Discrete Predefined-Time Distributed Optimization Algorithm for Resource Allocation Problem in Smart Grids

Resource allocation problems usually seek to find an optimal allocation of a limited amount of resources to a number of activities. The allocation solutions of different problems usually optimize different objectives under constraints [1, 2]. If the activities and constraints among them are presented as nodes and edges respectively, the resource allocation problem can be modeled as a k-coloring problem with additional optimization objectives [3, 4]. Since the amount of resources is limited, it is common that some of the activities (nodes) cannot obtain a resource (color). Because the importance of the nodes is usually different, let the weight of a node denote the cost if it cannot obtain a resource, then the resource allocation problem can be described by a node-weighted graph G(E,V), where E and V are the edge and node set, respectively. Some of the nodes that cannot obtain a resource will incur cost to the allocation solution. The optimization objective of the resource allocation problem formulated in this paper is to minimize the total cost of all the nodes that do not obtain a resource. If the total cost is zero, the obtained solution is a k-coloring of the graph; otherwise, the obtained solution is a k -coloring of the graph after removing the nodes that do not obtain a resource. So the resource allocation problem is a generalization of the k-coloring problem.

In this research work, we present a combination of a memetic algorithm and an immunological algorithm that we call Memetic Immunological Algorithm - MIA. This algorithm has been designed to tackle the resource allocation problem on a communication network. The aim of the problem is to supply all resource requested on a communication network with minimal costs and using a fixed number of providers, everyone with a limited resource quantity to be supplied. The scheduling of several resource allocations is a classical combinatorial problem that finds many applications in real-world problems. MIA incorporates two deterministic approaches: (1) a local search operator, which is based on the exploration of the neighbourhood; and (2) a deterministic approach for the assignment scheme based on the Depth First Search (DFS) algorithm. The results show that the usage of a local search procedure and mainly the DFS algorithm is an effective and efficient approach to better exploring the complex search space of the problem. To evaluate the performances of MIA we have used 28 different instances. The obtained results suggest that MIA is an effective optimization algorithm in terms of the quality of the solution produced and of the computational effort.

Distributed algorithm for resource allocation problems under persistent attacks

In this paper, the distributed resource allocation problem over undirected networks, considering box constraints and demand–supply constraints, is studied. Furthermore, each agent has one private cost function that is only known by itself. The aim is to minimize the sum of all local cost functions in a distributed manner while meeting the constraints. To solve the dispatch problem, a continuous‐time distributed algorithm with fixed step‐size is proposed. After that, a modified distributed algorithm of discrete‐time multi‐agent system is provided. Moreover, the proposed algorithms obtain the global optimal solution with an exponential convergence speed by the conventional Lyapunov methods. Finally, several simulations of IEEE‐14 bus are presented to validate and illustrate the proposed designed algorithm.

The vision of smart grids lie in large scale, distributed electrical power systems, equipped with communication and information technologies for automated and sustainable electricity production and distribution. Smart grids require communication with individual energy consumers for enabling energy providers to create a control-feedback loop using time-dependent pricing. Emerging broadband wireless networks hold the key in providing this efficient communication between energy consumers and smart power grids. In this paper we first propose the use of LTE multicast between the aggregator and energy consumers for efficient demand-response management in smart grids. We formulate the multicast scheduling and resource allocation problem for optimal SG-DR programs and provide a greedy solution. Performance analysis and OMNET++ simulation results, using data from Korean power corporation, demonstrate that our proposed LTE multicast and MBSFN solutions are capable of providing low latency (≤ 40ms) and packet drop (≤ 6%), thus resulting in almost 25% shift in peak energy load and up to 30% reduction in energy production cost.

Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.

