Problem Of Optimal Load Distribution Research Articles

Technique of Hydrothermal Power System Balance Calculation with Allowance for the Regime Features of a Hydropower Station Based on the Cost of a Hydraulic Resource

The present paper proposes a universal method for analyzing the efficiency of technical systems using the combination of an optimization method and a method of marginal utility evaluation. At present, the problem of optimal load distribution in the power system between a hydropower plant (HPP) and thermal power plants (TPP) is solved using the equality of the differential incremental rate characteristics of fuel consumption at TPP and water consumption at HPP by the Lagrangian multiplier method. In this case, the number of iterations can be five or more. The proposed approach is based, first of all, on the correct representation of the differential characteristics and calculation of a hydro resource price for the operational control of the HPP. Based on the comparison of water volume at a HPP and fuel amount at combined heat and power plants (CHPP) used for generation of 1 kW power, it is possible to determine a water price for a HPP. Using the examples of Novosibirsk HPP and CHPP, it is expected to develop an estimation of economic effect from the implementation of the developed criteria, the proposed method of determination of a water price at HPP. As a result of implementing the developed method for the HPP, a price of sold electricity in the flexible energy market will be comparable with the price for sold electricity produced at CHPPs, being equal to approximately 12 rubles/kW∙h

Rational modes of operation of complex gas transmission systems

The principles of formation of mathematical models for the choice of rational modes of operation of complex gas transmission systems with multi-shop compressor stations equipped with different types of gas pumping units are given. It is proposed to optimize the modes on the basis of the use of energy-economic characteristics of the gas transmission system, which are built on the basis of the parameters of the gas transmission system taking into account the technical condition of its elements and economic performance for a certain period. This approach ensures compliance with the principle of operation of the gas transmission system in the field of permissible modes and maximum energy consumption on the one hand and minimizing the cost of gas transportation on the other. If it is possible to use the actual data on the actual technical condition of the equipment and gas pipelines of the system, this method allows to evaluate and take into account in the further procedure the parameters of gas supply reliability. An important requirement for the optimization procedure is the stability of energy efficiency characteristics, which means that in any practical changes of non-basic regime parameters, the energy efficiency characteristic should not be shifted beyond the allowable error. The problem of optimal load distribution in a complex gas transmission system is considered based on their minimum total energy costs at a given volume of gas supply and other planning and technological constraints. The task of optimizing a multiparameter object is to find the extremum of the goal function by selecting a vector of control effects that would satisfy the constraint. The distribution of loads at the highest hierarchy for the system of main gas pipelines is obtained by finding the minimum total cost of gas compression for each pipeline. The method of construction of such characteristics and the procedure of establishing their stability are shown, which allows the formalization of the construction of the criterion dependence of the regime parameters on energy economic factors. The stated basic decisions allow to formalize the function of the purpose for a choice of a rational mode of operation of gas transmission system in the conditions of its incomplete loading.

A Queue Waiting Cost-Aware Control Model for Large Scale Heterogeneous Cloud Datacenter

Optimal load distribution and speed scaling in a heterogeneous data center that takes account of the tradeoff between the maintenance and operation costs and system performance are crucial issues of cloud computing. Due to the changing states of cloud centers and diversity of the task arrival rate, a static control model is infeasible for cloud computing. In this article, we aim to provide a novel multi-server control model with dynamic feedback to acquire dynamic states of the cloud system, and with queue waiting cost-awareness to optimize the queue wait time and load distribution in task assignment and server configuration management. Using the technology of speed scaling, each server in a data center is configured as an <inline-formula><tex-math notation="LaTeX">$M/M/1$</tex-math></inline-formula> queue system with variable service rate, and the service rate is a function of the length of the task queue. We formulate two optimization problems, the optimal load distribution problem and the optimal service rate controlling problem, and provide algorithms to solve these problems, facilitating load distributions and service rate adjustments. We also present numerical simulations to validate our model. The results show our model to be efficient in multi-server dynamic configurations and task assignments according to the feedback information for the tradeoff between the system cost and performance.

Energy-efficient scheduler for MPTCP data transfer with independent and coupled channels

Over the last few years, one can observe a shift in preferences to employ wireless media, including WiFi and cellular, rather than wired technologies for the communication purposes. Since even simple networking devices are nowadays equipped with a few communication interfaces, the user’s experience can be elevated by simultaneous transmission over different paths, as offered by the recently standardized Multipath TCP (MPTCP). This paper addresses the problem of optimal load distribution among the interfaces available at a networking device, e.g., a mobile phone, with the objective of minimizing the overall energy expenditure. Depending whether the corresponding data paths are unrelated or influence each other, two cases are considered and two dynamic allocation algorithms are designed. The design procedures are set on the formal grounds of convex optimization and extremum seeking control. An experimental comparison with the earlier MPTCP scheduling solutions shows superiority of the proposed algorithms in terms of energy requirements. In particular, it is shown that in the case of coupled channels, the energy savings can reach several percent without throughput degradation.

Optimal load distribution for multiple classes of applications on heterogeneous servers with variable speeds

SummaryPerformance and power are 2 significant issues in cloud computing. It is a critical issue on how to provide the best quality of service by consuming certain available power resource. For a given application environment and a given group of servers, optimal load distribution and optimal server speed setting can be an effective way to deal with the power‐performance tradeoff. The technique of variable and task‐type–dependent server speed management can be explored to optimize the server performance and to minimize the power consumption of a server with mixed applications. In this paper, we consider the problem of optimal load distribution for multiple classes of applications on heterogeneous servers with variable speeds. Given several classes of applications characterized by their arrival rates and expected execution requirements, several heterogeneous servers characterized by their power consumption parameters, and certain power supply, our problem is formulated as a multivariable optimization problem, ie, finding an optimal load distribution and an optimal server speed setting, such that the average task response time is minimized. To study the problem analytically, each server is treated as an M/G/1 queueing system with mixed classes of tasks such that both the average response time and the average power consumption can be calculated analytically. We define a power constrained performance optimization problem and develop a numerical algorithm to solve our optimization problem by solving a system of nonlinear equations. We also demonstrate numerical examples to show the effectiveness of our model and method. To the best of our knowledge, such analytical study of optimal load distribution and optimal server speed setting for multiple classes of applications on heterogeneous servers with variable speeds has not been available in the existing literature.

