A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

A review of uncertain factors and analytic methods in long-term energy system optimization models

A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.

A New Modified GM (1,1) Model: Grey Optimization Model

Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.

Optimization model of unascertained measurement for underground mining method selection and its application

An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.

Enterprise resource planning implementation decision & optimization models

To study the uncertain optimization problems on implementation schedule, time-cost trade-off and quality in enterprise resource planning （ERP） implementation, combined with program evaluation and review technique （PERT）, some optimization models are proposed, which include the implementation schedule model, the timecost trade-off model, the quality model, and the implementation time-cost-quality synthetic optimization model. A PERT-embedded genetic algorithm （GA） based on stochastic simulation technique is introduced to the optimization models solution. Finally, an example is presented to show that the models and algorithm are reasonable and effective, which can offer a reliable quantitative decision method for ERP implementation.

A dynamic optimization model of an integrated coal supply chain system and its application

Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.

Collaborative optimization model of renewable energy development considering peak shaving costs of various flexibility resources

China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.

Hybrid optimization model and its application in prediction of gas emission

According to the complex nonlinear relationship between gas emission and its effect factors, and the shortcomings that basic colony algorithm is slow, prone to early maturity and stagnation during the search, we introduced a hybrid optimization strategy into a max-rain ant colony algorithm, then use this improved ant colony algorithm to estimate the scope of RBF network parameters. According to the amount of pheromone of discrete points, the authors obtained from the interval of net- work parameters, ants optimize network parameters. Finally, local spatial expansion is introduced to get further optimization of the network. Therefore, we obtain a better time efficiency and solution efficiency optimization model called hybrid improved max-min ant system （H1-MMAS）. Simulation experiments, using these theory to predict the gas emission from the working face, show that the proposed method have high prediction feasibility and it is an effective method to predict gas emission.

Optimization Model of Cattle Husbandry for Rural Household in Cambodia

Cambodia is one of the Southeast Asia. With the agricultural market integration, Cambodia rural household is adjusting livestock structure naturally. In order to provide suitable support for agriculture policy, the authors conducted a survey on 204 rural household in Cambodia. This article uses the optimization model, considering rural labor, cattle size, and animal disease risk, to analyze and get optimum result range. The result shows that the more off-farm job opportunity, suitable cattle feed structure, and investment on public health for cattle, the household income in rural Cambodia will increase.

Analyzing the Relation Between Earthquakes and Active Faults Based on the Parameter Optimization Model

The gestation and occurrence of strong earthquakes are closely related to fault activity, which is not only revealed by abundant experimentation and seismism but also proved by modern seismology. On the Chinese mainland, the relation between earthquake activity and active faults is one of the bases for partitioning potential seismic sources, analyzing the seismotectoulcs and estimating location of strong earthquakes.Due to the nonuniformity of earth media, instability of observation systems and disturbance of the environment, etc, the variety of observational data is complicated, that is, there is no absolutely ＂normal＂ or ＂abnormal＂, and seismic anomalies can be divided into many mutually exdusive＂ abnormal states＂. In different conditions of combined time-spacestrength, determining seismic anomalies by different monomial forecast methods and its efficiency could be different due to the uncertainty of a precursor itself or complexity of the relationship between a precursor and earthquake gestation. It is very difficult to discover and dispose of this difference in actual application in a ＂two-state＂ model. But in a ＂multi-state＂ model, the difference can be easily reflected and the optimal combination of forecasting parameters for a forecast method can also be determined easily. Based on the ＂multi-state＂ precursory model and the optimization method for parameters of earthquake forecast model under the condition of optimal forecast efficiency, the relationship of the spatial location of earthquake with M ≥ 6.0 and active faults in three seismic belts are analyzed. The results demonstrate that in the Hetao Seismic Belt, seismicity is mostly concentrated in the range of 20 km along the fault, the optimization model can forecast the location of potential earthquakes of M ≥ 6.0 near the faults with a relatively high accuracy and the reliability is 0.5 ; while in the Qilian Mt. Seismic Belt, the reliability only reaches 0.14 when we use the model to estimate earthquakes within 30 km range along the faults. The ＂multi-state＂ precursory model, the efficiency-evaluating model and the parameter selection of individual earthquake forecast model based on optimal efficiency are of certain revelatory and practicable meanings for developing knowledge about precursors, investigating the laws of earthquake preparation and searching for optimal forecasting methods.

