Journal articles on the topic 'Aggregate residential load'
To see the other types of publications on this topic, follow the link: Aggregate residential load.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Aggregate residential load.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Zhou, Xiao, Jing Shi, Yuejin Tang, Yuanyuan Li, Shujian Li, and Kang Gong. "Aggregate Control Strategy for Thermostatically Controlled Loads with Demand Response." Energies 12, no. 4 (February 20, 2019): 683. http://dx.doi.org/10.3390/en12040683.
Full textAbstract:
The improvement of intelligent appliances provides the basis for the demand response (DR) of residential loads. Thermostatically controlled loads (TCLs) are one of the most important DR resources and are characterized by a large load and a high degree of control. Due to its distribution characteristic, the aggregation of TCLs and their control are key issues in implementing the load control for the DR. In this study, we focus on air conditioning loads as an example of TCLs and propose a simple and transferable aggregate model by establishing a virtual house model, which accurately captures the aggregate flexibility. The deviation of the aggregate model is analyzed for the model evaluation. An air conditioning DR control scheme is proposed based on the aggregate model; it has the advantage of simple implementation and convenient control for the individual units. Simulations are performed in Gridlab-D to evaluate the accuracy and effectiveness of the proposed model and control method.
2
Nashrullah, Erwin, and Abdul Halim. "Polynomial Load Model Development for Analysing Residential Electric Energy Use Behaviour." MATEC Web of Conferences 218 (2018): 01007. http://dx.doi.org/10.1051/matecconf/201821801007.
Full textAbstract:
Analysing and simulating the dynamic behaviour of home power system as a part of community-based energy system needs load model of either aggregate or dis-aggregate power use. Moreover, in the context of home energy efficiency, development of specific and accurate residential load model can help system designer to develop a tool for reducing energy consumption effectively. In this paper, a new method for developing two types of residential polynomial load model is presented. In the research, computation technique of model parameters is provided based on median filter and least square estimation and implemented by MATLAB. We use AMPDs data set, which have 1-minute data sampling, to show the effectiveness of proposed method. After simulation is carried out, the performance evaluation of model is provided through exploring root mean-squared error between original data and model output. From simulation results, it could be concluded that proposed model is enough for helping system designer to analyse home power energy use.
3
Djokic, Sasa Z., and Igor Papic. "Smart Grid Implementation of Demand Side Management and Micro-Generation." International Journal of Energy Optimization and Engineering 1, no. 2 (April 2012): 1–19. http://dx.doi.org/10.4018/ijeoe.2012040101.
Full textAbstract:
This paper analyses the influence and effects of demand side management (DSM) and micro-generation (MG) on the operation of future “smart grids.” Using the residential load sector with PV and wind-based MG as an example, the paper introduces a general methodology allowing to identify demand-manageable portion of the load in the aggregate demand, as well as to fully correlate variable power outputs of MG with the changes in load demands, including specific DSM actions and schemes. The presented analysis is illustrated using a detailed model of a typical UK LV/MV residential network.
4
Afzaal, Muhammad Umar, Intisar Ali Sajjad, Muhammad Faisal Nadeem Khan, Shaikh Saaqib Haroon, Salman Amin, Rui Bo, and Waqas ur Rehman. "Inter-temporal characterization of aggregate residential demand based on Weibull distribution and generalized regression neural networks for scenario generations." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4491–503. http://dx.doi.org/10.3233/jifs-200462.
Full textAbstract:
The characterization of electrical demand patterns for aggregated customers is considered as an important aspect for system operators or electrical load aggregators to analyze their behavior. The variation in electrical demand among two consecutive time intervals is dependent on various factors such as, lifestyle of customers, weather conditions, type and time of use of appliances and ambient temperature. This paper proposes an improved methodology for probabilistic characterization of aggregate demand while considering different demand aggregation levels and averaging time step durations. At first, a probabilistic model based on Weibull distribution combined with generalized regression neural networks (GRNN) is developed to extract the inter-temporal behavior of demand variations and, then, this information is used to regenerate aggregate demand patterns. Average Mean Absolute Percentage Error (AMAPE) is used as a statistical indicator to assess the accuracy and effectiveness of proposed probabilistic modeling approach. The results have demonstrated that the performance of proposed approach is better in comparison with an existing Beta distribution-based method to characterize aggregate electrical demand patterns.
5
Lindberg, K. B., S. J. Bakker, and I. Sartori. "Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts." Utilities Policy 58 (June 2019): 63–88. http://dx.doi.org/10.1016/j.jup.2019.03.004.
Full text
6
Roth, Jonathan, Jayashree Chadalawada, Rishee K. Jain, and Clayton Miller. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification." Energies 14, no. 5 (March 8, 2021): 1481. http://dx.doi.org/10.3390/en14051481.
Full textAbstract:
As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamental limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use.
7
Ahajjam, Mohamed Aymane, Daniel Bonilla Licea, Mounir Ghogho, and Abdellatif Kobbane. "IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings." Sensors 20, no. 4 (February 14, 2020): 1048. http://dx.doi.org/10.3390/s20041048.
Full textAbstract:
Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.
