46 research outputs found
Circular economy meets building automation
This paper demonstrates the concept of reusing discarded smartphones to
connect the end-of-life of e-wastes with the start-of-life of smart buildings.
Two control-related and one communication-related case studies have been
conducted experimentally to evaluate applicability. Diverse controlled systems,
control tasks, and algorithms have been considered. In addition, the
sufficiency of communication with external agents has been quantified. The
proof-of-concept experiments indicate technical feasibility and applicability
to typical tasks with satisfactory performance. As smartphones improve over
time, higher computing performance and lower communication latency can be
expected, enhancing the prospect of the proposed reuse concept
Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision
Emission-aware and flexible building operation can play a crucial role in the
energy transition. On the one hand, building operation accounts for a
significant portion of global energy-related emissions. On the other hand, they
may provide the future low-carbon energy system with flexibility to achieve
secure, stable, and efficient operation. This paper reports an experimental
implementation of an emission-aware flexible prosumer considering all
behind-the-meter assets of an actual occupied building by incorporating a model
predictive control strategy into an existing building energy management system.
The resultant can minimize the equivalent carbon emission due to electricity
imports and provide flexibility to the energy system. The experimental results
indicate an emission reduction of 12.5% compared to a benchmark that maximizes
PV self-consumption. In addition, flexibility provision is demonstrated with an
emulated distribution system operator. The results suggest that flexibility can
be provided without the risk of rebound effects due to the flexibility envelope
self-reported in advance
An evolving experience learned for modelling thermal dynamics of buildings from live experiments: the Flexhouse story:Power and Energy Systems Engineering
Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes
Real-time quantification of residential building energy flexibility is needed
to enable a cost-efficient operation of active distribution grids. A promising
means is to use the so-called flexibility envelope concept to represent the
time-dependent and inter-temporally coupled flexibility potential. However,
existing optimization-based quantification entails high computational burdens
limiting flexibility utilization in real-time applications, and a more
computationally efficient quantification approach is desired. Additionally, the
communication of a flexibility envelope to system operators in its original
form is data-intensive. In order to address the computational burdens, this
paper first trains several machine learning models based on historical
quantification results for online use. Subsequently, probability distribution
functions are proposed to approximate the flexibility envelopes with
significantly fewer parameters, which can be communicated to system operators
instead of the original flexibility envelope. The results show that the most
promising prediction and approximation approaches allow for a minimum reduction
of the computational burden by a factor of 9 and of the communication load by a
factor of 6.6, respectively
Optimal planning for partially self-sufficient microgrid with limited annual electricity exchange with distribution grid
Existing research on on-grid microgrid planning is carried out with a free trading assumption and without considering the limitation of annual electricity exchange. Therefore, the existing planning and sizing scheme may be not viable for the application of partially self-sufficient microgrid (PSSMG) with a limited amount of electricity exchange. To address this issue, a new planning method for PSSMG is proposed in this paper considering the limited annual electricity exchanging amount (AEEA). The sizing model and energy management are linearized and simultaneously integrated into one model, which could be solved in polynomial time. In order to effectively reduce the number of variables of a full year horizon and to cope with the uncertainty both of DGs and loads, a data-driven method based on K-means algorithm is utilized to choose a set of typical days that are representative of historical data of one full year. Finally, the validity and effectiveness of the proposed model are verified by comparative numerical simulations, and the sensitivity of limited AEEA to the planning scheme is analyzed