Measures to improve energy demand flexibility in buildings for demand response (DR): A review

Chen, Yongbao; Xu, Peng; Gu, Jiefan; Schmidt, Ferdinand; Li, Weilin

doi:10.1016/j.enbuild.2018.08.003

Cited by 264 publications

(110 citation statements)

References 122 publications

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“…Several research streams recently dealt with the efficient operation of equipment and energy resources in smart buildings, by investigating different fields. Examples are the optimization of HVAC systems based on Model Predictive Control (MPC) algorithms or Neural Networks (NNs) [14][15][16][17], or the application of flexible control strategies, Demand Side Management (DSM) actions, and Demand Response (DR) programs [2,[18][19][20]. The application of such control strategies is implemented in single family houses through the so-called Home Energy Management Systems (HEMSs), such as well as in large buildings, by means of Building Energy Management Systems (BEMSs).…”

Section: Distributed Management Of Energy Resourcesmentioning

confidence: 99%

“…This in fact implies larger opportunities of energy savings, which in the case of public buildings, may be particularly critical, due to the use of public money.The efficient use of energy intensive equipment, such as Heating, Ventilation, and Air Conditioning (HVAC) systems, plays a key role for the development of sustainable energy buildings. It is worth noting that this applies not only to the design stage, i.e., by making use of high performing insulating materials, advanced construction technologies, efficient equipment, or renewable sources [2,3], but also to the operation stage, i.e., by defining how equipment and energy resources must be operated to ensure an efficient use of energy [4]. The efficient operation of equipment and energy resources, particularly in the case of large buildings, requires the monitoring and control of several parameters, concerning the use of facilities (i.e., the presence of users, the thermo-hygrometric conditions in rooms, etc.…”

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confidence: 99%

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On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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The application of the most recent advances of the Internet-of-Things (IoT) technology to the automation of buildings is emerging as a promising solution to achieve greater efficiencies in energy consumption, and to allow the realization of sustainable models. The application of IoT has been demonstrated as effective in many fields, such as confirmed, for instance, by the Industry 4.0 concepts, which are revolutionizing modern production chains. By following this approach, the use of distributed control architectures and of IoT technologies (both wired and wireless) would result in effective solutions for the management of smart environments composed of groups of buildings, such as campuses. In this case, heterogeneous IoT solutions are typically adopted to satisfy the requirements of the very diverse possible scenarios (e.g., indoor versus outdoor coverage, mobile versus fixed nodes, just to mention a few), making their large-scale integration cumbersome. To cope with this issue, this paper presents an IoT architecture able to transparently manage different communication protocols in smart environments, and investigates its possible application for the monitoring and control of distributed energy resources in a smart campus. In particular, a use–case focused on the integration of the Long Range Wide Area Network (LoRaWAN) technology is considered to cope with heterogeneous indoor and outdoor communication scenarios. The feasibility analysis of the proposed solution is carried out by computing the scalability limits of the approach, based on the proposed smart campus data model. The results of the study showed that the proposed solution would be able to manage more than 10,000 nodes. An experimental validation of the LoRaWAN technology confirms its suitability in terms of coverage and latency, with a minimum LoRaWAN cell coverage range of 250 m, and a communication latency of about 400 ms. Finally, the advantages of the proposed solution in the supervision and management of a PV system are highlighted in a real-world scenario.

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Section: Distributed Management Of Energy Resourcesmentioning

confidence: 99%

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confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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“…Investments into new power plants are based on capacity-based costs and the variable energy prices [15], which are again influenced by the instantaneous power demand and the available energy generation capacity. Increase of peak power demand may call for increased investments to power transmission lines [16] or to demand response services which are used to shift peak demand to lower load hours [17]. Strategies for reducing peak demand under uncertain loads are being developed such as in [18], which highlights the importance of energy flexibility in buildings.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Deep Energy Retrofit on The Hourly Power Demand of Finnish Detached Houses

2020

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This study examines how the energy renovation of old detached houses affects the hourly power consumption of heating and electricity in Finland. As electrification of heating through heat pumps becomes more common, the effects on the grid need to be quantified. Increased fluctuation and peak power demand could increase the need for fossil-based peaking power plants or call for new investments to the distribution infrastructure. The novelty in this study is the focus on hourly power demand instead of just annual energy consumption. Identifying the influence of building energy retrofits on the instantaneous power demand can help guide policy and investments into building retrofits and related technology. The work was done through dynamic building simulation and utilized building configurations obtained through multi-objective optimization. Deep energy retrofits decreased both the total and peak heating power consumption. However, the use of air-source heat pumps increased the peak power demand of electricity in district heated and wood heated buildings by as much as 100%. On the other hand, peak power demand in buildings with direct electric heating was reduced by 30 to 40%. On the building stock level, the demand reduction in buildings with direct electric heating could compensate for the increase in the share of buildings with ground-source heat pumps, so that the national peak electricity demand would not increase. This prevents the increase of demand for high emission peaking power plants as heat pump penetration rises. However, a use is needed for the excess solar electricity generated by the optimally retrofitted buildings, because much of the solar electricity cannot be utilized in the single-family houses during summer.

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“…The work in this paper has been done in the sequence of the work presented in [1]. Sometimes unimportant actions during the day can impact the environment and, in the end, affect the survival of the earth [2]. That has been the motivation for automation in buildings in order to prevent the loss of energy as much as possible [3].…”

Section: Introductionmentioning

confidence: 99%

Consumption Optimization in an Office Building Considering Flexible Loads and User Comfort

Khorram

Faria

Abrishambaf

et al. 2020

Sensors

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This paper presents a multiperiod optimization algorithm that is implemented in a Supervisory Control and Data Acquisition system. The algorithm controls lights and air conditioners as flexible loads to reduce the consumption and controls a dishwasher as a deferrable load to implement the load shifting. Several parameters are considered to implement the algorithm for several successive periods in a real building operation. In the proposed methodology, optimization is done regarding user comfort, which is modeled in the objective function related to the indoor temperature in each room, and in the constraints in order to prevent excessive power reduction, according to users’ preferences. Additionally, the operation cycle of a dishwasher is included, and the algorithm selects the best starting point based on the appliance weights and power availability in each period. With the proposed methodology, the building energy manager can specify the moments when the optimization is run with consideration of the operational constraints. Accordingly, the main contribution of the paper is to provide and integrate a methodology to minimize the difference between the actual and the desired temperature in each room, as a measure of comfort, respecting constraints that can be easily bounded by building users and manager. The case study considers the real consumption data of an office building which contains 20 lights, 10 ACs, and one dishwasher. Three scenarios have been designed to focus on different functionalities. The outcomes of the paper include proof of the performance of the optimization algorithm and how such a system can effectively minimize electricity consumption by implementing demand response programs and using them in smart grid contexts.

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Measures to improve energy demand flexibility in buildings for demand response (DR): A review

Cited by 264 publications

References 122 publications

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

The Effect of Deep Energy Retrofit on The Hourly Power Demand of Finnish Detached Houses

Consumption Optimization in an Office Building Considering Flexible Loads and User Comfort

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