System Optimization Problems Research Articles

Commitment of Fast-Responding Storage Devices to Mimic Inertia for the Enhancement of Primary Frequency Response

One distinctive feature of renewable energy resources is that they contribute little inertia to power systems. With less system inertia, power grid is less capable of resisting frequency deviation from its nominal value in the first few seconds after disturbances. However, fast-responding storage devices can mimic inertial responses through some specified control algorithm. Thus, this paper focuses on the economic aspect of resource inertia as a service to the grid operation and aims to find a minimum-cost commitment of fast-acting storage devices for the enhancement of primary frequency responses. Costs are assigned to those storage resources and a commitment cost minimization problem is formulated with nonlinear primary frequency response constraints. A tractable iterative solution approach is developed and a sensitivity-based method through a rapidly solvable linear approximation of the nonlinear constraints is proposed to reduce the computational burdens of solving the optimization problem for large-scale systems. The effectiveness of the proposed methods is demonstrated using simulation results obtained using the 118-bus IEEE reliability test system.

Energy Efficiency Optimization in MIMO Interference Channels: A Successive Pseudoconvex Approximation Approach

In this paper, we consider the (global and sum) energy efficiency optimization problem in downlink multi-input multi-output multi-cell systems, where all users suffer from multi-user interference. This is a challenging problem due to several reasons: 1) it is a nonconvex fractional programming problem, 2) the transmission rate functions are characterized by (complex-valued) transmit covariance matrices, and 3) the processing-related power consumption may depend on the transmission rate. We tackle this problem by the successive pseudoconvex approximation approach, and we argue that pseudoconvex optimization plays a fundamental role in designing novel iterative algorithms, not only because every locally optimal point of a pseudoconvex optimization problem is also globally optimal, but also because a descent direction is easily obtained from every optimal point of a pseudoconvex optimization problem. The proposed algorithms have the following advantages: 1) fast convergence as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, 2) easy implementation as each approximate problem is suitable for parallel computation and its solution has a closed-form expression, and 3) guaranteed convergence to a stationary point or a Karush-Kuhn-Tucker point. The advantages of the proposed algorithm are also illustrated numerically.

Impact of Control System Structure and Performance of Inventory Goods Flow System with Long-Variable Delay

In this paper, we use a mathematical model of a inventory system with time-varying delivery delay and three control systems in order to compare their properties for the goods flow optimization problem in the inventory systems. Structures of the chosen control systems are based on mathematical discrete equations: a periodic inventory system with adaptive maximal inventory level and perpetual inventory system with adaptive order quantity level as well as the methods proposed by the authors in previous works based on the Smith predictor. The selection of the control systems parameters is done by solving optimization tasks for a specific scenario of time-varying market demand using a genetic algorithm in Matlab/Simulink. In this article, we mainly want to compare which of the control structures is able to achieve a high service level with maintaining of a given inventory maximal level in response to assumed consumer demand scenario.DOI: http://dx.doi.org/10.5755/j01.eie.24.1.14244

Distributed event-triggered scheme for a convex optimization problem in multi-agent systems

Abstract In this paper, a distributed event-triggered algorithm was proposed to solve a convex quadratic optimization problem of multi-agent systems under undirected and connected topologies. The event-triggered condition of each agent just requires its own state value and the state values of its neighbors at the triggering time, and hence the continuous communication and calculation are not required. Moreover, the minimum event-triggered interval is bounded by the sampling time and the Zeno behavior is therefore naturally avoided. The result is also extended to the networks with undirected and switching topologies. Numerical simulations show the effectiveness of the proposed approach.

Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms

Hydro-thermal-wind generation scheduling (HTWGS) with economic and environmental factors is a multi-objective complex nonlinear power system optimization problem with many equality and inequality constraints. The objective of the problem is to generate an hour-by-hour optimum schedule of hydro-thermal-wind power plants to attain the least emission of pollutants from thermal plants and a reduced generation cost of thermal and wind plants for a 24-h period, satisfying the system constraints. The paper presents a detailed framework of the HTWGS problem and proposes a modified particle swarm optimization (MPSO) algorithm for evolving a solution. The competency of selected heuristic algorithms, representing different heuristic groups, viz. the binary coded genetic algorithm (BCGA), particle swarm optimization (PSO), improved harmony search (IHS), and JAYA algorithm, for searching for an optimal solution to HTWGS considering economic and environmental factors was investigated in a trial system consisting of a multi-stream cascaded system with four reservoirs, three thermal plants, and two wind plants. Appropriate mathematical models were used for representing the water discharge, generation cost, and pollutant emission of respective power plants incorporated in the system. Statistical analysis was performed to check the consistency and reliability of the proposed algorithm. The simulation results indicated that the proposed MPSO algorithm provided a better solution to the problem of HTWGS, with a reduced generation cost and the least emission, when compared with the other heuristic algorithms considered.

Optimization of Temperature‐Control Measures for Concrete Structures: A Case Study of the Sluice Project

Temperature control and crack prevention in sluice pier concrete is a key issue in the early design and construction period. Strong surface insulation may lead to cracks after formwork removal, while weak surface insulation may result in a high crack risk in the early age. The water‐cooling measure may also cause severe cracks at a rapid cooling rate. Therefore, the optimum temperature control scheme should be comparatively studied against the alternatives. In this paper, we investigate crack prevention in sluice pier concrete as a multiple‐factor system optimization problem and investigate an optimization method for temperature‐control measures using the uniform design method and a neural network model. The minimum ratios for the internal and surface points of the sluice pier concrete are taken as inputs, and the corresponding combinations of temperature‐control parameters based on the uniform design method are taken as outputs. Combined with a sluice project, the optimization method for the temperature‐control measures is implemented. The analysis results show that internal pipe cooling combined with reasonable surface heat preservation measures should be employed, and a low concrete pouring temperature is more beneficial than a low cooling temperature and long duration for crack prevention in sluice pier concrete.

An Overlapped Decomposition Optimization Method for Dynamic Economic Dispatch

The dynamic economic dispatch (DED) that considers valve-point effects is a complex non-convex and non-smooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. However, many of these approaches have high complexity which is not easily implemented. In contrast to most research efforts, which focus on enhancing the performance of a specific optimization algorithm, this paper presents an efficient overlapped decomposition optimization (ODO) method to solve DED with valve-point effects, by converting DED into a few easily solvable sub-problems. We first exploit the characteristics of the problem itself and present a decomposition strategy. The analysis shows that this strategy can be adopted as an effective local search operator to further improve the optimal solutions. Furthermore, an overlapped decomposition optimization method is proposed in which the study period of sub-problems has overlap. The proposed method efficiently reduces the difficulty of solving DED, so that high-quality solutions can be obtained by combining ODO with some ordinary heuristic-based algorithms. The effectiveness of the proposed method is demonstrated on multiple DED problems. The simulation results indicate that the proposed method can be used as a powerful optimizer for coupled spatial–temporal scheduling problems in power systems.

Scenario Generation for Wind Power Using Improved Generative Adversarial Networks

Wind power scenarios have a significant impact on stochastic optimization problems for power systems in which wind power is a significant component. Generative adversarial networks (GANs) are a powerful class of generative models, and can generate realistic scenarios for renewable power sources without the need for any modeling assumptions. However, the performance of GANs in generating scenarios can further be improved by modifying the way in which a Lipschitz constraint on discriminator network is imposed. Another critical problem of applying deep neural networks is overfitting, a phenomenon especially prone to appear on small training sets. In this paper, we propose an improved GAN for the generation of wind power scenarios. To improve the training speed, we use a gradient penalty term to enforce the Lipschitz constraint based on the output and input of the discriminator network. To improve the scenario quality, we further use a consistency term in the training procedure. Besides, the overfitting problem can be effectively alleviated by the enforced Lipschitz continuity. The proposed method is applied to actual time series data from the NREL wind integration data set. The experimental results demonstrate that our method outperforms the existing methods.

