Optimization Of Network Operation Research Articles

A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.

End-to-End Network Slicing Security Across Standards Organizations

This article makes a holistic analysis of the security aspects specified for 5G network slicing across the main standards and industry organizations, namely 3GPP, ETSI, and GSMA. A network slice is a logical end-to-end network that provides specific network capabilities and characteristics to serve a defined business purpose of communications service providers' (CSPs') customers. That purpose can be motivated by CSP internal reasons including network operation optimization, services classification, resources optimization, cost savings, support of network automation, or other specific customer demands. Network slicing can be defined as a paradigm where network slices are created with appropriate isolation, set of resources, and optimized topology, becoming a key feature and business driver for 5G. The overall security architecture of the 5G network is being constantly enhanced with new security features available as well in network slices as logical networks created within the 5G network. In contrast, the threat surface is increased with network slicing as new factors such as business models, tenants, functions, interfaces, and signaling flows are introduced, especially when the isolation among network slices is not well designed and effectively enforced. By analyzing the underlying security threats on network slicing, the article derives the corresponding security requirements and studies the specified mechanisms to protect the network slices. The article concludes pointing out several gaps in current standards with respect to 5G network slicing security and depicts possible next steps for further investigation.

Operation Optimization Method of Distribution Network with Wind Turbine and Photovoltaic Considering Clustering and Energy Storage

The problem of distribution network operation optimization is diversified and uncertain. In order to solve this problem, this paper proposes a method of distribution network operation optimization considering wind-solar clustering, which includes source load and storage. Taking the total operating cost as the objective function, it includes network loss cost, unit operating cost, and considers a variety of constraints such as energy storage device constraints and demand response constraints. This paper aims to optimize the operation according to different wind-solar clustering scenes to improve the economy of distribution network. Taking the 365-day wind-solar output curves as the research object, K-means clustering is carried out, and the best k value is obtained by elbow rule. The second-order cone programming method and solver are used to solve the optimization model of each typical scenario, and the operation optimization analysis of each typical scenario obtained by clustering is carried out. Taking IEEE33 system and local 365-day wind-solar units output scenes as examples, the period is 24 h, which verifies the effectiveness of the proposed method. The proposed method has guiding significance for the operation optimization of distribution network.

Peer-to-Peer Joint Electricity and Carbon Trading Based on Carbon-Aware Distribution Locational Marginal Pricing

This paper proposes a novel Peer-to-Peer (P2P) joint electricity and carbon (E&C) trading model to co-optimize the energy and carbon emissions permit transactions considering the trading preferences in the distribution network. To realize the secure and low-carbon network operation, a decomposable carbon-aware distribution locational marginal pricing (CDLMP)-based operation service pricing scheme of the distribution system operator (DSO) is proposed to guide the P2P transactions among prosumers. In practice, the coordination between the P2P market and the DSO is cast as a bi-level optimization. In the upper level, the network operation optimization of the DSO is modeled as a carbon-aware optimal power flow (COPF) problem based on a carbon emission flow model to ensure the security of network operation and derive the CDLMP. In the lower level, a decentralized P2P joint E&C trading is modeled to simultaneously clear energy and carbon permit. Later, this paper develops an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> (p)-Box ADMM method to relax the binary variables originating from the peer-matching process of the carbon trading. Finally, a novel demurrage protocol is designed to facilitate the convergence of the bi-level interaction with an incentive for trading adjustments. Case studies illustrate the effectiveness of the proposed method in motivating “grid-friendly” and “low-carbon” P2P trading.

Increased Resilience Using Close Loop Renewable Microgrids in the Surabaya Distribution System as a Self-Healing Application

Distribution system reconfiguration has two main objectives, namely as optimization of network operations and as system recovery in case of disturbances. It is necessary to consider the reconfiguration process and the process of using Distributed Generation (DG) or microgrid as an additional supply to reduce the possibility of failure, loss of power, or a voltage drop. Therefore the first step in research This results in the candidate bus 14 and bus 52 on the Mulyosari feeder which must be reconfigured by cutting off the flow at L13 and L50 because the losses on both buses are the worst with a value of 0.01370MW and 0.00900MW using the calculation algorithm of Binary Particle Swarm Optimization (BPSO) to predict the location of possible failures and the placement DG, and selected on bus 29 and bus 45 which were injected DG with a PV capacity of 15 kW each, after the self-healing was applied to the system, power flow analysis was carried out, and get good results g is very satisfactory with the total losses in the system decreased from 0.363MW to 0.116MW from this study we can conclude that self-healing can only be done if the distribution system is a complex radial.

