Profile Of Demand Research Articles

Optimal sizing of PV–Wind generators with a smart EV charging framework to build grid friendly Net Zero Energy Campus

Electric Vehicles (EVs) are creating a significant challenge on the electric grid as they demand huge amounts of power for their charging. On-site power generation through renewable energy sources (RES) is one of the viable approaches to meet the demands of EVs and other loads of a campus. Hence, an optimal sizing of a hybrid PV–Wind system is considered in this work to build a Net Zero Energy Campus (NZEC). Generally, EVs are scheduled after obtaining the size of RES through a sizing procedure. As a unique approach of this work, a smart EV charging framework is developed to model the profile of EV charging demand and this modeled load profile of EV is considered along with the local load profile of the campus for optimal sizing procedure. The size of PV–Wind sources are optimized by minimizing three objective functions such as the annual operating cost of the system (AOC), loss of power supply probability (LPSP) and total energy transfer (TET). The optimization is carried out by Non-Dominated Sorting Genetic Algorithm II (NSGA II). The maximum utilization of RES and the reduced grid congestion attained through the proposed sizing approach showcase the grid friendliness of the designed NZEC.

Look-Ahead Unit Commitment With Adaptive Horizon Based on Deep Reinforcement Learning

The highly variable nature of renewable energy has led to the concept of intra-day look-ahead unit commitment (LAUC), which aims to determine the on/off status and power outputs of generating units in a rolling-horizon fashion. LAUC is traditionally performed based on look-ahead horizon (LAH) with fixed length and resolution. Such practice can neither capture the high-risk time periods, nor achieve maximum computational efficiency. To address these issues, this paper proposes a LAUC method with adaptive horizon (LAUC-AH). The method uses three parameters to describe the shape of LAH, namely length, resolution, and myopia. Taking these parameters, the forecast profile of renewable energy and load demand is aggregated using a hierarchical clustering procedure to form the LAH, which is then used to construct the LAUC optimization model. Furthermore, a deep reinforcement learning-based agent is used to dynamically adjust the parameters of LAH, such that the LAUC model can adapt to different operation statuses of the power grid. The case studies are carried out on a modified IEEE-118 test case to validate the feasibility and effectiveness of the proposed method.

Simulation of a Solar Domestic Water Heating System with Different Collector Efficiences and Different Volumen Storage Tanks.

This paper shows the modeling and dynamic simulation, of a domestic solar water heating installation. The results of simulations performed on an annual basis for a solar system, operated in Santander (Spain), which provides hot water for a family (four persons) are presented. The installation consists in a solar flat collector, a water storage tank, a source of auxiliary energy, and a device for blending water. The mathematical model is used to evaluate the annual variation of the solar fraction respect to the volume of the storage tank, demand hot water temperature required, difference of this temperature and preset storage tank water temperature, and consumption profile of the domestic hot water demand. The results for a number of designs with different storage tank volumes, show that the systems with greater volume yield higher solar fraction values. However, when a larger storage tank volume is used, the solar fraction is less sensitive to a variation of these operation parameters. The results of this simulation may be used to design a solar collector system.

Removal of organic pollutants from sugarcane stillage using UV-assisted Fenton process

Stillage or distillery wastewater have been reported as industrial effluent that are difficult to be treated as its discharge in the water bodies presents contamination with recalcitrant and non-degradable pollutants. Due to their complicated structure, stillage treatment process using traditional methods such as adsorption, coagulation, and flocculation is challenging. Fenton reaction has been found as an effective method to degrade these contaminants through hydroxyl radicals. In this study, the removal of pollutants in stillage was investigated using UV-assisted Fenton process. Experiments was conducted for 60 minutes in a batch reactor with initial pH of stillage 4.5, UV lamp of 395-400 nm, and constant speed of 250 rpm, while the dosages of H2O2 and Fe (II) were kept constant at 3.3 g/L and 0.6 g Fe/L, respectively. The profile of chemical oxygen demand (COD) removal, pH, temperature, and oxidation-reduction potential (ORP) was evaluated under three dilution factors of raw stillage (1/10; 1/25; and 1/50) during treatment process to investigate the effects of these parameters on treatment efficiency. It has been observed that initial COD of stillage had considerable effect on the performance of UV-photo-Fenton treatment, as higher dilution factor increases the removal efficiency of COD from stillage. The degradation of COD by UV-assisted Fenton was found to be suitably described by the pseudo-first-order kinetics. The results obtained from this work indicated that UV-Fenton can be introduced as a successful advanced treatment process for efficient degradation of stillage.

