Load Scheduling Method Research Articles

Enhanced PSO Optimized Leader in Cloud-Fog Task Scheduling for IoT and Mobile Crowdsensing Environments

The data generated by the IoT needs a powerful platform such as cloud computing for data processing. However, the cloud faces challenges when dealing with various types of resources, high delay, and cost, this represents a substantial challenge in scheduling tasks. Therefore, the need appeared to introduce the concept of fog. To address these limitations, optimization algorithms such as PSO were used. In traditional PSO, all particles in the swarm are influenced by a single global best particle (Gbest), if it becomes stuck in a local optimum, all the particles will move closer to it, thus, the PSO may easily get trapped in premature convergence. This paper proposed an adaptive cloud-fog integrated approach based on modified PSO called PSO Optimized Leader (PSO-OL). These modifications on four stages: Firstly, a method to ensure swarm diversity in the initialization phase is introduced. Secondly, to reduce the chance of the population getting trapped in a local optimum, the farthest-best particle is introduced. Third, in addition to the primary Gbest, a second Gbest represents a different good particle presented to explore multiple promising regions. Finally proposed a new crossover operator to get an optimized leader. The PSO-OL approach was evaluated and the results show the effectiveness of the enhanced leader by 40% with farthest-best, 45% with second-Gbest when compared to standard PSO, and when compared to scheduling algorithms where outperforms the other algorithms by minimizing makespan by 34%, cost by 14%, and increasing throughput by 75%, in comparison to existing load balancing and scheduling methods: RR, BLA, MPSO, ETS, and TCaS.

Operational Scheduling of Household Appliances by Using Triple-Objective Optimization Algorithm Integrated with Multi-Criteria Decision Making

Load scheduling is a key factor in demand side management (DSM), which manages available generation capacity with regard to the required demand. In this paper, a triple-objective load scheduling optimization problem (LSOP) is formulated for achieving optimal cost and peak demand as well as minimum customer inconvenience. A Henry gas solubility optimization (HGSO) algorithm that is based on multi-objective is used for solving LSOP. The proposed HGSO offers a set of compromise solutions that represent the tradeoff between the three objectives of the formulated problem. A set of all compromise solutions from the dominant Pareto front is achieved first, and then ranked by using MCDM so as to optimally sort these solutions. An entropy weighting method (EWM) is then used for computing the weights of various criteria that dominate the LSOP and is provided as a technique for ordering preferences by similarity to achieve the ideal solution (TOPSIS) so as to rank the sorted solutions. Two types of end-users are considered so as to show the effectiveness of the proposed LSOP: non-cooperative and cooperative users. The results of the proposed load scheduling method show the significance of the proposed method for both the cooperative and non-cooperative end-users. The proposed method achieves a cost of energy of R50.62 as a total cost of energy consumed by four non-cooperative end-users. The cost of energy for the cooperative end-users is found to be R47.39. Thus, saving in the energy cost unit is found to be around 5.5% by using the proposed method; moreover, the peak demand value is reduced by 9.7% when non-cooperative end-users becomes cooperative.

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.

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Electricity authorities need capacity assessment and expansion plans for efficiently charging the growing Electric vehicle (EV) fleet. Specifically, the distribution grid needs significant capacity expansion as it faces the most impact to accommodate the high variant residential EV charging load. Utility companies employ different scheduling policies for the maintenance of their distribution transformers (hereinafter, XFR). However, they lack scenario-based plans to cope with the varying EV penetration across locations and time. The contributions of this paper are twofold. First, we propose a customer feedback-based EV charging scheduling to simultaneously minimize the peak load for the distribution XFR and satisfy the customer needs. Second, we present a deep reinforcement learning (DRL) method for XFR maintenance, which focuses on the XFR’s effective age and loading to periodically choose the best candidate XFR for replacement. Our case study for a distribution feeder shows the adaptability and success of our EV load scheduling method in reducing the peak demand to extend the XFR life. Furthermore, our DRL-based XFR replacement policy outperforms the existing rule-based policies. Together, the two approaches provide a complete capacity planning tool for efficient XFR maintenance to cope with the increasing EV charging load.

Research on user-side flexible load scheduling method based on greedy algorithm

In the context of increasing flexible load represented by smart household appliances, this study aims to establish a demand response model of residential flexible load to minimize electricity cost and reduce grid load variance. In this study, the flexible loads of residents are classified considering different load demand response modes, and demand response models are established for all kinds of loads. To make full use of residential electricity data, this paper established a user side flexible load multi-objective optimization scheduling model, the model for electricity cost and power grid load variance minimizing the objective function, and the safe operation of the meter and the adjustable resources as constraint, load and energy storage battery time-sharing electricity price for resident’s flexible optimization scheduling. Finally, the greedy algorithm is use to calculate and analyze the model. The result show that the model is effective and feasible.

