A home energy management algorithm in a smart house integrated with renewable energy

Saha, Avijit; Kuzlu, Murat; Khamphanchai, Warodom; Pipattanasomporn, Manisa; Rahman, Saifur; Elma, Onur; Selamoğulları, Ugur Savaş; Uzunoğlu, Mehmet; Yağcıtekin, Bünyamin

doi:10.1109/isgteurope.2014.7028970

Cited by 17 publications

(4 citation statements)

References 18 publications

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“…The public good consists of the total energy surplus

, which is available to all households for charging the EVs. Its value is computed from the current production of renewable energy

, and the current network load

[ 14 ]. We assume that the energy surplus is lower than the total demand, i.e., at any given time

; hence, a black-out is inevitable if all households decide to use the surplus simultaneously.…”

Section: Modelmentioning

confidence: 99%

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

Bennati

Dusparić

Shinde

et al. 2018

Sensors

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Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.

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“…The public good consists of the total energy surplus

, which is available to all households for charging the EVs. Its value is computed from the current production of renewable energy

, and the current network load

[ 14 ]. We assume that the energy surplus is lower than the total demand, i.e., at any given time

; hence, a black-out is inevitable if all households decide to use the surplus simultaneously.…”

Section: Modelmentioning

confidence: 99%

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

Bennati

Dusparić

Shinde

et al. 2018

Sensors

View full text Add to dashboard Cite

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“…where Pw is wind energy and upper limit of generation from this source is P max with respect to time. In particular, when renewable energy sources are included, the available power sources for power generation on a per-grid basis may difer due to environmental factors, so it is necessary to integrate and operate them [5].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Framework of the Integrated Renewable Energy and Power Quality Model for the Smart Grid

Kumar

Singh

Kebede

2023

International Transactions on Electrical Energy Systems

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The smart grid’s structure is distinctive because it incorporates numerous cutting-edge communication and sensor technologies. It is challenging to manage smart grids using conventional power grids’ unified optimum delivery strategy effectively. This work offers a smart grid power production and maintenance collaborative optimization framework with renewable energy based on power generation and maintenance analysis. The operating costs of conventional units, the price of generating solar or wind power, and the cost of maintaining units are the problems of objective functions; the constraints taken into account are primarily system constraints. The genetic algorithm (GA) is used in this research to examine the optimization solution strategy. Depending on the properties of the system, various loads, power sources, and constraints are considered. The difficulty of adjusting load scheduling is transformed into a control problem by developing an efficient objective function. When uncertain system components are considered, the real-time control system can dynamically alter load scheduling to meet the needs of the actual system. In this study, the real-time algorithm put forward in this research is based on a strategy that satisfies the load optimization requirement and achieves dynamic compensation for unpredictable changes in renewable energy power generation. Moreover, the simulation compares the proposed algorithm capabilities with the existing algorithms/study, demonstrating the effectiveness of the suggested approach.

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“…There are several studies considering this integration area in home energy management system framework. Such studies can be found in [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account

Yağcıtekin

Uzunoğlu

2016

Applied Energy

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A home energy management algorithm in a smart house integrated with renewable energy

Cited by 17 publications

References 18 publications

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

An Optimized Framework of the Integrated Renewable Energy and Power Quality Model for the Smart Grid

A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account

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