8
Yousefi, Ali, Waiching Tang, Mehrnoush Khavarian, Cheng Fang, and Shanyong Wang. "Thermal and Mechanical Properties of Cement Mortar Composite Containing Recycled Expanded Glass Aggregate and Nano Titanium Dioxide." Applied Sciences 10, no. 7 (March 26, 2020): 2246. http://dx.doi.org/10.3390/app10072246.
Full textAbstract:
One of the growing concerns in the construction industry is energy consumption and energy efficiency in residential buildings. Moreover, management of non-degradable solid glass wastes is becoming a critical issue worldwide. Accordingly, incorporation of recycled expanded glass aggregates (EGA) as a substitution for natural fine aggregate in cement composites would be a sustainable solution in terms of energy consumption in the buildings and waste management. This experimental research aims to investigate the effects of EGA on fresh and hardened properties and thermal insulating performance of cement mortar. To enhance the mechanical properties and water resistance of the EGA-mortar, nano titanium dioxide (nTiO2) was used as nanofillers. The results showed an increase in workability and water absorption of the EGA-mortar. In addition, a significant decrease in bulk density and compressive strength observed by incorporating EGA into the cement mortar. The EGA-mortar exhibited a low heat transfer rate and excellent thermal insulation property. Furthermore, inclusion of nTiO2 increased compressive strength and water resistance of EGA-mortar, however, their heat transfer rate was increased. The results demonstrated that EGA-mortar can be integrated into the building envelop or non-load bearing elements such as wall partition as a thermal resistance to reduce the energy consumption in residential buildings.
9
Olama, Mohammed, Teja Kuruganti, James Nutaro, and Jin Dong. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid." Energies 11, no. 7 (July 16, 2018): 1852. http://dx.doi.org/10.3390/en11071852.
Full textAbstract:
Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.
10
Kapustin, Fedor, and Vladimir A. Belyakov. "Application of Modified Peat Aggregate for Lightweight Concrete." Solid State Phenomena 309 (August 2020): 120–25. http://dx.doi.org/10.4028/www.scientific.net/ssp.309.120.
Full textAbstract:
The scientific article "Application of Modified Peat Aggregate for Lightweight Concrete" presents the results of studies of the properties of a new composite material for use in enclosing structures of residential and public buildings. Physical and mechanical characteristics of possible aggregates of local production for this type of concrete affecting its operational properties are considered. The prospects of using fly ash as an additive improving the characteristics of polystyrene concrete with the addition of modified peat have been established. The analysis was made and the optimal compositions for obtaining lightweight concrete based on peat and polystyrene foam were selected. The desorption properties of lightweight concrete important for its effective operation as a wall material were tested. It was found that the use of new types of surfactants can improve the water wettability of peat particles and polystyrene granules, thereby reducing the water-cement ratio and improving the compressive strength of the material. Possible efficiency of application of this type of concrete for use in enclosing structures of buildings and constructions under construction in seismic regions of Russia is considered. The presence of damping effect manifested in the material due to the presence of polystyrene granules in the perception of a certain level of load, which is important for the work of concrete under seismic influences, was experimentally established.
11
Ribarov, Lubomir A., and David S. Liscinsky. "Microgrid Viability for Small-Scale Cooling, Heating, and Power." Journal of Energy Resources Technology 129, no. 1 (May 9, 2006): 71–78. http://dx.doi.org/10.1115/1.2424967.
Full textAbstract:
Cooling, heating, and power (CHP) energy systems provide higher fuel efficiency than conventional systems, resulting in reduced fuel consumption, reduced emissions, and other environmental benefits. Until recently the focus of CHP system development has been primarily on medium-scale commercial applications in a limited number of market segments where clear value propositions lead to short term payback. Small-scale integrated CHP systems that show promise of achieving economic viability through significant improvements in fuel utilization have received increased attention lately. In this paper the economic potential is quantified for small-scale (microgrid) integrated CHP systems suitable for groups of buildings with aggregate electric loads in the 15-120kW range. Technologies are evaluated for community building groups (CBGs) consisting of aggregation of pure residential entities and combined residential and light commercial entities. Emphasis is on determination of the minimum load size (i.e., the smallest electric and thermal load for a given CBG that is supplied with electric, heating, cooling power from a CHP) for which a microgrid CHP system is both technically and economically viable. In this paper, the operation of the CHP system is parallel with the public utility grid at all times, i.e., the grid is interconnected. Evaluations of CHP technology options using simulation studies in a “three-dimensional” space (CHP technology option, CBG load aggregation, and geographical location in the USA) were evaluated based on comparisons of net present value (NPV). The simulations indicated that as electric load increases, the viability of the CHP system (independent of the system’s size) becomes more favorable. Exceeding a system runtime (utilization) of 70% was shown to pass the break-even line in the NPV analysis. Finally, geographic location was found to have a relatively weak effect on the reported trends. These results suggest that microgrid CHP systems have the potential to be economically viable with relative independence of geographic location if adequately sized to match the specific load requirements.
12
Nazemi, Seyyed Danial, Mohsen A. Jafari, and Esmat Zaidan. "An Incentive-Based Optimization Approach for Load Scheduling Problem in Smart Building Communities." Buildings 11, no. 6 (May 31, 2021): 237. http://dx.doi.org/10.3390/buildings11060237.
Full textAbstract:
The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model’s objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk’s method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model’s effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants’ satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 residential and commercial buildings.
