Efficient Load Scheduling Research Articles

Empirical Validation of Fuzzy Logic Based Predictive Load Scheduling in Mimic Home Energy Management System

<p>Load scheduling plays a vital role in the home energy management systems. The main objective of this load scheduling is to balance the power demand and supply power without degrading the performance of the loads and consumers tolerance. Though many research works concentrate on load scheduling, very few works concentrated on real time scenario. On the other hand, research work concentrated on optimal load scheduling through fuzzy logic requires incorporation of fuzzy based system in either simulated or real-time home energy management system. Hence, the proposal aims to schedule the loads in a simulated home energy management system through fuzzy logic controller using MATLAB Simscape with required subsystems. The proposed simulated environment considers four different resistive loads. Intelligent scheduling is aimed to achieve efficient load scheduling. A fuzzy controller has three inputs namely integrated source, state of charge of battery, and power demand whereas probability of scheduling is considered as output. The efficiency of the proposed fuzzy-based load scheduling scheme is evaluated under various load conditions for different sub-systems with varying input power. The results exhibit the efficacy of the proposed scheme.</p> <p> </p>

Backpropagation neural network based adaptive load scheduling method in an isolated power system

This work introduces an efficient load scheduling method for handling the day-to-day power supply needs. At peak load times, due to its instabilitythe power generation system fails and as a measure, the load shedding process is followed. The presented method overcomes this problem by scheduling the load based on necessity. For this load scheduling is handled with an artificial neural network (ANN). For the training purpose the backpropagation (BP) algorithm is used. The whole load essential is the input of the neural network (NN). The power generation of all resources and power losses at the instant of transmission is the NN output. The optimum scheduling of different power sources is important when considering all the available sources. Load scheduling shares the feasibility of entire load and losses. It is well-known as optimal scheduling if the constraints such as availability of power, load requirement, cost and power losses are considered. Training the system using a large number of parameters would be a difficult task. So, finest number of communally independent inputs is selected. The presented method aims to lower the power generation expenditure and formulate the power available on demand without alteration. The network is designed using MATLAB.

Design, control, reliability, economic and energy management of microgrid: A review

Currently, fossil fuels still dominate as the primary energy source, accounting for around 80% of global output. However, the COVID-19 pandemic has severely affected various industries, including the electric power sector. Consequently, the energy industry has recognized the importance of renewable energy sources in addressing environmental concerns and establishing a sustainable future. Recently, microgrid systems based on renewable energy have emerged as crucial players in reducing pollution and promoting environmental sustainability. This paper offers a comprehensive assessment of microgrid systems, starting with the optimal design of the microgrid and extending to the evaluation of its control system. Through a detailed analysis of existing literature and case studies, the review identifies several key findings. Firstly, effective design and control strategies are crucial for optimizing the operation of microgrid's and maximizing their economic and energy management potential. Secondly, the integration of renewable energy sources and energy storage systems can significantly enhance the reliability and resilience of microgrid's. Thirdly, advanced control techniques and optimization algorithms play a vital role in achieving optimal energy management, cost reduction, and efficient load scheduling within microgrid systems. Furthermore, the paper explores energy management, reliability assessment, and economic analysis within the microgrid context. Lastly, the paper highlights the application of cutting-edge trends like artificial intelligence, data analysis, and blockchain in the field of microgrid systems. The numerical results showcase the enhanced reliability and resilience of microgrid's with the integration of energy storage systems, reducing the frequency and duration of power outages by 30% compared to traditional grids.

AN EFFICIENT LOAD SCHEDULING TECHNIQUE USING OPPOSITIONAL SPARROW SEARCH ALGORITHM FOR CLOUD COMPUTING ENVIRONMENT

Cloud computing (CC) environment is commonly applied for provisioning effectual services and offers diverse benefits, along with some challenges like load balancing and load scheduling. The load scheduling can be defined as the procedure of assigning and balancing the load like tasks or cloudlets to virtual machines (VMs) in the cloud platform. A main goal of the load scheduling algorithm is to lessen the transfer time and total cost incurred in the system. This paper presents a new oppositional sparrow search algorithm (OSSA) for efficient load scheduling in CC environment. The classical sparrow search algorithm (SSA) is based on the group wisdom, foraging and anti-predation behaviour of sparrows. To improve the convergence speed of SSA, Opposition based learning (OBL) concept is incorporated to it. The proposed OSSA based load scheduler has the ability of scheduling the load effectively in the CC platform. An extensive set of simulations were carried out to ensure the goodness of the OSSA and the results are investigated under several aspects. The experimental results indicated the superior nature of the OSSA over the compared methods on all the applied scenarios.

