Consumption Curve Research Articles

Analyses of Distributed Generation and Storage Effect on the Electricity Consumption Curve in the Smart Grid Context

The householders’ electricity consumption is about 20–30% of the total consumption that is a significant space for demand response. Mainly, the householders are becoming more and more active and interested in diminishing their expenses related to the electricity consumption, considering different rates of the advanced tariffs. Therefore, in the smart grid context, especially for prosumers with energy sources and storage devices (SD), the electricity consumption optimization becomes attractive since they obtain significant benefits. On the other hand, the electricity suppliers design appropriate tariffs in order to reduce the consumption peaks and avoid the occurrence of new peaks. Based on the effect of these tariffs on consumers’ behavior, the stress on generators decreases and the electricity suppliers improve the demand forecast and adjust their strategies on the market. In addition, the grid operators are interested in the minimization of the consumption peak that leads to loss reduction and avoidance of congestions that would ensure at least the delay of the onerous investment in grid capacities. In this paper, we will run several scenarios for electricity consumption optimization in the context of smart grid that includes: sensors, actuators, smart meters, advanced tariff schemes, smart appliances and electricity home control applications. Our goal is to analyze the effect of the Renewable Energy Systems (RES) distributed generation (such as photovoltaic panels—PV) and storage on the consumption curve. The results show that consumption optimization with RES distributed generation and SD brings sustainable development of the power systems and significant benefits from the consumption peak and savings point of view.

Prediction of Ship Fuel Consumption and Speed Curve by Using Statistical Method

Prediction of Ship Fuel Consumption and Speed Curve by Using Statistical Method

Thermodynamic evaluation of bi-directional solid oxide cell systems including year-round cumulative exergy analysis

Bi-directional solid oxide cell systems (Bi-SOC) are being increasingly considered as an electrical energy storage method and consequently as a means to boost the penetration of renewable energy (RE) and to improve the grid flexibility by power-to-gas electrochemical conversion. A major advantage of these systems is that the same SOC stack operates as both energy storage device (SOEC) and energy producing device (SOFC), based on the energy demand and production. SOEC and SOFC systems are now well-optimised as individual systems; this work studies the effect of using the bi-directionality of the SOC at a system level.Since the system performance is highly dependent on the cell-stack operating conditions, this study improves the stack parameters for both operation modes. Moreover, the year-round cumulative exergy method (CE) is introduced in the solid oxide cell (SOC) context for estimating the system exergy efficiencies. This method is an attempt to obtain more insightful exergy assessments since it takes into account the operational hours of the SOC system in both modes. The CE method therefore helps to predict more accurately the most efficient configuration and operating parameters based on the power production and consumption curves in a year.Variation of operating conditions, configurations and SOC parameters show a variation of Bi-SOC system year-round cumulative exergy efficiency from 33% to 73%. The obtained thermodynamic performance shows that the Bi-SOC when feasible can prove to be a highly efficient flexible power plant, as well as an energy storage system.

An analysis of utility meter data aggregation and tenant privacy to support energy use disclosure in commercial buildings

A growing number of cities are adopting energy use benchmarking ordinances, which require building owners report their buildings' total energy usage annually. It also requires that utilities supply aggregated building-level monthly energy consumption data. The data aggregation poses privacy concerns, as it is possible to estimate the individual tenant load consumption curve by dividing aggregated energy data by the number of meters. A solution is to quantify and assess the impact of adjusting the utility meter aggregation threshold on tenant privacy and on buildings that are eligible for energy usage reporting. As the threshold increases, fewer buildings are eligible for energy use data disclosure and therefore lessening data value. This study aims to investigate the similarity between individual utility meters and whole-building totals at various aggregation levels. Based on statistical analysis of 715,000 anonymized, non-residential meter accounts from six utilities across the U.S., we developed the “Meter Aggregation Selection Threshold” as a metric to assess tenant privacy risk. The metric estimates the portion of individual customer energy use patterns that are similar to the aggregated building consumption profile. It allows policy makers to make an informed decision on whether required disclosure regulations compromise business sensitive information and tenant privacy.

Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization algorithm

Abstract Nowadays, by means of smart meters and sensors, more and more electricity consumers can shift the operation of some of their appliances thus, reducing the electricity expenses calculated on advanced tariff schemes such as time-of-use (ToU) tariff. In this paper, we propose an optimization algorithm that can be automatically implemented by the electricity suppliers without knowing the specific characteristics of residential consumers’ appliances (preserving privacy) to flatten the daily consumption curve and identify the ToU tariff that would reduce the consumers’ electricity payment. This way the consumers will receive incentives to provide the daily operation schedule of the appliances indicating their type, considering that the electricity appliances are divided into programmable and non-programmable appliances. The dependency between operations of some appliances brings certain constraints to the optimization model that shift programmable appliances from peak to off-peak hours. Therefore, the flattening of the consumption peak relies on programmable appliances, while the operation of the non-programmable appliances forms the base of the daily consumption curve. By transparently implementing the optimization algorithm in relation to several ToU tariffs, the suppliers show the benefits in terms of consumption peak and electricity payment reduction. Also, this would stimulate the consumer to shift the operation of appliances and choose the tariff that minimizes the electricity payment that is a function of the consumption flexibility. Although the proposed algorithm is mainly designed to flatten the consumption peak, it can further restrict the operation of the programmable appliances at high-rate time intervals that rewards the flexible consumers with more savings.

An optimization algorithm for evaluation of kinetic parameters for crude oil combustion

The design of enhanced oil recovery processes that employ in-situ combustion requires high-fidelity simulation models that accurately describe the physics and chemistry of reactive, exothermic multiphase fluid flow. Therefore, a detailed description of oil combustion kinetics is required. The goal of this work is to present and apply a workflow to define and match pseudo-kinetic reaction models based on Arrhenius kinetics. These models are meant to be used at the laboratory scale with high-resolution, fully-resolved simulation models as a tool to generate high density data required by upscaled models at reservoir scale.The workflow starts by performing ramped temperature oxidation (RTO) experiments for a wide range of heating rates. New experimental equipment has allowed us to obtain data for large heating rates (>15°Cmin ) that were not achievable in the past. A subset of the experiments are selected to match the oxygen consumption curves by tuning kinetic parameters of several global reaction models using pattern search optimization. Those models that obtain a match are validated by predicting a second subset of the data that was not considered during the optimization. The workflow is tested using a heavy oil and two satisfactory matches with two different global reaction models are obtained.

Intelligent system for non-technical losses management in residential users of the electricity sector

The identification of irregular users is an important assignment in the recovery of energy in the distribution sector. This analysis requires low error levels to minimize non-technical electrical losses in power grid. However, the detection of fraudulent users who have billing does not present a generalized methodology. This issue is complex and varies according to the case study. This paper presents a novel methodology to identify residential fraudulent users by using intelligent systems. The proposed intelligent system consists of three fundamental modules. The first module performs the classification of users with similar power consumption curves using self-organizing maps and genetic algorithms. The second module allows carrying out the monthly electricity demand forecasting through of recursive adjustment of ARIMA models. The third module performs the detection of fraudulent users through an artificial neural network for pattern recognition. For the design and validation of the proposed intelligent system, several tests were performed in each developed module. The database used for the design and evaluation of the modules was constructed with data supplied by the energy distribution company of the Colombian Caribbean Region. The results obtained by the proposed intelligent system show a better performance versus the detection rates obtained by the company.

Intelligent system for non-technical losses management in residential users of the electricity sector

The identification of irregular users is an important assignment in the recovery of energy in the distribution sector. This analysis requires low error levels to minimize non-technical electrical losses in power grid. However, the detection of fraudulent users who have billing does not present a generalized methodology. This issue is complex and varies according to the case study. This paper presents a novel methodology to identify residential fraudulent users by using intelligent systems. The proposed intelligent system consists of three fundamental modules. The first module performs the classification of users with similar power consumption curves using self-organizing maps and genetic algorithms. The second module allows carrying out the monthly electricity demand forecasting through of recursive adjustment of ARIMA models. The third module performs the detection of fraudulent users through an artificial neural network for pattern recognition. For the design and validation of the proposed intelligent system, several tests were performed in each developed module. The database used for the design and evaluation of the modules was constructed with data supplied by the energy distribution company of the Colombian Caribbean Region. The results obtained by the proposed intelligent system show a better performance versus the detection rates obtained by the company.