13
Obaro, Adewale Zakariyahu, Josiah Lange Munda, and Adedayo Adedamola YUSUFF. "Modelling and Energy Management of an Off-Grid Distributed Energy System: A Typical Community Scenario in South Africa." Energies 16, no. 2 (January 6, 2023): 693. http://dx.doi.org/10.3390/en16020693.
Full textAbstract:
Conventional power systems have been heavily dependent on fossil fuel to meet the increasing energy demand due to exponential population growth and diverse technological advancements. This paper presents an optimal energy model and power management of an off-grid distributed energy system (DES) capable of providing sustainable and economic power supply to electrical loads. The paper models and co-optimizes multi-energy generations as a central objective for reliable and economic power supply to electrical loads while simultaneously satisfying a set of system and operational parameters. In addition, mixed integer nonlinear programing (MINLP) optimization technique is exploited to maximize power system generation between interconnected energy sources and dynamic electrical load with highest reliability and minimum operational and emission costs. Due to frequent battery cycling operation in the DES, rainflow algorithm is applied to the optimization result to estimate the depth of discharge (DOD) and subsequently count the number of cycles. The validity and performance of the power management strategy is evaluated with an aggregate load demand scenario of sixty households as a benchmark in a MATLAB program. The simulation results indicate the capability and effectiveness of optimal DES model through an enhanced MINLP optimization program in terms of significant operational costs and emission reduction of the diesel generator (DG). Specifically, the deployment of DES minimizes the daily operational cost by 71.53%. The results further indicate a drastic reduction in CO2 emissions, with 22.76% reduction for the residential community load scenario in contrast to the exclusive DG system. This study provides a framework on the economic feasibility and effective planning of green energy systems (GESs) with efficient optimization techniques with capability for further development.
14
Drozdzol, Krzysztof. "Thermal and Mechanical Studies of Perlite Concrete Casing for Chimneys in Residential Buildings." Materials 14, no. 8 (April 16, 2021): 2011. http://dx.doi.org/10.3390/ma14082011.
Full textAbstract:
Chimneys are structures designed to convey exhaust gases from heating devices to the outside of buildings. The materials from which they are made have a great impact on their fire safety, as well as on the safety of the whole building. As current trends in the construction industry are moving towards improving the environmental impact and fire safety, changes to building materials are constantly being introduced. This also applies to the development of chimney technology, as there is still a recognised need for new solutions when it comes to materials used in the production of chimney systems. This article presents the findings of tests carried out on a chimney made from innovative perlite concrete blocks. Four different perlite concrete blocks that differed in bulk densities were analysed. The obtained results were then compared with widely used leca (lightweight expanded clay aggregate) concrete blocks. The test results confirmed high insulation properties of the perlite concrete block, from which the innovative chimney casing was made. The fire safety level was maintained even in high temperatures that occur during soot fire (1000 °C). These properties were retained despite there being no additional insulation of the flue duct. Even though the thermal load decreased the compressive strength of the chimney blocks, they still displayed sufficient average strength of 4.03 MPa. Additionally, the test results confirmed the possibility of recovering heat from the chimney with the efficiency of 23–30%, which constitutes a considerable increase compared to chimneys made from leca concrete blocks.
15
Mascherbauer, Philipp, Franziska Schöniger, Lukas Kranzl, and Songmin Yu. "Impact of variable electricity price on heat pump operated buildings." Open Research Europe 2 (December 7, 2022): 135. http://dx.doi.org/10.12688/openreseurope.15268.1.
Full textAbstract:
Background: Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods: In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results: This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions: Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.
16
Yedilbayev, Bauyrzhan, Akmaral Shokanova, Zauresh Akhmetova, Gani Askarov, and Nurlan Kalganbayev. "Structural and bit-by-bit modeling of the cities." E3S Web of Conferences 159 (2020): 05001. http://dx.doi.org/10.1051/e3sconf/202015905001.
Full textAbstract:
Developed economic - ecological model of modern large city on the example of Almaty, based on the main provisions of statistical theory, theories of logistics and the similarity of the General plan of development of Kazakhstan megapolis, the strategy of transport development of Kazakhstan, programs to reduce the traffic load on the highways regulations of international and national importance, as well as on the basis of predictive decisions arising from the comprehensive consideration of the issues city transport road ecology (CTRE). It includes for the first time scientifically grounded ecological and economic indicators and daily ecological model of Almaty which were initial data for further researches and calculations. However, this complex problem practically in all textbooks and manuals on ecology is considered factually, i.e. separately without interrelation, and questions of interrelation or mutual influence of emissions of motor transport in aggregate with infrastructure SRN (traffic lights, intersections, sidewalks, avenues, etc.) on environment are not still considered in the world literature. Besides there are no data on distribution of exhaust gases (EG) of motor vehicle in the residential area near highways in any source. Therefore, even it is difficult to expert to define the main sources of pollution of urban air environment.
17
Wang, Yizhen, Ningqing Zhang, and Xiong Chen. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features." Energies 14, no. 10 (May 11, 2021): 2737. http://dx.doi.org/10.3390/en14102737.
Full textAbstract:
With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the related research of power load forecasting and introduce a short-term residential load forecasting model based on a long short-term memory (LSTM) recurrent neural network with weather features as an input.