Internet traffic matrix prediction with convolutional LSTM neural network

With the rapid growing trend of Internet, prediction‐based network operation optimization and management has drawn the attention from both the academia and the industry. For predicting Internet traffic, deep learning models have been proven more effective than linear and statistical models. However, some of the previous studies model the Internet traffic prediction as a multivariate time series prediction problem simply, without using the Internet traffic matrix structure. In this letter, the Internet traffic matrix prediction problem is firstly modeled as a video prediction task. Then a ConvLSTM‐based Seq2Seq model named ConvLSTM‐TM is proposed for predicting the traffic matrix in the next time slot. Based on three real‐world traffic matrix datasets, ConvLSTM‐TM outperforms five deep learning baselines with a lower prediction error. For replication, the dataset, code, and results are publicly available in a Github repository.

Real-time coordination optimization method of active distribution network considering different stakeholders

In order to reduce the impact of renewable energy output and load forecasting error on the network operation optimization and realize the mutual benefit and win-win situation of multiple stakeholders, a real-time coordination optimization method of active distribution network considering different stakeholders is proposed in this paper. Firstly, the daily real-time coordination and optimization framework of active distribution network is proposed. Then, the intraday real-time rolling two-level optimal scheduling model was solved by nonlinear particle swarm optimization and primal dual interior point method. The results of the calculation examples show that the proposed scheduling model and method can maximize the common interests of different subjects of the distribution network and flexible resources, so that the network can guarantee lower operating costs on the basis of safe operation.

Utilization of Active Distribution Network Elements for Optimization of a Distribution Network Operation

Electricity Distributions Networks (DNs) are changing from a once passive to an active electric power system element. This change, driven by several European Commission Directives and Regulations in the energy sector prompts the proliferated integration of new network elements, which can actively participate in network operations if adequately utilized. This paper addresses the possibility of using these active DN elements for optimization of a time-discrete network operation in terms of minimization of power losses while ensuring other operational constraints (i.e., voltage profiles and line currents). The active elements considered within the proposed optimization procedure are distributed generation units, capable of reactive power provision; remotely controlled switches for changing the network configuration; and an on-load tap changer-equipped substation, supplying the network. The proposed procedure was tested on a model of an actual medium voltage DN. The results showed that simultaneous consideration of these active elements could reduce power losses at a considered point of operation while keeping the voltage profiles within the permitted interval. Furthermore, by performing a series of consecutive optimization procedures at a given time interval, an optimization of network operations for extended periods (e.g., days, months, or years) could also be achieved.

Global optimization of an industrial gas network operation

Air Liquide operates several industrial gas pipeline networks around the world, connecting air separation plants to customers of industrial gases. The operation of such a network of plants, pipelines, and customers is complicated due to fluctuating electricity prices and customer demands. We describe a complex industrial problem for real‐time optimization of network operations in the presence of these challenges. We then summarize a concerted modeling and algorithmic effort toward global optimization of this model. The resulting advances include development of a regression‐based fully‐deterministic nonconvex optimization model, a tool for diagnosing infeasibilities during model development, reformulations and scaling to make the model more amenable for optimization, and development of strengthened relaxations for its efficient solution. We provide details on the development of these tools and techniques that facilitated the solution of this model in a reasonable computational time with the global solver BARON. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3215–3224, 2016

Cognitive Network Management Framework and Approach for Video Streaming Optimization in Heterogeneous Networks

The future Internet will be highly heterogeneous in supporting a multitude of access technologies and networks with overlapping coverages. Optimization of network operations like management of resources, mobility or Quality of Service in order to ensure smooth network operation and high user satisfaction will be very challenging in the future networks. Cognitive network management can provide a solution of managing such complex systems. This paper studies cognitive network management in the context of optimizing video streaming performance in heterogeneous multi-access networks. The paper proposes a network management framework that relies on cognitive decision techniques in the joint optimization of network and video service performance. The proposed solution is also implemented and validated in part in a testbed environment. The results attest the feasibility of the solution as well as the benefits of cognitive decision techniques over non-learning or non-adaptive approaches.