A data-driven approach for building energy benchmarking using the Lorenz curve

A building energy benchmark indicates the reasonable level of energy demand, and it facilitates the policy making when facing with uncertain information at the planning stage. Traditional benchmarks are established based on statistical indicators or physical simulations of prototype buildings, while the diversified consumption levels of various buildings are rarely considered carefully. Pointing to this, a data-driven benchmarking methodology is explored based on the Lorenz curve, which is a specific measurement tool of income inequality. In this paper, the Lorenz curve is used to identify the distribution inequality of energy consumption among buildings. In particular, the Lorenz curves of energy consumption were firstly derived from 195 buildings of three building types in Beijing. Afterwards, a variable was extracted from the fitting Lorenz curve, in order to quantify the nonlinear distribution principles of energy use intensity along with the accumulative building area. Finally, empirical benchmarking formulae for the three building types were achieved together with stepwise applications. The validation showed that relative errors of accumulated energy consumption for offices, hotels and shopping malls were −5.27%, 3.57% and −3.56% respectively. This data-driven approach is developed based on the limited information, and it attempts to assist energy-policy planners in establishing energy benchmarking targets and promoting energy efficiency.

EKC Analysis and Decomposition of Influencing Factors in Building Energy Consumption of Three Municipalities in China

The acceleration of the urbanization process has brought new opportunities for China’s development. With the rapid economic development and people’s living standards improving, building energy consumption also showed a rigid growth trend. With the continuous improvement of the level of industrialization, industrial energy-saving potential declines. The construction industry to bear the task of energy-saving emission reduction will face more severe challenges. As the three municipalities of China, Beijing, Shanghai and Chongqing have significant radiation effects in the economy, urbanization level and construction industry development of the region. Therefore, it is of great significance to study the building energy consumption in the three regions with the change of urbanization level and the key factors. Based on the data of Beijing, Shanghai and Chongqing from 2001 to 2015, this paper attempts to find out whether the EKC curve of building energy consumption exists. At the same time, based on the results of the model, the data of the three regions are divided into three periods. The exponential decomposition method (LMDI) is used to find out the factors that have the greatest impact on the energy consumption of buildings in different stages. Moreover, analyzes the policy background of each stage and puts forward some policy suggestions on this basis.

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

Speed control with incremental algorithm of minimum fuel consumption tracking for variable speed diesel generator

This paper presents a model of a diesel-combustion-engine-based variable speed generation unit driving a permanent magnet generator loaded by a power converter. Variable speed operation of the internal combustion engine facilitates fuel saving when the load profile changes, but it requires a power conditioning unit such as an electronic converter. Power converter operation is well known from wind energy conversion systems, in which variable speed operation increases their efficiency. The paper presents modeling and speed control of an exemplary diesel engine, and control of output voltage and current of the power electronics converter loading the generator. Special attention has been paid to elaboration of the methods of minimum specific fuel consumption points tracking for the given load. Incremental algorithms can find minimum specific fuel consumption in the case in which the details of fuel consumption curves are not exactly known. The incremental algorithm has been adopted from wind energy conversion systems and partly modified to avoid torque peaks during incremental step changes of reference speed. The concept has been validated in a simulation using data of a real model of an internal combustion engine through a three-dimensional approximation of fuel consumption characteristics.

The Functional Latent Block Model for the Co-Clustering of Electricity Consumption Curves

SummaryAs a consequence of recent policies for smart meter development, electricity operators nowadays can collect data on electricity consumption widely and with a high frequency. This is in particular so in France where the leading electricity company Électricité de France will be able soon to record the consumption of its 27 million clients remotely every 30 min. We propose in this work a new co-clustering methodology, based on the functional latent block model (LBM), which enables us to build ‘summaries’ of these large consumption data through co-clustering. The functional LBM extends the usual LBM to the functional case by assuming that the curves of one block live in a low dimensional functional subspace. Thus, the functional LBM can model and cluster large data sets with high frequency curves. A stochastic expectation–maximization–Gibbs algorithm is proposed for model inference. An integrated information likelihood criterion is also derived to address the problem of choosing the number of row and column groups. Numerical experiments on simulated and original Linky data show the usefulness of the methodology proposed.

Research on Development of Simulation Interface of Engine Speed Characteristics Based on GT-POWER

The engine speed characteristic simulation operation interface based on GT-POWER was developed by using VB6.0. VB has the characteristics of simple operation, high efficiency and powerful function. It can realize the background data modification, operation control and calculation result reading and storage of GT-Power model, and draw the engine speed characteristics after finishing the data. curve. Based on ACCESS database, the automatic input and storage of interface input data is realized. The data access, operation control and result reading of GT-POWER model are realized based on VB6.0 programming. The engine is validated by VB6.0 PictureBox control. Power, effective torque and effective ratio fuel consumption curve. For the needs of engine speed characteristic simulation, VB6.0 software is used for secondary development of GT-Power, and Chinese operation interface is obtained to simplify the operation process of engine speed characteristic simulation, reduce operation difficulty and improve calculation efficiency, which has certain engineering significance. The simulation method has the advantages of high flexibility, low cost, strong repeatability, and the like, and can obtain parameters that are inconvenient to measure. The results show that the developed engine speed characteristic simulation operation interface can simplify the operation process of engine speed characteristic simulation, reduce the operation difficulty and improve the calculation efficiency.

