Realistic and Intelligent Management of Connected Storage Devices in Future Smart Homes Considering Energy Price Tag

Aznavi, Sima; Fajri, Poria; Asrari, Arash; Harirchi, Farshad

doi:10.1109/tia.2019.2956718

Cited by 37 publications

(17 citation statements)

References 33 publications

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“…Due to such technological advancements, smart homes can enhance energy efficiency, and improve both stability and reliability by allowing owners to regulate electricity usage [1]- [3]. They can also change the operations of utilities by minimizing both energy usage and peak demand in the residences [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

et al. 2021

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Domestic load profiles in the residential sectors are being modified with the adoption of smart home management systems and solar generation. In addition, houses with rooftop PV behave like local generators, contributing to the growth of the penetration of PV energy. Hence, the demand for power is declining day by day. However, the increasing PV penetration causes technical challenges for the power system, such as the "duck curve". This can be addressed through home energy management (HEM) techniques including peak shaving, load shifting with smart home devices. In this regard, electric water heaters (EWH), with high thermal mass and being ubiquitous, are attractive and low-cost energy storage systems. In this paper, a case study for one of the largest rural field smart energy technology demonstrators involving business, industries, and more than 5,000 residences, located in Glasgow, KY, US, is presented. Furthermore, a HEM system, which aims to minimize the total energy usage and peak demand by regulating the heating, ventilation, and airconditioning (HVAC) systems, water heaters, and batteries, thereby benefiting both the utility and the consumer is proposed. This work also demonstrates the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries are calculated and compared with respect to different peak reduction targets. Long term load prediction by considering different fractions of smart homes for the utility is also provided.

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Section: Introductionmentioning

confidence: 99%

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

et al. 2021

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“…Similarly, in [12], a mathematical energy cost formula was developed and optimized using PSO algorithm. The authors in [13] and [14] also proposed a price-based EMS algorithm to reduce the operation cost. However, their works did not consider the energy forecast and battery State of Heath (SOH) to optimize the battery utilization.…”

Section: Introductionmentioning

confidence: 99%

Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study

et al. 2021

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This paper presents a predictive Energy Management System (EMS), aimed to improve the performance of a domestic PV-battery system and maximize self-consumption by minimizing energy exchange with the utility grid. The proposed algorithm facilitates a self-consumption approach, which reduces electricity bills, transmission losses, and the required central generation/storage systems. The proposed EMS uses a combination of Fuzzy Logic (FL) and a rule based-algorithm to optimally control the PV-battery system while considering the day-ahead energy forecast including forecast error and the battery State of Health (SOH). The FL maximizes the lifetime of the battery by using SOH and State of Charge (SOC) in decision making algorithm to charge/discharge the battery. The proposed Battery Management System (BMS) has been tested using Active Office Building (AOB) located in Swansea University, UK. Furthermore, it is compared with three recently published methods and with the current BMS utilized in the AOB to show the effectiveness of the proposed technique. The results show that the proposed BMS achieves a saving of 18% in the total energy cost over six months compared to a similar day-ahead forecast-based work. It also achieves a saving up to 95% compared to other methods (with a similar structure) but without a day-ahead forecast-based management. The proposed BMS enhances the battery's lifetime by reducing the average SOC up to 47% compared to the previous methods through avoiding unnecessary charge and discharge cycles. The impact of the PV system size and the battery capacity on the net exchanged energy with the utility grid is also investigated in this study.INDEX TERMS Battery management system, energy management system, fuzzy logic, state of charge, state of health.

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“…The variations between dynamic/realtime pricing and time of use tariff are explained in Ref. 19 There are multiple research works like, [20][21][22] which presents the dynamic scheduling of ESSs, and corresponding load scheduling. This research work presents and proves the battery scheduling in a static condition based on the relation between the real-time pricing and efficiency of the battery.…”

Section: Introductionmentioning

confidence: 99%

Economic analysis and control strategy of residential prosumer

Badar

Sanjari

2020

Int Trans Electr Energ Syst

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Summary A smart home requires energy management systems to operate optimally. It has an integrated renewable energy source along with a storage system. The output of the renewable energy source cannot be controlled. The ESS is optimally scheduled for charging and discharging in order to derive the maximum utilization of it. Optimal scheduling of battery integrated smart home is proposed in this paper to minimize the energy costs for a given set of daily electricity tariffs scheme. The sizing of the battery installed in a prosumer system is investigated. An SHEMS is modelled, and an appropriate objective function is defined to reflect the operational costs of a prosumer. The battery efficiency is related to the applied tariff for deciding the operational characteristics of the storage system. The optimal control of the battery operation is then proposed considering its constraints. The sensitivity analysis is performed on the battery to observe its performance in different conditions. The results show the effectiveness of the proposed algorithm to reduce the operational costs of the smart home.

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Realistic and Intelligent Management of Connected Storage Devices in Future Smart Homes Considering Energy Price Tag

Cited by 37 publications

References 33 publications

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study

Economic analysis and control strategy of residential prosumer

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