Combined Optimization Model Research Articles

Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model

Wind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA, BP)-PSOLSSVM. In this model, variational mode decomposition (VMD) is combined with phase space reconstruction (P) as data processing method to determine intrinsic mode function (IMF) and its input–output matrix in the prediction model. Then, the linear model autoregressive integrated moving average model (ARIMA) and typical nonlinear model back propagation neural network (BP) are adopted to forecast each IMF separately and get the prediction of short-term wind speed by adding up the IMFs. In the final stage, particle swarm optimization least squares support vector machine (PSOLSSVM) uses the prediction results of the two separate models from previous step for the secondary prediction. For the proposed method, the PSOLSSVM employs different mathematical principles from ARIMA and BP separately, which overcome the shortcoming of using just single models. The proposed combined optimization model has been applied to two datasets with large fluctuations from a northern China wind farm to evaluate the performance. A performance comparison is conducted by comparing the error from the proposed method to six other models using single prediction techniques. The comparison result indicates the proposed combined optimization model can deliver more accurate and robust prediction than the other models; meanwhile, it means the power grid dispatching work can benefit from implementing the proposed predicting model in the system.

Evaluating investment strategies for distribution networks based on yardstick competition and DEA

The traditional investment for distribution networks is mainly evaluated and conducted from the static perspective. A dynamic comprehensive evaluation method is essential to obtain an efficient investment strategy. Firstly, a hierarchical evaluation index system is constructed to describe the overall performance of the distribution network, which includes six types of criteria such as information level, power supply capacity, asset utilization efficiency and economy, power supply quality and loss, power supply reliability, and sustainable development level. Each criterion is composed of input and output indicators. Then, using projection pursuit method considering subjective weight constraints, a combined weight optimization model is proposed to calculate the weight vector of each criterion. Furthermore, the data envelopment analysis (DEA) based dynamic evaluation approach is set up to compute the investment efficiency defined as ratio of output and input. The time degree theory is also introduced to realize dynamic evaluation. Via yardstick competition among multiple regions and “reward and punishment” rule, a precise investment planning strategy can be achieved while minimizing construction cost and maximizing efficiency of the resource allocation. The numerical results on a practical 10-region distribution system in four planning cycles verify the effectiveness of the proposed method.

Simultaneous optimization of fiber orientations and topology shape for composites compliant leading edge

A morphing leading edge, based on the compliant mechanism, is a novel concept which can improve the aerodynamic performance of aircraft for different missions. This can obviously reduce the weight and enhance the aircraft’s fuel efficiency. A simultaneous optimization of topology and fiber orientations for a composite leading edge has been achieved in the paper. It mainly included two steps. Initially, the lamination parameters and density were taken as design variables to determinate the fiber angles and topology, respectively. The least squares between the actual and desired displacements at key points were set as the objective function. Under the volume constraints, the combined optimization model of the leading edge was built to simultaneously obtain the optimal lamination parameters and topology shape. Then the optimal fiber angles were matched with the optimal lamination parameters by transforming the nonlinear equations to the least squares optimization. The physical model of the composite leading edge and experiments for it were manufactured and designed. The morphing capability of the composite’s compliant leading edge was investigated through simulation and experiments.

Optimizing operation of a large-scale pumped storage hydropower system coordinated with wind farm by means of genetic algorithms

<p>Due to the intermittent and fluctuating nature of wind and other renewable energy sources, their integration into electricity systems requires large-scale and flexible storage systems to ensure uninterrupted power supply and to reduce the percentage of produced energy that is discarded or curtailed. Storage of large quantities of electricity in the form of dynamic energy of water masses by means of coupled reservoirs has been globally recognized as a mature, competitive and reliable technology; it is particularly useful in countries with mountainous terrain, such as Greece. Its application may increase the total energy output (and profit) of coupled wind-hydroelectric systems, without affecting the availability of water resources. Optimization of such renewable energy systems is a very complex, multi-dimensional, non-linear, multi modal, nonconvex and dynamic problem, as the reservoirs, besides hydroelectric power generation, serve many other objectives such as water supply, irrigation and flood mitigation. Moreover, their function should observe constraints such as environmental flow. In this paper we developed a combined simulation and optimization model to maximize the total benefits by integrating wind energy production into a pumped-storage multi-reservoir system, operating either in closed-loop or in open-loop mode. In this process, we have used genetic algorithms as the optimization tool. Our results show that when the operation of the reservoir system is coordinated with the wind farm, the hydroelectricity generation decreases drastically, but the total economical revenue of the system increases by 7.02% when operating in closed-loop and by 7.16% when operating in open-loop mode. We conclude that the hydro-wind coordination can achieve high wind energy penetration to the electricity grid, resulting in increase of the total benefits of the system. Moreover, the open-loop pumped-storage multi-reservoir system seems to have better performance, ability and flexibility to absorb the wind energy decreasing to a lesser extent the hydroelectricity generation, than the closed-loop.</p>

