Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning

Sani, Sajad Aliakbari; Bahn, Olivier; Delage, Erick

doi:10.1016/j.ejor.2022.02.035

Cited by 3 publications

(2 citation statements)

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“…A static robust optimization approach is used to solve the problem. In [ 32 ], a robust multiperiod formulation of the ETEM, with uncertain demand response, is developed to better control the level of conservatism in planning. Finally, in [ 33 , 34 ], two robust versions of the ETEM are linked to a mean field game model that presents the charging behavior of a large fleet of electric vehicles (EVs).…”

Section: Literature Reviewmentioning

confidence: 99%

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Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework

et al. 2022

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More than half of the world’s population live in cities, and by 2050, it is expected that this proportion will reach almost 68%. These densely populated cities consume more than 75% of the world’s primary energy and are responsible for the emission of around 70% of anthropogenic carbon. Providing sustainable energy for the growing demand in cities requires multifaceted planning approach. In this study, we modeled the energy system of the Greater Montreal region to evaluate the impact of different environmental mitigation policies on the energy system of this region over a long-term period (2020–2050). In doing so, we have used the open-source optimization-based model called the Energy–Technology–Environment Model (ETEM). The ETEM is a long-term bottom–up energy model that provides insight into the best options for cities to procure energy, and satisfies useful demands while reducing carbon dioxide (CO2) emissions. Results show that, under a deep decarbonization scenario, the transportation, commercial, and residential sectors will contribute to emission reduction by 6.9, 1.6, and 1 million ton CO2-eq in 2050, respectively, compared with their 2020 levels. This is mainly achieved by (i) replacing fossil fuel cars with electric-based vehicles in private and public transportation sectors; (ii) replacing fossil fuel furnaces with electric heat pumps to satisfy heating demand in buildings; and (iii) improving the efficiency of buildings by isolating walls and roofs.

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Section: Literature Reviewmentioning

confidence: 99%

“…Note again that a full description of the ETEM can be found in [ 11 , 31 ]. Here, we mostly followed the notation used in [ 32 ]. Namely, index

indicates time periods, and

is the set of time slices inside a period.…”

Section: Table A1mentioning

confidence: 99%

Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework

et al. 2022

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Smart Grid Business Portfolio Investment Decision Based on MOPSO Algorithm

2022

2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

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A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm

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Khaki

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et al. 2022

International Transactions on Electrical Energy Systems

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The concept of energy management in smart homes has received increasing attention in recent years, particularly on issues such as creating a balance between user privacy and reducing energy costs. Accordingly, this article proposes a user-oriented multi-objective approach, which minimizes energy costs and maximizes consumer privacy. In addition, a home energy management system is suggested for smart homes to optimize the energy consumption pattern of appliances. On the other hand, considering challenges in energy management of smart homes, the concept of demand-side management (DSM) is introduced. The objective of the proposed method is to reduce energy consumption to lower consumers’ electricity bills. Also, it improves user comfort (UC) in average waiting time conditions. In this research, a smart home equipped with an energy management system and smart home appliances that can inject electric power into the upstream network is considered the main system. This framework leads to a multi-objective optimization problem in which the two objectives mentioned above are considered two separate dimensions. To solve the problem, an ITS-BF Algorithm is used, which employs a random search to schedule home appliances and batteries based on the application of flexible devices in smart homes. The case studies show that the proposed method can considerably respect and satisfy users’ privacy and reduce the energy cost to an acceptable level. Finally, the numerical results obtained from the simulation have been analyzed to evaluate the proposed method’s efficiency. The simulation results show that an ITS-BF algorithm performs better than the existing methods in reducing costs and waiting time.

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Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning

Cited by 3 publications

References 39 publications

Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework

Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework

Smart Grid Business Portfolio Investment Decision Based on MOPSO Algorithm

A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm

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