Zero-Carbon Power-to-Hydrogen Integrated Residential System Over a Hybrid Cloud Framework

The study pioneers in proposing a hybrid cloud-based framework for designing a totally stand-alone, green residential house, stable over load variations and fluctuations of renewable energy sources (RES). The framework uses wind turbine (WT) and photovoltaic panels (PV) as the main power supplies, while employing a fuel cell (FC), fed with an electrolyzer, as the secondary source of energy. A battery is also used, which together with the FC and electrolyzer, constitute the compensation system in the proposed framework. Taking into account the various types of residential electrical loads, including an electrical vehicle (EV), and applying a deep learning method, the proposed framework makes the day-ahead scheduling of all components in the house based on the forecasted profile of load demand and the energy generated by the RES. The compensation system comes into use to balance the real-time scheduling error caused by the uncertainties of the main sources of power. To ascertain the practicality of the proposed framework for real-life implementation, it is examined on a residential house considering components with authentic technical features. The real-time operation of the suggested residential system is also tested on the SpeadGoat real-time simulator, whose results corroborate the practicability of both the real-time and day-ahead operation of the proposed framework.

Психологические особенности профессиональной востребованности и мотивации сотрудников полиции

<p style="text-align: justify;">The article considers the peculiarities of professional demand and motivational profile of the personality of employees of operational subdivisions and patrol-guard service of the police. The sample is represented by 89 employees of internal affairs bodies in age from 29 to 36 years, with the service experience of 2-3 years. Purpose of the study: to determine the influence of the specifics of service activity, motivation and social characteristics on the experience of professional demand for police officers. Methods: anonymous questionnaire of assessment of socio-psychological factors; methodology “Professional demand for personality” (E.V. Kharitonova, B.A. Yasko); questionnaire “Motivational profile” (S. Ritchie, P. Martin). As a result of the study it was found that the specifics of service activities of police officers and their motivation have a significant impact on the overall level of professional demand for personality and its individual components, as well as on the socio-psychological adaptation, “turnover” and psychological health of specialists. Operational unit officers differ from patrol officers (p ≤ 0,05) with a more harmonious profile of the professional demand for personality.</p>

STOCHASTIC TRAFFIC DEMAND PROFILE

Traffic surveys routinely estimate the profile of traffic demand in a certain road section, showing the expected evolution of the demand over a workday or weekend. However, the actual demand fluctuates around this value. That can lead to brief excess of the capacity at the moment of high demand and consequent congestion due to the capacity drop. This type of traffic demand variability has not yet been properly studied despite the fact it can play significant role in traffic modelling and engineering applications. This paper presents results of analysis of demand variability in five-minute aggregation intervals. The results do not clearly show a single random distribution that would accurately model the demand variability. Normal, lognormal and gamma distributions all show reasonably well fit to the data for individual intervals. Based on count of best fits, the lognormal distribution seems best, but in most cases, the difference between the distributions is not statistically significant. There appears to be a pattern where certain distributions have better fit in different times of day and week. The regularity and magnitude of demand (e.g. morning peak hour) probably play a role in this, as well as the aggregation interval.

The Energy Storage System Integration Into Photovoltaic Systems: A Case Study of Energy Management at UTFPR

Energy storage system integration can reduce electricity costs and provide desirable flexibility and reliability for photovoltaic (PV) systems, decreasing renewable energy fluctuations and technical constraints. In this sense, this study aimed to propose energy management strategies through this integration, aiming to improve the demand profile of a university commercial consumer for compensation during peak hours. To achieve the objectives of this research, a pilot project was installed which included a 10 kWp grid-tie PV system (GTPVS), bidirectional inverters, and battery bank, as part of a research and development (R&D) project implemented in the Federal University of Technology–Paraná (UTFPR) Curitiba campus at Neoville headquarters. The methodology consisted of the analysis of variables that interfere with the operation of this system, such as a detailed university profile of demand and consumption, local energy pricing, PV generation profile as well as the interaction between PV generator and the energy storage system, and the variation in power fluctuations throughout the year. Additionally, a technical feasibility study was carried out aimed to determine this consumer’s optimal size through the Homer Grid software. The detailed demand profile allowed us to know in depth the peak values were above 80 kW, for university working days. Furthermore, the daily analysis of the PV generation profile confirmed that although the PV source is quite variable, the scheduled dispatch operation is not interfered by the variability and intermittency of the solar source, as the system was designed and programmed to operate in parallel with the electrical network. Regarding the energy consumed at the university, very significant values were obtained, in which the month of July presented the highest consumption for the base year 2019, with a total monthly value of 48,670 kWh. The established operating strategy for the battery bank with a 10% discharge depth resulted in a life cycle of 2,500 cycles which can extend its useful life by approximately 9.5 years. The results obtained by the simulation show different levels of energy contribution with percentages of 60.0, 33.6, and 2.7%, respectively.