On the Generalization and Stability Analysis of Pareto Seeking Control

In this letter, we address a multi-objective on-line optimization problem for unknown dynamical systems by providing a generalization of the Pareto seeking controller (PSC). The idea of the PSC is to drive the system to the Pareto front in the absence of knowledge of the values of the objectives or the input values to achieve Pareto optimality. Two special cases are presented, for two objectives and three objectives. A stability analysis is presented for the case of two objectives and two inputs, which shows that the controller practically drives the system into the neighborhood of the Pareto front, for any initial condition. Two simulation examples for the special cases are also presented.

Уравнения Гамильтона–Якоби в задачах динамической оптимизации систем нейтрального типа

Установлена связь между дифференциальной игрой в системах нейтрального типа и функциональным уравнением Гамильтона-Якоби с коинвариантными производными. Доказано совпадение функционала цены игры и минимаксного решения этого уравнения. Указаны оптимальные стратегии игроков.

Harnessing the Power of HPC in Simulation and Optimization of Large Transportation Networks: Spatio-Temporal Traffic Management in the Greater Toronto Area

The significant growth of many urban areas comes at a cost of increasing demand for mobility and traffic congestion in large-scale urban environments. As congestion levels soar to unprecedented levels, and researchers and governments are challenged addressing the basic needs for transportation and mobility; solutions are becoming more complex and untraditional, creating a strong potential for optimization and simulation of large-scale transportation networks. In this paper, we present a generic traffic management framework for solving large-scale constraint optimization problems in advanced Intelligent Transportation systems (ITS) applications. The framework employs a distributed computing approach to enable replicating analysis of large-scale traffic simulation networks, providing a practical mechanism for solving complex optimization problems in transportation applications. We discuss employing the framework in two use cases, congestion pricing and emergency evacuation, targeting optimization of spatial and temporal traffic management.

Distributed Energy Optimization for HVAC Systems in University Campus Buildings

Educational buildings consume about 2% of the total energy in a country, which leads to energy cost concerns for building operators. To reduce the building energy cost, an effective way is to intelligently schedule heating, ventilation, and air conditioning (HVAC) systems, which account for above 40% of the total energy consumption in educational buildings. In this paper, we investigate an energy optimization problem for HVAC systems in university campus buildings. To be specific, we first formulate a total cost optimization problem that minimizes the sum of energy cost related to HVAC systems and thermal discomfort cost associated with occupants considering zone occupancy pattern and thermal preference of occupants in each zone. Due to the existence of uncertain parameters as well as spatially and temporally coupled constraints, it is very challenging to solve the formulated problem. To this end, we propose a distributed model predictive control algorithm based on the alternating direction method of multipliers, which has high scalability with an increasing number of zones and can protect user privacy. Extensive simulations based on the real-world traces show that the proposed distributed algorithm could effectively reduce the total cost and offer a flexible tradeoff between energy cost and thermal discomfort.

A clustering and datamining technique for analysis of non-dominated solutions in manufacturing system optimization problem

本研究では，生産システムの構想設計を対象とする体系的な設計支援の手法として，データマイニングに基づく設計案の分析手法を提案する．本研究においては, ユークリッドノルムを用いた従来のクラスター分析手法を発展させた, EMアルゴリズムに基づく新たなクラスター分析手法を開発し, 数値例として生産システム設計問題へ適用し, その有効性を確かめた.

