Grid Resource Allocation Research Articles

Electricity user behavior analysis and marketing strategy based on internet of things and big data

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies.

Deep learning-based evaluation of photovoltaic power generation

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

Research on 5G Network Slicing Algorithm Based on the Smart Grid

To address the problem of network-slicing resource allocation in the smart grid, this paper puts forward a hierarchical network-slicing resource scheduling strategy for the smart grid based on priority and network utility, which is a study of the 5G net slice resource allocation (the max algorithm of utility). It is divided into two layers, which are the user layer and the slice allocation layer. By adopting different resource allocation methods to better meet the service and the experience quality of power users in different slices, the simulation results playing the strategy are better than the other number of resource allocation schemes.

SOLAR ENERGY FORECASTING WITH DEEP LEARNING TECHNIQUE

The increasing reliance on renewable energy sources, such as solar power, necessitates accurate forecasting to ensure efficient grid integration and stability. This study explores the application of deep learning techniques, particularly deep neural networks (DNNs), in predicting solar irradiance and power output. By leveraging advanced algorithms and large datasets, this research aims to enhance the precision of solar energy forecasts, thereby optimizing grid management and resource allocation. Deep learning techniques, characterized by their ability to model complex nonlinear relationships, offer significant advantages over traditional statistical methods in forecasting solar energy. This study focuses on the development and evaluation of deep neural networks for predicting solar irradiance, which directly influences the power output of photovoltaic (PV) systems. The models are trained on extensive historical weather data, satellite imagery, and real-time solar output measurements to capture temporal and spatial variations in solar energy. Key components of the research include data preprocessing to handle missing values and noise, feature extraction to identify relevant patterns, and model training using various architectures of DNNs such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The study also employs ensemble learning techniques to combine predictions from multiple models, further enhancing forecast accuracy. Results demonstrate that deep neural networks significantly outperform traditional forecasting methods in terms of accuracy and reliability. The improved forecasts enable better scheduling of energy generation and distribution, reducing reliance on fossil fuels and minimizing grid imbalances. Additionally, accurate solar energy predictions support the efficient integration of solar power into the grid, enhancing overall system stability and reducing operational costs. The study also addresses challenges in implementing deep learning models, such as computational requirements and the need for large, high-quality datasets. Solutions include leveraging cloud computing resources and developing standardized data collection protocols to facilitate broader application and scalability of the models. Application of deep learning techniques in solar energy forecasting represents a promising advancement for the renewable energy sector. By improving the accuracy of solar irradiance and power output predictions, deep neural networks contribute to more reliable grid integration and efficient energy management. This research advocates for the continued exploration and adoption of advanced machine learning methods to support the transition to a sustainable energy future. Keywords: Deep Learning Techniques; Solar Energy; Forecasting; Grid Integration; Power Output.

A Mid/Long-Term Optimization Model of Power System Considering Cross-Regional Power Trade and Renewable Energy Absorption Interval

With the integration of large-scale renewable energy into the power grids, cross-regional power trade can play a major role in promoting renewable energy consumption, as it can effectively achieve the optimal allocation of interconnected power grid resources and ensure the safe and economic operation of the power grid. An optimization model on a mid/long-term scale is established, considering the relationship between the renewable energy absorption interval and the regulation of resources in the system. The model is based on the load block curve and the renewable energy power model, considering the maintenance constraints of conventional units, the operation constraints of conventional units and renewable energy units, cross-regional power trade constraints and system operation constraints. By analyzing the results of the adapted IEEE RELIABILITY TEST SYSTEM (IEEE-RTS), the validity of the model and method proposed in this paper is proven. The results show that the coordinated optimization of conventional energy and renewable energy in the system can be achieved, and the complementarity of power supply and load can be promoted.

GCN based virtual resource allocation scheme for power internet of things

With the construction of smart grid, increasing number of smart devices will be connected to the power communication network. Therefore, how to allocate the resources of access devices has become an urgent problem to be solved in smart grid. However, due to the diversity and time-variability of access devices at the edge of the power grid, such dynamic changes may lead to untimely and unbalanced resource allocation of the power grid and additional system overhead, resulting in reducing the efficiency of power grid operation, unbalanced workload and other problems. In this paper, a grid resource allocation scheme based on Gauss optimization is proposed. The grid virtualization application resources are managed through three main steps: decomposition, combination and exchange, so as to realize the reasonable allocation of grid resources. Considering the time-variability of the grid topology and the diversity of the access device, the computational complexity of the traditional data analysis model is too high to be suitable for time-sensitive power network structure. This paper proposes an MPNN framework combined with the Graph Convolutional Network (GCN) to enhance the calculation efficiency and realize the rapid allocation of network resources. Since the smart gateway connected by the grid terminal has certain computation ability, the cloud computing used in distribution model in deep learning to find the optimal solution can be distributed in the cloud and edge computing gateway. In this way, The entire electricity network can efficiently manage and orchestrate virtual services to maximize the utility of grid virtual resources. Furthermore, this paper also adopt the GG-NN (Gated Graph Neural Network) which is based on the MPNN framework in the training. Finally, we carry out simulation for the Gauss optimization scheme and the MPNN-based scheme to verify that the convolutional diagram neural network is suitable for virtual resource allocating in multi-access power Internet-of –Things (IoTs).