18
Hou, Tingting, Rengcun Fang, Jinrui Tang, Ganheng Ge, Dongjun Yang, Jianchao Liu, and Wei Zhang. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms." Energies 14, no. 22 (November 22, 2021): 7820. http://dx.doi.org/10.3390/en14227820.
Full textAbstract:
Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method.
19
Carpaneto, E., and G. Chicco. "Probabilistic characterisation of the aggregated residential load patterns." IET Generation, Transmission & Distribution 2, no. 3 (2008): 373. http://dx.doi.org/10.1049/iet-gtd:20070280.
Full text
20
Lv, Wenjie, Jian Wu, Zhao Luo, Min Ding, Xiang Jiang, Hejian Li, and Qian Wang. "Load Aggregator-Based Integrated Demand Response for Residential Smart Energy Hubs." Mathematical Problems in Engineering 2019 (April 18, 2019): 1–14. http://dx.doi.org/10.1155/2019/6925980.
Full textAbstract:
In order to attract more flexible resource to take part in integrated demand response (IDR), this can be realized by introducing load aggregator-based framework. In this paper, based on residential smart energy hubs (S.E. Hubs), a two-level IDR framework is proposed, in which S.E. Hub operators play the role of load aggregators. The framework includes day-ahead bidding and real-time scheduling. In day-ahead bidding, S.E. Hub operators have to compete dispatching amount for maximal profit; hence, noncooperative game approach is formulated to describe the competition behavior among operators. In real-time scheduling, the dispatching model is formulated to minimize the error between real-time scheduling amount and bidding amount. Moreover, in order to reduce the influence of IDR on residential users, 4 categories of users’ flexible loads are modeled according to load consumption characteristic, and then these models are considered as the constraints in real-time scheduling. A case study is designed to validate the effectiveness of the proposed two-level IDR framework. And simulation results confirm that smart grid, S.E. Hub operators, and residential users can benefit simultaneously.
21
Hosseini, Seyed Ali, Mehrdad Hojjat, and Azita Azarfar. "An integrated home energy management system by the load aggregator in a microgrid using the internet of things infrastructure." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6796. http://dx.doi.org/10.11591/ijece.v12i6.pp6796-6805.
Full textAbstract:
<span lang="EN-US">Smart technologies enable the significant participation of consumers in demand-side management programs. In this paper, the management of electrical energy consumption for a set of residential houses in a microgrid by a load aggregator for a 24-h planning horizon is studied. In this study, consumption management programs are implemented on controllable equipment by sending binary codes by the load aggregator via the internet of things (IoT) infrastructure to residential sockets. To increase the level of customer convenience and provide more flexibility for consumers to participate in demand response programs, a parameter called the value of lost load (VOLL) has been introduced. According to the results, in addition to no need to use the energy management system for each residential house, only by moving shiftable loads to off-peak hours, 18.34% of energy consumption costs are saved daily. Also, from the load aggregator’s viewpoint for every 10% change in status from normal to the scheduled priority, there is a reduction of about 3.4% in the consumer’s peak-load cost. If solar arrays and storage resources are used, more than 18% of the total consumption cost can be saved.</span>
22
Eslami, Abolfazl, Ali Nabizadeh, and Hossein Akbarzadeh Kasani. "Geotechnical and geophysical characterisations of construction waste-infilled quarry for housing and commercial developments: Case study of Tehran, Iran." Waste Management & Research: The Journal for a Sustainable Circular Economy 40, no. 3 (October 19, 2021): 349–59. http://dx.doi.org/10.1177/0734242x211052851.
Full textAbstract:
The fast population growth in the metropolitan areas of the province of Tehran has led to the scarcity of land and inevitable expansion of urban construction to non-engineered fills and construction/demolition waste disposal sites. An abandoned aggregate quarry, infilled with construction wastes over 16 years, has been recently selected for a new development project consisting of several multi-storey commercial and residential complexes (up to 7 storeys). This study was aimed at delineation of the waste materials, geophysical and field and laboratory geotechnical characterisations prior to foundation design, and the design of the excavation programme. Geo-electric resistivity test was used to delineate the waste materials from natural ground materials. Surface and downhole P- and S-wave velocity measurements were used for the estimation of dynamic elastic properties of the wastes. In total, 12 boreholes (15–30 m deep) along with 10 test pits (4–8.5 m deep) provided the opportunity for visual observations of the waste materials, necessary sampling for compositional analyses, laboratory shear strength tests and determination of waste deposit thickness in different regions of the site. Manual standard penetration test (SPT) was also used to evaluate in situ stiffness of the fine materials of the waste. Six field plate load tests were performed on the waste materials at their natural water content conditions and at saturated (flooded) ground conditions to determine their compressibility and the ground reaction modulus. Based on the results from extensive characterisation programme, it was concluded that the waste materials are in a metastable state and exhibit heterogeneity across the site. The findings of current case study can provide new insight into construction/demolition waste behaviour, using available geophysical and geotechnical tools and testing procedures for characterisation, and eventually helping in reliable design of foundations for new development projects.
23
Sajjad, Intisar Ali, Gianfranco Chicco, and Roberto Napoli. "Definitions of Demand Flexibility for Aggregate Residential Loads." IEEE Transactions on Smart Grid 7, no. 6 (November 2016): 2633–43. http://dx.doi.org/10.1109/tsg.2016.2522961.
