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.
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. This paper demonstrates a novel method to establish robust control and stability of microgrid connected with photovoltaic (PV) grid tie inverter, battery storage with bidirectional inverter, and diesel generator. The proposed control scheme enables optimum power sharing among the sources based on the changes in generation due to intermittent nature of PV source and level of energy stored in battery. Power flow control has been achieved by varying the active power generated by the PV inverter as a function of the system frequency in the islanded mode of operation. Detailed analysis has been carried out in MATLAB Simulink tool and the captured results are found to effective and same has been confirmed with hardware prototype setup with PV inverter and bidirectional battery charger. 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. 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. A novel distributed reinforcement learning (DRL) strategy is proposed in this study to coordinate current sharing and voltage restoration in an islanded DC microgrid. Firstly, a reward function considering both equal proportional current sharing and cooperative voltage restoration is defined for each local agent. The global reward of the whole DC microgrid which is the sum of the local rewards is regarged as the optimization objective for DRL. Secondly, by using the distributed consensus method, the predefined pinning consensus value that will maximize the global reward is obtained. An adaptive updating method is proposed to ensure stability of the above pinning consensus method under uncertain communication. Finally, the proposed DRL is implemented along with the synchronization seeking process of the pinning reward, to maximize the global reward and achieve an optimal solution for a DC microgrid. Simulation studies with a typical DC microgrid demonstrate that the proposed DRL is computationally efficient and able to provide an optimal solution even when the communication topology changes. The trading is a well known, easy and efficient method of distributing goods. Energy cannot be stored efficiently, which implies that supply and demand of power has to be balanced at all times; this adds complexity to the trading of electric power. Introduction of microgrids, with internal balancing of power and trading with external power grid, brings many questions about power distribution, sharing of costs and revenues. Using market mechanism for such purpose seems appropriate. In this article we present an example of balancing a microgrid with its own energy production sources and connected to a higher voltage distribution grid, by introduction of the continuous double auction market with agents following the Adaptive-Aggressive Strategy.