Advances in renewable energy generation and introduction of the government targets to improve energy efficiency gave rise to a concept of a Zero Energy Building (ZEB). A ZEB is a building whose net energy usage over a year is zero, i.e., its energy use is not larger than its overall renewables generation. A collection of ZEBs forms a Zero Energy Community (ZEC). This paper addresses the problem of energy sharing in such a community. This is different from previously addressed energy sharing between buildings as our focus is on the improvement of community energy status, while traditionally research focused on reducing losses due to transmission and storage, or achieving economic gains. We model this problem in a multi-agent environment and propose a Deep Reinforcement Learning (DRL) based solution. Each building is represented by an intelligent agent that learns over time the appropriate behaviour to share energy. We have evaluated the proposed solution in a multi-agent simulation built using osBrain. Results indicate that with time agents learn to collaborate and learn a policy comparable to the optimal policy, which in turn improves the ZEC's energy status. Buildings with no renewables preferred to request energy from their neighbours rather than from the supply grid.
There have been other works that focus on energy sharing between buildings but no direct work has been done on intra-nZEC energy sharing for community benefit. Previous works on only energy sharing between buildings focus on economic gains @cite_10 @cite_9 , optimizing transactions @cite_7 @cite_23 and thereby reducing transmission and storage loses at the individual building level. @cite_6 defines nZEC as a collection of ZEBs having its annual energy balance as zero. However, @cite_4 argues that achieving a ZEB status without a grid is very difficult. The authors propose a solution by defining using a basic energy matching algorithm and conclude that energy sharing can help achieve ZEB status for individual buildings. Additionally, in the previous section, we have argued that in a ZEC buildings may have varying generation capacities and some may even have none, as in reality, very few communities have all buildings exhibiting .
@cite_4 @cite_7 @cite_9 @cite_6 @cite_23 @cite_10
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Abstract Nowadays, micro-generation technologies are well developed and these make local energy production more possible. In terms of building scale, in April 2009 the Japanese government decided to accelerate its efforts towards zero energy building (ZEB). However, it is said that achieving a ZEB status without a grid connection would be quite difficult. In this study, thus, the definition of zero energy building (ZEB) is extended to the community level, ‘energy community’, which is defined here as a cluster of buildings in which every building generates both heat and electricity using micro-generation technologies and shares both types of energy with the other buildings. This article describes energy-sharing possibilities among four buildings in Japan: an office building, a hotel, a hospital and a shopping centre. The comparisons of primary energy consumption of the separate and shared cases of buildings show that the energy-sharing cases have the advantage of energy management within the boundary compared with the buildings studied as separated cases. The combination of hotel and hospital has the higher potential for achieving ZEB status. The results of this study show that the advantages of energy sharing are dependent on the type of buildings in the combination and CHP operation strategy. The work reported here addresses load matching improvement in Net Zero Energy Buildings (Net-ZEBs). The related relevant literature shows that currently research work is mainly focused on improving the load matching of individual buildings. In this paper the concept of a Cooperative Net Zero Energy Community (CNet-ZEC) is introduced, extending discussion to the enhancement of load matching at a wider community level. Both building and community levels are compared in order to assess the work proposed here, through the analysis of three distinct scenarios where five Net-ZEBs work individually or in community. Abstract During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework that combines several anomaly detection classifiers using majority voting. To ensure diversity of anomaly classifiers, the EAD is implemented by combining pattern-based (e.g., CCAD-SW) and prediction-based anomaly classifiers. The research was evaluated using real-world data provided by Powersmiths, located in Brampton, Ontario, Canada. Results show that the EAD framework improved the sensitivity of the CCAD-SW by 3.6 and reduced false alarm rate by 2.7 . Buildings consume a significant amount of electricity, which is normally generated from dirty sources causing an increase in carbon footprints. To reduce carbon footprint, distributed renewable energy generation has been proposed. However, the amount of renewable energy harvested normally does not match the amount of energy consumed in individual homes. To address this mismatch, we propose a distributed solution to share renewable energy among homes, which form a microgrid. Specifically we (i) design an incentive-driven distributed energy sharing system (iDES) in a microgrid to enable effective energy sharing and reduce the communication overhead, and (ii) develop energy sharing pricing model to incentivize energy sharing. The energy sharing price generally reflects the installation costs of on-site renewable and energy storage units, the dynamic changes of energy supply-demand relationship, and the remaining energy level of batteries. We validated the effectiveness of our system with extensive evaluations that use empirical traces. The results show that our energy sharing pricing model can effectively motivate and encourage homes to share energy.