Optimal Operations of Local Energy Market With Electric Vehicle Charging and Incentives for Local Grid Services

The development of a local energy market (LEM) in Thailand involves prosumers and electric vehicle (EV) owners in a low-voltage distribution system (LVDS). However, both maximizing social welfare through independent energy transactions and managing distribution network constraints (DNCs) remain challenging. Additionally, defining fair incentives and ensuring equitable participation in local grid service programs further add to these challenges. This paper proposes an optimal LEM operation incorporating participants’ selection of energy transaction partners and independent price negotiations to maximize social welfare. The distribution system operator provides grid service programs to address violations, define fair incentives, ensure equitable participation, and guarantee the resolution of DNC violations while participants maintain control over their power consumption. A novel penalty scheme is proposed to support these grid service programs. Numerical simulations on a 380-V LVDS in Thailand demonstrate that social welfare is maximized at 19.17 THB/h across all preference cases during the study period. The LEM trades electricity without violating DNCs while allowing self-management. Results show that EVs contribute 83.66% of the demand reduction required by the distribution system operator, while the penalty scheme discourages 100% of individual benefit pursuit. Revenue compensation covers 100% of all prosumers’ revenue before implementing the grid service programs for all periods.

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Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition

The pivoted QLP decomposition is computed through two consecutive pivoted QR decompositions. It is an approximation to the computationally prohibitive singular value decomposition (SVD). This work is concerned with a partial QLP decomposition of matrices through the exploitation of random sampling. The method presented is tailored for low-rank matrices and called Randomized Unpivoted QLP (RU-QLP). Like pivoted QLP, RU-QLP is rank-revealing and yet it utilizes randomized column sampling and the unpivoted QR decomposition. The latter modifications allow RU-QLP to be highly scalable and parallelizable on advanced computational platforms. We provide an analysis for RU-QLP, thereby bringing insights into its characteristics and performance behavior. In particular, we derive bounds in terms of both spectral and Frobenius norms on: i) the rank-revealing property; ii) principal angles between approximate subspaces and exact singular subspaces and vectors; and iii) the errors of low-rank approximations. Effectiveness of the bounds is illustrated through numerical tests. We further use a modern, multicore machine equipped with a GPU to demonstrate the efficiency of RU-QLP. Our results show that compared to the randomized SVD, RU-QLP achieves a speedup of up to 7.1 and 8.5 times using the CPU and up to 2.3 and 5.8 times using the GPU for the decomposition of dense and sparse matrices, respectively.

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Dynamic Network Slice Scaling Assisted by Attention-Based Prediction in 5G Core Network

Network slicing is a key technology in fifth-generation (5G) networks that allows network operators to create multiple logical networks over a shared physical infrastructure to meet the requirements of diverse use cases. Among core functions to implement network slicing, resource management and scaling are difficult challenges. Network operators must ensure the Service Level Agreement (SLA) requirements for latency, bandwidth, resources, etc for each network slice while utilizing the limited resources efficiently, i.e., optimal resource assignment and dynamic resource scaling for each network slice. Existing resource scaling approaches can be classified into reactive and proactive types. The former makes a resource scaling decision when the resource usage of virtual network functions (VNFs) exceeds a predefined threshold, and the latter forecasts the future resource usage of VNFs in network slices by utilizing classical statistical models or deep learning models. However, both have a trade-off between assurance and efficiency. For instance, the lower threshold in the reactive approach or more marginal prediction in the proactive approach can meet the requirements more certainly, but it may cause unnecessary resource wastage. To overcome the trade-off, we first propose a novel and efficient proactive resource forecasting algorithm. The proposed algorithm introduces an attention-based encoder-decoder model for multivariate time series forecasting to achieve high short-term and long-term prediction accuracies. It helps network slices be scaled up and down effectively and reduces the costs of SLA violations and resource overprovisioning. Using the attention mechanism, the model attends to every hidden state of the sequential input at every time step to select the most important time steps affecting the prediction results. We also designed an automated resource configuration mechanism responsible for monitoring resources and automatically adding or removing VNF instances.

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Lightweight Multifactor Authentication Scheme for NextGen Cellular Networks

With increased interest in 6G (6th Generation) cellular networks that can support intelligently small-cell communication will result in effective device-to-device (D2D) communication. High throughput requirement in 5G/6G cellular technology requires each device to act as intelligent transmission relays. Inclusion of such intelligence relays and support of quantum computing at D2D may compromise existing security mechanisms and may lead towards primitive attacks such as impersonation attack, rouge device attack, replay attack, MITM attack, and DoS attack. Thus, an effective yet lightweight security scheme is required that can support existing low computation devices and can address the challenges that 5G/6G poses. This paper proposes a Lightweight ECC (elliptic curve cryptography)-based Multifactor Authentication Protocol (LEMAP) for miniaturized mobile devices. LEMAP is the extension of our previous published work TLwS (trust-based lightweight security scheme) which utilizes ECC with Elgamal for achieving lightweight security protocol, confidentiality, integrity, and non-repudiation. Multi-factor Authentication is based on OTP (Biometrics, random number), timestamp, challenge, and password. This scheme has mitigated the above-mentioned attacks with significantly lower computation cost, communication cost, and authentication overhead. We have proven the correctness of the scheme using widely accepted Burrows-Abadi-Needham (BAN) logic and analyzed the performance of the scheme by using a simulator. The security analysis of the scheme has been conducted using the Discrete Logarithm Problem to verify any quantum attack possibility. The proposed scheme works well for 5G/6G cellular networks for single and multihop scenarios.

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