Smart Irrigation System Considering Optimal Energy Management Based on Model Predictive Control (MPC)

Traditional irrigation techniques usually cause the wasting of water resources. In addition, crops that are located in rural areas require water pumps that are powered by environmentally unfriendly fossil fuels. This research proposes a smart irrigation system energized by a microgrid. The proposal includes two stages: the first generates the daily irrigation profile based on an expert system for the adequate use of the water. Then, considering the irrigation profile, the power required for the water pump is measured—the optimal daily profile of electricity demand is determined in the second stage. The energy system is a microgrid composed of solar energy, a battery energy storage system (BESS) and a diesel generator. The microgrid is managed by an energy management system (EMS) that is based on model predictive control (MPC). The system selects the optimal start-up time of the water pump considering the technical aspects of irrigation and of the microgrid. The proposed methodology is validated by a simulation with real data from an alfalfa crop in an area of Ecuador. The results show that the smart irrigation proposed considers technical aspects that benefit the growth of the crops being studied and also avoids the waste of water.

Profitability of Prosumers According to Various Business Models—An Analysis in the Light of the COVID-19 Effect

The European Union has set itself ambitious emission-reduction targets—becoming carbon neutral by 2050. The member states’ energy systems are increasingly switching to renewable and decentralized configurations, in line with the EU guidelines. This is accompanied by the support for “prosumers”—entities that both consume and generate renewable energy. In parallel, a number of prosumer support schemes are emerging as a result of the search for optimal development paths. At the same time, the COVID-19 pandemic has significantly affected the power market, causing huge anomalies mainly in demand and billing. The objective of the study was to assess the impact of the proposed changes in the prosumer schemes on the level of benefits prosumers obtained, including the performance of sensitivity analyses reflected in different levels and stability of electricity consumption resulting from extreme situations caused by the COVID-19 pandemic. Numerical computer simulations for five predefined prosumer support schemes were carried out in the R-project environment. The basic conclusion is that the prosumer benefits most from participating in the applicable discount mechanism, in which the unused energy is fed into a network storage, from which the prosumer can take 70% or 80% of the stored volume. The research also allows us to conclude that in Poland the COVID-19 pandemic has had a very significant impact on the level and profile of energy demand due to the introduction of restrictions on selected areas and economic sectors. The reduced demand is particularly visible in services (−58%), tourism and sport (−39%), and education (−19%). The analysis is an important contribution to the search for an optimal model of prosumer market development in Europe.

Predictive analytics for building power demand: Day-ahead forecasting and anomaly prediction

The power demand forecast for buildings can generate useful day-ahead predictions in power system planning and operation. However, the information in the forecast needs to be interpreted by a person with domain expertise. Moreover, automated interpretation of upcoming abnormal behaviors needs ground-truth labeling, but labels are not always available from power meter data. In this paper, we propose a novel Predictive Power Demand Analytics Methodology (PPDAM), based on deep neural networks and symbolic aggregate approximation, to predict the pattern profile of power demand in a building and upcoming normal (motif) and anomalous (discord) behaviors. The experimental results indicate that a power forecast could be mapped as different foreseeable demand patterns, each with a specific probability of occurrence. The reliability of anomaly prediction is evaluated by a classification test of which the accuracy is 88% and the F1 score is 87.38%. The outcomes of this work could provide building operators with a solution to derive latent information in power consumption data. The derived information could be used to improve the working conditions of the building’s power system.