A green energy model for resource allocation in computational grid using differential evolution algorithm

A green energy model for resource allocation in computational grid using differential evolution algorithm

An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization

The success of an efficient and effective aggregator-based residential demand response system in the smart grid relies on the day-ahead customer incentive pricing (CIP) and the load shifting protocols. An artificial neural network model is designed to generate the day-ahead CIP for the aggregator based on historical data. Load scheduling is proposed as a day-ahead optimization problem that is solved using a blocked sliding window technique using parallel computing. With the assumptions made, the proposed algorithm improved the aggregator performance by reducing the overall simulation time from 275 to 45 min and increasing the aggregator forecast profits and customer savings by 11.85% and 35.99% compared to the previous genetic algorithm-based approach.

Combined Impact of Demand Response Aggregators and Carbon Taxation on Emissions Reduction in Electric Power Systems

An emission rate-based carbon tax is applied to fossil-fueled generators with a demand response approach called Smart Grid resource allocation (SGRA). The former reduces the capacity factors (CFs) of base load serving fossil-fueled units, while the latter reduces the CFs of peak load serving units. The objective is to quantify the integration of the carbon tax and the SGRA approach on CO2 emissions and electricity prices in a multi-area power grid. We illustrate this using the Roy Billinton test system and the results show potential for significant reductions in fossil fuel-based generation and CO2 emissions.

Energy consumption optimisation based on mobile edge computing in power grid internet of things nodes

With deployment and application of the power grid internet of things (IoT), all the nodes need more and more resources which bring a great challenge to the power grid system. Mobile edge computing (MEC) and rational use of renewable energy in the grid are effective way to solve above problem of grid resource allocation. In this paper, we propose a MEC framework and an offloading policy that considers renewable energy. The model fully considers the stochasticity of renewable energy arrival randomness and task offloading. By analysing the renewable energy's dedicate character and adopt alternating direction method of multipliers (ADMM) algorithm, the method introduce a task computing offloading method based on renewable energy is proposed. The algorithm realises the optimisation strategy of the power grid internet node delay and grid energy consumption minimisation. The simulation results show that the proposed algorithm performance exceeds the two baseline computation and offloading strategies, which can effectively optimise the grid energy consumption of the nodes.

Energy consumption optimisation based on mobile edge computing in power grid internet of things nodes

With deployment and application of the power grid internet of things (IoT), all the nodes need more and more resources which bring a great challenge to the power grid system. Mobile edge computing (MEC) and rational use of renewable energy in the grid are effective way to solve above problem of grid resource allocation. In this paper, we propose a MEC framework and an offloading policy that considers renewable energy. The model fully considers the stochasticity of renewable energy arrival randomness and task offloading. By analysing the renewable energy's dedicate character and adopt alternating direction method of multipliers (ADMM) algorithm, the method introduce a task computing offloading method based on renewable energy is proposed. The algorithm realises the optimisation strategy of the power grid internet node delay and grid energy consumption minimisation. The simulation results show that the proposed algorithm performance exceeds the two baseline computation and offloading strategies, which can effectively optimise the grid energy consumption of the nodes.

Decomposition and Equilibrium Achieving Distribution Locational Marginal Prices Using Trust-Region Method

We propose a new distribution locational marginal price (DLMP) model which is based on a linearized variant of the global energy balance formulation along with trust-region-based solution methodology. Compared to existing DLMP works in the literature, the proposed DLMP model has shown to depict the following features: 1) it decomposes into most general components, i.e., energy, loss, congestion, and voltage; 2) it presents market equilibrium conditions; and 3) it is capable of achieving an efficient flexibility resource allocation in local day-ahead distribution grid markets. The developed model is tested first on a benchmark IEEE 33-bus distribution grid and then on much larger grids with the inclusion of dispatch from flexible loads and distributed generators.

Generic Resource Allocation in Distribution Grid

This study presents a generic methodology for allocating active and reactive power resources in the distribution network. It is based on the conventional AC load flow model and can provide accurate results without solving sophisticated optimization programs. Numerical results of standard test systems are presented which indicate the proposed method, compared to the optimization based approaches, can quickly attain accurate and easy-to-compute decisions.

Threshold‐based negotiation framework for grid resource allocation

Computational grid provides virtual powerful computer for solving any large-scale scientific, commercial, and engineering problems by aggregating and sharing heterogeneous resources. However, designing an effective resource allocation method is a complicated task. To undertake the task, several economic-based resource management models have been studied. Bargaining (or negotiation) is one of the most used and effective models even though it has several main defects such as high communication demand and the risk of losing a deal. Instead of repeating negotiation for every generated grid job (GJ), the novel contribution of threshold-based negotiation framework is to determine whether a new negotiation is needed or not, according to the current market conditions which depend on supply of resource providers and demand of consumers. In the proposed framework, agents request for renewing negotiation, provided that the amount of change in market conditions exceeds pre-defined threshold. Therefore, communication overhead and the risk of losing the deal are minimised by avoiding unnecessary negotiations.

