Smart home energy optimization with incentives compensation from inconvenience for shifting electric appliances

Paudyal, Priti; Ni, Zhen

doi:10.1016/j.ijepes.2019.02.016

Cited by 48 publications

(28 citation statements)

References 21 publications

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“…4) Electric vehicle model: The State-of-Charging (SoC) of the battery at a time 't' is the ratio of remaining ( ) or residual capacity at that time to the maximum battery capacity, as shown in equation (7). ON/OFF status of EV is illustrated in equation (8).…”

Section: A Controllable Loads 1) Ewh Modelmentioning

confidence: 99%

“…The utility commonly control loads of the customer remotely in Interruptible / Curtailable Service (CS) to achieve the required load reduction level. Several researchers [2][3][4][5][6][7][8][9] suggested the implementation of optimal DR scheduling via incentives. Demand Bidding (DB) is a process that encourages consumers to involve actively in electricity usage trading.…”

Section: Introductionmentioning

confidence: 99%

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Reward-Based DSM Program for Residential Electrical Loads in Smart Grid

Muthuselvi¹,

Saravanan²

2020

IJACSA

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There is a positive attitude towards the use of different strategies for engaging in demand response (DR) programs in energy markets through the innovation and trend of smart grid technologies. In this paper, a reward-based approach is proposed that enhances the involvement of customers in the DR program by assuring the customer's comfort level. Most of the previous works considered limited controllable loads like thermal loads for demand side management (DSM). In this approach thermal and all active electrical loads are considered for the analysis. Comfort indicator is used for the analysis which is an important parameter for measuring comfort of each resident. This technique significantly reduces the utility reward cost and maximizes the user satisfaction level compared with existing approach. The numerical example considered in this work illustrates the fruitfulness of the proposed approach. This problem is formulated as mixed-integer linear programming (MILP) and solved by using CPLEX solver in General Algebraic Modelling Software (GAMS).

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Section: A Controllable Loads 1) Ewh Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reward-Based DSM Program for Residential Electrical Loads in Smart Grid

Muthuselvi¹,

Saravanan²

2020

IJACSA

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“…Recently, more contributions considering controllable load appliances management in an SH have been proposed. An example of this is found in [38], where the strategy of smart energy management system was proposed to deal with the scenario of a complete power outage in an area with partial load shedding, as well as taking into consideration RCs' preferences. To this end, DR strategies were also applied, through the maximum demand bound levels, changing the priorities assigned to an appliance.…”

Section: Related Work Considering Management Of Controllable Applianmentioning

confidence: 99%

Analysis Application of Controllable Load Appliances Management in a Smart Home

et al. 2019

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The residential sector is one of the sectors with the highest rates of electricity consumption worldwide. For years, many studies have been presented in order to minimize energy consumption at the residential level. The idea of such studies is that the residential customer (RC) is the interested party of their own consumption. Moreover, the algorithms that have been developed to predict and manage the energy consumption, also analyze the behavior of the loads, with the objective of minimizing the energy costs, with good safety, robustness, and comfort levels. In the context of the smart house (SH), one of the objectives of smart grids (SGs) is to enable the RC, with home energy management systems (HEM), to actively participate, allowing for higher reliability at different levels. In this work, a new model that simulates the behavior of an SH, considering heating, ventilation and air conditioning (HVAC) and sanitarian water heater (SWH) devices, is presented. For this purpose, the proposed model considers realistic physical parameters of the SH, together with customer comfort, in order to mitigate the RC disinterest. The proposed model considers the electric vehicle (EV), a battery-based energy storage system (ESS), a micro production unit, and different types of tariffs that the RC might choose, aiming to maximize the benefits, and temporarily shifting the proposed loads.

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“…Smart‐energy usage in residences has been investigated in several studies. Paudyal & Ni 3 proposed an optimized use of smart‐home energy, whereas Zhu et al 4 considered real‐time pricing in different types of smart homes. Miyagi et al 5 proposed a phase synchronization control system for an uninterruptible smart house.…”

Section: Introductionmentioning

confidence: 99%

Battery control for leveling the amount of electricity purchase in smart‐energy houses

2020

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An optimal operation method in smart-energy houses with photovoltaics (PV) and a storage battery was investigated in a trial production system. In this method, the inverse current of the PV output is not conveyed to the commercial electricity system as operation conditions. Instead, the excess of the consumed PV power is applied to leveling the electricity purchase by appropriately charging and discharging the storage battery. To validate the proposed system, a lithium battery (4 kWh) and PV cell (3 kW) used in average individual houses was installed in a smart-energy house in a local city (Kitami) in Japan. Another example was introduced into a wider area (Hokkaido, Japan). Accounting for the error between the weather forecast and actual solar radiation, the trial production system reduced the range in the electricity purchase amount by 75.0%, 77.0%, and 73.0% on a representative day in January, April, and July, respectively. The accuracy of the reduction effect in the trial production system, obtained in the proposed optimization analysis, ranged from 1.9% to 7.2%. Moreover, the CO 2 emissions were reduced by 1.990 kg-CO 2 /(Day-House) in January, 2.910 kg-CO 2 /(Day-House) in April, and 2.210 kg-CO 2 / (Day-House) in July.

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Smart home energy optimization with incentives compensation from inconvenience for shifting electric appliances

Cited by 48 publications

References 21 publications

Reward-Based DSM Program for Residential Electrical Loads in Smart Grid

Reward-Based DSM Program for Residential Electrical Loads in Smart Grid

Analysis Application of Controllable Load Appliances Management in a Smart Home

Battery control for leveling the amount of electricity purchase in smart‐energy houses

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