Energy management for a commercial building microgrid with stationary and mobile battery storage

Wang, Yübo; Wang, Bin; Chu, Chi-Cheng; Pota, H. R.; Gadh, Rajit

doi:10.1016/j.enbuild.2015.12.055

Cited by 112 publications

(42 citation statements)

References 29 publications

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“…A solar panel consists of several cells, which are coupled together to produce power. In a similar work [37], the authors use renewable energy generation and storage systems, where deterministic and stochastic methods have been used to consider uncertainties.…”

Section: Power Output Of Renewable Energy Sourcesmentioning

confidence: 99%

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

et al. 2017

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Today's buildings are responsible for about 40% of total energy consumption and 30-40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

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Section: Power Output Of Renewable Energy Sourcesmentioning

confidence: 99%

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

et al. 2017

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“…A state-of-the-art approach is to use a microgrid as in [27,28], while our work targets sites where a microgrid is not required; further, one must make buy and sell decisions on the previous day which is problematic when the EV behaviour for the next day is not known. Several scenarios for exploiting PV based EV charging are thoroughly investigated in [29] and the scenario of the consumption peak shaving is similar to our work; however, in [29] the solar energy cannot be directly supplied to the building, resulting in battery degradation costs that are not present in our system.…”

Section: Related Research In Electric Vehicle Battery Exploitationmentioning

confidence: 99%

Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings

2018

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Vehicle-to-building (V2B) technology permits bypassing the power grid in order to supply power to a building from electric vehicle (EV) batteries in the parking lot. This paper investigates the hypothesis stating that the increasing number of EVs on our roads can be also beneficial for making buildings sustainably greener on account of using V2B technology in conjunction with local photovoltaic (PV) generation. It is assumed that there is no local battery storage other than EVs and that the EV batteries are fully available for driving, so that the EVs batteries must be at the intended state of charge at the departure time announced by the EV driver. Our goal is to exploit the potential of the EV batteries capacity as much as possible in order to permit a large area of solar panels, so that even on sunny days all PV power can be used to supply the building needs or the EV charging at the parking lot. A system architecture and collaboration protocols that account for uncertainties in EV behaviour are proposed. The proposed approach is proven in simulation covering one year period for three locations in different climatic regions of the US, resulting in the electricity bill reductions of 15.8%, 9.1% and 4.9% for California, New Jersey and Alaska, respectively. These results are compared to state-of-the-art research in combining V2B with PV or wind power generation. It is concluded that the achieved electricity bill reductions are superior to the state-of-the-art, because previous work is based on problem formulations that exploit only a part of the potential EV battery capacity.

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“…However, there are other more efficient approaches for solving the convex PEV coordination problem associated with radial networks [33,34]. This paper does not consider the stochastic behavior of the wind and solar (rooftop PV) generations [35,36]; however, the stochastic nature of EVs are included by storing the random plug-in times of each vehicle in the "PEV Queue Table" and updating them at each time interval.…”

Section: Coordinated Ol-f/dl-mss Pev Charging Scheme Without/with Winmentioning

confidence: 99%

Combined Online and Delayed Coordinated Charging of Plug-In Electric Vehicles Considering Wind and Rooftop PV Generations

Masoum

Abu-Siada

Islam

2016

Technol Econ Smart Grids Sustain Energy

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Emerging smart grid technology is creating unique opportunity for wide applications of renewable distributed generation resources, smart appliances and plugin electric vehicles (PEVs). This paper presents a combined online (real-time) and delayed (overnight and next day) PEV coordination scheme based on fuzzy reasoning and maximum sensitivity selections (MSS) considering wind and rooftop PV generations. The proposed combined online fuzzy-based and delayed MSS-based (OL-F/DL-MSS) algorithm aims at reducing energy generation cost by charging the electric vehicles during high renewable energy generation periods. To include vehicle owners' satisfaction, they are classified into three groups; high priority consumers requiring expensive PEV charging as soon as arriving home during early evening hours, low priority consumers requesting cheap overnight PEV charging and medium priority consumers requiring inexpensive PEV charging within 24 hours. PEV coordination is performed to minimize the cost of generating energy and regulate node voltages while utilizing the available renewable energy resources for vehicle battery charging. Detailed simulations for uncoordinated and combined online/overnight coordinated PEV charging are presented for a 449 node smart grid

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Energy management for a commercial building microgrid with stationary and mobile battery storage

Cited by 112 publications

References 29 publications

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings

Combined Online and Delayed Coordinated Charging of Plug-In Electric Vehicles Considering Wind and Rooftop PV Generations

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