Research on the Steady Operation Optimization Model of Natural Gas Pipeline Considering the Combined Operation of Air Coolers and Compressors

Based on the mutual coupling effect among the compressor, the air cooler and pipes in the system of natural gas pipeline, innovatively with the goal of minimum energy consumption, this paper established a combined operation optimization model of the air cooler and compressor through the optimization of the switching scheme of compressors and air coolers, which can greatly reduce the production energy consumption of the pipeline system. Moreover, when the air temperature is taken as an optimization variable, the most proper temperature to start the air cooler of each compressor station can be worked out to guide the optimized operation of the pipeline, which is of high value for promotion and application. The case analysis of west-east natural gas pipeline II showed that among genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithm that are used to solve the optimization model, the genetic algorithm is the fastest, and the simulated annealing algorithm the slowest, but the optimization results of the simulated annealing algorithm is the best, in which the reduced production energy consumption accounted for 33.77%, testifying the practicability and creativity of the optimization model.

Optimization of Combining Fiber Orientation and Topology for Constant-Stiffness Composite Laminated Plates

This paper deals with an efficient optimization method of combining fiber orientation and topology for constant-stiffness composite laminated plates. The optimal topology and fiber orientation can be simultaneously obtained, using the proposed method. To overcome the non-monotonous behaviors derived from directly optimizing fiber orientation, the lamination parameters are selected as design variable. The proposed method mainly includes two steps. Initially, lamination parameters and density are taken as the design variables for determining the fiber orientation and topology shape. A combined optimization model is built based on the penalization theory. The optimal lamination parameter and topology shape can be achieved simultaneously in this step. Then, solving nonlinear equations is transformed into a least squares optimization problem. The optimal fiber orientation is obtained and matched with the optimal lamination parameter. Finally, numerical examples of designing short cantilever beam and compliant inverter are performed to illustrate the validity of this method.

Optimization for Blasting Scheme of Crown-Sill Pillar Based on CW-GT and GC-WTOPSIS

Blasting scheme for crown-sill pillar of a lead–zinc mine was optimized by a new combination optimization model on the basis of CW-GT and GC-WTOPSIS. Nine main evaluation indices influencing blasting were chosen from economy, technology and safety aspects to establish the synthetic evaluation index system of four blasting schemes. Then the synthetic superiority degrees of the four schemes were determined with the basic theory of CW-GT and GC-WTOPSIS. Scheme- III (burn cut, inclined hole and side collapse with an angle of 80°) had the highest superiority degree and hence was confirmed as the best. The result was consistent with AHP-TOPSIS, BP neural network and catastrophe progressing model. The practice showed that the selected blasting scheme achieved the desired blasting effect, and the new method was suitable for optimization of blasting scheme, which provided a new way for scientific and reliable optimization of similar programmes.

Mid and long-term optimize scheduling of cascade hydro-power stations based on modified GA-POA method

Abstract. In this paper, to explore the efficiency and rationality of the cascade combined generation, a cascade combined optimal model with the maximum generating capacity is established, and solving the model by the modified GA-POA method. It provides a useful reference for the joint development of cascade hydro-power stations in large river basins. The typical annual runoff data are selected to calculate the difference between the calculated results under different representative years. The results show that the cascade operation of cascaded hydro-power stations can significantly increase the overall power generation of cascade and ease the flood risk caused by concentration of flood season.