Energy-efficient anycast scheduling and resource allocation in optical grids

In recent years, optical grid networks has been used as an ideal infrastructure to support high-performance computing environment, data intensive applications and interconnection of data centers. Due to rapid increase in the high-bandwidth applications, the power consumption of communications equipment for such networks has been increasing steadily over the past decade. Therefore, energy efficient routing schemes and traffic models can be developed to reduce the energy consumption. In many applications it is possible to select the destination node from a set of possible destinations, which have the required computing/storage resources. This is known as anycasting compared to unicasting where there is only one destination for each communication. In this paper we adopt the sliding scheduled traffic model, where setup and tear down times may vary within larger window frame. We propose a novel problem that exploits knowledge of demand holding times using anycasting model. We show how the flexibility of anycast routing can lead to additional energy saving. The problem was formulated as an integer linear program to optimally schedule demands (in time) and route them in order to minimize overall network energy consumption. The problem of energy consumption is addressed by switching off idle network components in low utilization periods. We analyze the performance of the proposed approach for anycast and unicast routing models. Our simulation results demonstrate that the proposed approach can lead to significant reductions in energy consumption, compared to traditional routing schemes.

Resource allocation decision model for dependable and cost-effective grid applications based on Grid Bank

Distributed and parallel computing systems such as grid- or cloud-computing have been widely applied, studied and developed for various large-scale computing requirements in cross-administrative domains. However, two important issues, the economy of computation and the dependability of service-oriented computing, have not been thoroughly deliberated. In this regard, we formulate a grid resource allocation decision model that includes (1) a service reliability assessment, which derives the computing dependability from the universal generating function methodology (UGFM), and (2) a virtual payment assessment, which appraises the service expense for the contribution of each resource based on the expense function. For the near-optimal (or optimal) non-dominant solutions (service expense and service reliability), this paper presents two bi-objective soft-computing techniques, PC-GA (genetic algorithm) and PC-PSO (particle swarm optimization), where the Pareto-set cluster (PC) contains elite-selected and reborn mechanisms to generate new non-dominant solutions and strengthen the optimization effectiveness of the Pareto frontier. Finally, a virtual grid system is provided as a case study to illustrate the performance of two optimization methodologies and to analyze the pros and cons in terms of different resource allocation decisions.

Grid Resource Allocation for Real-Time Data-Intensive Tasks

Grid resource allocation mechanism maps tasks to the available grid resources according to some predefined criterion, such as minimizing makespan or execution cost, load balancing, energy efficiency, maintaining user-defined task deadlines, and efficiently using resource memory. The minimization of the makespan is a dominant criterion and is more challenging when computationally intensive tasks have realtime deadlines and data requirements. Such tasks require data files for processing that are transferred from data storage resources to the computing resources, which consume network bandwidth. Resource allocation mechanism for these tasks takes into account the data files transfer time and processing power of the computing resources to complete execution within deadlines. The problem of allocating real-time data-intensive tasks to the grid heterogeneous computing resources with the assumption that the data resources are decoupled from the computing resources, remain challenging. This paper addresses the aforementioned problem as the global optimization problem by considering heterogeneous computing resources of various processing capabilities connected to the data storage resources by network links of various bandwidths. We have analytically formulated the resources with the aim to maximize total number of mapped tasks while possibly minimizing the makespan subject to the time QoS constraints of deadlines, execution time, and data files transfer time. The experimental results reveal that the proposed technique outperforms the other alternatives when real-time tasks are considered.

Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

AbstractThe operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

Online Auction-Based Resource Allocation in Grids: Model and Protocols

Online Auction-Based Resource Allocation in Grids: Model and Protocols

A novel resource allocation mechanism for live cloud-based video streaming service

With the recent emergence of cloud computing, growing numbers of clients are using online cloud services through the Internet such as video streaming service. The rent costs of cloud service providers increase when the resource utilizations of the cloud-servers are not well. Therefore, resource allocation is a crucial problem for cloud data centers. The resource allocation problem is an NP-hard problem. This paper proposes a novel cloud resource allocation mechanism based on a winning strategy for a Nim game. This mechanism offers all clients an effective number of running cloud servers, and allocates cloud resources rapidly and effectively by using a pre-pairing approach. The proposed mechanism does not require searching for remaining resources of the running cloud server; hence, it can reduce the time taken to arrange resources. The experimental results show that the proposed mechanism can improve utilization of cloud servers and reduce the rent costs of the cloud service providers. The proposed mechanism can reach the utilization of cloud servers by as much as 99.96 %. The proposed mechanism is approximately 9 % more efficient than the market-based grid resource allocation algorithm, and 19 % more efficient than the modified best fit decreasing algorithm.