System Optimization Problems Research Articles

Solving economic dispatch problem with valve-point effects using swarm-based mean–variance mapping optimization (MVMOS)

AbstractMean–variance mapping optimization (MVMO) is a new population-based metaheuristic technique which is successfully applied for different power system optimization problems. The special feature of MVMO is the mapping function applied for the mutation based on the mean and variance of n-best population. Recently, the modified version of MVMO has been developed to become more powerful, named as swarm-based mean–variance mapping optimization (MVMOS). This paper proposes MVMOS as a new approach for solving the economic dispatch (ED) problem considering valve-point effects. To validate the performance of the proposed method, the MVMOS is tested on three systems including 3, 13, and 40 thermal generating units with valve-point effects and the obtained results from MVMOS are compared to those from other existing methods in the literature. Test results have indicated that the proposed MVMOS is more robust and produces better solution quality than many other methods. Therefore, the MVMOS is efficient for sol...

CrowdOp: Query Optimization for Declarative Crowdsourcing Systems

We study the query optimization problem in declarative crowdsourcing systems. Declarative crowdsourcing is designed to hide the complexities and relieve the user of the burden of dealing with the crowd. The user is only required to submit an SQL-like query and the system takes the responsibility of compiling the query, generating the execution plan and evaluating in the crowdsourcing marketplace. A given query can have many alternative execution plans and the difference in crowdsourcing cost between the best and the worst plans may be several orders of magnitude. Therefore, as in relational database systems, query optimization is important to crowdsourcing systems that provide declarative query interfaces. In this paper, we propose CrowdOp , a cost-based query optimization approach for declarative crowdsourcing systems. CrowdOp considers both cost and latency in query optimization objectives and generates query plans that provide a good balance between the cost and latency. We develop efficient algorithms in the CrowdOp for optimizing three types of queries: selection queries, join queries, and complex selection-join queries. We validate our approach via extensive experiments by simulation as well as with the real crowd on Amazon Mechanical Turk.

Sensitivity of algorithm parameters and objective function scaling in multi-objective optimisation of water distribution systems

This paper presents an extensive analysis of the sensitivity of multi-objective algorithm parameters and objective function scaling tested on a large number of parameter setting combinations for a water distribution system optimisation problem. The optimisation model comprises two operational objectives minimised concurrently, the pump energy costs and deviations of constituent concentrations as a water quality measure. This optimisation model is applied to a regional non-drinking water distribution system, and solved using the optimisation software GANetXL incorporating the NSGA-II linked with the network analysis software EPANet. The sensitivity analysis employs a set of performance metrics, which were designed to capture the overall quality of the computed Pareto fronts. The performance and sensitivity of NSGA-II parameters using those metrics is evaluated. The results demonstrate that NSGA-II is sensitive to different parameter settings, and unlike in the single-objective problems, a range of parameter setting combinations appears to be required to reach a Pareto front of optimal solutions. Additionally, inadequately scaled objective functions cause the NSGA-II bias towards the second objective. Lastly, the methodology for performance and sensitivity analysis may be used for calibration of algorithm parameters.

New hybrid shrinking projection algorithm for common fixed points of a family of countable quasi-Bregman strictly pseudocontractive mappings with equilibrium and variational inequality and optimization problems

The purpose of this paper is to introduce and consider a new hybrid shrinking projection algorithm for finding a common element of the set of solutions of a system of equilibrium problems, the set of solutions of a system of variational inequality problems, the set of solutions of a system of optimization problems, the common fixed point set of a uniformly closed family of countable quasi-Bregman strictly pseudocontractive mappings in reflexive Banach spaces. Strong convergence theorems have been proved under the appropriate conditions. The main innovative points in this paper are as follows: (1) the notion of the uniformly closed family of countable quasi-Bregman strictly pseudocontractive mappings is presented and the useful conclusions are given; (2) the relative examples of the uniformly closed family of countable quasi-Bregman strictly pseudocontractive mappings are given in classical Banach spaces $l^{2}$ and $L^{2}$ ; (3) the hybrid shrinking projection method presented in this paper modified some mistakes in the recent result of Ugwunnadi et al. (Fixed Point Theory Appl. 2014:231, 2014). These new results improve and extend the previously known ones in the literature.