Real-Time Management of Groundwater Resources Based on Wireless Sensors Networks

Groundwater plays a vital role in the arid inland river basins, in which the groundwater management is critical to the sustainable development of area economy and ecology. Traditional sustainable management approaches are to analyze different scenarios subject to assumptions or to construct simulation–optimization models to obtain optimal strategy. However, groundwater system is time-varying due to exogenous inputs. In this sense, the groundwater management based on static data is relatively outdated. As part of the Heihe River Basin (HRB), which is a typical arid river basin in Northwestern China, the Daman irrigation district was selected as the study area in this paper. First, a simulation–optimization model was constructed to optimize the pumping rates of the study area according to the groundwater level constraints. Three different groundwater level constraints were assigned to explore sustainable strategies for groundwater resources. The results indicated that the simulation–optimization model was capable of identifying the optimal pumping yields and satisfy the given constraints. Second, the simulation–optimization model was integrated with wireless sensors network (WSN) technology to provide real-time features for the management. The results showed time-varying feature for the groundwater management, which was capable of updating observations, constraints, and decision variables in real time. Furthermore, a web-based platform was developed to facilitate the decision-making process. This study combined simulation and optimization model with WSN techniques and meanwhile attempted to real-time monitor and manage the scarce groundwater resource, which could be used to support the decision-making related to sustainable management.

A Logistic-BP Classifier for Assessing Personal Credit Risk

In view of the present personal credit risk problems in peer-to-peer industry, this paper proposes the Logistic-BP (Back Propagation) combined optimal model. The method combines the traditional statistical method with the artificial intelligence method by building a unified error function. The simulation is carried out with a German commercial banks’ credit data, and the result shows that the Logistic-BP model has higher accuracy and robustness than single model. The average classification accuracy for the test sample is 77.3%, 2-7% higher than the single model and has an obvious promotion effect. In this paper, we compare Logistic-BP with other methods such as LDA, Naive Bayes, RBF-LS-SVM and C4.5, the result shows that Logistic-BP is superior to other algorithms.

Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology

More and more lines are using the Communication Based Train Control (CBTC) systems in urban rail transit. Trains are operated by tracking a pre-determined target speed curve in the CBTC system, so one of the most effective ways of reducing energy consumption is to fully understand the optimum curves that should prevail under varying operating conditions. Additionally, target speed curves need to be calculated with optimum real-time performance in order to cope with changed interstation planning running time. Therefore, this paper proposes a fast and effective algorithm for optimization, based on a two-stage method to find the optimal curve using a max-min ant colony optimization system, using approximate calculations of a discrete combination optimization model. The first stage unequally discretizes the line based on static gradient and speed limit in low-density and it could conduct a comprehensive search for viable energy saving target speed curves. The second stage unequally discretizes the line based on first stage discretion results, it makes full use of first-stage optimization information as pheromone, quickly optimizing the results to satisfy real-time demands. The algorithm is improved through consideration of the experience of train drivers. Finally, the paper presents some examples based on the operation data of Beijing Changping Subway Line, which is using CBTC system. The simulation results show that the proposed approach presents good energy-efficient and real-time performance.

Effect of Inflow Class Selection on Multi-Objective Reservoir Operation Using Stochastic Dynamic Programming

A combined simulation optimization model was developed to derive optimal operational policies for a multi-objective reservoir in a semiarid environment. Stochastic dynamic programming was selected as the optimization technique. Different scenarios were considered to optimize the current operational policy as well as different possible future uses of the reservoir. The first scenario was used to maximize hydropower production within the framework of the current operational policy. Other scenarios were used to address multiple objective operation of the reservoir including hydropower, agricultural, and domestic uses. Generated policies for all the scenarios were simulated in real time using historical inflow data for the Qar’awn reservoir within the Litani Basin in Lebanon. Sensitivity analysis on number of inflow classes was performed. Results showed that the newly derived policies decrease failure by a range of threefold to sixfold and improve hydropower production by more than 15 %. The model was able to derive policies that decreased system failure and shortages to less than 10 %. Best inflow classes were found to be in the range of 3–5.

Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting

The paper improves the prediction accuracy of the monthly electricity consumption of power system, a hybrid prediction model is put forward aiming at the problems existing in the traditional grey prediction method, which is based on the combined optimization model of particle swarm and K nearest value. Grey prediction equation is solved by particle swarm optimization algorithm, which is a good solution to the problem of the choice of parameters of gray prediction equation, with strong global optimization ability; A combinatorial optimization algorithm of the K- nearest value and particle swarm was proposed, which solves the problem of prediction error caused by large fluctuations of raw data, and improves the accuracy of prediction results. Through the prediction of monthly electricity consumption in the past years, the results show that the combination prediction method proposed in this paper can effectively predict the monthly electricity consumption, and is practical.