Train timetabling in rail transit network under uncertain and dynamic demand using Advanced and Adaptive NSGA-II

It is critical to design an adaptable and stable train timetable for long-term use in rail transit network that not only meets the dynamicity of passenger demand in different hours within one day, but also meets the uncertainty of passenger demand in different days. In this study, a scenario-based train timetabling framework is constructed to classify the possibilities of passenger demand in multiple days into a set of scenarios based on profile and volume of passenger demand. On this basis, multi-scenario demand input method (MM) is introduced to deal with the uncertainty of passenger demand, which is different from one-scenario method (OM) and average-scenario method (AM). A MM-based mixed-integer linear programming model is formulated for the bi-objective train timetabling problem under uncertain and dynamic demand at acyclic network level, in which multi-scenario small-granularity passenger demand follows actual distribution processed from historical data. The two objectives are to minimize train service cost and penalized passenger waiting time from perspectives of enterprises and passengers. Advanced and Adaptive NSGA II (AANSGA-II) is proposed to cope with the high-complexity bi-objective problem, which applies advanced population sorting based on neighborhood distance, adaptive genetic operation based on scoring mechanism and improved population initialization based on boundary individuals. The model and algorithm are testified by a small-scale numerical experiment on a virtual line and a large-scale real-world instance in Shenyang Metro network. As a result, MM-based train timetables are generally better than AM-based and OM-based train timetables in reducing generalized cost and raising robustness. Besides, AANSGA-II is more applicable than NSGA-II and CPLEX in shortening computation time at the same time of improving computation result.

Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States

Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class.

Months of monotony – moments of mayhem: Planning for the human role in a transitioning world of work

Events that can be characterized as hours of boredom which rapidly change into moments of terror, are now evolving into mere milliseconds of mayhem interjected into perhaps even months of monotony. This organisation of human supervisory work, while perhaps one inevitable consequence of the present evolutionary line of automation and autonomy’s proliferation, may profoundly disserve the human operators involved. These latter individuals readily become ever-more remote from the inner loops of control. Such distancing is characterised by a progressively diminishing necessity for the operator to interface, interfere, interrupt, or even interpret on-going operations. This emerging and increasing profile of task demand threatens to make human workers inefficient, ineffective, or perhaps altogether redundant even during such off-nominal and/or emergency events. In framing this general argument, a specific analysis is provided here of such transition events as a foundation from which to suggest remediating strategies that may, at least temporarily, address such a profoundly inhuman work profile. Other avenues to help redress and even dissolve this growing discord and dissonance are identified and advocated. The question as to the degree to whether such strategic HF/E exhortations swim against an inexorable tide of technological development is also considered.

Product customization: A profile of consumer demand

Increasingly, many brands are handing over to consumers the control of the design process, allowing for the customization of products, experiences, and services. While there is a clear demand for customization, the general assumption is that such demand is homogeneous among consumers. We propose that heterogeneity exists in consumer demand for customization and that a better understanding of these differences is important for brand managers to effectively develop and deliver to consumers the opportunity to customize. Drawing on configuration theory, we provide insights into consumer market segments for firms seeking strategic fit for more effective customization. Across multiple studies, a Latent Cluster Analysis is utilized to profile customization demand. Four distinct segments are identified: Non-Customizers, New Customizers, Active Customizers, and Lapsing Customizers, while studying their outcome considerations in terms of new product design. The varying patterns of customization preference and consumption patterns among segments provide opportunities for retail and service providers to target customization offers more effectively.

Estimating electricity impact profiles for building characteristics using smart meter data and mixed models

Understanding the impact of building characteristics on electricity demand is important for policy and management decision making. Certain building characteristics and equipment may increase or decrease electricity consumption. Due to different operating practices, these impacts on electricity consumption may vary both across the day and across seasons. Quantifying the magnitude and statistical significance of these impacts will help managers and policy makers make better informed decisions. Here we present a mixed effects model to assess the importance of several variables on building electricity consumption. We use smart meter and building attribute data for 129 commercial office buildings. Our building attribute data includes information on installed equipment and meter characteristics of each building. To account for uncertainty in both variable significance and model selection we follow a multimodel inference approach. Demand impact profiles that show the expected change in electricity demand when a characteristic is absent or present are produced for each season. A discussion of the commercial office building characteristics we use and their impact on the daily profile of electricity demand is presented. Our approach has the advantage of only requiring building level demand and characteristic data. No equipment level sub-metering is required. Furthermore, our approach can also be used to quantify changes in electricity consumption caused by other factors that do not directly draw electricity from the grid, such as management decisions or occupant behaviour. We conclude with a discussion of applications for our methodology and future research directions.