Construction and validation of optimal design framework for marine systems based on neuro-response surface method

The geometry of systems including the marine engineering problems needs to be optimized in the initial design stage. However, the performance analysis using commercial code is generally time consuming. To solve this problem, many engineers perform the optimization process using the response surface method (RSM) to predict the system performance, but RSM presents some prediction errors for nonlinear systems. The major objective of this research is to establish an optimal design framework for marine systems. The framework is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the response surface is generated using the back-propagation neural network (BPN) which is considered as neuro-response surface method (NRSM). The optimization process is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II).A nonlinear mathematical function problem is used to compare the accuracy of response surface generated by RSM and NRSM. Through case study of substructure of floating offshore wind turbine considering hydrodynamic performance, we have confirmed the proposed framework applicability for marine system optimization. In the future, we will try to apply the optimization problem for marine systems using the constructed framework.

Design of CSP plants with optimally operated thermal storage

Concentrated solar power plants are increasingly considered worldwide, in order to meet the demand for renewable power generation. A distinctive feature of these plants is the possibility of integrating thermal energy storage such that full-load operation can be sustained for several hours in the absence of solar radiation. A number of design software tools are available for sizing the plant and evaluating the return on investment. These usually assume a short-sighted strategy for storage management. This work presents a new methodology aimed at assessing the potential of optimal control techniques when applied to the operation of energy storage systems in general. The design method is applied to a test case, a state-of-the-art central receiver plant with direct storage, using molten salts as working fluid, and operating in a context of variable electricity prices. The system modeling and optimization problems are formulated and implemented using modern high-level modeling languages, demonstrating the potential of the approach. The different operating strategies are compared based on a detailed financial analysis. A wide system design space is explored, and the results presented for all the foreseeable combinations of solar field size and storage system capacity, considering two plant locations, i.e., Daggett (US-CA) and Almeria (ES). Potential gains up to 10% in terms of yearly revenue are estimated, in case improved control strategies are adopted. These figures translate into increases of more than 30% of the investment profitability by considering over-life financial figures. It is further shown how, in case of state-of-the-art systems, it is always profitable to adopt optimal control to the end of increasing electricity production. However, the potential of these techniques is discussed also under the point of view of investment cost reduction, since the same yearly revenue can be harvested with smaller energy storage, if optimally operated. This aspect, unveiled here for the first time, might become significant if technologies with different cost structure are of interest, i.e., in case the storage cost constitutes a comparatively large part of the total investment. The novel method is thus an additional decision tool allowing to treat the storage operation strategy as a new relevant variable for the design of next generation energy systems.

Canonical duality theory and triality for solving general global optimization problems in complex systems

General nonconvex optimization problems are studied by using the canonical duality-triality theory. The triality theory is proved for sums of exponentials and quartic polynomials, which solved an open problem left in 2003. This theory can be used to find the global minimum and local extrema, which bridges a gap between global optimization and nonconvex mechanics. Detailed applications are illustrated by several examples.

Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem

This paper proposes a hybrid of genetic algorithm (GA) and differential evolution (DE), termed hGADE, to solve one of the most important power system optimization problems known as the unit commitment (UC) scheduling. The UC problem is a nonlinear mixed-integer combinatorial high-dimensional and highly constrained optimization problem consisting of both binary UC variables and continuous power dispatch variables. Although GA is more capable of efficiently handling binary variables, the performance of DE is more remarkable in real parameter optimization. Thus, in the proposed algorithm hGADE, the binary UC variables are evolved using GA while the continuous power dispatch variables are evolved using DE. Two different variants of hGADE are presented by hybridizing GA with two classical variants of DE algorithm. Additionally, in this paper a problem specific heuristic initial population generation method and a replacement strategy based on preservation of infeasible solutions in the population are incorporated to enhance the search capability of the hybridized variants on the UC problem. The scalability of the proposed algorithm hGADE is demonstrated by testing on systems with generating units in the range of 10 up to 100 in one-day scheduling period and the simulation results demonstrate that hGADE algorithm can provide a system operator with remarkable cost savings as compared to the best approaches in the literature. Finally, an ensemble optimizer based on combination of hGADE variants is implemented to further amplify the performance of the presented algorithm.