Smart grids have emerged as a transformative technology in the power sector, enabling efficient energy management. However, the increased reliance on digital technologies also exposes smart grids to various cybersecurity threats and attacks. This article provides a comprehensive exploration of cyberattacks and cybersecurity in smart grids, focusing on critical components and applications. It examines various cyberattack types and their implications on smart grids, backed by real-world case studies and quantitative models. To select optimal cybersecurity options, the study proposes a multi-criteria decision-making (MCDM) approach using the analytical hierarchy process (AHP). Additionally, the integration of artificial intelligence (AI) techniques in smart-grid security is examined, highlighting the potential benefits and challenges. Overall, the findings suggest that “security effectiveness” holds the highest importance, followed by “cost-effectiveness”, “scalability”, and “Integration and compatibility”, while other criteria (i.e., “performance impact”, “manageability and usability”, “compliance and regulatory requirements”, “resilience and redundancy”, “vendor support and collaboration”, and “future readiness”) contribute to the evaluation but have relatively lower weights. Alternatives such as “access control and authentication” and “security information and event management” with high weighted sums are crucial for enhancing cybersecurity in smart grids, while alternatives such as “compliance and regulatory requirements” and “encryption” have lower weighted sums but still provide value in their respective criteria. We also find that “deep learning” emerges as the most effective AI technique for enhancing cybersecurity in smart grids, followed by “hybrid approaches”, “Bayesian networks”, “swarm intelligence”, and “machine learning”, while “fuzzy logic”, “natural language processing”, “expert systems”, and “genetic algorithms” exhibit lower effectiveness in addressing smart-grid cybersecurity. The article discusses the benefits and drawbacks of MCDM-AHP, proposes enhancements for its use in smart-grid cybersecurity, and suggests exploring alternative MCDM techniques for evaluating security options in smart grids. The approach aids decision-makers in the smart-grid field to make informed cybersecurity choices and optimize resource allocation.

Electrification of the transportation sector can play an essential role in curbing fossil fuel scarcity and oil shortages in the world. Electric vehicles (EVs) can significantly reduce CO2 emissions, improve urban pollution. Apart from these advantages of EVs, they may also pose challenges to the distribution grid. Increasing penetration of EVs puts an extra burden and leads to affect the grid severely. Load congestions, voltage drops/regulation, overloading are some of the issues that might incur in the grid because of improper charging of EVs. The uncertain nature of EV owners makes it very difficult to predict the charging pattern. So the grid operator may face a daunting task to avoid overloading if huge EVs are charged without following any smart charging management strategies. We propose a framework that allows EV users to follow a well-coordinated charging schedule and fulfill various objectives like load variance minimization and social welfare maximization for both electric utility and EV owners. A proof of concept of distributed resource allocation for EV charging is implemented using the output consensus approach. Microgrids are components of a smart power distribution grid that can operate as a decentralized/localized grid when disconnected from the main grid. A microgrid can enhance the grid resiliency and provide adequate power to a community or a region in case of grid failure caused by natural disasters or any other disruptions. We also propose the optimal scheduling model for Distributed Energy Resources (DERs) including an EV charging station and battery energy storage system (BESS) in a community microgrid. This model also minimizes the cost incurred in operating the DERs, charging station, BESS, and the grid load. Due to the two-way power flow in the smart grid because of the EVs and distributed energy resources (DERs) involvement, the distribution system operator (DSO) must provide control & protection to equipment and manage the reverse power flow without violating system constraints. Artificial Intelligence (AI) leads the path for a promising smart grid future and helps the DSO tackle such challenges. Given the large size of data flow in the smart grid due to phasor measurement units (PMUs) and smart sensors, AI-based techniques help the grid operator to manage and analyze such data effectively and improve grid resiliency. Machine learning can help the grid operator forecast the uncertainty in supply-demand because of the integration of renewable energy sources. For system planning and operation, the utility needs to perform the

A survey on applications of Alternating Direction Method of Multipliers in smart power grids

The 21st century world is more dependent on electrical power than ever. This dependency will increase manifold in the near future with more systems being computerized and a plethora of new electronic devices emerging for household, commercial and industrial applications. In such a scenario, availability of secure, reliable and efficient power is crucial for sustaining current development and economic growth trends. India is struggling to meet electric power demands of the fast expanding economy. In this situation, “smart grid” is the only solution, as it provides an open platform for electricity market and power transactions and offers high quality service and optimizes resource allocation. Simply stating smart grid refers to the application of information technology to power systems. Due to the complexity of such systems, the possibilities have yet to be clearly defined. Undertaking smart grid research in India is necessary for safe and stabilization of national power grid, optimization of national energy structure etc. This paper explores the definition, characteristics of smart grid and the gap between today’s grid and smart grid and its milestones are analyzed. Also the necessity of smart grid and enormous challenges which Indian power enterprises are facing is analyzed through which the inspiration of developing smart grid in India can be gained. Finally some of the ongoing smart grid activities in India and the barriers to implement the smart grid are discussed. The power system development as the world’s new trend of change, smart grid will lead the Indians to restore confidence, revive industry and promote sound and fast economic growth.

