Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation

Amin, Amin; Kem, Oudom; Gallegos, Pablo; Chervet, Philipp; Ksontini, Feirouz; Mourshed, Monjur

doi:10.1016/j.apenergy.2021.117836

Cited by 25 publications

(4 citation statements)

References 28 publications

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“…Customers are also expected to benefit from energy flexibility. For example, the TABEDE project modeled a district consisting of 66 residential buildings with seven archetypes including apartment buildings and terraced houses in Cardiff, UK, and estimated up to 30% in energy cost savings and up to a 25% increase in the penetration of distributed RES [24]. A study of a community with 498 all-electric homes showed that with the increase in energy flexibility by using home energy management systems (HEMS) and batteries, homeowners can reduce their electricity cost by $590/year [4].…”

Section: Q1 How Can Building Energy Flexibility Contribute To a Low-c...mentioning

confidence: 99%

Ten questions concerning energy flexibility in buildings

Satchwell

Finn

et al. 2022

Building and Environment

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Section: Q1 How Can Building Energy Flexibility Contribute To a Low-c...mentioning

confidence: 99%

Ten questions concerning energy flexibility in buildings

Satchwell

Finn

et al. 2022

Building and Environment

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“…An et al [22] found that 46.2% of the surplus energy can be generated by renewable energy systems based on energy sharing between buildings in an existing community. Amin et al [23] designed a cloud-based framework for intelligent optimization to enhance the effectiveness of demand response and tested it in a UK district comprising 66 dwellings. The results show that the energy cost reduction potential could achieve about 30% for the tested district.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Peak Demand Reduction and Energy Saving in a Mixed-Use Community through Urban Building Energy Modeling

Zhao,

Deng,

et al. 2024

Energies

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Energy saving in buildings is essential as buildings’ operational energy use constitutes 30% of global energy consumption. Urban building energy modeling (UBEM) effectively understands urban energy consumption. This paper applied UBEM to assess the potential of peak demand reduction and energy saving in a mixed-use community, using 955 residential buildings, 35 office buildings and 7 hotels in Shenzhen, China, as a case study. The building type and period were collected based on the GIS dataset. Then, the baseline models were generated by the UBEM tool—AutoBPS. Five scenarios were analyzed: retrofit-window, retrofit-air conditioner (AC), retrofit-lighting, rooftop photovoltaic (PV), and demand response. The five scenarios replaced the windows, enhanced the AC, upgraded the lighting, covered 60% of the roof area with PV, and had a temperature reset from 17:00 to 23:00, respectively. The results show that using retrofit-windows is the most effective scenario for reducing peak demand at 19.09%, and PV reduces energy use intensity (EUI) best at 29.96%. Demand response is recommended when further investment is not desired. Retrofit-lighting is suggested for its low-cost, low-risk investment, with the payback period (PBP) not exceeding 4.54 years. When the investment is abundant, retrofit-windows are recommended for public buildings, while PV is recommended for residential buildings. The research might provide practical insights into energy policy formulation.

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“…This consumption has an increasing trend [8,9], despite improvements in their efficiency in recent years [10]. Households in the European Union account for 26% of the final energy consumption, yet their share in demand response (DR) systems is practically nonexistent [11]. Moreover, the energy consumption of refrigerators and freezers accounts for about half of the corresponding households [12], while HVAC is responsible for around 20-30% [13].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Household Appliances Power Consumption

et al. 2022

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The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats.

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Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation

Cited by 25 publications

References 28 publications

Ten questions concerning energy flexibility in buildings

Ten questions concerning energy flexibility in buildings

Analysis of Peak Demand Reduction and Energy Saving in a Mixed-Use Community through Urban Building Energy Modeling

Modeling and Simulation of Household Appliances Power Consumption

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