The energy system is now at the forefront of achieving net-zero by integrating sustainable distributed energy resources (DERs) with low carbon footprints. Due to the increasing DER uptake, the operation and planning of the energy system faces the following challenges: Firstly, the inherent variability and uncertainty of weather-dependent renewables will fundamentally change the way (scheduled supply to meet demand) that the energy system is planned and operated. Secondly, DERs have a significant impact on the power quality, especially voltage and frequency, yet the current distribution system is not designed to accommodate a high penetration of DERs and cannot support two-way power flows. Thirdly, the centralized market mechanism cannot provide effective economic signals that capture the particular incentives of DERs. Integrating DERs to leverage their diversity and flexibility has become one of the most challenging problems in energy market designs. AI and optimization have been reshaping different sectors and are promising prospects to address the aforementioned challenges in DER integration toward Net-Zero.
This Research Topic aims to highlight the state of the art in DER integration. Thanks to information and communication technologies, such as the Internet of Things and digital twins, a large amount of data is collected to improve the understanding of DER behaviors, and two-way communications enable the real-time interactions between DERs and the smart grid. These new developments make AI and optimization techniques promising tools to address the challenges. For example, AI can improve the energy data analytics for DERs, such as renewable generation and load forecasting, DER consumer load profiling and segmentation, to helping hedge against uncertainty. Optimization can improve the efficiency and effectiveness of system operations with a high uptake of DERs, via Volt/VAR optimization, battery storage scheduling, electric vehicle coordination, and demand response, etc. AI and optimization techniques can also contribute to the data-driven designs of local market mechanisms to facilitate the DER integration, such as optimal tariff and pricing for DER scheduling, peer-to-peer energy trading, transactive energy, DER aggregation, and blockchain for DER markets. In this regard, this Research Topic will provide a forum to promote the research on AI and optimization for DERs toward Net Zero for future energy systems.
We welcome all types of manuscripts, including Original Research Articles, Reviews, Perspectives, and Brief Research Reports in AI, optimization, and emerging techniques for DER Integration, including but not limited to the following themes:
• AI-enabled DER renewable and load forecasting for prosumers and DER aggregators
• Data-driven modeling and analysis of DERs, such as profiling, segmentation, and classification
• AI and optimization for distribution system operations and planning
• AI and optimization theory and practice for DER coordination and scheduling
• Market designs and business models for DER integration (including tariffs, pricing, business models, and incentive mechanisms)
• Design and implementation of blockchain systems for DER management and markets
• Online learning and reinforcement learning for DERs
• Distributed intelligence, collective intelligence, and multi-agent systems for DERs
• Data-driven optimization and predict-then-optimize methods for DERs
• Demo and Implementation of AI and optimization solutions for DERs
• Virtual Power Plants (VPP)
• Frequency control ancillary services.
The energy system is now at the forefront of achieving net-zero by integrating sustainable distributed energy resources (DERs) with low carbon footprints. Due to the increasing DER uptake, the operation and planning of the energy system faces the following challenges: Firstly, the inherent variability and uncertainty of weather-dependent renewables will fundamentally change the way (scheduled supply to meet demand) that the energy system is planned and operated. Secondly, DERs have a significant impact on the power quality, especially voltage and frequency, yet the current distribution system is not designed to accommodate a high penetration of DERs and cannot support two-way power flows. Thirdly, the centralized market mechanism cannot provide effective economic signals that capture the particular incentives of DERs. Integrating DERs to leverage their diversity and flexibility has become one of the most challenging problems in energy market designs. AI and optimization have been reshaping different sectors and are promising prospects to address the aforementioned challenges in DER integration toward Net-Zero.
This Research Topic aims to highlight the state of the art in DER integration. Thanks to information and communication technologies, such as the Internet of Things and digital twins, a large amount of data is collected to improve the understanding of DER behaviors, and two-way communications enable the real-time interactions between DERs and the smart grid. These new developments make AI and optimization techniques promising tools to address the challenges. For example, AI can improve the energy data analytics for DERs, such as renewable generation and load forecasting, DER consumer load profiling and segmentation, to helping hedge against uncertainty. Optimization can improve the efficiency and effectiveness of system operations with a high uptake of DERs, via Volt/VAR optimization, battery storage scheduling, electric vehicle coordination, and demand response, etc. AI and optimization techniques can also contribute to the data-driven designs of local market mechanisms to facilitate the DER integration, such as optimal tariff and pricing for DER scheduling, peer-to-peer energy trading, transactive energy, DER aggregation, and blockchain for DER markets. In this regard, this Research Topic will provide a forum to promote the research on AI and optimization for DERs toward Net Zero for future energy systems.
We welcome all types of manuscripts, including Original Research Articles, Reviews, Perspectives, and Brief Research Reports in AI, optimization, and emerging techniques for DER Integration, including but not limited to the following themes:
• AI-enabled DER renewable and load forecasting for prosumers and DER aggregators
• Data-driven modeling and analysis of DERs, such as profiling, segmentation, and classification
• AI and optimization for distribution system operations and planning
• AI and optimization theory and practice for DER coordination and scheduling
• Market designs and business models for DER integration (including tariffs, pricing, business models, and incentive mechanisms)
• Design and implementation of blockchain systems for DER management and markets
• Online learning and reinforcement learning for DERs
• Distributed intelligence, collective intelligence, and multi-agent systems for DERs
• Data-driven optimization and predict-then-optimize methods for DERs
• Demo and Implementation of AI and optimization solutions for DERs
• Virtual Power Plants (VPP)
• Frequency control ancillary services.