Full text
24
Song, Zhaofang, Jing Shi, Shujian Li, Zexu Chen, Wangwang Yang, and Zitong Zhang. "Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling." Applied Sciences 10, no. 20 (October 19, 2020): 7310. http://dx.doi.org/10.3390/app10207310.
Full textAbstract:
As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechanism requires the LA to formulate reasonable bidding strategies to obtain the maximum profit. Accordingly, this paper focuses on how the LA formulate the optimal bidding strategy considering the uncertainty of residents’ participation in DR. Firstly, the physical models of flexible loads are established to evaluate the ideal DR potential. On this basis, to quantify the uncertainty of the residential consumers, this paper uses a fuzzy system to construct a model to evaluate the residents’ willingness to participate in DR. Then, based on the queuing method, a bidding decision-making model considering the uncertainty is constructed to maximize the LA’s income. Finally, based on a case simulation of a residential community, the results show that compared with the conventional bidding strategy, the optimal bidding model considering the residents’ willingness can reduce the response cost of the LA and increase the LA’s income.
25
Ponocko, Jelena, and Jovica V. Milanovic. "Forecasting Demand Flexibility of Aggregated Residential Load Using Smart Meter Data." IEEE Transactions on Power Systems 33, no. 5 (September 2018): 5446–55. http://dx.doi.org/10.1109/tpwrs.2018.2799903.
Full text
26
Huang, Nantian, Wenting Wang, Sining Wang, Jun Wang, Guowei Cai, and Liang Zhang. "Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting." IEEE Access 8 (2020): 25198–209. http://dx.doi.org/10.1109/access.2020.2971033.
Full text
27
Lucas, Alexandre, Luca Jansen, Nikoleta Andreadou, Evangelos Kotsakis, and Marcelo Masera. "Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector." Energies 12, no. 14 (July 16, 2019): 2725. http://dx.doi.org/10.3390/en12142725.
Full textAbstract:
Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.
28
Huber, Patrick, Melvin Ott, Martin Friedli, Andreas Rumsch, and Andrew Paice. "Residential Power Traces for Five Houses: The iHomeLab RAPT Dataset." Data 5, no. 1 (February 5, 2020): 17. http://dx.doi.org/10.3390/data5010017.
Full textAbstract:
Datasets with measurements of both solar electricity production and domestic electricity consumption separated into the major loads are interesting for research focussing on (i) local optimization of solar energy consumption and (ii) non-intrusive load monitoring. To this end, we publish the iHomeLab RAPT dataset consisting of electrical power traces from five houses in the greater Lucerne region in Switzerland spanning a period from 1.5 up to 3.5 years with a sampling frequency of five minutes. For each house, the electrical energy consumption of the aggregated household and specific appliances such as dishwasher, washing machine, tumble dryer, hot water boiler, or heating pump were metered. Additionally, the data includes electric production data from PV panels for all five houses, and battery power flow measurement data from two houses. Thermal metadata is also provided for the three houses with a heating pump.
29
Carcangiu, S., A. Fanni, P. A. Pegoraro, G. Sias, and S. Sulis. "Forecasting-Aided Monitoring for the Distribution System State Estimation." Complexity 2020 (February 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/4281219.
Full textAbstract:
In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid.
30
Alahyari, Arman, and Mohammad Jooshaki. "Fast energy management approach for the aggregated residential load and storage under uncertainty." Journal of Energy Storage 62 (June 2023): 106848. http://dx.doi.org/10.1016/j.est.2023.106848.
Full text
31
Ungureanu, Stefan, Vasile Topa, and Andrei Cristinel Cziker. "Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market." Energies 14, no. 21 (October 23, 2021): 6966. http://dx.doi.org/10.3390/en14216966.
Full textAbstract:
Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and historical data, which is frequently not consistent. Our objective is to develop viable and market-oriented machine learning models for short-term forecasting for non-residential consumers. Multiple algorithms were implemented and compared to identify the best model for a cluster of industrial and commercial consumers. The article concludes that the sliding window approach for supervised learning with recurrent neural networks can learn short and long-term dependencies in time series. The best method implemented for the 24 h forecast is a Gated Recurrent Unit (GRU) applied for aggregated loads over three months of testing data resulted in 5.28% MAPE and minimized the cost with 5326.17 € compared with the second-best method LSTM. We propose a new model to evaluate the gap between evaluation metrics and the financial impact of forecast errors in the power market environment. The model simulates bidding on the power market based on the 24 h forecast and using the Romanian day-ahead market and balancing prices through the testing dataset.
32
Estebsari, Abouzar, and Roozbeh Rajabi. "Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques." Electronics 9, no. 1 (January 1, 2020): 68. http://dx.doi.org/10.3390/electronics9010068.
Full textAbstract:
The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.
33
El-Hendawi, Mohamed, Zhanle Wang, Raman Paranjape, Shea Pederson, Darcy Kozoriz, and James Fick. "Electric Vehicle Charging Model in the Urban Residential Sector." Energies 15, no. 13 (July 4, 2022): 4901. http://dx.doi.org/10.3390/en15134901.
Full textAbstract:
Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.