A combined external delivery optimization model for wind power and thermal power considering carbon trading and tradable green certificates based on Credibility theory

Abstract To reduce the impact of wind power prediction error on power system operation, Credibility theory and Fuzzy chance constrained programming method are introduced while solving wind power inter-regional scheduling optimization problems. Firstly, the paper introduces the carbon trading (CT) and Tradable Green Certificates (TGC). Then, an energy independent external delivery model is constructed, which contains wind power independent delivery model and thermal power independent delivery model. Secondly, the paper put forward energy combined external delivery model considering CT and TGC. Meanwhile, Credibility theory and Fuzzy chance constrained programming method are used to deduce credibility distribution function of wind power prediction errors and define the feasible region under a certain confidence level, respectively. Since credibility distribution is applied as a measure function and the feasible region covers fuzzy event set, the optimal solution will be in the credible solution space. Thirdly, four scenarios are set according to CT and TGC. Finally, this paper uses six thermal power units and one wind farm as the simulation system. The simulation results show Credibility theory is applicable to overcome wind power uncertainty. Energy combined external delivery is better for improving system consuming wind power. CT and TGC could increase wind power grid-connection, but CT’s optimization effect is more obvious than TGC. The optimization effect reaches the optimum when both CT and TGC are implemented, and energy external delivery profits should be reasonable allocated for encouraging thermal power to participate in wind power delivery.

MRA와 POD를 적용한 공력특성 최적설계

This paper attempts to evaluate the accuracy and efficiency of a design optimization procedure by combining wavelets-based multi resolution analysis method and proper orthogonal decomposition (POD) technique. Aerodynamic design procedure calls for high fidelity computational fluid dynamic (CFD) simulations and the consideration of large number of flow conditions and design constraints. Thus, even with significant computing power advancement, current level of integrated design process requires substantial computing time and resources. POD reduces the degree of freedom of full system by conducting singular value decomposition for various field simulations. In this research, POD combined Design Optimization model is proposed and its efficiency and accuracy are to be evaluated. For additional efficiency improvement of the procedure, multi resolution analysis method is also being employed during snapshot constructions (POD training period). The proposed design procedure was applied to the optimization of wing aerodynamic performance. Throughout the research, it was confirmed that the POD/MRA design procedure could significantly reduce the total design turnaround time and also capture all detailed complex flow features as in full order analysis.

Optimum Part Build Orientation in Additive Manufacturing for Minimizing Part Errors and Support Structures

Additive Manufacturing (AM) is the process of part building by stacking layers of material on top of each other. Various challenges for a metal powder based process include reducing the staircase effect which leads to poor surface finish of the part, and minimal use of support structures for regions with overhangs or internal hollow volumes. Part build orientation is a crucial process parameter which affects part quality, in particular, Geometric Dimensioning & Tolerancing (GD&T) errors on the part, the energy expended and the extent of support structures required. This paper provides an approach to identify an optimal build orientation which will minimize the volume of support structures while meeting the specified GD&T criteria of the part for a DMLS based process. Siemens PLM NX API is used to extract the GD&T callouts and associated geometric information of the CAD model. The regions requiring support structures are identified and a Quadtree decomposition is used to find the volume of support structures. The mathematical relationships between build orientation and GD&T are developed as part of a combined optimization model to identify best build orientations for minimizing support structures while meeting the design tolerances. The feasible build orientations along with the corresponding support structures are depicted using a visual model.

Truck-and-Trailer Distribution Based on a Hybrid Evolutionary Algorithm

AbstractA simple DTT is a problem to design a hybrid trucking route for single and double trailers as a new version of the routing problem using sub-tours for servicing truck customers. The local optimization approach, 2-opt, is employed to optimize the route. An 0/1 integer linear programming model is built to segment the route that visits all customers, and choose a sub-tours combination that minimizes the incremental cost of the sub-tours. A hybrid evolutionary algorithm is built upon them to search for an optimal hybrid route. Numerical samples are provided to show the features of the DTT problem. The performance of the sub-tours combination optimization model and the evolution of the fitness of the algorithm with or without 2-opt are studied by simulation. The results demonstrate the validity of the proposed approach.