Finite State Machine-Based Load Scheduling Algorithm for Smart Home Energy Management

The problem of scheduling household appliances with the availability of renewable energy is the biggest challenge in the smart home energy management system. The components such as renewable energy resources, household appliances, utility grid, storage batteries are pooled into a nonlinear, time-varying, indefinite, and dynamic structure that is impossible to control and refine. For this, real-time pricing is applied in most nations to withstand the burden on the grid. This requires attention to utilize renewable energy effectively. In this paper, a load scheduling method to schedule the loads based on the availability of solar energy and customer preferences is presented. First, the availability of solar energy is forecasted ahead one day using Regression Analysis. Second, the finite state machine approach-based load scheduling algorithm is implemented and tested using MATLAB Simulink and Lab VIEW. LabVIEW-based GUI is developed to visualize the MATLAB schedule for loads. The problem is divided into several states with the availability of solar power, and if solar power is unavailable, grid power is utilized. The loads preferred by the consumers are scheduled in alignment with the production of solar power with the finite state machine scheduling algorithm. Also, the loads considered are able to consume instantaneous energy with the instantaneous production of energy, thereby reducing CO2 emission by not consuming power from the grid. Finally, the loads are scheduled accordingly, and it is concluded that coordination can be established between energy providers, and the system proposed can flatten out the load profile.

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.

Development of Consumer Preference based Demand Response Model for Home Energy Management

Demand response has become an effective method for energy saving and to reduce the energy cost. By adjusting the residential loads, it reacts quickly for the mismatches of supply and demand. In this paper, the Internet of Things (IoT) based Demand Response (Load scheduling method) methodology is proposed to mitigate the energy waste and tariff. Two different alarm provisions are made at the consumer side to indicate the normal and demand modes operated by the supplier. The consumer is provided with a controller that read the market signal and answers with consumer preferences. Whenever demand mode arises, the consumer is completely free to change the load setting; also the developed system will propose the load pattern. The demand mode will be given during peak hour and the tariff will be high at that time and normal mode will consist of the minimum tariff. The consumer may control their load through cloud-MQTT by giving a specific command or from the MQTT dashboard android app. Based on the tariff, the consumption profile could be reduced

Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory

Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.

Wireless Photoelectric Sensor Network Scheduling Algorithm Based on Divisible Load

To discuss the divisible load scheduling in wireless photoelectric sensor networks, a load scheduling algorithm called EDDLT based on residual energy landscape is proposed. In the algorithm, the constant of the time needed for the induction and reporting unit data is adjusted to the variable parameters based on the residual energy. In addition, the ratio of the initial energy to the residual energy is used to carry out the effective load scheduling. By using this algorithm, when the load is allocated to each sensor node, the remaining energy of nodes is considered, and a lighter load is allocated to sensor nodes with less residual energy. EDDLT, compared to the standard divisible load scheduling method SDLT, the number of execution rounds is greatly increased when the first sensor node is dead. The experimental results showed that EDDLT had a certain effect on prolonging the lifetime of wireless photoelectric sensor networks. To sum up, the scheduling algorithm has good performance in exploring divisible load.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

Model-based optimal load control of inverter-driven air conditioners responding to dynamic electricity pricing

Dynamic electricity pricing provides a great opportunity for residential consumers to participate into demand response (DR) programs to reduce the electricity bills. The lack of automatic response to time-varying electricity prices is one of the challenges faced by the residential electric appliances. Most of the existing studies on DR of residential air conditioner (ACs) focus on the single-speed ACs, rather than the inverter-driven ACs which are more energy efficient and extensively installed in today’s residential buildings. This paper presents a novel model-based optimal load scheduling method for residential inverter-driven ACs to realize automatic DR to the day-ahead dynamic electricity prices. The models of the inverter-driven ACs and room thermal dynamics are firstly developed, identified and integrated for the development of the model-based scheduling. The tradeoff problem between the electricity costs, resident’s comfort and peak power reductions is formulated as a mixed-integer nonlinear programming problem with adjustable weightings. The optimal solution of the nonlinear programming problem is searched by the genetic algorithm (GA). Simulation results show that multiple goals can be achieved via GA optimization and the regulations of the weights in the objective function. The developed framework can be implemented in the programmable communicating thermostats (PCTs) or the smart home energy manage systems (HEMSs) to enable residential inverter-driven ACs automatically respond to day-ahead dynamic electricity pricing.

A Load Scheduling Algorithm for the Smart Home Using Customer Preferences and Real Time Residential Prices

A load scheduling method in the Smart Home using a combination of customer preferences and the price of electricity is presented. To translate the customer preference of loads into a time-varying priority curve, the Analytical Hierarchy Process (AHP) and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) methods are used. The resulting curves of customer priority are combined with the available time-varying pricing information for determining the schedule for each load. An example with four loads is presented to demonstrate the effectiveness of the algorithm.