34
Stephen, Bruce, Xiaoqing Tang, Poppy R. Harvey, Stuart Galloway, and Kyle I. Jennett. "Incorporating Practice Theory in Sub-Profile Models for Short Term Aggregated Residential Load Forecasting." IEEE Transactions on Smart Grid 8, no. 4 (July 2017): 1591–98. http://dx.doi.org/10.1109/tsg.2015.2493205.
Full text
35
Schlemminger, Marlon, Raphael Niepelt, and Rolf Brendel. "A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles." Energies 14, no. 8 (April 13, 2021): 2167. http://dx.doi.org/10.3390/en14082167.
Full textAbstract:
End-use specific residential electricity load profiles are of interest for energy system modelling that requires future load curves or demand-side management. We present a model that is applicable across countries to predict consumption on a regional and national scale, using openly available data. The model uses neural networks (NNs) to correlate measured consumption from one country (United Kingdom) with weather data and daily profiles of a mix of human activity and device specific power profiles. We then use region-specific weather data and time-use surveys as input for the trained NNs to predict unscaled electric load profiles. The total power profile consists of the end-use household load profiles scaled with real consumption. We compare the model’s results with measured and independently simulated profiles of various European countries. The NNs achieve a mean absolute error compared with the average load of 6.5 to 33% for the test set. For Germany, the standard deviation between the simulation, the standard load profile H0, and measurements from the University of Applied Sciences Berlin is 26.5%. Our approach reduces the amount of input data required compared with existing models for modelling region-specific electricity load profiles considering end-uses and seasonality based on weather parameters. Hourly load profiles for 29 European countries based on four historical weather years are distributed under an open license.
36
Gu, Wei, Shuai Lu, Zhi Wu, Xuesong Zhang, Jinhui Zhou, Bo Zhao, and Jun Wang. "Residential CCHP microgrid with load aggregator: Operation mode, pricing strategy, and optimal dispatch." Applied Energy 205 (November 2017): 173–86. http://dx.doi.org/10.1016/j.apenergy.2017.07.045.
Full text
37
Andruszkiewicz, Jerzy, Józef Lorenc, and Agnieszka Weychan. "Price-Based Demand Side Response Programs and Their Effectiveness on the Example of TOU Electricity Tariff for Residential Consumers." Energies 14, no. 2 (January 7, 2021): 287. http://dx.doi.org/10.3390/en14020287.
Full textAbstract:
Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on the power system’s load and this impact changes substantially for particular hours of a day or season. Such efficiency assessment may be used by both energy suppliers to optimize their market purchases and by distribution system operators in order to ensure adequate generation during peak load periods.
38
Andruszkiewicz, Jerzy, Józef Lorenc, and Agnieszka Weychan. "Price-Based Demand Side Response Programs and Their Effectiveness on the Example of TOU Electricity Tariff for Residential Consumers." Energies 14, no. 2 (January 7, 2021): 287. http://dx.doi.org/10.3390/en14020287.
Full textAbstract:
Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on the power system’s load and this impact changes substantially for particular hours of a day or season. Such efficiency assessment may be used by both energy suppliers to optimize their market purchases and by distribution system operators in order to ensure adequate generation during peak load periods.
39
Martín-Crespo, Alejandro, Sergio Saludes-Rodil, and Enrique Baeyens. "Flexibility Management with Virtual Batteries of Thermostatically Controlled Loads: Real-Time Control System and Potential in Spain." Energies 14, no. 6 (March 19, 2021): 1711. http://dx.doi.org/10.3390/en14061711.
Full textAbstract:
Load flexibility management is a promising approach to face the problem of balancing generation and demand in electrical grids. This problem is becoming increasingly difficult due to the variability of renewable energies. Thermostatically-controlled loads can be aggregated and managed by a virtual battery, and they provide a cost-effective and efficient alternative to physical storage systems to mitigate the inherent variability of renewable energy sources. However virtual batteries require that an accurate control system is capable of tracking frequency regulation signals with minimal error. A real-time control system allowing virtual batteries to accurately track frequency or power signals is developed. The performance of this controller is validated for a virtual battery composed of 1000 thermostatically-controlled loads. Using virtual batteries equipped with the developed controller, a study focused on residential thermostatically-controlled loads in Spain is performed. The results of the study quantify the potential of this technology in a country with different climate areas and provides insight about the feasibility of virtual batteries as enablers of electrical systems with high levels of penetration of renewable energy sources.
40
Bashir, Arslan, and Matti Lehtonen. "Optimal Coordination of Aggregated Hydro-Storage with Residential Demand Response in Highly Renewable Generation Power System: The Case Study of Finland." Energies 12, no. 6 (March 18, 2019): 1037. http://dx.doi.org/10.3390/en12061037.
