Stochastic model for lighting's electricity consumption in the residential sector. Impact of energy saving actions

García, Emilio José; Chen, A.; Santiago, I.; Bellido-Outeiriño, F.J.; Flores-Arias, J.M.; Moreno-Munoz, A.

doi:10.1016/j.enbuild.2014.12.028

Cited by 46 publications

(36 citation statements)

References 24 publications

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“…Additional conclusions can be obtained if a cluster of 200 households similar to the previously studied ones is simulated for the same range of dates (18)(19)(20)(21). As can be seen in Figure 8, although the consumption profiles were stochastically obtained, the aggregate trend presented the expected behavior with two consumption peaks located during middays and evenings.…”

Section: High Temporal Resolution Simulationsmentioning

confidence: 60%

“…In addition, a general user application (GUI) was developed to ease the use of the system, where the simulation parameters can be configured using the configuration tab from Figure 5 and the results visualized as shown in Figure 6. This GUI is integrated into the previous one developed in [19]. …”

Section: Resultsmentioning

confidence: 99%

“…In addition, by means of a probability distribution of lighting technologies and powers, the lamps that are installed in the household are randomly selected. Further details regarding this model can be found in a previous work [19].…”

Section: Number Ofmentioning

confidence: 99%

“…In addition, Mckenna and Thomson [18] implemented the simulation of heating consumption in terms of hot water demand. What is more, the authors have previously developed various models based on the active occupancy, which are able to simulate the lighting [19], appliances andheating, ventilation and air conditioning (HVAC) systems' consumption [20].…”

Section: Introductionmentioning

confidence: 99%

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PV Hosting Capacity Analysis and Enhancement Using High Resolution Stochastic Modeling

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Reduction of CO 2 emissions is a main target in the future smart grid. This goal is boosting the installation of renewable energy resources (RES), as well as a major consumer engagement that seeks for a more efficient utilization of these resources toward the figure of 'prosumers'. Nevertheless, these resources present an intermittent nature, which requires the presence of an energy storage system and an energy management system (EMS) to ensure an uninterrupted power supply. Moreover, network-related issues might arise due to the increasing power of renewable resources installed in the grid, the storage systems also being capable of contributing to the network stability. However, to assess these future scenarios and test the control strategies, a simulation system is needed. The aim of this paper is to analyze the interaction between residential consumers with high penetration of PV generation and distributed storage and the grid by means of a high temporal resolution simulation scenario based on a stochastic residential load model and PV production records. Results of the model are presented for different PV power rates and storage capacities, as well as a two-level charging strategy as a mechanism for increasing the hosting capacity (HC) of the network.

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Section: High Temporal Resolution Simulationsmentioning

confidence: 60%

Section: Resultsmentioning

confidence: 99%

Section: Number Ofmentioning

confidence: 99%

Section: Introductionmentioning

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“…These appliances account for a significant proportion of household energy consumption. Alternatively, non-shiftable loads, such as lighting and brown appliances, cannot delay their operation [31]. At present, there is no deployed infrastructure that allows remote activation of these appliances.…”

Section: Smart Appliances Overviewmentioning

confidence: 99%

A Novel Direct Load Control Testbed for Smart Appliances

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The effort to continuously improve and innovate smart appliances (SA) energy management requires an experimental research and development environment which integrates widely differing tools and resources seamlessly. To this end, this paper proposes a novel Direct Load Control (DLC) testbed, aiming to conveniently support the research community, as well as analyzing and comparing their designs in a laboratory environment. Based on the LabVIEW computing platform, this original testbed enables access to knowledge of major components such as online weather forecasting information, distributed energy resources (e.g., energy storage, solar photovoltaic), dynamic electricity tariff from utilities and demand response (DR) providers together with different mathematical optimization features given by General Algebraic Modelling System (GAMS). This intercommunication is possible thanks to the different applications programming interfaces (API) incorporated into the system and to intermediate agents specially developed for this case. Different basic case studies have been presented to envision the possibilities of this system in the future and more complex scenarios, to actively support the DLC strategies. These measures will offer enough flexibility to minimize the impact on user comfort combined with support for multiple DR programs. Thus, given the successful results, this platform can lead to a solution towards more efficient use of energy in the residential environment.Energies 2019, 12, 3336 2 of 16 since 2010 [3]. Although the electricity demand for major appliances has slightly decreased since 2007, mainly due to improvements in their energy efficiency, the rapid proliferation of small appliances and brown goods has absorbed these savings. The energy consumption due to these small loads has grown twice as fast as that of large appliances in the last decade. In addition, only one-third of domestic appliances consumption is under regulatory protection, particularly in emerging markets. This may become even more relevant in the near future as the demand for electricity in buildings increases due to the impact of the charging infrastructure for electric vehicles. While it is true that there is a need to increase the rigor of existing policies by extending regulatory coverage to a broader range of devices, on the other hand, user awareness may be the key factor. However, to achieve this, consumers should be rewarded to some extent when changing their behavior. The availability of information and communication technologies (ICT) on SG can be decisive in meeting this commitment through the widespread adoption of DR strategies.In other areas, such as power electronics, it is common to find a complete chain of modeling, development, testing, optimization, virtual validation, and rapid prototyping commercial tools that integrate seamlessly into a convenient testing and development environment such as these tools of Typhoon (Typhon, Somerville, USA) [4] and dSPACE (dSPACE, Paderborn, Germany) [5]. It is possible to find testbed...

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Stochastic model for lighting's electricity consumption in the residential sector. Impact of energy saving actions

Cited by 46 publications

References 24 publications

PV Hosting Capacity Analysis and Enhancement Using High Resolution Stochastic Modeling

PV Hosting Capacity Analysis and Enhancement Using High Resolution Stochastic Modeling

A Novel Direct Load Control Testbed for Smart Appliances

Genetic Algorithm for a Coordinated Control to Improve Performance for a Building Cluster with Energy Storage, Electric Vehicles, and Energy Sharing

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