Smart Distribution Grid Modeling
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".
Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 5909
Special Issue Editors
Interests: smart distribution network planning and operation; distributed generation; demand response; energy storage systems
Special Issue Information
Dear Colleagues,
Distribution systems are characterized by great diversity in Europe and worldwide. Common and shared network models are becoming essential in planning and operation studies and in many advanced applications for fully assessing the performances of distribution in response to the main trends in power systems evolution. One trend is the increasing shares of variable renewable energies, mainly wind and solar, in the production mix, especially connected to distribution networks. Such resources introduce significant planning and operational challenges from the technical point of view and important externalities to the electricity markets. Another trend at distribution level is represented by new electric loads with high coincident peaks (e.g., electric vehicles, heat pumps, induction cookers) that impact the exploitation of existing distribution assets and make the balance of generation and demand at every single point in time harder than in the past.
Comprehensive studies of distribution network behavior should be conducted to find solutions suitable for solving these issues. Unfortunately, the observability level of distribution is very low, and some remedies to overcome this are becoming urgent for meeting the needs of planners and operators towards the smart grid era.
Models of general validity or methods for building the model of given networks are required by researchers, system operators, and other power system actors, such as aggregators, microgrid controllers etc. intent on orienting themselves into the new power system scenario.
Detailed models of distribution should include two aspects. The first concerns new and updated demand and production profiles, relevant to the behaviors of current customers, distributed generators owners, and prosumers. The second involves the network topology structures useful for testing control strategies and foreseeing criticalities that could happen in the future (e.g., resulting from the opening of the services market to distribution).
Clustering techniques for aggregating customer behaviors or the resort to representative networks, with common features, for modeling the great variability of distribution networks have been proposed in the literature, but there is further space for new contributions.
Contributions on the following topics are welcome:
- Customer behaviors
- Daily curves
- Clustering techniques
- Distribution network representation
- Use of public data
- Representative networks
- Synthetic networks
- Spatial modeling and GIS analysis
- Model aggregation
- Network reduction
Dr. Giuditta Pisano
Dr. Simona Ruggeri
Guest Editors
Manuscript Submission Information
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Keywords
- Distribution networks
- Modeling
- Clustering algorithms
- Reference networks
- Representative networks
- Representative profiles
- Public data
- Renewable energy sources
- Distributed generation
- Electric vehicles
- Load demand profiles
- GIS applications
- Topological models
- Network reduction
- Energy losses analysis
- Network observability
- Smart metering
- Data processing
- Big data
- Time series analysis
- Customer characterization
- Customer classification
- Synthetic network
- LV customers characterization
- Load forecasting
- Production forecasting