A New Type of Countermeasure against DPA in Multi‐Sbox of Block Cipher

The Internet of Things (IoT) provides the network for physical devices, like home appliances, embedded with electronics, sensors, and software, to share and exchange data. With its fast development, security of IoT has become a crucial problem. Among the methods of attack, side‐channel attack has proven to be an effective tool to compromise the security of different devices with improving techniques of data processing, like DPA and CPA. Meanwhile, many countermeasures have risen accordingly as well, such as masking and noise addition. However, their common deficiency was that every single countermeasure might not be able to protect the key information completely after statistical analysis. Sensitive information will be disclosed during differential power analysis of Sbox, since it is the only nonlinear component in block cipher. Thus, how to protect Sbox effectively was the highlight of researches. Based on Sbox‐reuse concept proposed by Bilgin, this paper put forward a new type of a countermeasure scheme against DPA in multi‐Sbox of block cipher. We first converted the multi‐Sbox into 4 × 4 permutations and then reused permutation with the algebraic degree of more than one so as to turn it into a special reusable Sbox and then numbered 4 × 4 permutation input. Finally, we made these inputs of permutations completely random by masking. Since it was necessary to make the collected power consumption curve subject to alignment process in DPA by chosen‐plaintext attack, this scheme combined the concept from DPA countermeasures of masking and noise addition. After the experiment with the proposed implementation, successful prevention of the attacker from accurately aligning the power consumption curve of the target Sbox has been proven, and the level of security has been improved by adding more random noise to protect key information and decrease the accuracy of statistical analysis.

Clustering of Complementary Electricity Consumers Based on Their Usage Patterns

In the electricity market, the real-time balance of electricity generation and consumption is a main task. In view of this, power providers usually sign contracts with their critical consumers (i.e., usually large-scale industrial companies) for managing their capacity demands. On the other hand, aggregators group commercial and residential consumers, and integrate their demands to negotiate with power providers. With a proper grouping of numerous electricity consumers, aggregators help to ensure stable electric supply, and reduce the burden of managing many consumers. In this work, we thus propose a novel data clustering approach to group complementary consumers based on their usage patterns (i.e., daily electricity consumption curves.) Furthermore, we incorporate the technique of discrete wavelet transform to speed up the clustering process. Specifically, approximations reconstructed from only a few wavelet coefficients may precisely capture the shape of original usage patterns. Experimental results based on a real dataset show that our approach is promising in practical applications.

Use of weather forecast for increasing the self-consumption rate of home solar systems: An Italian case study

With the aim of increasing the self-consumption rate of grid-connected Photovoltaic (PV) home systems, two main options can be implemented: the inclusion of an energy storage system, in particular a battery bank, and the adoption of a Demand Side Management (DSM) strategy. However, both the reshaping of the load consumption curve with the displacement of deferrable loads and the optimal management of the battery bank require estimation of the daily PV generation profile. The assessment of the on-site energy production can be carried out based on weather forecast data. However, the latter are characterized by uncertainty, which may affect the achievable self-consumption rate.This work investigates the influence of weather forecast errors on the performance of home PV systems equipped with a battery bank and characterized by a certain share of deferrable loads. Two different weather forecast services are considered, referring to the annual meteorological conditions occurring in Rome, and energy consumption data for 150 different households are analysed. The self-consumption rate is maximized by solving a suitable optimization problem, while different combinations of relative battery capacity, PV-to-load ratio and share of deferrable loads are considered. Two different approaches—deterministic and stochastic—are adopted and compared with an ideal approach where the PV generation profile is perfectly forecasted. The results show that the adoption of the deterministic approach leads to a reduction in the achievable self-consumption rate in the range of 0.5–4.5% compared to the ideal approach. The adoption of a stochastic approach further reduces the deviations from the ideal case, especially in the case of consumption profiles with a high share of deferrable loads. Finally, a preliminary economic analysis proves that the use of a battery bank is not yet a cost-effective solution and a price reduction of the current battery prices is therefore required.

