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

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