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Scheduling of Smart Meter Data Access using Hungarian Algorithm
Linear assignment problems are well-known combinatorial optimization problems involving domains such as logistics, robotics and telecommunications. In general, obtaining an optimal solution to such problems is computationally infeasible even in small settings, so heuristic algorithms are often used to find near-optimal solutions. In order to attain the right assignment permutation, this study investigates a general-purpose learning strategy that uses a bipartite graph to describe the problem structure and a message passing Graph Neural Network (GNN) model to learn the correct mapping. Comparing the proposed structure with two existing DNN solutions, simulation results show that the proposed approach significantly improves classification accuracy, proving to be very efficient in terms of processing time and memory requirements, due to its inherent parameter sharing capability. Among the many practical uses that require solving allocation problems in everyday scenarios, we decided to apply the proposed approach to address the scheduling of electric smart meters access within an electricity distribution smart grid infrastructure, since near-real-time energy monitoring is a key element of the green transition that has become increasingly important in recent times. The results obtained show that the proposed graph-based solver, although sub-optimal, exhibits the highest scalability, compared with other state-of-the-art heuristic approaches. To foster the reproducibility of the results, we made the code available at https://github.com/aircarlo/GNN_LSAP .
Urban heat island (UHI) effects significantly impact building energy. Traditional UHI investigation methods are often incapable of providing the high spatial density of observations required to distinguish small-scale temperature differences in the UHI. Crowdsourcing offers a solution. Building cooling/heating load in 2018 has been estimated in London, UK, using crowdsourced data from over 1300 Netatmo personal weather stations. The local climate zone (LCZ) scheme was used to classify the different urban environments of London (UK). Inter-LCZ temperature differences are found to be generally consistent with LCZ temperature definitions. Analysis of cooling degree hours in July shows LCZ 2 (the densest urban LCZ in London) had the highest cooling demand, with a total of 1550 cooling degree hours. The suburban related LCZs 5 and 6 and rural LCZs B and D all had about 80% of the demand of LCZ 2. In December, the rural LCZs A, B and D had the greatest heating demand, with all recording around 5750 heating degree hours. Urban LCZs 2, 5 and 6 had 91%, 86% and 95% of the heating demand of LCZ D, respectively. This study has highlighted both advantages and issues with using crowdsourced data for urban climate and building energy research.
Important changes in electricity consumption model require a communication network within the electrical grid, which is called the Advanced Metering Infrastructure (AMI) that can handle the increase of multiple and fluctuating production sites. Recently, we witness the convergence of this communication network and the electricity network into what is now called Smart Grid. This communication network will serve the purpose of automatic meter reading, but new usages and services could be developed on top of this network. The massive integration of renewable energy sources will shift the production level close to the consumption sites. Therefore, new communication models are required to control the energy consumption depending on the production capabilities. In this Chapter, we explain why a two‐way communication network is necessary for future Smart Grid. Furthermore, we present the required network characteristics, and we particularly describe what are the current protocols selected to achieve these goals.
Abstract In this paper, we present the dynamic Hungarian algorithm, applicable to optimally solving the assignment problem in situations with changing edge costs or weights. This problem is relevant, for example, in a transportation domain where the unexpected closing of a road translates to changed transportation costs. When such cost changes occur after an initial assignment has been made, the new problem, like the original problem, may be solved from scratch using the well-known Hungarian algorithm. However, the dynamic,version of the algorithm which we present solves the new problem,more efficiently by repairing the initial solution obtained before the cost changes. We present proofs of the correctness and efficiency of our algorithm and present simulation results illustrating its efficiency. I Contents
A source provides status updates to monitors through a network with state defined by a continuous-time finite Markov chain. An age of information (AoI) metric is used to characterize timeliness by the vector of ages tracked by the monitors. Based on a stochastic hybrid systems (SHS) approach, first order linear differential equations are derived for the temporal evolution of both the moments and the moment generating function (MGF) of the age vector components. It is shown that the existence of a non-negative fixed point for the first moment is sufficient to guarantee convergence of all higher order moments as well as a region of convergence for the stationary MGF vector of the age. The stationary MGF vector is then found for the age on a line network of preemptive memoryless servers. From this MGF, it is found that the age at a node is identical in distribution to the sum of independent exponential service times. This observation is then generalized to linear status sampling networks in which each node receives samples of the update process at each preceding node according to a renewal point process. For each node in the line, the age is shown to be identical in distribution to a sum of independent renewal process age random variables.
The Hungarian algorithm solves the linear assignment problem in polynomial time. A GPU/CUDA implementation of this algorithm is proposed. GPUs are massive parallel machines. In this implementation, the alternating path search phase of the algorithm is distributed by several blocks in a way to minimize global device synchronization. This phase is very important and has a big contribution to the execution time. Other advanced features also implemented are: parallel graph traversal; the parallel detection of multiple alternating paths in a single iteration; a simplified and fast matrix compression that stores the zeros of the slack matrix, resulting in very fast graph traversal; highly optimized reductions for the initial slack matrix calculation and update. This results in a fast implementation for moderate size problems.
