Demand response control strategy of groups of central air-conditionings for power grid energy saving

Zhou, Yu; Yi, Yunhui; Cui, Gaoying; Jin, Ping; Guo, Xingxi; Yang, Chenxing

doi:10.1109/icpre.2016.7871225

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…Papers in the literature that approach the problem at group level usually describe techniques that centralise the optimisation and control schemes [33,34,[40][41][42][43][44][45]. This imposes computational complexity (and associated costs), which increases exponentially with the number of coordinated buildings [41], as identify-ing optima requires considering the entire search space [46].…”

Section: Introductionmentioning

confidence: 99%

Decentralised peak energy demand minimisation in networks of buildings

Poghosyan

McCullen

Natarajan

2021

Preprint

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Simultaneous peaks in the energy demand from networks of buildings can decrease system stability and increase operational costs. However, reducing these peaks can require complicated centralised control schemes. Here, taking inspiration from biological systems, we investigate a decentralised, building-to-building load coordination schema that requires very little information and no human intervention. Using agent-based modelling, we investigate both the optimal system size and robustness of the results to changes in the system parameters. It is found that substantial reductions are readily achieved through coordination between a small number of buildings, analogous to models of coordination between flocks of birds. Strikingly, the schema significantly outperforms existing techniques and is robust to varying network topology and the inclusion of large time-constrained thermal loads. These results imply that significant reductions in network peaks are achievable through simple low-cost controllers implemented at the building level; particularly important for developing countries with fragile networks.

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Section: Introductionmentioning

confidence: 99%

Decentralised peak energy demand minimisation in networks of buildings

Poghosyan

McCullen

Natarajan

2021

Preprint

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“…Even techniques that approach the problem at group level usually centralise the optimisation and control schemes [24,25,[31][32][33][34][35][36]. This imposes computational complexity complexity and a higher associate cost, which increases exponentially with the number of coordinated buildings [32], as identifying optima requires considering the entire search space [37].…”

Section: Introductionmentioning

confidence: 99%

Optimising peak energy reduction in networks of buildings

Poghosyan

McCullen

Natarajan

2021

Preprint

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Buildings are amongst the world's largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Here, we investigate a new schema for decentralised, self-organising building-to-building load coordination that requires very little information and no direct intervention. We find that the theoretically optimal size for load-coordination networks can be surprisingly small, analogous to other complex systems such as coordination between flocks of birds. The schema outperforms existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals theoretical results and new insights which which will be of great interest to the complexity and network science communities.

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“…Some studies for CACs focus on optimising their operation modes to achieve energy saving and consumption reduction, and the control objects are usually the main device and the circulating water system. Such homogeneous regulation methods can affect the operating modes of all terminal devices, which reduce the flexibility of the user's choice [17,18]. Recently, some modelling and control methods designed for certain types of CACs participating in DR have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A bang‐bang control based water‐loop heat pump load aggregation method for power levelling dispatch

Wang

et al. 2021

IET Smart Grid

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In this article, a bang‐bang control based water‐loop heat pump (WLHP) load aggregation method is proposed to facilitate its attending demand response service for the purpose of power levelling dispatch. A WLHP aggregation model is first established to determine its power limit, in which a priority list based on the state of temperature (SOT) index for each heat pump unit (HPU) is developed to measure the HPU's power regulation capability. Users' comfort is also well guaranteed by introducing a forced switching rule in the model. The proposed model is then applied for a power levelling dispatch problem with WLHP load and a bi‐section based bang‐bang control algorithm is developed to determine HPUs' ON/OFF states. A microgrid test system including wind and solar generation, WLHP load and a battery storage is used to validate the effectiveness of the proposed method. To further test its robustness, an influence analysis of initial operating conditions such as input power profiles and initial ON/OFF states as well as multiple operating‐condition simulations are both conducted.

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Demand response control strategy of groups of central air-conditionings for power grid energy saving

Cited by 13 publications

References 12 publications

Decentralised peak energy demand minimisation in networks of buildings

Decentralised peak energy demand minimisation in networks of buildings

Optimising peak energy reduction in networks of buildings

A bang‐bang control based water‐loop heat pump load aggregation method for power levelling dispatch

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