Technical and economic analysis of home energy management system incorporating small-scale wind turbine and battery energy storage system

Hemmati, Reza

doi:10.1016/j.jclepro.2017.04.174

Cited by 132 publications

(64 citation statements)

References 54 publications

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“…Falling costs of BESS in recent years has encouraged combining PV with it to provide stable power supply. Both linear and nonlinear mixed integer programming (MIP) models have been used to optimize PV and BESS capacity [17][18][19][20][21][22]. Zhao et al [17] co-optimizes BESS and PV for microgrids and households.…”

Section: Literature Surveymentioning

confidence: 99%

“…However, it is economic to install a PV-only system under a stepwise power tariff, whereas the PV together with BESS can only be justified under a time-of-use and real-time-pricing tariff. Hemmati [19] and Hemmati and Saboori [20] adopt a similar approach. Hemmati and Saboori [20] introduces uncertainty in PV output using a Monte Carlo simulation model to minimize annual utility bills of customers.…”

Section: Literature Surveymentioning

confidence: 99%

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Battery Storage for Resilient Homes

Chatterji

Bazilian

2020

IEEE Access

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Small-scale battery energy storage systems (BESS), especially for behind-the-meter applications, are still relatively expensive, but we show that it can be a potent solution to render homes resilient to storm related power outages. We present a stochastic programming model formulation to optimize PV/BESS explicitly accounting for resilience benefits these investments entail, over and above their ability to reduce cost of supply. The stochastic optimization considers uncertainties around storm related grid supply failures as well as variability of solar PV. The model includes an embedded Monte Carlo simulation module that considers storm related outage risks using climate model reanalysis data. It is a least-cost planning framework that optimizes selection of BESS, solar PV, grid supply, and diesel generator, from a homeowner's perspective. We present two case studies with low and high storm risks that demonstrate how different risk exposures can impact on the selection of alternative options to build resilience. Duals, or shadow prices, of demand-supply constraints from the model for both normal days and for storm related contingencies, provide interesting insights into the marginal cost of supply that can inform innovative pricing schemes to promote customer level resilience measures. The case study results reveal significant merits of BESS, in combination with PV, to enhance resilience. We find that in low-risk areas like Bethesda, MD, incremental PV and BESS required for a more resilient system can add $79 (4%) to expected annual electricity costs for a typical household, and a considerably higher $208 (10.6%) in Miami, FL which is at a much greater risk. These options are, however, 27% (in Bethesda) and 20% (in Florida) less expensive than the conventional solution of installing a diesel generator. These results provide insights into the value of BESS as part of a resilient and clean energy solution for households.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Battery Storage for Resilient Homes

Chatterji

Bazilian

2020

IEEE Access

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“…The developed HEMS were selected as a new energy management approach to ensure energy savings and peak demand [12]- [13]. Broadly speaking, HEMS can be defined as a precise approach system that allows households to control their energy demand by adding various network-wide hardware / software connections [14].…”

Section: B Summary Of Home Energy Management System (Hems) Using Thementioning

confidence: 99%

Smart Home Energy Management System Design: A Realistic Autonomous V2H / H2V Hybrid Energy Storage System

Zafar¹,

Slama²,

Sihem³

et al. 2019

IJACSA

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The hybrid fuel cell electric vehicle powered by household power during peak use is another opportunity to reduce emissions and save money. For this reason, Vehicle-to-Home (V2H) and Home-to-Vehicle (H2V) systems were proposed as a new method of exchanging smart energy and a new method of exchanging smart energy. The main goal of this paper is to develop a smart home energy management based on IoT, generate more energy efficiency and share production between home and vehicle. In fact, the Hybrid Fuel cell electric vehicle will be used simultaneously to power household appliances during peak demand for electricity to solve energy consumption. The household's energy is derived from an accurate Autonomous hybrid power system. Several technologies such as Proton Exchange Membrane Fuel Cell, solar panel, Supercapacitor (SC) device and water electrolyzer are incorporated into the proposed system. Two-way electrical energy from the PEMFC-Hybrid Electric Vehicle and household power will be exchanged by discharging vehicle energy storage to balance energy demand and supply. To this end, a smart energy management unit (EMU) will be developed and discussed in order to meet the estimated fuel consumption mitigation goals during peak periods when demand is highest, coordinating between household power and vehicle energy storage. The Matlab / Simulink software based on an experimental database extracted from household power to demonstrate the effectiveness of the proposed strategy and its effects on V2H / H2V operations will simulate the presented design for one day. The Matlab / Simulink software based on an experimental database extracted from household power to demonstrate the effectiveness of the proposed strategy and its effects on V2H / H2V operations will simulate the presented design for one day.

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“…Power coefficient. Power coefficient is an important parameter as it describes the wind turbines aerodynamics efficiency in converting kinetic energy into electrical energy (Cooney , 2017;Shahizare et al, 2016). Betz limit sets highest value of 0.59 on the power coefficient value associated with wind turbine.…”

Section: Performance Analysis Modelmentioning

confidence: 99%

Assessment of the apparent performance characterization along with levelized cost of energy of wind power plants considering real data

Hulio

Jiang

2018

Energy Exploration & Exploitation

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Pakistan pursued the renewable energy policy to minimize the cost of energy per kWh as well as dependence on costly imported oil. Jhimpir site is termed as wind corridor and has tremendous proven wind power potential. The site is hosted for the first installed wind power plant. The aim of paper is to investigate the performance and levelized cost of energy of a wind farm. The methodology covers assessment of wind characteristics, performance function and levelized cost of energy model. The measured mean wind speed was found to be 8 m/s at 80 m above the ground level. The average values of standard deviation, Weibull k and c parameters, obtained using entire data set, were found to be 2.563, 3.360 and 8.940 m/s at 80 m. Performance assessment including technical, real availability and average capacity factor was found to be 97, 90 and 34.50%, respectively. It is evident that the power coefficient dropped if wind speed crosses the rated power. So it can be concluded that the efficiency of wind turbine decreased by increased wind speed. Tip speed ratio shows that a wind turbine operating close to optimal lift and drag will exhibit the performance level. Wind turbine performs better at the wind speed between 6 and 10 m/s. The estimated average levelized cost of energy was US $0.11371 and US $0.04092/kWh for 1-10 and 11-20 years, respectively. This makes it competitive in terms of low production cost per kWh to other energy technologies.

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Technical and economic analysis of home energy management system incorporating small-scale wind turbine and battery energy storage system

Cited by 132 publications

References 54 publications

Battery Storage for Resilient Homes

Battery Storage for Resilient Homes

Smart Home Energy Management System Design: A Realistic Autonomous V2H / H2V Hybrid Energy Storage System

Assessment of the apparent performance characterization along with levelized cost of energy of wind power plants considering real data

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