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Demand response through decentralized optimization in residential areas with wind and photovoltaics

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  • Dengiz, Thomas
  • Jochem, Patrick
  • Fichtner, Wolf

Abstract

A paradigm shift has to be realized in future energy systems with high shares of renewable energy sources. The electrical demand has to react to the fluctuating electricity generation of renewables. To this end, flexible electrical loads like electric heating devices and electric vehicles are necessary in combination with optimization approaches. In this paper, we develop a novel privacy-preserving approach for decentralized optimization to exploit load flexibility in residential areas. This approach, which is based on a set of schedules, is referred to as SEPACO-IDA. Compared to the approaches from the literature for decentralized optimization, SEPACO-IDA leads to improvements of between 0.8% and 13.3% regarding the surplus energy and the peak load. Furthermore, this paper illustrates the suboptimal results for uncoordinated decentralized optimization and thus the need for coordination approaches. Another contribution of this paper is the development and evaluation of two methods for distributing a central wind power profile to the local optimization problem of different buildings in a residential area (Equal Distribution and Score-Rank-Proportional Distribution). These wind profile assignment methods are combined with different decentralized optimization approaches. The results reveal the dependency of the best wind profile assignment method on the used decentralized optimization approach.

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  • Dengiz, Thomas & Jochem, Patrick & Fichtner, Wolf, 2021. "Demand response through decentralized optimization in residential areas with wind and photovoltaics," Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:energy:v:223:y:2021:i:c:s0360544221002334
    DOI: 10.1016/j.energy.2021.119984
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    1. Rawat, Tanuj & Niazi, K.R. & Gupta, Nikhil & Sharma, Sachin, 2022. "A linearized multi-objective Bi-level approach for operation of smart distribution systems encompassing demand response," Energy, Elsevier, vol. 238(PC).
    2. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    3. Corneliu Marinescu, 2022. "Progress in the Development and Implementation of Residential EV Charging Stations Based on Renewable Energy Sources," Energies, MDPI, vol. 16(1), pages 1-31, December.
    4. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    5. Liu, Youquan & Li, Huazhen & Zhu, Jiawei & Lin, Yishuai & Lei, Weidong, 2023. "Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm," Energy, Elsevier, vol. 262(PA).

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