The case for efficient renewable energy management in smart homes

Abstract

Distributed generation (DG) uses many small on-site energy sources deployed at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to many homes is difficult. In this paper, we explore a different approach that combines residential TOU pricing models with on-site renewables and modest energy storage to incentivize DG. We propose a system architecture and control algorithm to efficiently manage the renewable energy and storage to minimize grid power costs at individual buildings. We evaluate our control algorithm by simulation using a collection of real-world data sets. Initial results show that the algorithm decreases grid power costs by 2.7X while nearly eliminating grid demand peaks, demonstrating the promise of our approach.

An energy transmission and distribution network using electric vehicles

“…CONCLUSIONS Energy is one of the most precious resources in the world. Researchers have proposed various approaches to either reduce energy consumption [20], [7] or efficiently and securely utilize energy [12], [24], [30], [18], [5], [28], [27], [26], [25], [29]. In this paper, we proposed a novel concept called EV energy network, for energy transmission and distribution using EVs.…”

Residential energy simulation and scheduling: A case study approach

“…Other building simulators demonstrate the effectiveness of this approach [1] [2]. The computation at each step, called the execute step, distinguishes HomeSim.…”

“…As such, recent research has turned to improving residential energy use. Some works focus on reducing a single aspect of consumption [1] [2], while others seek to provide more granular energy information to endusers in order to facilitate user-driven improvements [1]. While these works are indeed beneficial, they require considerable overhead in data collection and testing, and results cannot be quantitatively compared.…”

“…Home energy simulators have been developed as part of other research endeavors [3] [1] [2], but they are either specific to the residence or scenario being investigated, or particular to one aspect of energy consumption. For example, the Department of Energy's NZERTF project [4] has established a user-model centric energy simulation, but the results are determined by actually emulating the user models on a real house, prohibiting widespread use.…”

Control Methodology for Peak Demand through Multi-Source Environment at Demand Side

“…The peak demand will be pre-calculated through various algorithms through the daily load profiles as shown in Figure 4. The load was categorized based on the starting current and the block diagram as shown in Figure 2 will give clear idea on classification and will explain the control logic of relays peak and non-peak hours based on the mode in which system operating (i.e., [8], [9]). Generally, the load like refrigerator, Air conditioner, water heaters come under category-2 loads.…”