Combining Machine Learning Analysis and Incentive-Based Genetic Algorithms to Optimise Energy District Renewable Self-Consumption in Demand-Response Programs
| Autor: | Matteo Verber, Giuseppe Raveduto, Denisa Ziu, Vincenzo Croce |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Energy management Computer science 020209 energy forecast lcsh:TK7800-8360 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 7. Clean energy 01 natural sciences Multi-objective optimization Energy storage Demand response load shifting 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering 0105 earth and related environmental sciences electric vehicles business.industry Photovoltaic system second-life batteries lcsh:Electronics peak shaving Renewable energy machine learning multi-objective optimization RES Hardware and Architecture Control and Systems Engineering demand response Peaking power plant Signal Processing Artificial intelligence Electricity storage system business computer FIWARE |
| Zdroj: | Electronics, Vol 9, Iss 945, p 945 (2020) Electronics Volume 9 Issue 6 |
| ISSN: | 2079-9292 |
| Popis: | The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, we propose a methodology that combines machine learning with multi-objective optimization to accurately plan the exploitation of the energy district&rsquo s flexibility with the objective of reducing peak consumption and avoiding reverse power flow. Using historical data, acquired by the smart meters deployed on the pilot district, the district&rsquo s power profile can be predicted daily and analyzed to identify potentially critical issues on the network. District&rsquo s resources, such as electric vehicles, charging stations, photovoltaic panels, buildings energy management systems, and energy storage systems, have been modeled by taking into account their operational constraints and the multi-objective optimization has been adopted to identify the usage pattern that better suits the distribution operator&rsquo s (DSO) needs. The district is subject to incentives and penalties based on its ability to respond to the DSO request. Analysis of the results shows that this methodology can lead to a substantial reduction of both the reverse power flow and peak consumption. |
| Databáze: | OpenAIRE |
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