Optimizing resource allocation and enhancing security in smart grid environments through a decentralized access control system with power theft detection mechanism

A smart grid is an intelligent power grid. In recent years, the smart grid environment and its applications are incorporated into a variety of areas. The smart grid environment, however, can expose much more information than the old environments. In the environment, smart devices can be media in the exposure of various and specific pieces of information as well as energy consumption. This poses a huge risk in that it, combined with other pieces of information, may expose much more information. The current smart grid environment raises a need to develop anonymous signature and authentication techniques to prevent privacy breaches. Trying to meet this need, the principal investigator conducted research for three years. This paper discusses both the research trends investigated by him and the limitations of the development research and future research in need. Smart grid security requires the development of encrypted anonymous authentication that is applicable to power plant security, including nuclear power plants as well as expandable test beds.

A new model of power system to identify critical elements for smart and bulk power transmission grid environment is presented in this paper. Proposed bidirectional power flow model under complex network framework can quantify the criticality of nodes whose removal would trigger cascading failure. The proposed model based centrality, which is an extension of nominal power flow based model, is able to find nodes whose removal exerts more stress on the system. Several measures of impact like path length increase, boosted connectivity loss, raised load loss and increase in number of overloaded lines justify the use of bidirectional power flow based model to evaluate the criticality in smart grid environment. Change in direction of power flow in various random lines of the test system has a very little effect on the rank of nodes in the critical list.

This paper proposes an innovative developed framework to implement and evaluate an effective dynamic pricing (dP) methodology for demand-side management (DSM) within smart grid (SG) environment. The proposed framework is realized through two phases. In the first phase, a generic set of formulas is developed based on a composite particle swarm-heuristic optimization technique to enable informing consumers about the required demand reduction based on the system conditions. The developed generic formulas define the best and critical required demand reductions at each load centre. The focus in the second phase is to model the proposed composite time-incentive based pricing technique. The approach is applied to the IEEE 30-bus system under different demand conditions. The results ensure the possibility of achieving consumer-bill saving, defining break points of consumers' response, managing transmission system congestion, and reducing peak demand. Thus, the network upgrading can be postponed.

Electricity theft-induced power loss is a pressing issue in both traditional and smart grid environments. In smart grids, smart meters can be used to track power consumption behaviour and detect any suspicious activity. However, smart meter readings can be compromised by deploying intrusion tactics or launching cyber attacks. In this regard, machine learning models can be used to assess the daily consumption patterns of customers and detect potential electricity theft incidents. Whilst existing research efforts have extensively focused on batch learning algorithms, this paper investigates the use of online machine learning algorithms for electricity theft detection in smart grid environments, based on a recently proposed dataset. Several algorithms including Naive Bayes, K-nearest Neighbours, K-nearest Neighbours with self-adjusting memory, Hoeffding Tree, Extremely Fast Decision Tree, Adaptive Random Forest and Leveraging Bagging are considered. These algorithms are evaluated using an online machine learning platform considering both binary and multi-class theft detection scenarios. Evaluation metrics include prediction accuracy, precision, recall, F-1 score and kappa statistic. Evaluation results demonstrate the ability of the Leveraging Bagging algorithm with an Adaptive Random Forest base classifier to surpass all other algorithms in terms of all the considered metrics, for both binary and multi-class theft detection. Hence, it can be considered as a viable option for electricity theft detection in smart grid environments.

MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things

High-data-rate mobile multimedia applications can greatly increase energy consumption, leading to an emerging trend of addressing the “energy efficiency” aspect of mobile networks. Cognitive mobile networks with small cells are important techniques for meeting the high-data-rate requirements and improving the energy efficiency of mobile multimedia communications. However, most existing works do not consider the power grid, which provides electricity to mobile networks. Currently, the power grid is experiencing a significant shift from the traditional grid to the smart grid. In the smart grid environment, only considering energy efficiency may not be sufficient since the dynamics of the smart grid will have significant impacts on mobile networks. In this paper, we study green cognitive mobile networks with small cells in the smart grid environment. Unlike most existing studies on cognitive networks, where only the radio spectrum is sensed, our cognitive networks sense not only the radio spectrum environment but also the smart grid environment, based on which power allocation and interference management for multimedia communications are performed. We formulate the problems of electricity price decision, energy-efficient power allocation, and interference management as a three-stage Stackelberg game. A homogeneous Bertrand game with asymmetric costs is used to model price decisions made by the electricity retailers. A backward induction method is used to analyze the proposed Stackelberg game. Simulation results show that our proposed scheme can significantly reduce operational expenditure and CO2 emissions in cognitive mobile networks with small cells for multimedia communications.