Electric power grids are lagging in flexibility and time-response. A smart grid is an improved version of electrical grids that leverages Internet of Things (IoT) based devices to improve the overall infrastructure from the grid stations to intelligent appliances. It provides better understanding of supply and demand and overall flow of data depending based upon the requirements. Modern approach towards Smart grid envisions to provide electricity consumers with the opportunity to manage their respective power usage. Population increase has played a major role in the adoption of smart grid as a lot of electrical energy is consumed in the residential sector and a lot of architectures have been proposed for better flow of information from the smart meter to connectors and devices for improved customer participation. Customer needs have been very important in the smart grid. However, the customers have never been provided with the ease of choosing their own kind of benefits from the smart grid. In this work, we propose an enhanced architecture working effectively for multiple users based on their requirements. The users would be able to choose their type of scheduling techniques based on their requirements. These requirements may include cost reduction and increasing user comfort for better consumption of electricity and reliable systems. These requirements can be achieved using different Bio inspired computing based scheduling algorithms. Furthermore, in this work, we provide a comparison of these bio inspired scheduling techniques, i.e., Enhanced Differential Evolution, Bacterial Foraging Algorithm and Grey Wolf Optimization integrated in smart grid architecture for providing better consumption of electricity and achieving reliable systems. These algorithms mainly aim to schedule load, minimize electricity bills and maximize the user comfort depending on user demand.

In this paper, we propose a dynamic event-based distributed reinforcement learning algorithm to address the resource allocation problem in a smart grid. The main challenge lies in the presence of unknown resource transfer functions and cost functions. Our objective is to discover a set of resource allocation solutions that minimise the total cost functions over a specified period. Our approach involves re-constructing a new convex optimisation problem to facilitate the approximation of Q-values in the reinforcement learning training process. Additionally, to avoid continuous communication, we integrate an event-based distributed optimisation approach to approximate the action-value function. We support our methodology with theoretical proofs that establish the validity of the event-triggered conditions and demonstrate the algorithm's convergence. We further validate the algorithm's feasibility through an illustrative example involving output power allocation in a smart grid.

Smart grid planning and construction is a very complex engineering project that requires intelligent technology to protect systems such as grid lines and substations to control grid stability and power quality. The smart grid GIS resource allocation technology has designed a new system architecture for the needs of building smart grids. The article expounds the key technologies and typical applications of smart grid GIS resource allocation technology, and describes its broad application prospects in smart grid. As a public service platform for graphical display applications, GIS platform constructs a spatial geographic information data center, which is intelligent. Applications such as grid integration, data mining, and intensive resource utilization provide a massive data storage, management, and analysis environment. The grid GIS platform provides geospatial information sharing and business application integration for smart grid power generation, transmission, substation, power distribution, power consumption, dispatching, etc., providing planning methods and visual analysis services for the construction of smart grid systems.

The inclusion of a previously non-existent traffic class required by the Smart Grid poses many difficulties on OFDMA based wireless communication systems. In this paper, we develop a resource allocation algorithm based on the ecological research Lotka-Volterra equations. Each class of traffic is modelled as a species and they compete over the telecommunications resources. Constraints were then placed on the classes to ensure fairness between the traffic classes and improved average user throughput. Our results show a high level of fairness between competing classes of communication data, whilst ensuring Smart Grid packets attain their low delay constraints. Furthermore, we apply an optimization algorithm to ensure efficient allocation of subcarriers to Smart Grid periodic traffic. When compared against other resource allocation schemes, the proposed Lotka-Volterra solution provides low variability in performance. This shows that the resource allocation is able to adapt to changing channel conditions and still ensure fairness between competing user equipment of other traffic classes.

Smart Grid is the trend of next generation power distribution and network management that enable a two -- way interactive communication and operation between consumers and suppliers, so as to achieve intelligent resource management and optimization. The wireless mesh network technology is a promising infrastructure solution to support these smart functionalities, while it has some inherent vulnerabilities and cyber-attack risks to be addressed. As Smart Grid is heavily relying on the underlie communication networks, which makes their security and dependability issues critical to the entire smart grid technology. Several studies have been conducted in the field of Smart Grid security, but few works were focused on the dependability and its associated resource analysis of the control center networks. In this paper, we have investigated the dependability modeling and also resource allocation in redundant communication networks by adopting two mathematical approaches, Reliability Block Diagrams (RBD) and Stochastic Petri Nets (SPNs), to analyze the dependability of control center networks in Smart Grid environment. We have applied our proposed modeling approach in an extensive case study to evaluate the availability of smart gird networks with different redundancy mechanisms. A combination of dependability models and reliability importance are used to analyze the network availability according to the most important components. We also show the variation of network availability in accordance with Mean Time to Failure (MTTF) in different network architectures.