Finding optimal schedules in a home energy management system

Merdanoglu, Hakan; Yakıcı, Ertan; Dogan, Osman Tufan; Duran, Serhan; Karataş, Mümtaz

doi:10.1016/j.epsr.2020.106229

Cited by 60 publications

(31 citation statements)

References 39 publications

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“…However, the battery degradation costs, and the generation costs of the renewable components were overlooked. In [21], the EMS problem of a grid-connected home was formulated as an optimisation problem. The house was integrated with wind turbine, solar PV, and battery.…”

Section: Literature Review and Research Gapsmentioning

confidence: 99%

Energy Management Systems for Grid-Connected Houses with Solar PV and Battery by Considering Flat and Time-of-Use Electricity Rates

et al. 2021

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This paper develops new practical rule-based energy management systems (EMSs) for typical grid-connected houses with solar photovoltaic (PV) and battery by considering different rates for purchasing and selling electricity. The EMSs are developed to supply the household’s loads and reduce operating costs of the system based on different options of flat and time-of-use (ToU) rates for buying and selling electricity prices. Four different options are evaluated and compared in this study: (1) Flat-Flat, (2) ToU-Flat, (3) Flat-ToU, and (4) ToU-ToU. The operation cost is calculated based on the electricity exchange with the main grid, the equivalent cost of PV generation, as well as the degradation cost of battery storage. The operation of the grid-connected house with rooftop solar PV and battery is evaluated for a sunny week in summer and a cloudy week in winter to investigate the proper performance for high and low generations of PV. While the developed rule-based EMS are generic and can be applied for any case studies, a grid-connected house in Australia is examined. For this purpose, real data of solar radiation, air temperature, electricity consumption, and electricity rates are used. It is found that the ToU-Flat option has the lowest operating cost for the customers.

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Section: Literature Review and Research Gapsmentioning

confidence: 99%

Energy Management Systems for Grid-Connected Houses with Solar PV and Battery by Considering Flat and Time-of-Use Electricity Rates

et al. 2021

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“…In the recent decade, various studies [29][30][31][32][33][34][35][36][37][38][39][40][41][42] also have been documented to solve home energy scheduling problems for the smart home with only RESs integration, or both RESs and ESS integration. In study [29], authors proposed a hybrid of stochastic programming and robust optimization model for the HEM system by incorporating the uncertainties of solar photovoltaic (PV) electricity output and electricity tariffs.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…In the proposed model, the home is furnished with a solar PV module and ESS batteries, and various dynamic electricity pricing models such as ToU, RTP, and IBR are considered to alleviate consumers' electricity bills. In [31], authors proposed a mathematical model based on stochastic MILP to develop a HEM system for the smart home in which RESs and storage devices are integrated. A scenario-based stochastic modeling methodology is applied in the proposed model to reduce energy cost by considering the stochastic RESs electricity output, time-varying electricity tariffs, weather conditions, household appliances energy usage pattern, ESS capacity, and real-time electric sales prices.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Cost-Effective Optimization for Scheduling of Household Appliances and Energy Resources

et al. 2021

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In literature, proposed approaches mostly focused on household appliances scheduling for reducing consumers' electricity bills, peak-to-average ratio, electricity usage in peak load hours, and enhancing user comfort level. The scheduling of smart home deployed energy resources recently became a critical issue on demand side due to a higher share of renewable energy sources. In this paper, a new hybrid genetic-based harmony search (HGHS) approach has been proposed for modeling the home energy management system, which contributes to minimizing consumers' electricity bills and electricity usage during peak load hours by scheduling both household appliances and smart home deployed energy resources. We have comparatively evaluated the optimization results obtained from the proposed HGHS and other approaches. The experimental results confirmed the superiority of HGHS over genetic algorithm (GA) and harmony search algorithm (HSA). The proposed HGHS scheduling approach outperformed more efficiently than HSA and GA. The electricity usage cost for completing one-day operation of household appliances was limited to 1305.7 cents, 953.65 cents, and 569.44 cents in the proposed scheduling approach for case I, case II, and case III, respectively and was observed as lower than other approaches. The electricity consumption cost was reduced upto 23.125%, 43.87% and 66.44% in case I, case II, and case III, respectively using proposed scheduling approach as compared to an unscheduled load scenario. Moreover, the electrical peak load was limited to 3.07 kW, 2.9478 kW, and 1.9 kW during the proposed HGHS scheduling approach and was reported as lower than other approaches.INDEX TERMS Demand side management, demand response program, home energy scheduling, smart grid, metaheuristic algorithm.

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“…e proposed model reduces the peaks and the electricity bills using evolutionary algorithms (EAs) such as binary particle swarm optimization (BPSO), genetic algorithm (GA), and cuckoo search. e authors in [19] utilize a stochastic optimization approach to find the optimal schedules for a home energy management system (HEMS) where energy prices and generation from renewable sources are time-varying. e aim of the proposed method is to find the minimum energy cost while taking into account the supply and demand constraints.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Dynamic Time- and Load-Based Preference toward Optimal Appliance Scheduling in a Smart Home

Ali

Ouassaid

Maâroufi

2021

Mathematical Problems in Engineering

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In this paper, the household appliance scheduling based on the user predefined preferences is addressed. Previous works generally deal with this problem without integration of renewable energy sources (RESs) in smart home. The present paper proposes a new demand side management (DSM) technique considering time-varying appliance preferences and solar panel generation. The branch and bound (B&B) algorithm is developed based on three postulations that allow the time-varying preferences to be quantified in terms of time- and load-based features. Based on the input data including the load’s power rating, the absolute comfort derived from time- and load-preferences, the total energy available from the solar panels as well as the energy purchased from the utility grid, the (B&B) algorithm is run to generate the optimal energy consumption model that would give maximum comfort to the householder based on the mixed-integer linear programming (MILP) technique. To test the performance of the proposed mechanism, three scenarios are considered with local energy production and limited budget for purchasing the energy from the utility grid to cover the user needs. The simulation results reveal that the proposed DSM mechanism based on the MILP method offers maximum level of comfort for all the scenarios within the available energy limitation.

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Finding optimal schedules in a home energy management system

Cited by 60 publications

References 39 publications

Energy Management Systems for Grid-Connected Houses with Solar PV and Battery by Considering Flat and Time-of-Use Electricity Rates

Energy Management Systems for Grid-Connected Houses with Solar PV and Battery by Considering Flat and Time-of-Use Electricity Rates

A Cost-Effective Optimization for Scheduling of Household Appliances and Energy Resources

Dynamic Time- and Load-Based Preference toward Optimal Appliance Scheduling in a Smart Home

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