Optimization problems in natural gas transportation systems: A state-of-the-art review

Abstract This paper provides a review on the most relevant research works conducted to solve natural gas transportation problems via pipeline systems. The literature reveals three major groups of gas pipeline systems, namely gathering, transmission, and distribution systems. In this work, we aim at presenting a detailed discussion of the efforts made in optimizing natural gas transmission lines. There is certainly a vast amount of research done over the past few years on many decision-making problems in the natural gas industry and, specifically, in pipeline network optimization. In this work, we present a state-of-the-art survey focusing on specific categories that include short-term basis storage (line-packing problems), gas quality satisfaction (pooling problems), and compressor station modeling (fuel cost minimization problems). We discuss both steady-state and transient optimization models highlighting the modeling aspects and the most relevant solution approaches known to date. Although the literature on natural gas transmission system problems is quite extensive, this is, to the best of our knowledge, the first comprehensive review or survey covering this specific research area on natural gas transmission from an operations research perspective. The paper includes a discussion of the most important and promising research areas in this field. Hence, this paper can serve as a useful tool to gain insight into the evolution of the many real-life applications and most recent advances in solution methodologies arising from this exciting and challenging research area of decision-making problems.

Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory

The interconnection of maintenance and spare part inventory management often puzzles managers and researchers. The deterioration of the inventory affects decision-making and increases losses. Block replacement and periodic review inventory policies were here used to evaluate a joint optimization problem for multi-unit systems in the presence of inventory deterioration. The deterministic deteriorating inventory (DDI) model was used to describe deteriorating inventory when deteriorating inventory data were available and the stochastic deteriorating inventory (SDI) model was used when they were not. Analytical joint optimization models were established, and the preventive replacement interval and the maximum inventory level served as decision variables to minimize the expected system total cost rate. This work proved the existence of the optimal maximum inventory level and gave the uniqueness condition under the DDI model. Numerical experiments based on the electric locomotives in Slovenian Railways were performed to confirm the effectiveness of the proposed models. Results showed the total cost rate to be sensitive to the maximum inventory level, which indicates that the research of this work is necessary. Further, the optimal preventive replacement interval was reduced and the optimal maximum inventory level was increased to balance the influence of deteriorating inventory. Monte Carlo experiments were used to show that the proposed policy is better than policies that do not take deteriorating inventory into account.

English

This paper reviews the optimal power flow solutions under variable load conditions. In this article we review the recent trend towards non-deterministic (random) search techniques and hybrid methods for OPF and give the review conclusions. These methods have become popular because they have a theoretical advantage over the deterministic methods with respect to handling of non convexity, dynamics, and discrete variables. Present commercial OPF programs can solve very large and complex power systems optimization problems in a relatively less time. In recent years many different solution methods have been suggested to solve OPF problems. The review contributes a comprehensive discussion of specific optimization techniques that can be applied to OPF Solution methodology.

A hybrid multi-objective optimization method considering optimization problems in power distribution systems

Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world. In actual implementation of these technologies, mostly, there are multiple objectives with trade off relationships among each other, and also various constraints in the achievement of these objectives. Therefore, it should be essential to solve multi-objective optimization problems effectively in the applications of these new technologies in power systems. This paper proposes an improved method to realize multi-objective optimization for critical challenges in advanced power systems. To realize that, in an optimal dispersed generation installation problem, that is, one of effective measures for low-carbon power systems, various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed. The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.

Evolutionary Approach to jointly resolve the Power and the Capacity Optimization problems in the multi-user OFDMA Systems

This paper deals with the problem of resources allo-cation in the downlink of the radio mobile systems. The allocation of resources is established in the context of multi-path effect and Doppler effect. These phenomena cause randomly variations of the channel and make difficult the allocation of the resources. Therefore, the problem of resources allocation is a high non linear optimization problem. Thus, we propose an implementation of genetic algorithms approach to increase the total throughput of users while minimizing the consumption of the total power of the base station. Like the evolutionary algorithms approach, this method is characterized by its robustness, permitting to efficiently solve a non linear optimization problem.

Performance Enhancement for Adaptive Beam-Forming Application Based Hybrid PSOGSA Algorithm

Recently researchers were interested in hybrid algorithms for optimization problems for several communication systems. In this paper, a novel algorithm based on hybrid PSOGSA technique (combination of Gravitational Search Algorithm and Particle Swarm Optimization) is presented to enhance the performance analysis of beam-forming for smart antennas systems using N elements for Uniform Circular Array (UCA) geometry. Complex excitations (phases) of the array radiation pattern are optimized using hybrid PSOGSA technique for a set of simultaneously incident signals. Our results have shown tremendous improvement over the previous work was done using Uniform Linear Array (ULA) geometry and standard GSA in terms of normalized array factor and computational speed for normalized fitness values.

