental, economic and technical results. The study analysed expert opinions published in foreign and Russian journals indexed in the SCOPUS, Science Direct, MDPI, WIT Press databases, as well as analytical reports of international energy agencies and consulting firms. The analysis revealed that the most appropriate form of organisation of the regional electric power system is a flexible model combining system and regional circuits and involving power plants with a wide range of generating capacities. The integrated power system and decentralised energy sources are seen as energy transition objects. The study presented an energy transition scheme from the implementation of low-cost energy demand management measures to the reconstruction of coal-fired power plants and construction of renewable energy facilities. An ideal model of the regional electric power system was developed as a strategic guideline in the energy transition implementation. Possible scenarios and stages of the energy transition were discussed. The findings can be applied by government and industry regulators, energy companies, large electricity consumers when planning structural, organisational and market transformations in the regional energy sector under uncertainty.

Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage

The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation.

Energy Optimization for Fenestration Design: Evidence-Based Retrofitting Solution for Office Buildings in the UAE

With the prevalent use of large glazings, particularly in office buildings, offices receive an abundance of light and are among the largest consumers of electricity. Moreover, in an extreme hot arid climate such as in the UAE, achieving comfortable daylighting levels without increasing solar heat gain is a challenge, in which the window or fenestration design plays an essential role. This research adopts a case study of a higher education (HE) office building on the United Arab Emirates University (UAEU) campus, selected to investigate an evidence-based retrofitting solution for the west façade that can be applied in existing office buildings in the UAE in order to reduce cooling energy load as well as enhance indoor environmental quality. To achieve an evidence-based retrofitting solution, the research design built upon a comprehensive exploratory investigation that included indoor environmental quality physical monitoring and occupant satisfaction surveying. Model simulation was performed by means of DesignBuilder software to perform a single- and multiparameter sensitivity analysis for three key passive window design parameters, i.e., window-to-wall ratio, glazing type, and external shading, aimed towards minimizing annual cooling load and solar heat gain, while maintaining appropriate indoor daylight illuminance levels. The results highlight the importance of the window-to-wall ratio (WWR), as it is the single most significant parameter effecting total energy consumption and daylighting levels. The results recommend 20–30% WWR as the optimum range in the west façade. However, by utilizing high performance glazing types and external shading, equal energy savings can be achieved with a larger WWR. Double Low E tinted glazing and 0.4 projection shading overhang and side fin revealed a noteworthy reduction of energy use intensity of 14%. The study concludes with final retrofitting solutions and design recommendations that aim to contribute validated knowledge towards enhancing window performance in a hot arid climate to guide architects and stakeholders to apply a range of passive parameters towards reducing energy consumption and improving occupant comfort in office buildings.

Multi-retailer energy procurement in smart grid environment with the presence of renewable energy resources and energy storage system

An electricity retailer, as a profit-oriented company, is an intermediary between large producers and end consumers of electricity. The smart grid structure provides retailers with facilities such as telecommunications infrastructure, energy management systems, distributed generation resources, and energy storage systems to meet the needs of end consumers. Therefore, energy procurement of retailer in smart grid environment is a big problem and challenge. In a way, it is possible to use the capacities of other retailers to achieve the goals of each retailer in such an environment. In this paper, the structure of the network is considered, with several retailers present in the smart grid environment. Therefore, a model has been proposed in which each retailer in his area to settle energy and also in accordance with a cooperative game with other retailers, to reach an agreement with them on the amount of exchangeable power and its price. A distributed method is used to day-ahead energy procurement of retailers. Each retailer clears energy in their area by solving a Mixed Integer Linear Programing (MILP). The Mayfly optimization (MO) algorithm is used to find the best energy exchange price between retailers. To evaluate the efficiency of the proposed method, a network with the presence of three retailers has been studied. Also, 4 different case studies have been used to compare the proposed method with the state without energy exchange between retailers and also to investigate the effect of the presence of renewable energy resources and energy storage system on the profitability of retailers. In the absence of distributed generation resources and energy storage system, the use of the proposed method compared to the method of non-exchange of energy between retailers has increased the total profit of retailers by 2.1 %. This rate of profit increase using the proposed method in the presence of distributed generation sources and energy storage system was 7.8 %. Additionally, to evaluate the efficiency of using MO in the method proposed in this paper, this algorithm has compared with Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) algorithms. Also in this paper, the effect of considering the uncertainty of the pool market price in the proposed method has investigated. In this paper, the effect of parameters of storage systems and wind power generation on retailers' profits has evaluated.

The demand response potential in copper production

Demand response (DR) of large industrial electricity consumers is a promising option to balance the fluctuating supply by renewable energies in the electricity grid. Renewable energy technologies themselves depend on copper as a key material. At the same time, the production of copper is a power-intensive process but its DR potential has not yet been quantified in detail via process scheduling. Here, we analyze the DR potential of copper production by optimally scheduling the batch and continuous tasks of a representative copper process. To determine the optimal schedule, we formulate a mixed-integer linear program (MILP) based on the resource-task network (RTN) formulation approach. We first optimize the production volume to define a reference case and then minimize the electricity costs under time-varying electricity prices while retaining the production target. A sensitivity analysis evaluates the impact of task capacities on the production volume and DR potential. The results indicate a significant DR potential, as optimal scheduling can reduce the annual electricity costs by up to 14.2% while still producing the maximum copper output as the reference process schedule. The power-intensive electrolytic refining shows the largest potential for reducing costs. Offgas handling, air separation, and air compression further show significant cost reduction potentials. These tasks must process large material streams that are directly connected to operating the smelting and refining tasks. Our model shows the potential of considering these interlinked tasks in one scheduling model that focuses on DR. The results suggest that DR scheduling in copper production has a significant economic potential without compromising production goals. Further, the DR scheduling shifts large amounts of electricity demand by responding to fluctuating electricity prices, which enables flexibility of the demand side and can thus support the integration of renewable energy into the electric grid. • Process scheduling of copper production to assess demand response (DR) potential. • Discrete-time resource-task network model to resolve electricity demands. • DR can reduce electricity costs by up to 14% based on German day-ahead market. • Substantial load shifting potential of 16% of the annual electricity demand. • Task-specific DR potentials and impact of process debottlenecking analyzed.

Виробництво зеленого амоніаку для зеленого вибору України

Ukraine's Green Energy Transition by 2050 involves a number of energy transformations in the economy, including decarbonisation, fossil fuel abandonment and the further development of renewable energy sources (RES). For a long-term storage of energy generated by RES, the chemical systems are most suitable that convert electricity into chemical energy of such types of fuels like hydrogen and ammonia, which after being burnt do not produce emissions of carbon monoxide and oxide, sulfur dioxide, or dust. Ammonia manufacturers that use traditional production technology are being themselves large consumers of fossil fuels and electricity and emit hundreds of millions of tons of carbon dioxide. An ecological alternative is the synthesis of green ammonia based on the electrolytic production of hydrogen using electricity produced by RES. But this option requires a lot of electricity. In the context of Ukraine, with an annual demand for the production of 5 million tons of carbon-free ammonia, the required consumption of electricity amounts to 55 billion kWh. To obtain green ammonia in Ukraine, it is necessary to dramatically increase the scope of nuclear power plants and RES capacities, while abandoning the use of coal-fired power plants. Decentralized production of green ammonia can become an effective regulator of electric power in the power system without restrictions on the operation of nuclear power plants and RES. The start of this production will come after the development of synthesis technologies of green ammonia and the expiration of RES preferences in the energy market of Ukraine. Keywords: ammonia, carbon dioxide, emission, electricity, RES, demand-side load regulation.