Economic analysis of a nuclear hybrid energy system in a stochastic environment including wind turbines in an electricity grid

The electricity market in the U.S. is moving toward an energy mix including more variable renewable energy (VRE) sources like wind and solar. This increasing VRE penetration is altering the profile of the net electricity demand. Traditionally, flexible generators (e.g. gas turbines) are used to absorb fluctuations in the electricity demand. Therefore, an increase in VRE also leads to an increase in installed capacity for such flexible generators which, in turn, may increase the price of electricity. Nuclear-Renewable Hybrid Energy Systems (N-R HES) are seen as a solution to this problem. The N-R HES contains a nuclear power plant electrically and/or thermally linked to an industrial process (IP). This paper demonstrates a new methodology and presents the corresponding RAVEN/Modelica-based software framework to evaluate the financial performance of N-R HES. The novelty of the method is that it explicitly incorporates the stochastic nature of N-R HES inputs such as electricity demand and wind speeds. The method is applied to a generic (rather than region-specific) N-R HES. The analysis shows that under the right market conditions, economic benefits can be achieved by adding a suitable IP to the base-load generator. In particular, there exists an optimal relationship between the IP capacity and the demand. Global trends also indicate VRE penetration raises the cost of electricity due to the added volatility, an issue only mitigated when operating with high profit margins for the IP.

A NEW METHODOLOGY TO ANALYZE THE DYNAMIC OF DAILY POWER DEMAND WITH ATTRACTORS INTO THE MANDELBROT SET

The time series plot of electricity daily load demand is seasonal as shown by its regular repetitive pattern during the same period each year. Its behavior is determined by phase-space diagrams that are able to identify any of the following states of the series: fixed point, periodic, or chaotic. The first two deal with predictable systems. This paper focuses on presenting a new methodology to analyze the dynamics of the series in reference by using the curve formed by attractors that move in the complex plane over the Mandelbrot set according to the law dictated by the load curve. Because electrical power is a variable, it is also defined in the complex plane with the components of active power on the real axis and reactive power on the imaginary axis. Therefore, electrical power facilitates a new field of analysis in Mandelbrot fractal space. The obtained temporal curve confirms that the profile of the electric power demand is also mapped with the new fractal geometric space of the Mandelbrot set, thus providing a new contribution that extends knowledge about the dynamics of systems in fractal geometry.

Synthesis of Solar Thermal Network for Domestic Heat Utilization

Strategic and systematic integration of solar thermal in the building sector can lead to energy security and emission reduction. The impact of such renewable energy application could be boosted when an integration scheme is formulated by considering the changing meteorological conditions and periodic variabilities in the profile of heat demand. The efficiency of such networks could be enhanced by incorporating heat storage technology and backup utility system to form an integrated design. In this paper, a new approach based on multi-period Mixed-Integer Non-Linear Programming (MINLP) which captures such features is developed for integrating solar thermal with the space heat and hot water supply network of a cluster of buildings. The integration scheme is implemented and solved in GAMS and considers the hourly changes in solar irradiation and ambient temperatures, together with the effects of these changes on integration variables across different time horizons. Two scenarios are considered, the first involves four winter months while the second entails four summer months. The results obtained show that solar thermal satisfies 7.4 % of the total heat required in the winter months, while it fully supplies the total heat demand in the winter months.

The times they are a changin’: Current and future trends in electricity demand and supply

The increasing penetration of variable renewable electricity generation in Australia’s National Electricity Market over the past decade has led to a sustained change in the shape of electricity demand. In particular, intra-day demand has become more volatile, with demand in some Australian regions increasingly resembling the widely-cited ‘duck curve’ or more appropriately the ‘emu curve’. The changes in demand have, in turn, economically driven out generators whose technical characteristics are ill-suited to supplying this demand profile: high capacity-factor, slow-start plant. In this article, we describe the changes to date in the profile of electricity demand, and draw on other studies to argue that these trends are likely to accelerate going forward based on projected future uptake of variable renewables. In combination with technological and policy developments, these trends imply that flexible plant, such as peaking gas, hydro, and dispatchable storage, are likely to be better suited to the changing profile of demand, in contrast to slow-start and relatively inflexible technologies such as coal and combined-cycle gas plants. These trends are based on the flexibility of different generation technologies and their interaction with emissions considerations.