Encrypted Decentralized Optimization for Data Masking in Energy Scheduling
Pages 103 - 109
Abstract
In agent-based, decentralized optimization for coordinated energy generation in virtual power plants, often plain information on possible generation (or consumption) profiles is communicated to other agents in the network. If the virtual power plant consists of members from a local vicinity like an energetic neighborhood, e.g. an industrial estate, schedules can easily be assigned to specific entities in the neighborhood. Patterns of generation and consumption can be derived from collected schedules and from the aggregated information therein. We present a fully decentralized, agent-based optimization algorithm for orchestration of generation and consumption that works with encrypted schedules and thus masks the sent data, preventing machine learning methods from deriving information on other agents. We demonstrate the feasibility of still being able to conduct optimization for energy scheduling when using order preserving encryption.
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Index Terms
- Encrypted Decentralized Optimization for Data Masking in Energy Scheduling
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ISBN:9781450372015
DOI:10.1145/3372454
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- The University of Versailles Saint-Quentin: The University of Versailles Saint-Quentin, Versailles, France
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Published: 21 January 2020
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ICBDR 2019: 2019 The 3rd International Conference on Big Data Research
November 20 - 22, 2019
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