A binary Bird Swarm Optimization based load balancing algorithm for cloud computing environment

Abstract Task scheduling and load balancing are a concern for service providers in the cloud computing environment. The problem of scheduling tasks and balancing loads in a cloud is categorized under an NP-hard problem. Thus, it needs an efficient load scheduling algorithm that not only allocates the tasks onto appropriate VMs but also maintains the trade-off amidst VMs. It should keep an equilibrium among VMs in a way that reduces the makespan while maximizing the utilization of resources and throughput. In response to it, the authors propose a load balancing algorithm inspired by the mimicking behavior of a flock of birds, which is called the Bird Swarm Optimization Load Balancing (BSO-LB) algorithm that considers tasks as birds and VMs as destination food patches. In the considered cloud simulation environment, tasks are assumed to be independent and non-preemptive. To evaluate the efficacy of the proposed algorithm under real workloads, the authors consider a dataset (GoCJ) logged by Goggle in 2018 for the execution of cloudlets. The proposed algorithm aims to enhance the overall system performance by reducing response time and keeping the whole system balanced. The authors have integrated the binary variant of the BSO algorithm with the load balancing method. The proposed technique is analyzed and compared with other existing load balancing algorithms such as MAX-MIN, RASA, Improved PSO, and other scheduling algorithms as FCFS, SJF, and RR. The experimental results show that the proposed method outperforms when being compared with the different algorithms mentioned above. It is noteworthy that the proposed approach illustrates an improvement in resource utilization and reduces the makespan of tasks.

Efficient Load Control Based Demand Side Management Schemes Towards a Smart Energy Grid System

In this paper, we propose efficient load scheduling based demand side management schemes for the objective of peak load reduction. We propose two heuristic algorithms, named G-MinPeak and LevelMatch, which are based on the generalized two-dimensional strip packing problem, where each of the appliances has their specific timing requirements to be fulfilled. Furthermore, we have proposed some improvement schemes that try to modify the resulted schedule from the proposed heuristic algorithms to reduce the peak. All the proposed algorithms and improvement schemes are experimented using benchmark data sets for performance evaluation. Extensive simulation studies have been conducted using practical data to evaluate the performance of the algorithms in real life. The results obtained show that all the proposed methodologies are thoroughly effective in reducing peak load, resulting in smoother load profiles. Specifically, for the benchmark datasets, the deviation from the optimal values has been about 6% and 7% for LevelMatch and G-MinPeak algorithms respectively and by using the improvement schemes the deviations are further reduced up to 3% in many cases. For the practical datasets, the proposed improvement schemes reduce the peak by 5.21− 7.35 % on top of the peaks obtained by the two proposed heuristic algorithms without much computation overhead.

Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.

A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid

The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.

New Challenges of Wireless Power Transfer and Secured Billing for Internet of Electric Vehicles

Smart grid modernization and integration of Internet of electric vehicles (IoEVs) have attracted much attention due to their processing capability and convenience. In addition, the developments of magnetic resonance or inductive-based wireless power transfer (WPT) technologies can improve the efficiency of power transmission and user convenience. In particular, the use of renewable energy resources (RERs) is very attractive for low-carbon clean energy, and thus we can imagine integration services that utilize RERs and IoEVs. Accordingly, energy trading markets among IoEVs and power stations will appear in future smart grids. Thus, this article proposes an auction mechanism for energy trading markets of IoEV and RER power stations. Moreover, we can imagine a convenient system where IoEVs can automatically be charged from the WPT charging pad when they are parked in an IoEV parking lot. However, to realize such a convenient system, there are several problems that need to be resolved. Suppose that many IoEVs are parked in the parking lot; then due to power system constraints, it is impossible to simultaneously charge all IoEVs at maximum power. Therefore, an efficient load scheduling algorithm that allows several electric vehicle service equipments (EVSEs) to charge many IoEVs is required. Furthermore, when IoEVs are automatically charged in the anticipated convenient system, it is particularly challenging to design a suitable and secure billing system. In this article, we introduce the advantages of a convenient system that integrates IoEVs and a smart grid. We also discuss the challenges and solutions for future charging and billing systems.

Improved Hyper-Heuristic Scheduling with Load-Balancing and RASA for Cloud Computing Systems

Nowadays cloud computing has turned into a key innovation and has become a great solution for indulging a flexible utility oriented , online allocation and storage of computing resources and client’s information in lower expense, oninterest and dynamically scalable framework on pay per use premise. This technology is a new pattern emerging in IT environment with immense necessities of framework and resources. Job Scheduling Problem is an essential issue. For efficient usage and managing resources, administrations, scheduling plays a critical role. This paper apportion the performance enhancement of HyperHeuristic Scheduling Approach to schedule cloudlets and resources, by taking account of both , computation time and transmission cost with two detection operators. Load Balancing and RASA concept is applied for efficient Load Scheduling, resource utilization and thereby enhancing the overall performance of cloud computing environment. The numerical investigations of HHSA were performed on CloudSim. Experimental results generated via simulation shows that enhanced heuristic scheduling approach is much better than individual heuristic approach in terms of minimizing makespan time.