Optimization of joint operation of district and distributed heat sources for effective and reliable heat supply to consumers

The problem of optimal load distribution between district and distributed heat sources (heat sources of prosumers) in the district heating systems are considered. The methodical approach and mathematical model for solving this problem are proposed. They are based on methods of the theory of system energy researches, theory of industrial organization, theory of hydraulic circuits, and basic laws of cogeneration. The case study results obtained by using the developed methodology are presented. The potential economic effect of the prosumer adoption under specified conditions is demonstrated. The conclusions and directions for further research are formulated.

Optimal element loading for linear sliding window systems

Many engineering systems are designed to support load with different amounts. The load carried by the system has a significant effect on the deterioration of the system elements. This article presents an algorithm for determining the optimal load given to each multi-state element in a linear sliding window system such that the reliability of the system can be maximized. The model considers both the effect of load on the failure rate and the relationship between load and performance for each element. A reliability evaluation algorithm based on universal generating function is suggested for the proposed load-dependent linear sliding window system. The optimal load distribution problem is solved using genetic algorithms. Numerical experiments are presented to illustrate the proposed methods.

Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

A Study of Multi-Objective Load Optimal Dispatch in Thermal Power Unit Based on Improved Particle Swarm Optimization Algorithm

In order to fit in with the demands of the development of electricity market in China, a multi-objective optimization mathematical model is presented to dispatch load within the units, taking economy, environmental protection and quick responsiveness to dispatching commands into consideration at the same time. And take the minimal whole plants power-supply coal cost rate, the minimal pollutant emissions and the minimal load adjusting time as these three objective functions respectively. The four constraint conditions are unit power balance constraint, load bound constraint, ramping constraint and pollution discharge standards constraint. An improved particle swarm optimization algorithm is used to get the Pareto solution set. The optimal solution was obtained by using the method of multi-attribute decision making, through sequencing the solution set by comprehensive evaluation. A case study based on a power plant with 4×600MW units was carried out. The result shows that the method can solve the multi-objective optimal load distribution problem accurately and quickly, and get the good effect in energy conservation and emissions reduction.

Optimal Load Distribution for Multiple Heterogeneous Blade Servers in a Cloud Computing Environment

Given a group of heterogeneous blade servers in a cloud computing environment or a data center of a cloud computing provider, each having its own size and speed and its own amount of preloaded special tasks, we are facing the problem of optimal distribution of generic tasks over these blade servers, such that the average response time of generic tasks is minimized. Such performance optimization is important for a cloud computing provider to efficiently utilize all the available resources and to deliver the highest quality of service. We develop a queueing model for a group of heterogeneous blade servers, and formulate and solve the optimal load distribution problem of generic tasks for multiple heterogeneous blade servers in a cloud computing environment in two different situations, namely, special tasks with and without higher priority. Extensive numerical examples and data are demonstrated and some important observations are made. It is found that server sizes, server speeds, task execution requirement, and the arrival rates of special tasks all have significant impact on the average response time of generic tasks, especially when the total arrival rate of generic tasks is large. It is also found that the server size heterogeneity and the server speed heterogeneity do not have much impact on the average response time of generic tasks. Furthermore, larger (smaller, respectively) heterogeneity results in shorter (longer, respectively) average response time of generic tasks.

Finding Multiple Optimal Solutions to Optimal Load Distribution Problem in Hydropower Plant

Optimal load distribution (OLD) among generator units of a hydropower plant is a vital task for hydropower generation scheduling and management. Traditional optimization methods for solving this problem focus on finding a single optimal solution. However, many practical constraints on hydropower plant operation are very difficult, if not impossible, to be modeled, and the optimal solution found by those models might be of limited practical uses. This motivates us to find multiple optimal solutions to the OLD problem, which can provide more flexible choices for decision-making. Based on a special dynamic programming model, we use a modified shortest path algorithm to produce multiple solutions to the problem. It is shown that multiple optimal solutions exist for the case study of China’s Geheyan hydropower plant, and they are valuable for assessing the stability of generator units, showing the potential of reducing occurrence times of units across vibration areas.

ASYMPTOTICALLY OPTIMAL LOAD DISTRIBUTION FOR MULTIPATH STREAMING UNDER FEC

Multipath streaming protocols have recently attracted much attention because they provide an effective means to provide high-quality streaming over the Internet. Most existing multipath streaming schemes also apply forward error correction (FEC) encoding in the stream so as to provide high-quality streaming of prestored or live media content. However, the problem of how to intelligently split the FEC-encoded stream among multiple available paths has not been fully addressed. Most previous work focused on protocol design or heuristic-based engineering approaches. Exact analysis turns out to be hard, as it involves heavy combinatorics computation. In this article, we develop an analytical model and use asymptotic analysis to address the problem of optimal load distribution. Using asymptotic approximations, we propose a closed-form formulation for the optimal load distribution problem. We then develop interesting properties of the optimal solution based on majorization, interchanging argument, and optimization techniques. These results are surprisingly simple yet insightful. We further demonstrate through simulation that our asymptotic solution works quite well in practice.