Subject-specific planning of femoroplasty: A combined evolutionary optimization and particle diffusion model approach

A potential effective treatment for prevention of osteoporotic hip fractures is augmentation of the mechanical properties of the femur by injecting it with agents such as (PMMA) bone cement – femoroplasty. The operation, however, is only in research stage and can benefit substantially from computer planning and optimization. We report the results of computational planning and optimization of the procedure for biomechanical evaluation. An evolutionary optimization method was used to optimally place the cement in finite element (FE) models of seven osteoporotic bone specimens. The optimization, with some inter-specimen variations, suggested that areas close to the cortex in the superior and inferior of the neck and supero-lateral aspect of the greater trochanter will benefit from augmentation. We then used a particle-based model for bone cement diffusion simulation to match the optimized pattern, taking into account the limitations of the actual surgery, including limited volume of injection to prevent thermal necrosis. Simulations showed that the yield load can be significantly increased by more than 30%, using only 9ml of bone cement. This increase is comparable to previous literature reports where gross filling of the bone was employed instead, using more than 40ml of cement. These findings, along with the differences in the optimized plans between specimens, emphasize the need for subject-specific models for effective planning of femoral augmentation.

A New Combined Optimization Model for Wireless Sensor Networks

Abstract In this paper we proposed a multiobjective optimization model for wireless sensor networks (WSNs). The proposed model optimized several objectives, simultaneously. Indeed, by starting from a generic configuration we found new location for sensors, that the network have appropriate performance in terms of energy consumption and travelled distance. For the monotony of energy consumption and life time of sensors, the rate of energy consumption in each stage have been associated the previous stage. Through a series of calculations the behavior of the proposed model has been compared with other one-objective models.

화음탐색법을 이용한 교통망 링크 통행비용함수 정산기법 개발

본 연구에서는 최근 개발된 화음 탐색법을 이용해 교통망 통행배정 모형의 통행비용 함수의 하나인 BPR 함수의 계수들을 추정하였다. 기존 연구에서는 교통량과 통행시간 자료를 실측해 이를 통계적으로 분석해 계수를 추정하는 방법과 관측교통량과 통행배정 교통량을 일치시키는 계수값을 찾는 것을 목표로 통행배정 모형과 최적화 기법을 결합시킨 방법을 이용하여왔다. 이중 대형 교통망의 계수 정산에 자주 이용되어온 최적화 기법은 관측 통행패턴을 최대한 근접하게 재현하는 계수를 추정할 수 있다는 장점이 있으나 그 수학적 성질과 추정 계수값에 대한 수학적 검토가 충분히 이루어지지 못했다. 본 연구에서는 이러한 문제 인식아래 최근 개발된 전역 탐색 기법인 화음탐색법 기반의 교통망 비용함수 정산 방법을 개발하였다. 화음탐색법은 2000년대 초반 개발된 이후 다양한 분야에서 기존에 사용되던 전역탐색기법들에 비해 우수한 성질을 입증하여 왔으나 교통분야에는 그 적용 예가 거의 없었다. 본 연구는 화음탐색법의 개념을 설명하고 이를 이용해 개발된 정산 알고리즘을 기존 연구에서 사용된 점진증가법 및 황금율법과 성능 비교하였다. 화음탐색법 기반 정산기법은 기존 기법들에 비해 관측 통행패턴을 보다 근접하게 재현할 수 있는 비용함수 계수값들을 찾을 수 있는 것으로 나타났다. 또, 관측 교통량 기반 계수추정법은 BPR식의 <TEX>${\beta}$</TEX>값 추정에는 적합하지만 초기속도나 <TEX>${\alpha}$</TEX>값 정산을 위해서는 통행 속도나 시간과 같은 추가 자료가 필요한 것으로 판단된다. Some previous studies adopted a method statistically based on the observed traffic volumes and travel times to estimate the parameters. Others tried to find an optimal set of parameters to minimize the gap between the observed and estimated traffic volumes using, for instance, a combined optimization model with a traffic assignment model. The latter is frequently used in a large-scale network that has a capability to find a set of optimal parameter values, but its appropriateness has never been demonstrated. Thus, we developed a methodology to estimate a set of parameter values of BPR(Bureau of Public Road) function using Harmony Search (HS) method. HS was developed in early 2000, and is a global search method proven to be superior to other global search methods (e.g. Genetic Algorithm or Tabu search). However, it has rarely been adopted in transportation research arena yet. The HS based transportation network calibration algorithm developed in this study is tested using a grid network, and its outcomes are compared to those from incremental method (Incre) and Golden Section (GS) method. It is found that the HS algorithm outperforms Incre and GS for copying the given observed link traffic counts, and it is also pointed out that the popular optimal network calibration techniques based on an objective function of traffic volume replication are lacking the capability to find appropriate free flow travel speed and <TEX>${\alpha}$</TEX> value.