A brief concept of the smart city consists of using the technology to improve the urban infrastructure, to optimize the use of resources and services to turn the urban center a better place to live. This chapter provides a review of the communications technologies and network protocols used in the power distribution grid, for example, Modbus, DNP 3.0, and IEC 61850. It reviews the communications requirements of the different applications for distribution automation (DA), that is distribution system monitoring, control, and protection outside the substations. The chapter presents a resource allocation strategy for a Long‐Term Evolution‐device‐to‐device (LTE‐D2D) system model for a power distribution grid based on an optimization formulation. It discusses the advantages and disadvantages of half/full‐duplex LTE‐D2D technology for power distribution grids. A smart grid (SG) consists of a huge number of devices distributed over a large geographical region, with different demands and connected through a communication network.
The Smart Grids that connect Smart Homes are being built by Power Utilities to collectively shape Smart Cities. These new generation Power Utilities will also play a distinctive role to facilitate many new applications and services. In the trend of moving towards Smart Cities, the Telecommunications infrastructure and Services for Power Grid Utilities have been a challenge due to its complex and costly operations and constant improvements that are needed for the networks which span over multiple domains. Additionally, data centers and public and/or private clouds have already become parts of the Power Grid IT Infrastructure (IT/OT Convergence). The current Power Grid IT and Telecom network infrastructure are based on purpose‐built hardware for each network function and use technology‐specific interfaces. Recent paradigm shift in networking towards SDN/NFV have already started and next generation Power Grids should be part of this modernization. An obvious benefit is the simplification that manifests itself in cost reduction for network devices (white‐boxes) and their operations. In addition to improving quality of life, new applications will help revenue grow for Smart Grid Utilities and create sustainable environment for Smart Cities. The chapter covers issues, problems, and solutions of Smart Grid by the introduction of SDN and NFV to the Power Utilities Telecommunications Networks.
Smart meter data is usually accessed periodically with low time granularity, which creates limitations for near real-time information. This paper first introduces information quality metrics that can be used to optimize real-time data access to smart meter data. Then, a systematic parametric study assesses the impact of smart meter access scheduling on information quality. Finally, the paper evaluates a previously proposed heuristic optimization of scheduling of smart meter data access. The result shows that the heuristic optimization algorithm in all investigated scenarios shows less than 15% degradation as compared to the achievable best schedule.
Typical smart city applications generally require two different communications in-frastructures, a wide area cellular network to provide connectivity and long-range communications and efficient communication strategies for transmitting short data packets, particularly in case of Internet of Things (IoT) devices. The cellular infras-tructure is optimized for high data rates and large data sizes while IoT devices mostly exchange small data packets with high energy efficiency and low data rates. To fully exploit both communication infrastructures together, different strategies related to 5G and Device-to-Device (D2D) communication are proposed in literature. In this chapter, we survey these strategies and provide useful considerations for seamless integration of smart city applications in 5G networks. Moreover, we present smart city scenarios, their communication requirements and the potential impact on the life of citizens. Finally, we elaborate big data impact on smart cities with possible security and privacy concerns.
This paper presents the MATLAB simulation framework, DiSC, for verifying voltage control approaches in power distribution systems. It consists of real consumption data, stochastic models of renewable resources, flexible assets, electrical grid, and models of the underlying communication channels. The simulation framework makes it possible to validate control approaches, and thus advance realistic and robust control algorithms for distribution system voltage control. Two examples demonstrate the potential voltage issues from penetration of renewables in the distribution grid, along with simple control solutions to alleviate them.
Context-awareness is a key requirement in many of today’s networks, services and applications. Context management systems are used to provide access to distributed, dynamic context information. The reliability of remotely accessed dynamic context information is impacted by network delay, packet drop probability, its information dynamics and the access strategy used. Due to the characteristics of the different access strategies, different levels of reliability of context information can be ensured, but at the same time, these strategies lead to different access traffic which impacts also the network performance, and hence feeds back to the reliability of the information. Furthermore, different levels of QoS may be available and used in order to mitigate the impact of network performance degradation on the reliability of the dynamic context information. In this paper we describe a system and algorithms that are capable of configuring effectively context access strategies in order to maximize reliability of all accessed dynamic context information. The framework utilizes and extends existing information reliability models, and it can utilize different network performance models. Simulation results of scenarios in which the framework uses finite-buffer bottleneck performance models demonstrate the effectiveness of our algorithm to increase reliability. Furthermore, the framework is applied to a scenario with QoS classes that allows to trade off delay and loss via different buffer-size configurations.
Energy Watch: The European Union increases it commitment to the green transition
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Smart Distribution Grids Power Europes Transition to Green Energy DSOs-the Backbone of the Energy Transition
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Probabilistic models for access strategies to dynamic information elements,English
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Probabilistic models for access strategies to dynamic information elements,English
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