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.
The authors of @cite_20 define a Central Energy Sharing (CES) systems as the one that consists of a central controller that has information of all the distributed energy resources in the micro-grid as well as the forecasting systems, and schedules resources accordingly. This type of system allows broad observability but reduces the flexibility. Work in @cite_14 introduces a CES system that classifies homes into sets of suppliers and consumers, and then uses a greedy strategy to initiate energy exchange between homes. Another interesting system - IDES @cite_9 uses a sophisticated distributed energy generation and sharing approach (DES) with a novel pricing model to incentivize energy sharing between homes.
@cite_9 @cite_14 @cite_20
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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. Renewable energy harvested from the environment is an attractive option for providing green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either i) requiring batteries to store surplus energy, which typically incurs 20 energy conversion losses; or ii) using net metering to transmit surplus energy via the electric grid's AC lines, which severely limits the maximum percentage of possible renewable penetration. In this paper, we propose an alternative structure wherein nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when, to minimize system-wide efficiency losses. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system i) reduces the energy loss on the AC line by 60 without requiring large batteries, ii) scales up performance with larger battery capacities, and iii) is robust to changes in microgrid topology. The issue of controlled and reliable integration of distributed energy resources into microgrids and large power grids has recently gained considerable attention. The microgrid concept, which basically corresponds to the coordinated operation of a cluster of loads, distributed generators and energy storage systems, is quite appealing due to its flexibility, controllability and energy management capabilities. In order to provide uninterruptible power supply to the loads, microgrids are expected to operate in both grid-connected and stand-alone modes, and economically meet the demand on an instantaneous basis. The problem of optimal management of the resources in a microgrid is being widely investigated and recent studies have proposed the application of both centralized and distributed control schemes by using multi-agent systems, heuristic methods and optimization algorithms. This paper elaborates on the conceptual design of a centralized energy management system (EMS) and its desirable attributes for a microgrid in stand-alone mode of operation. A number of test protocols are proposed to analyze the performance of the system, as well as the impacts of relevant parameters.