Indexing is an important technique to optimize graph database performance. However, indexing in existing graph databases focuses mainly on property-value based query, and has not drawn much attention on graph traversa...
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The rising adoption rates and integration of Renewableenergy Sources (ReS) and electric Vehicles (eVs) into theenergy grid introduces complex challenges, including the need to balance supply and demand and smooth pe...
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ISBN:
(纸本)9798350318562;9798350318555
The rising adoption rates and integration of Renewableenergy Sources (ReS) and electric Vehicles (eVs) into theenergy grid introduces complex challenges, including the need to balance supply and demand and smooth peak consumptions. Addressing these challenges requires innovative solutions such as Demand Response (DR), Renewableenergy Communities (ReCs), and more specifically for eVs, Vehicle-to-grid (V2G). However, existing V2G approaches often fall short in real-world applicability, adaptability, and user engagement. To bridge this gap, this paper proposes energAIze, a Multi-Agent Reinforcement Learning (MARL) energy management algorithm leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. energAIzeenables user-centric multi-objectiveenergy management by allowing each prosumer to select from a range of personal management objectives. Additionally, it architects' data protection and ownership through decentralized deployment, whereeach prosumer can situate an energy management node directly at their own dwelling. The local node not only manages local eVs and other energy assets but also fosters ReC wide optimization. energAIze is evaluated through a case study using the CityLearn framework. The results show reduction in peak loads, ramping, carbon emissions, and electricity costs at the ReC level while optimizing for individual prosumers objectives.
This study aims to create metamodels for the annual energy production of a l-kWp grid-tied mono-Si residential solar PV system with net-metering and the annual net energy sourced from thegrid in terms of the plane ti...
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The smart distribution grid is a type of electrical supply network that has been widely applied in life. ensuring efficient and secure communication of information within the smart distribution grid has become one of ...
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Caching-assisted edgecomputing has been regarded as a promising technique to avoid duplicate data transmission and provide closer data access and computing by pre-storing popular data and offloading computing task to...
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The artificial intelligence standard knowledgecomputingengine system for gridequipment construction was applied to realize the functions of real-time monitoring, fault diagnosis, predictive analysis and optimizatio...
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To address the issue of reduced data transmission efficiency in cloud platforms due to microservice failures, this paper proposes a data transmission algorithm based on edgecomputing. Initially, data cleaning, dedupl...
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The construction of a distributed heterogeneous data platform for power grid dispatching faces challenges of diversity, large scale, and high performance. However, existing data platform design methods in both the pow...
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ISBN:
(纸本)9798350375145;9798350375138
The construction of a distributed heterogeneous data platform for power grid dispatching faces challenges of diversity, large scale, and high performance. However, existing data platform design methods in both the power and computer science fields struggle to meet practical production requirements effectively. This paper constructs a distributed data storage architecture model for power grid dispatching, defining theelements and their relationships within the architecture. Additionally, it proposes methods for managing massive source data and distributed heterogeneous database clusters. Based on these findings, a power grid dispatching business data platform is designed. Test results indicate that the proposed architectureeffectively supports theefficient execution of power grid dispatching business, providing a specialized data platform design paradigm for the power industry.
In this paper, an edgecomputing-based machine-learning study is conducted for solar inverter power forecasting and droop control in a remote microgrid. The machine learning models and control algorithms are directly ...
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The mission-critical operations of smart distribution grids necessitate highly reliable and low-latency communication to ensure uninterrupted electricity distribution with high security of supply. The 5G networks (and...
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ISBN:
(纸本)9798350318562;9798350318555
The mission-critical operations of smart distribution grids necessitate highly reliable and low-latency communication to ensure uninterrupted electricity distribution with high security of supply. The 5G networks (and beyond), will provide a diverse range of services for various customers, network operators, and verticals. This is enabled by the flexibility provided by increased softwarization and virtualization of the networks, including network slicing. In an intent-based network, the tenants can express the desired outcome of a communication service using intents at an abstract level. Due to the highly dynamic nature of communication service requests, it is very challenging for the network provider to make the trade-off between accepting or rejecting a request while allocating resources to meet the tenants' expectations as defined by the service level objectives. In this paper, we implemented a discreteevent simulation, including a two-tier admission control mechanism for service requests. The simulation results compare four admission control policies upon the arrival of service requests into the network-sliced environment and provide interesting parameters, such as the probability of violating a service level objective, which implies a breach of the service level agreement. Our findings in the paper highlight the importance of priority enforcement, two-tier admission control, and resource reserving strategies to meet the desired objectives of mission-critical traffic in smart distribution grid protection.
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