Full textAbstract:
Current energy policy-driven targets have led to increasing deployment of renewable energy sources in electrical grids. However, due to the limited flexibility of current power systems, the rapidly growing number of installations of renewable energy systems has resulted in rising levels of generation curtailments. This paper probes the benefits of simultaneously coordinating aggregated hydro-reservoir storage with residential demand response (DR) for mitigating both load and generation curtailments in highly renewable generation power systems. DR services are provided by electric water heaters, thermal storages, electric vehicles, and heating, ventilation and air-conditioning (HVAC) loads. Accordingly, an optimization model is presented to minimize the mismatch between demand and supply in the Finnish power system. The model considers proportions of base-load generation comprising nuclear, and combined heat and power (CHP) plants (both CHP-city and CHP-industry), as well as future penetration scenarios of solar and wind power that are constructed, reflecting the present generation structure in Finland. The findings show that DR coordinated with hydropower is an efficient curtailment mitigation tool given the uncertainty in renewable generation. A comprehensive sensitivity analysis is also carried out to depict how higher penetration can reduce carbon emissions from electricity co-generation in the near future.
41
Dolan, P. S., M. H. Nehrir, and V. Gerez. "Development of a Monte Carlo based aggregate model for residential electric water heater loads." Electric Power Systems Research 36, no. 1 (January 1996): 29–35. http://dx.doi.org/10.1016/0378-7796(95)01011-4.
Full text
42
Theocharis, Andreas, and Sahaphol Hamanee. "Battery Storage at the Secondary Distribution Electricity Grid by Investigating End-Users Load Demand Measurements." Energies 15, no. 8 (April 8, 2022): 2743. http://dx.doi.org/10.3390/en15082743.
Full textAbstract:
Energy storage systems is expected to be utilized to cover the increased electrification of energy demands and to alleviate the electrical energy production from intermittent energy sources such as solar and wind. Aggregated and distributed battery energy storage systems may improve electricity grids operability and security by providing smart energy management options and efficient resources allocation. In this paper, battery storage at the secondary distribution level is explored. The investigation is based on the end-user energy demand behavior. As such, the electrical energy consumption patterns are measured and analyzed in a residential area. Measurements were collected and analyzed in order to record the customers’ behaviors aiming to reveal their differences and similarities. Following this, aggregated and distributed battery energy storage systems are computed based on the features of the measured electrical power consumption patterns aiming to estimate the factors that could potentially incentivize the installation of a battery system either as aggregated at the low voltage transformer side or as distributed system at the load side. The parameters that affect the economic viability of the system are qualitatively evaluated with regard to the profitability of the system.
43
Ziras, Charalampos, Carsten Heinrich, Michael Pertl, and Henrik W. Bindner. "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data." Applied Energy 242 (May 2019): 1407–21. http://dx.doi.org/10.1016/j.apenergy.2019.03.156.
Full text
44
Mugnini, Alice, Fabio Polonara, and Alessia Arteconi. "Energy flexibility in residential buildings clusters." E3S Web of Conferences 197 (2020): 03002. http://dx.doi.org/10.1051/e3sconf/202019703002.
Full textAbstract:
The building sector represents one of the most energy-consuming worldwide and a great part of its consumption is accounted for residential demand for space heating and cooling. Although it is necessary to promote the buildings energy efficiency, energy flexibility is also of paramount importance to optimize the balance between demand and supply. In fact, an energy flexible building is defined as able to change, in a planned manner, the shape of its energy demand curve, electrical and thermal, while the comfort of the end-users is still guaranteed. Objective of this work is to exploit the energy demand management ability of different buildings composing a cluster, when their aggregated demand derived from electric heating systems (i.e. heat pumps) is subject to demand response (DR) strategies. Users with different occupancy profile are considered. By supposing to be able to activate the energy flexibility of the single building with thermostatic load control, different scenarios of cluster composition are evaluated in order to provide guidelines to implement optimal strategies for energy flexibility exploitation without drawback effects connected to the event.
45
Francke, M. K., and F. P. W. Schilder. "Losses on Dutch residential mortgage insurances." Journal of European Real Estate Research 7, no. 3 (October 28, 2014): 307–26. http://dx.doi.org/10.1108/jerer-01-2014-0008.
Full textAbstract:
Purpose – This paper aims to study the data on losses on mortgage insurance in the Dutch housing market to find the key drivers of the probability of loss. In 2013, 25 per cent of all Dutch homeowners were “under water”: selling the property will not cover the outstanding mortgage debt. The double-trigger theory predicts that being under water is a necessary but not sufficient condition to predict mortgage default. A loss for the mortgage insurer is the result of a default where the proceedings of sale and the accumulated savings for postponed repayment of the principal associated to the loan are not sufficient to repay the loan. Design/methodology/approach – For this study, the authors use a data set on losses on mortgage insurance at a national aggregate level covering the period from 1976 to 2012. They apply a discrete time hazard model with calendar time- and duration-varying covariates to analyze the relationship between year of issue of the insurance, duration, equity, unfortunate events like unemployment and divorce and affordability measures to identify the main drivers of the probability of loss. Findings – Although the number of losses increases over time, the number of losses relative to the active insurance is still low, despite the fact that the Dutch housing market is the world’s most strongly leveraged housing market. On average, the peak in loss probability lies around a duration of four years. The average loss probability is virtually zero for durations larger than 10 years. Mortgages initiated just prior to the beginning of the financial crisis have an increased loss probability. The most important drivers of the loss probability are home equity, unemployment and divorce. Affordability measures are less important. Research limitations/implications – Mortgage insurance is available for the lower end of the market only and is intended to decrease the impact of risk selection by banks. The analysis is based on aggregate data; no information on individual households, like initial loan-to-value and price-to-income ratios; current home equity; and unfortunate events, like unemployment and divorce, is available. The research uses averages of these variables per calendar year and/or duration. Information on repayments of insured mortgages is missing. Originality/value – This paper is the first to describe the main drivers of losses on insured mortgages in The Netherlands by using loss data covering two housing market crises, one in the early 1980s and the current crisis that started in 2008. Much has changed between the two crises. For instance, prices have risen steeply as has household indebtedness. Furthermore, alternative mortgage products have increased in popularity. Focusing a study on the drivers of mortgage losses exclusively on the current crisis could therefore be biased, given the time-specific circumstances on the housing market.