Optimal Representations for Adaptive Streaming in Interactive Multiview Video Systems

Interactive multiview video streaming (IMVS) services permit to remotely navigate within a 3D scene with an immersive experience. This is possible by transmitting a set of reference camera views (anchor views), which are used by the clients to freely navigate in the scene and possibly synthesize additional viewpoints of interest. From a networking perspective, the big challenge in IMVS systems is to deliver to each client the best set of anchor views that maximizes the navigation quality, minimizes the view-switching delay and yet satisfies the network constraints. Integrating adaptive streaming solutions in free-viewpoint systems offers a promising solution to deploy IMVS in large and heterogeneous scenarios, as long as the multiview video representations on the server are properly selected. Therefore, we propose to optimize the multiview data at the server by minimizing the overall resource requirements while offering a good navigation quality to the different users. We propose a representation set optimization problem for multiview adaptive streaming systems, and we show that it is NP-hard. Therefore, we introduce the concept of multiview navigation segment that permits to cast the video representation set selection as an integer linear programming problem with a bounded computational complexity. We then show that the proposed solution reduces the computational complexity, while preserving optimality in most of the 3D scenes. We finally provide simulation results for different classes of users and show the gain offered by an optimal multiview video representation selection compared to recommended representation sets (e.g., Netflix and Apple ones) or to a baseline representation selection algorithm, where the encoding parameters are decided a priori for all the camera views.

A Novel Multi-Objective Discrete Particle Swarm Optimization with Elitist Perturbation for Reconfiguration of Ship Power System

Abstract A novel multi-objective discrete particle swarm optimization with elitist perturbation strategy (EPSMODPSO) is proposed and applied to solve the reconfiguration problem of shipboard power system(SPS). The new algorithm uses the velocity to decide each particle to move one step toward positive or negative direction to update the position. An elitist perturbation strategy is proposed to improve the local search ability of the algorithm. Reconfiguration model of SPS is established with multiple objectives, and an inherent homogeneity index is adopted as the auxiliary estimating index. Test results of examples show that the proposed EPSMODPSO performs excellent in terms of diversity and convergence of the obtained Pareto optimal front. It is competent to solve network reconfiguration of shipboard power system and other multi-objective discrete optimization problems.

Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system

Hybrid energy system based on solar and wind power coupled with energy storage unit provides a reliable and cost effective energy alternative above the commonly used diesel based standalone power system. Various methodologies are adopted for modeling hybrid energy system component. They are modeled either by deterministic or probabilistic methods. The current study considers the hardware failure of photovoltaic panels while modeling. A probability distribution (PD) represents the various capacity states due to hardware failure of photovoltaic panels and corresponding probabilities. A concept based on random number generation is adopted to calculate the actual hourly available photovoltaic power. Wind turbine power output modeling incorporate force outage rate of the turbine. A new meta-heuristic algorithm called Cuckoo Search is applied for solving the hybrid energy system optimization problem. Photovoltaic-Battery, Wind-Battery and Photovoltaic-Wind-Battery system applicable to a remote area located in Almora district of Uttarakhand, India are considered. The effectiveness of Cuckoo Search in solving hybrid system design problem is investigated and its performance is compared with other well known optimization algorithms like Genetic Algorithm and Particle Swarm Optimization algorithm. Furthermore, this paper investigates the sensitivity of various input parameters like solar, wind resources and capital cost on the cost of energy.

Utility optimization framework for a distributed traffic control of urban road networks