Large-Scale Loan Portfolio Selection

We consider the problem of optimally selecting a large portfolio of risky loans, such as mortgages, credit cards, auto loans, student loans, or business loans. Examples include loan portfolios held by financial institutions and fixed-income investors as well as pools of loans backing mortgage- and asset-backed securities. The size of these portfolios can range from the thousands to even hundreds of thousands. Optimal portfolio selection requires the solution of a high-dimensional nonlinear integer program and is extremely computationally challenging. For larger portfolios, this optimization problem is intractable. We propose an approximate optimization approach that yields an asymptotically optimal portfolio for a broad class of data-driven models of loan delinquency and prepayment. We prove that the asymptotically optimal portfolio converges to the optimal portfolio as the portfolio size grows large. Numerical case studies using actual loan data demonstrate its computational efficiency. The asymptotically optimal portfolio's computational cost does not increase with the size of the portfolio. It is typically many orders of magnitude faster than nonlinear integer program solvers while also being highly accurate even for moderate-sized portfolios. Our method allows for tractable, large-scale data-driven optimization of loan portfolios and could also be applicable to large-scale optimization problems for stochastic systems in other areas.

Kriging Based Surrogate Modeling for Fractional Order Control of Microgrids

This paper investigates the use of fractional order (FO) controllers for a microgrid. The microgrid employs various autonomous generation systems like wind turbine generator, solar photovoltaic, diesel energy generator, and fuel-cells. Other storage devices like the battery energy storage system and the flywheel energy storage system are also present in the power network. An FO control strategy is employed and the FO-proportional integral derivative (PID) controller parameters are tuned with a global optimization algorithm to meet system performance specifications. A kriging based surrogate modeling technique is employed to alleviate the issue of expensive objective function evaluation for the optimization based controller tuning. Numerical simulations are reported to prove the validity of the proposed methods. The results for both the FO and the integer order controllers are compared with standard evolutionary optimization techniques, and the relative merits and demerits of the kriging based surrogate modeling are discussed. This kind of optimization technique is not only limited to this specific case of microgrid control, but also can be ported to other computationally expensive power system optimization problems.

A Linearization Approach for Improving the Computational Efficiency of Water Distribution System Ranking-based Optimization Algorithms

Abstract This paper presents a linearization approach to be used within the framework of water distribution system optimization problems where the searching algorithm is guided by the ranking of the possible solutions characterized by a system performance indicator such as the resilience index. The results obtained in addressing two different problems applied to real water distribution systems, namely, identification of the optimal placement of flow meters in a district meter area and multi-objective design of a water distribution system, show that a linearized hydraulic simulator although not capable of correctly describing the hydraulic functioning of the network, nonetheless enables each solution it finds to be assigned a value of the performance indicator whose ranking closely approximates the ranking of the corresponding solution that would be obtained if the full hydraulic simulator was used. This ensures significant reductions in computational times.

Research on gain scheduling controller of the networked control system with long delay

This paper addresses the stabilization and optimization problem of networked control systems (NCSs) with long time delays and parameter scheduling. According to the actual network conditions, the network time delay is divided into the fixed time delay and the random time delay. Then, the stabilization controller and the gain scheduling controller are constructed, where the stabilizing control parameters are obtained with cone complementary linearization (CCL) approach and the optimizing control parameters are solved with estimation of distribution algorithm (EDA). Simulation results demonstrate the effectiveness of the proposed methods.

Evolution of the passive harmonic filters optimization problem in industrial power systems

Several authors have treated the optimization of passive filters in electric distribution systems. Optimization methods like: sequential quadratic programming (SQP), simulated annealing (SA), differential evolution (DE), artificial neural networks (ANN), particle swarm optimization (PSO), genetic algorithm (GA), etc., have been employed for optimizing certain configurations of passive filters. These optimization methods have been employed to solve several formulations of the problem of the project of filters. These formulations can be classified in: formulations of one or several objectives. The objective of the present work is to show the evolution of the formulation of this problem in the lasts years respect to the objective functions and constraints used. This analysis shows how the formulations employed have been upgraded from single-objective to multi-objective formulations to achieve a better representation of this complex problem.

Dynamic system optimal model for multi-OD traffic networks with an advanced spatial queuing model

This paper provides an approach to solve the system optimal dynamic traffic assignment problem for networks with multiple O–D pairs. The path-based cell transmission model is embedded as the underlying dynamic network loading procedure to propagate traffic. We propose a novel method to fully capture the effect of flow perturbation on total system cost and accurately compute path marginal cost for each path. This path marginal cost pattern is used in the projection algorithm to equilibrate the departure rate pattern and solve the system optimal dynamic traffic assignment. We observe that the results from projection algorithm are more reliable than those from method of successive average algorithm (MSA). Several numerical experiments are tested to illustrate the benefits of the proposed model.