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

The smart grid incorporates a two-way communication system between customers and the utility for advanced monitoring and intelligent control of supply and demand. Wireless multimedia sensor network can be treated as an organic supplement and a peripheral network in this two-way communication system. However, the challenging smart grid environment makes it difficult to achieve a high quality of service in wireless multimedia sensor network. This article proposes a prioritization mechanism that considers the heterogeneous characteristics of smart grid traffic. Specifically, an innovative channel allocation and traffic scheduling scheme, named the preemptive tidal flow queuing model, is presented. This scheme achieves differentiated services for diverse communication data when the wireless multimedia sensor network accesses the core network and ensures the performance for high-priority data at the expense of the performance for low-priority data. Simulation analyses show that the performance for high-priority messages can be reliably guaranteed and that the preemptive tidal flow queuing model satisfies the requirements for a wireless multimedia sensor network operating in the smart grid environment. This article offers three main contributions: the development of a prioritization mechanism specifically for a wireless multimedia sensor network in the smart grid environment, the proposal of the preemptive tidal flow queuing model, and the presentation of formulas and simulations to verify the performance of the preemptive tidal flow queuing model.

The objectives of the research are to determine the optimal allocation of resources available to the general company for food products using a linear programming(LP) technique to obtain high levels of production and high levels of profits by maximizing the value of the objective function and increasing the profitability of the company, and conducting a sensitivity analysis to see the extent of changes in the optimum commodity composition in terms of quality and quantity with the level of profits in a manner commensurate with the requirements of growth in the company , the study based on the use of (LP)technique to determine the optimum production plan with the highest net income for the products under study for vegetable oil factories as well as using sensitivity analysis by using the statistical program (Win QSB) .The research showed a number of results, perhaps the most important: proof of the hypothesis of achieving high levels of production and profits as well as the optimal allocation of available resources, as the quantity of production increased from 472 tons in the actual production plan to 974 tons in the derived plan , achieving a jump estimated at 106. 4%, and the results of the optimal solution using the (LP)technique showed that the value of net income at current prices has reached 348739500 dinars, an increase of 184.6% over the net income actually achieved at current prices of 122528565 dinars for the year 2018. The research had reached a number of conclusions, perhaps the most important is the matching of the research results to the research hypothesis as well as the optimal solution using the LP technique had showed the difference in the optimum commodity composition and its quantities from the actual commodity composition of the products , the research provides a number of recommendations, the most important of which is the application of a linear programming technique to know the extent of investing available resources in a way efficiency, which helps to increase production in order to achieve economic efficiency and the need to generalize it to production units with similar conditions in order to determine the optimal use of the various production resources.

This paper presents a case study of a charging stations with solar canopy within a smart grid environment. The charging stations and solar canopy are installed in the main campus of Illinois Institute of Technology (IIT) as part of its “Perfect Power Initiative”. This paper presents the configuration of the charging station and the solar canopy followed by the description of the technical solution for data acquisition, communication and display. The collected data from the installed charging stations and solar canopy, which can be utilized as reference for economic assessment, return of investment analysis, or other related researches, are presented. The optimal operation of charging stations within smart grid environment considering the real time electricity price and the Vehicle-to-grid (V2G) capacity is simulated and studied. Simulation results show that the charging stations with V2G capacity have the capability of both providing economic benefit and increasing the reliability of the smart grid system.

Cyber physical systems (CPSs) utilised in many of the critical infrastructures including the smart grid. The operational activities of smart grid's CPS use different industrial solution such as industrial control systems (ICS) for monitoring and controlling the physical architecture of smart grid. Therefore, it is quite essential to keep these industrial control systems secure and safe from potential hazards and cyber world as it may be harmful for human safety, strategic infrastructure of a country and also for the economy. The study gathered the scattered guidelines, best practices and policies available from various organisations such as National Institute of Standards and Technology (NIST), ISO, and North American Electric Reliability Corporation (NERC) for the purpose of developing safe and secure ICS security systems and also the knowledge about the issues involved in ICS security and protection. A conceptual framework is proposed and developed to address the shortcomings in-order to protect the ICS systems in a smart grid environment. The proposed framework was developed by reviewing related studies, standards and utilising IT security working experience. This framework will not only expand the horizon of knowledge but also provides pathway for the development of existing activities of CPS applications such as smart grid.

As one of the most important communication scenarios in the coming fifth generation (5G) cellular networks, ultrareliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems. However, one of the biggest challenges is that how to integrate URLLC and control performance together to maximize the overall system performance. In this paper, we investigate the resource allocation for URLLC uplink in real-time wireless control systems. Specifically, we first discuss the relationship between communication and control performance. Based on that, we convert the hybrid co-design problem into a regular wireless resource allocation problem. Then, we propose an iteration algorithm to obtain the optimal wireless resource allocation. Simulation results indicate the performance of our method.

In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

Optimal resource allocation across related channels