46
Garcia-Guarin, Julian, David Alvarez, Arturo Bretas, and Sergio Rivera. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints." Energies 13, no. 17 (September 3, 2020): 4567. http://dx.doi.org/10.3390/en13174567.
Full textAbstract:
Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.
47
Hassell Sweatman, Catherine Zoe Wollaston, N. Wichitaksorn, A. Jiang, Troy Farrell, N. Bootland, G. Miskell, G. Pritchard, C. Chrystall, and G. Robinson. "Challenge from Transpower: Determining the effect of the aggregated behaviour of solar photovoltaic power generation and battery energy storage systems on grid exit point load in order to maintain an accurate load forecast." ANZIAM Journal 60 (June 25, 2020): M1—M40. http://dx.doi.org/10.21914/anziamj.v60i0.14619.
Full textAbstract:
With limited data beyond the grid exit point (GXP) or substation level, how can Transpower determine the effect of the aggregated behaviour of solar photovoltaic power generation and battery energy storage systems on GXP load in order to maintain an accurate load forecast? In this initial study it is assumed that the GXP services a residential region. An algorithm based on non-linear programming, which minimises the financial cost to the consumer, is developed to model consumer behaviour. Input data comprises forecast energy requirements (load), solar irradiance, and pricing. Output includes both the load drawn from the grid and power returned to the grid. The algorithm presented is at the household level. The next step would be to combine the load drawn from the grid and the power returned to the grid from all the households serviced by a GXP, enabling Transpower to make load predictions. Various means of load forecasting are considered including the Holt--Winters methods which perform well for out-of-sample forecasts. Linear regression, which takes into account comparable days, solar radiation, and air temperature, yields even better performance.
48
Hussain, Shahid, Subhasis Thakur, Saurabh Shukla, John G. Breslin, Qasim Jan, Faisal Khan, Ibrar Ahmad, Mousa Marzband, and Michael G. Madden. "A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles." Energies 15, no. 4 (February 11, 2022): 1304. http://dx.doi.org/10.3390/en15041304.
Full textAbstract:
The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.
49
Delcroix, B., S. Sansregret, G. Larochelle Martin, and A. Daoud. "Quantile regression using gradient boosted decision trees for daily residential energy load disaggregation." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012107. http://dx.doi.org/10.1088/1742-6596/2069/1/012107.
Full textAbstract:
Abstract The building sector is responsible for approximately one-third of the total energy consumption, worldwide. This sector is undergoing a major digital transformation, buildings being more and more equipped with connected devices such as smart meters and IoT devices. This transformation offers the opportunity to better monitor and optimize building operations. In the province of Quebec (Canada), most buildings are equipped with smart meters providing electricity usage data every 15 minutes. A current major challenge is to disaggregate the different energy use from smart meter data, a discipline called non-intrusive load monitoring in literature. In this work, the aim is to develop and validate a potentially generalizable model for all houses that identifies the daily share of each energy use based on building information, weather data and smart meter data. Input features are selected and ordered using an aggregated score composed of the correlation coefficient, the feature importance given by a decision tree, and the predictive power score. Two modelling methods based on quantile regression are tested: linear regression (LR) and gradient boosted decision trees (GBDT). Compared to ordinary least squares regression, quantile methods inherently provide more robustness and confidence intervals. Both models are trained and validated using separate datasets collected in 8 houses in Canada where metering and sub-metering were performed during a whole year. Results on the test dataset indicate a better performance of the GBDT model, compared to the LR model, with a coefficient of determination of 0.88 (vs. 0.78), a mean absolute error of 6.34 % (vs. 8.89 %) and a maximum absolute error between the actual and predicted values in 95 % of the cases of 17.2 % (vs. 23.1 %).
50
Gong, Huangjie, Rosemary E. Alden, Aron Patrick, and Dan M. Ionel. "Forecast of Community Total Electric Load and HVAC Component Disaggregation through a New LSTM-Based Method." Energies 15, no. 9 (April 19, 2022): 2974. http://dx.doi.org/10.3390/en15092974.
Full textAbstract:
The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling and winter heating are introduced using a V-shaped hourly total load curve. The trained LTSM model is additionally run with TmHVAC and zero irradiance inputs yielding an estimated baseload, which is representative of typical occupancy patterns. The HVAC power component is disaggregated as the difference between total and baseload power. Total power forecasts of an aggregated residential community as seen by major distribution lines are experimentally validated with a satisfactory MAPE error below 10% based on a 4-year dataset from a representative suburban community with more than 1800 homes in Kentucky, U.S. Discussions regarding the validity of the separation method based on combined considerations of fundamental physics, statistics, and human behavior are also included.