On the model granularity and temporal resolution of residential PV-battery system simulation

دسته: , تاریخ انتشار: 31 فروردین 1400تعداد بازدید: 338
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جزئیات بیشتر

انتشار

۲۰۲۱

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scopus – master journals – JCR

ایمپکت فاکتور

۴٫۲۷۶ در سال ۲۰۲۰

شاخص H_index

۲۶ در سال ۲۰۲۱

شاخص SJR

۰٫۹۰۱ در سال ۲۰۲۰

شاخص Quartile (چارک)

Q1 در سال ۲۰۲۰

مدل مفهومی

ندارد

پرسشنامه

ندارد

متغیر

ندارد

رفرنس

دارد

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On the model granularity and temporal resolution of residential PV-battery system simulation


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On the model granularity and temporal resolution of residential PV-battery system simulation

Abstract

This paper investigates the  impact of model  granularity and  temporal resolution on simulated energy  flow items, self-sufficiency and  self-consumption of grid-connected residential PV-battery systems. For such  a purpose, three models  with  increasing levels  of granularity are  implemented for  both  PV modules and  batteries. In addition, three temporal resolutions (i.e.,  ۱ s, 1 min,  and  ۱ h) of weather data  and  building electrical loads  are considered. The  simulation results  for  a  PV-battery system  in  Lindenberg, Germany show  that  temporal resolutions have negligible impact on self-consumption and self-sufficiency, but cause noticeable differences of most power  profiles observed in the PV-battery system.  As for the impact of model  granularity, the self-consumption is approximately 44% for the coarsest models, 48% for the models  with  the intermediate level of granularity, and 52% for the most refined models;  the  self-sufficiency is 83%,  ۷۸%,  and  ۸۰%,  respectively, for the  three models.

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  1. Introduction

With the decreasing cost of photovoltaic (PV) modules  and the rising concerns  on environmental problems  caused by fossil fuel consumption, solar  PV has  been  the  fastest  growing  distributed power  generation technology. According  to the U.S. Department of Energy (Feldman  and Margolis 2020), there were 68 GW AC power capacity from PV in the U.S. by the end of September 2020.  Of the 68 GW, 41 GW were utility-scale PV and  ۲۷  GW were  distributed PV. Since  ۲۰۱۰,  the  residential PV market  has grown by approximately 40% per year on average, which has led to 14 GW AC power  installed for residential PV systems  in the U.S. More developed PV markets  exist in some  European countries. In Ger- many, for example, there was a total of about 43 GW of installed solar PV power  by the end of 2017 (Wirth 2018),  ۷۴% of which were distributed on buildings, mostly  on the  rooftop  of residential buildings. There  are two  basic  types  of residential PV systems:  standalone (also  known  as off-grid  PV systems)  and  grid-connected (also  known  as  grid-tied or utility-interactive PV systems). The majority of residential PV systems are grid-connected, relying  on the power  grid at all times to balance  the PV power  supply  and  the building  electricity demand. The ever-increasing penetration of solar  PV into  residential buildings contributes to a sus- tainable  society   by  cutting   user’s   utility   bills,   reducing  fossil  fuel consumption and  mitigating the  greenhouse gas emissions  to the  envi- ronment. However, due to the intermittent nature  of solar energy and the mismatch between power  generation and  power  demand, a high  pene- tration of PV capacity  in the  grid  may  lead  to technical challenges  of reliable  power  grid operation. Onsite storage  devices,  such as batteries, can be one of the effective means to not only smooth the PV system power generation and  significantly increase  the  degree  of autonomy, but  also increase  the economic  return on investment.

There exist many studies on residential PV-battery systems, as demonstrated by the  rich  body  of available publications included in a number of review  papers  (Chauhan and  Saini  ۲۰۱۴;  Hoppmann et al., 2014;  Luna-Rubio et al., 2012).  From the perspective of PV-battery  sys- tem    design,    numerous   previous    studies    are    related     to    either techno-economic    analysis     or     optimal     sizing.     The     work     on techno-economic analysis  (Brusco et al., 2016;  Hoppmann et al., 2014; Kosmadakis et al., 2019; Linssen et al., 2017; Parra and Patel 2016; Silva and  Hendrick  ۲۰۱۷)  intends  to evaluate the  costs,  benefits, sensitivity factors, and uncertainties that could potentially affect the performance of PV-battery      systems.      Several      performance     metrics—such     as self-consumption, self-sufficiency,  levelized  cost of energy, and life-cycle cost—are  often  used  to  facilitate the  comparison of  different  system configurations and designs. The work on optimal  sizing intends  to apply optimization techniques for  PV-battery  system  sizing.  In  this  regard, Mulder  et al. (2013) determined the optimal  sizing of a PV-battery  sys- tems in the context  of different remuneration schemes  (i.e., the combi- nation  of different selling prices and self-consumption fees of PV power). Okoye and Solyali (2017) developed a mixed integer linear programming (MILP) model  to optimize  the  size of standalone residential PV-battery systems.   Li  (۲۰۱۹)  applied   a  genetic   algorithm  to  size  residential PV-battery  systems  for minimizing the  total  annual  cost  of electricity. Mulleriyawage and Shen (2020) used MILP to optimally size the battery storage  capacity  for minimizing annual  cost including both  energy  and battery degradation-based cost.  Ru et al. (2013) developed an  optimi- zation model to determine the battery size for grid-connected PV systems for the purpose  of power arbitrage and peak shaving. Zhang et al. (2017) applied  a multi-objective genetic  algorithm to optimize  the battery size and  operation parameters, towards maximizing the self-sufficiency  and net present  value of a PV-battery  system.

All aforementioned studies  on techno-economic analysis and optimal sizing rely on electrical load profiles and PV generation profiles as the key inputs.   In  these  studies,   building   electrical   load  profiles   are  usually assumed  to be known in advance, which  can be obtained by either  field measurements or simulation models. For PV power profiles, some studies (e.g., Beck et al., 2016; Langenmayr et al., 2020; Linssen et al., 2017) rely on  field  measurements to  establish these  profiles   while  others   (e.g., Hoppmann et  al.,  ۲۰۱۴;  Zhang  et al.,  ۲۰۱۷)  calculate PV power  from meteorological data  (e.g., solar  irradiance and  air temperature).  Wher- ever the data are sourced from, the temporal resolutions of PV power and building  electrical  load profiles  need  to be considered carefully  because they  may affect  system  performance evaluation. Most previous  studies have used the temporal resolution ranging  from 1 min to 1 h.

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