Route choice behavior has been recognized as an important factor in the traffic management and control strategy design. State-of-the-art research in this field (e.g. anticipatory traffic control approaches) often formulates this as a bi-level optimization problem where users’ reactions to changes in signal control are taken into account in the design of the control policies. Solving such a combined optimization problem is possible but difficult and complex which often involves the determination of the dynamic user equilibrium (UE). Nevertheless, anticipatory control inherently aims to optimize the signal control that anticipates or reacts to the users’ route choice and thus is a reactive control. Furthermore, the approaches used in anticipatory control are based on the coupling between control and traffic assignment where the route choice behavior of road users is unrealistically assumed to be in equilibrium.This paper proposes a novel utility based optimization framework to design a distributed control scheme based on a so-called BackPressure principle that is scalable and proactively influences the drivers’ route choice behavior. In particular, as opposed to the well-studied anticipatory control and bi-level optimization, the proposed utility optimization formulation solves for a system optimal problem where the optimal turning fraction (as an output decision variable) is enforced as a desirable outcome. The optimal turning fractions would then be achieved in a practical deployment either by directly asking the users to follow the systems routing advice or by influencing, i.e. manipulating, the route choice decisions made by them so that the resulting turning fraction is (or is close to) the optimal value. Although drivers may not follow the computed optimal fractions as desire, there exist several ways, e.g. see Groot et al. (2015), to design appropriate incentives for drivers to follow the route guidance, and thus improve the compliance rate and overall journey experience for everyone inside the network. To this end, we demonstrate by numerical results that better network performance is achieved using the proposed framework even with a limited compliance from the users. Furthermore, we have formally proved that our proposed utility optimization based BackPressure scheme stabilizes the network for any feasible traffic demands thus maximizing network throughput. In addition, we show through simulation and numerical results that the new policy outperforms the original BackPressure-based distributed control scheme both in terms of network throughput and other congestion measures such as travel time.

Developing an optimal layout design of a satellite system by considering natural frequency and attitude control constraints

In recent years, there has been a growing research interest in layout design optimization of satellite systems. The layout design optimization of a satellite system is a complex process having a large number of design variables and constraints. This paper presents a hybrid optimization algorithm, which globally explores the design search space using Particle Swarm Optimization (PSO) and gradient-based Sequential Quadratic Programming (SQP) to rapidly locate optimum design point. The majority of the previous research works mainly focused on finding reasonable placement of components in satellite layout design, with some specific requirements, which are essential for the satellite stability, control and performance such as attitude control, non-interference and overlap constraints. In this study, additional requirements such as structural stiffness and natural frequency constraints are also considered. The proposed approach is employed on a simplified international global communication satellite. The obtained results indicate that the consideration of natural frequency and attitude control constraints in the configuration layout design of a satellite system can significantly improve the stability and control of the satellite and thus frequency coupling between satellite and launcher can be prevented. In addition, the results indicate that the proposed method provides an effective way of solving layout design optimization problem of satellite systems.

Hybrid stochastic optimization method for optimal control problems of chemical processes

In the paper, the optimal control problem of chemical process systems is considered. In general, it is very difficult to solve this problem analytically due to its nonlinear nature and the existence of control input constraints. To obtain the numerical solution, based on the time-scaling transformation technology and the control parameterization method, the problem is transformed into a parameter optimization problem with some variable bounds, which can be efficiently solved using the improved conjugate gradient algorithm developed by us. However, in spite of the improved conjugate gradient algorithm is very efficient for local search, the solution obtained is usually a local extremum for non-convex optimal control problems. In order to escape from the local extremum, a novel stochastic search method is developed. A large number of numerical experiments show that the novel stochastic search method is excellent in exploration, while bad in exploitation. In order to improve the exploitation, we propose a hybrid stochastic optimization approach to solve the problem based on the novel stochastic search method and the improved conjugate gradient algorithm. Convergence results indicate that any global optimal solution of the approximate problem is also a global optimal solution of the original problem. Finally, four chemical process system optimal control problems illustrate that the hybrid numerical optimization algorithm proposed by us is low CPU time and obtains a better cost function value than the existing approaches.

Symbiotic Organisms Search algorithm for economic load dispatch problem with valve-point effect

Symbiotic Organisms Search (SOS) is a brand new and effective metaheuristic optimization algorithm. This paper proposes the SOS algorithm to solve the Economic Load Dispatch (ELD) problem with valve-point effect, which is one of the essential optimization problems in modern power systems. The proposed algorithm is tested on five different test cases consisting 3-machines 6-bus, IEEE 5-machines 14-bus, IEEE 6-machines 30-bus, 13 and 40 unit test systems including both transmission loss and without transmission loss. These test cases show that SOS is able to converge to the global optima successfully. Moreover results obtained from proposed algorithm are compared through different methods used in solving the ELD problem existing in the literature. According to these results, SOS produces better values than all.