Segmentation of Residential Gas Consumers Using Clustering Analysis

The growing environmental concerns and liberalization of energy markets have resulted in an increased competition between utilities and a strong focus on efficiency. To develop new energy efficiency measures and optimize operations, utilities seek new market-related insights and customer engagement strategies. This paper proposes a clustering-based methodology to define the segmentation of residential gas consumers. The segments of gas consumers are obtained through a detailed clustering analysis using smart metering data. Insights are derived from the segmentation, where the segments result from the clustering process and are characterized based on the consumption profiles, as well as according to information regarding consumers’ socio-economic and household key features. The study is based on a sample of approximately one thousand households over one year. The representative load profiles of consumers are essentially characterized by two evident consumption peaks, one in the morning and the other in the evening, and an off-peak consumption. Significant insights can be derived from this methodology regarding typical consumption curves of the different segments of consumers in the population. This knowledge can assist energy utilities and policy makers in the development of consumer engagement strategies, demand forecasting tools and in the design of more sophisticated tariff systems.

PERFORMANCE, EMISSION AND EFFICIENCY ANALYSIS OF A DIESEL ENGINE OPERATED WITH DIESEL AND DIESEL-ETHANOL (E20) BLEND

Conventional fossil fuel sources face with scarcity risk. Therefore, researchers are trying to find new renewable alternative energy sources which have potential to replace conventional sources. Alcohol and biodiesel are prominent sources among others. In this study, performance and emission characteristics of a diesel fuel and ethanol added (20% vol.) diesel fuel (E20) operated compression ignition engine was studied in between 1000 - 2600 engine speed. Torque, power, specific fuel consumption curves as performance and carbon monoxide (CO), nitrogen oxides (NOx) curves as emission parameters with respect to engine speed were obtained. Energy and exergy efficiency curves versus engine speed in order to assess the system thermodynamically were also obtained. Results showed that, while torque, power, CO emission were decreased, specific fuel consumption, NOx emission were increased with use of diesel-ethanol blends. On the other hand, both energy and exergy efficiency values were decreased when alcohol was added to diesel fuel. Article DOI: https://dx.doi.org/10.20319/mijst.2017.33.5161 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

ВОЗРАСТНЫЕ И ГЕНДЕРНЫЕ ОСОБЕННОСТИ КАРДИОРЕСПИРАТОРНОЙ ВЫНОСЛИВОСТИ ЧЕЛОВЕКА В УСЛОВИЯХ УРБАНИЗИРОВАННОГО СИБИРСКОГО СЕВЕРА

Aim. The aim of this article is to reveal consistent patterns of human cardiorespiratory system reaction to walking at different speeds depending on sex and age in the conditions of the urbanized Siberian North. Materials and Methods. We studied three groups of healthy volunteers. The group of young people (GY, 22.1 ± 2.6 years, n = 25: 12 men and 13 women), the group of middle-aged people (GM, 42.7 ± 9.3 years, n = 25: 12; 13) and the elderly group (GE, 66.2 ± 5.1 years, n = 24: 11, 13) performed walking on the treadmill at a speed of 2–7 km/h for 5 minutes at each speed. We used FitMet PRO analyzer (COSMED, Italy) to measure breathing frequency (FB, times/min), lungs ventilation (VE, l/min), oxygen consumption (PO2, ml/min), relative oxygen consumption (RPO2, ml/min/kg), heart rate (HR, bpm), and oxygen concentration in the exhaled air (KO2, %). Before the beginning of the study, we measured leg length (m), body length (m), and body weight (kg). Results. As walking speed increased from 2 to 7 km/h, relative oxygen consumption increased regularly in the young group by 14.6 ml/kg/min (2.46 times), in the middle-aged group by 17 ml/kg/min (2.72 times), and in the elderly group by 13.9 ml/kg/min (2.53 times). We registered the decrease in the slope of the dependence curves of relative oxygen consumption on heart rate in young and older women in comparison with young people and the people of middle and old age. Regression equations of the dependence of relative oxygen consumption on pulmonary ventilation (VO2 = –12.9 + 40.3Ve (r = .98, p = .0000) in young men and VO2 = 116.3 + 34.7Ve in girls (r = .97; p = .0000)) allow to determine oxygen consumption in the group of students in the classroom using a spirometer. Conclusion. The data obtained show that the reaction of the cardiorespiratory system of the persons of different ages in response to walking with a stepwise increasing rate is adequate. Decrease in the slope of the dependence curves of relative oxygen consumption on heart rate in young and elderly women in comparison with young people, middle-aged and elderly people indicates a possible decrease in the cardiorespiratory endurance of women from this sample.