Residential Load Scheduling in Smart Grid: A Cost Efficiency Perspective

In smart grid, residential consumers adopt different load scheduling methods to manage their power consumptions with specific objectives. The conventional load scheduling methods aim to maximize the consumption payoff or minimize the consumption cost. In this paper, we introduce a novel concept of cost efficiency-based residential load scheduling framework to improve the economical efficiency of the residential electricity consumption. The cost efficiency is defined as the ratio of consumer’s total consumption benefit to its total electricity payment during a certain period. We develop a cost-efficient load scheduling algorithm for the demand-side’s day-ahead bidding process and real-time pricing mechanism by using a fractional programing approach. Results show that the proposed scheduling algorithm can effectively reflect and affect user’s consumption behavior and achieve the optimal cost-efficient energy consumption profile. For practical consideration, we also take into account the service fee and distributed energy resources (DERs) in our framework, and analyze their impacts on the cost efficiency. Simulation results confirm that the proposed algorithm significantly improves consumer’s cost efficiency. It is shown that a higher service fee will decrease the cost efficiency, while the integration of DERs can effectively improve the cost efficiency.

Demand side management in smart grid: A review and proposals for future direction

This paper mainly focuses on demand side management and demand response, including drivers and benefits, shiftable load scheduling methods and peak shaving techniques. Demand side management techniques found in literature are overviewed and a novel electricity demand control technique using real-time pricing is proposed. Currently users have no means to change their power consumption to benefit the whole system. The proposed method consists of modern system identification and control that would enable user side load control. This would potentially balance demand side with supply side more effectively and would also reduce peak demand and make the whole system more efficient.

Optimizations of Power Consumption and Supply in the Smart Grid: Analysis of the Impact of Data Communication Reliability

Data communications infrastructure will play an important role to transfer various information in smart grid. In this paper, we consider the reliability of the smart grid data communications infrastructure and its impact on the power consumption and supply optimizations. For optimizing the power consumption, we consider a deferrable load scheduling method which is modeled by using a constrained Markov decision process (CMDP) model, taking into account the unavailability of the home area network (HAN) and neighborhood area network (NAN) gateways. For optimizing the power supply, we consider an economic dispatch method which is modeled by using stochastic programming (SP), taking into account the unavailability of the exact power demand and supply information. The power consumption and supply costs are analyzed. In addition, we show how these costs can be reduced through the deployment of component redundancy in the smart grid data communications infrastructure.

Distributed dynamic load balancing for pipelined computations on heterogeneous systems

One of the most significant causes for performance degradation of scientific and engineering applications on high performance computing systems is the uneven distribution of the computational work to the resources of the system. This effect, which is known as load imbalance, is even more noticeable in the case of irregular applications and heterogeneous distributed systems. This motivated the parallel and distributed computing research community to focus on methods that provide good load balancing for scientific and engineering applications running on (heterogeneous) distributed systems. Efficient load balancing and scheduling methods are employed for scientific applications from various fields, such as mechanics, materials, physics, chemistry, biology, applied mathematics, etc. Such applications typically employ a large number of computational methods in order to simulate complex phenomena, on very large scales of time and magnitude. These simulations consist of routines that perform repetitive computations (in the form of DO/FOR loops) over very large data sets, which, if not properly implemented and executed, may suffer from poor performance. The number of repetitive computations in the simulation codes is not always constant. Moreover, the computational nature of these simulations may be in fact irregular, leading to the case when one computation takes (unpredictably) more time than others. For successful and timely results, large scale simulations require the use of large scale computing systems, which often are widely distributed and highly heterogeneous. Moreover, large scale computing systems are usually shared among multiple users, which causes the quality and quantity of the available resources to be highly unpredictable. There are numerous load balancing methods in the literature for different parallel architectures. The most recent of these methods typically follow the master-worker paradigm, where a single coordinator (master) is responsible for making all the scheduling decisions based on information provided by the workers. Depending on the application requirements, the scheduling policy and the computational environment, the benefits of this paradigm may be limited as follows: (1) its efficiency may not scale as the number of processors increases, and (2) it is quite probable that the scheduling decisions are made based on outdated information, especially on systems where the workload changes rapidly. In an effort to address these limitations, we propose a distributed (master-less) load balancing scheme, in which the scheduling decisions are made by the workers in a distributed fashion. We implemented this method along with other two master-worker schemes (a previously existing one and a recently modified one) for three different scientific computational kernels. In order to validate the usefulness and efficiency of the proposed scheme, we conducted a series of comparative performance tests with the two master-worker schemes for each computational kernel. The target system is an SMP cluster, on which we simulated three different patterns of system load fluctuation. The experiments strongly support the belief that the distributed approach offers greater performance and better scalability on such systems, showing an overall improvement ranging from 13% to 24% over the master-worker approaches.