Risk-constrained framework for residential storage space heating load management

Ali, Mubbashir; Safdarian, Amir; Lehtonen, Matti

doi:10.1016/j.epsr.2014.10.024

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…Since heating homes with low carbon electricity is a major element in UK strategy for reducing carbon emissions [11] and the value to the electricity system of electric heating that can deliver demand response is likely to increase by 2030 to between £0.015 and £0.03 per kWh of renewable energy consumed [12] it is realistic to expect that this form of heating, which is cheap to install and very reliable, will become more economically attractive. This potential for sustained and possibly increased use applies throughout northern Europe [13], [14], [15].…”

Section: Storage Heatingmentioning

confidence: 99%

Making legacy thermal storage heating fit for the smart grid

Boait

Snape

Darby

et al. 2017

Energy and Buildings

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Thermal storage heaters, charged using overnight off-peak electricity, have been used for domestic space heating in the UK and other countries since the 1980s. However, they have always been difficult for consumers to manage efficiently and, with the advent of a high proportion of renewables in the electricity generation mix, the time of day when they are charged needs to be more flexible. There is also a need to reduce peaks in the demand profile to allow distribution networks to support new sources of demand such as electric vehicles. We describe a trial of a smart control system that was retrofitted to a group of six dwellings with this form of heating, with the objectives of providing more convenient and efficient control for the users while varying the times at which charging is performed, to flatten the profile of demand and make use of locally-generated renewable electricity. The trial also employs a commercially-realistic combination of a static time-of-day tariff with a real time tariff dependent on local generation, to provide consumers with the opportunity and incentive to reduce their costs by varying times of use of appliances. Results from operation over the

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Section: Storage Heatingmentioning

confidence: 99%

Making legacy thermal storage heating fit for the smart grid

Boait

Snape

Darby

et al. 2017

Energy and Buildings

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“…A predictive control approach is developed in [17] to proactively operate the building system under weather uncertainty to minimize energy cost. An efficient residential area energy management framework based on a risk-constraint approach is developed in [18] which can achieve desired cost reduction and user thermal comfort level simultaneously. An operation problem for residential building with solar assisted HVAC and water heating system under real-time pricing environment is studied in [19].…”

Section: Single Building Operation/controlmentioning

confidence: 99%

A distributed decision framework for building clusters with different heterogeneity settings

Jafari-Marandi

Omitaomu

2016

Applied Energy

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“…The main reason behind this was their major contribution to annual residential demand in Finland [13]. Also, it is worthwhile mentioning that the two loads have great flexibility mainly due to the thermal inertia of hot water and building mass [14][15][16]. The EWH is an inherent component for domestic hot-water (DHW) consumption.…”

Section: Introductionmentioning

confidence: 99%

Matching of Local Load with On-Site PV Production in a Grid-Connected Residential Building

et al. 2018

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Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation using an electrical storage, as well as demand response solutions, in a building that was also capable of interacting with the main grid. Ten-minute resolution data were used to capture the temporal behavior of the weather. Extensive mathematical models were employed to estimate the demand for hot-water consumption, space cooling, and heating loads. The proposed framework is cast as mixed-integer linear programming model while minimizing the interaction with the grid. To evaluate the effectiveness of the proposed framework, it was applied to a typical Finnish household. Matching indices were used to evaluate the degree of overlap between generation and demand under different PV penetrations and storage capacities. Despite negative correlation of PV generation with Finnish seasonal consumption, a significant portion of demand can be satisfied solely with on-site PV generation during the spring and summer seasons.

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Risk-constrained framework for residential storage space heating load management

Cited by 9 publications

References 31 publications

Making legacy thermal storage heating fit for the smart grid

Making legacy thermal storage heating fit for the smart grid

A distributed decision framework for building clusters with different heterogeneity settings

Matching of Local Load with On-Site PV Production in a Grid-Connected Residential Building

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