Balancing timber production and habitat conservation of Okinawa Rails(Gallirallus okinawae): Application of a harvest scheduling optimization model in subtropical forest in Okinawa, Japan

Finding the right balance between timber production and the management of forest-dependent wildlife species,present a difficult challenge for forest resource managers and policy makers in Okinawa,Japan.A possible explanation of this can be found in the unique nature of the forest management area which is populated with various kinds of rare and endangered species.This issue has been brought to light as a result of the nomination of northern Okinawa Island in 2018 as a candidate for World Natural Heritage site.The nomination has raised public awareness to the possibility of conflicting management objectives between timber extraction and the conservation of habitat for forest-dependent wildlife species.Managing exclusively for one objective over the other may fail to meet the demand for both forest products and wildlife habitat,ultimately jeopardizing the stability of human and wildlife communities.It is therefore important to achieve a better balance between the objective of timber production and conservation of wildlife habitat.Despite the significance of this subject area,current ongoing discussions on how to effectively manage for forest resources,often lack scientific basis to make sound judgement or evaluate tradeoffs between conflicting objectives.Quantifying the effect of these forest management activities on wildlife habitat provides useful and important information needed to make forest management and policy decisions.In this study we develop a spatial timber harvest scheduling model that incorporates habitat suitability index(HSI)models for the Okinawa Rail(Gallirallus okinawae),an endangered avian species found on Okinawa,Japan.To illustrate how the proposed coupling model assembles spatial information,which ultimately aids the study of forest management effects on wildlife habitat,we apply these models to a forest area in Okinawa and conduct a simple simulation analysis.

Hybrid optimization model of product concepts

Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the ＂evolution - evaluation＂ iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the orooosed method and associated algorithms.

An Optimization Model for Aircraft Service Logistics

Scheduling is one of the most difficult issues in t he planning and operations of the aircraft services industry. In this paper, t he various scheduling problems in ground support operation of an aircraft mainte nance service company are addressed. The authors developed a set of vehicle rout ings to cover each schedule flights; the objectives pursued are the maximization of vehicle and manpower utilization and minimization of operation time. To obta in the goals, an integer-programming model with genetic algorithm is formulated . It is found that the company can produce an effective and efficient schedules to deploy the manpower and equipment resources. Simulation is used to verify the method and a MATLAB program is used to code the genetic algorithm. This model i s further illustrated by a case study in Hong Kong and the benefit elaborated. F inally, a conclusion is made to summarize the experience of this project and pro vide further improvement.

Research on the Multidimensional Constraint Stability of Bridge Structure Modeling based on Optimization Model

In this paper, we conduct research on the multidimensional constraint stability of bridge structure modeling based on the optimization model. The current internal and the external research results to the truss web structure, the high internode the aspect ratio and the stiffness of the middle truss brace of the truss web, deffection of composite beams of the impact of stress is a very important problem in the design of the bridge. Structural health monitoring is the use of the field of the non-destructive sensing technology, including the structural response, including structural system characteristics analysis, to achieve the purpose of monitoring structural damage or degradation. Under this basis, this paper proposes the new idea on the modelling and simulates the performance.

An Optimization Model of EVs Charging and Discharging for Power System Demand Leveling

This paper presents an advanced methodology for optimizing a UK network load demand with various uncertainties which are related to individual driving behaviours. Without the optimized regulation for traditional power system demand, EVs （electric vehicles） would have an adverse impact on the stability of power systems. This becomes more significant for large-scale EVs plugging into the power grid. Traditional optimized methodologies are effective only for EV charging. The proposed techniques improve the system flexibility and stability through an advanced optimization model and flexible bidirectional charging/discharging control. Three scenarios with different charging and discharging power levels and various penetration levels of EVs are discussed in detail in this paper. Simulation results demonstrate that bidirectional EV power flow control has vast potentials to improve the load demand profile, with increased proportion of EVs, and charging/discharging power levels.

Application of fuzzy optimization model in the evaluationof urban flood-waterloggedvulnerabilitybased on the synthetic weight ofgame theory

The evaluation of urban flood-waterlogged vulnerability is very important to the safety of urban flood control. In this paper, the evaluation of consolidated index is used. Respectively, AHP and entropy method calculate the subjective and objective weight of the evaluation indicators, and combine them by game theory. So we can obtain synthetic weight based on objective and subjective weights. The evaluation of urban flood-waterlogged vulnerability as target layer, a single variable multi-objective fuzzy optimization model is established. We use the model to evaluate flood-waterlogged vulnerability of 13 prefecture-level city in Hunan, and compare it with other evaluation method. The results show that the evaluation method has certain adaptability and reliability, and it＇ s helpfid to the construction planning of urban flood control.

A New Searching Strategy for the Lost Plane Based on RBF Neural Network Model and Global Optimization Model

In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.

A novel stiffness optimization model of space telescopic boom based on locking mechanism

The deployable telescopic boom,whose mass and stiffness play crucial roles,is extensively used in the design of space-deployable structures.However,the most existing optimal design that neglects the influence of the locking mechanisms in boom joints cannot raise the whole stiffness while reducing the boom mass.To tackle this challenge,a novel optimization model,which utilizes the arrangement of the locking mechanisms to achieve synchronous improvement of the stiffness and mass,is proposed.The proposed optimization model incorporates a novel joint stiffness model developed based on an equivalent parallel mechanism that enables the consideration of multiple internal stiffness factors of the locking mechanisms and tubes,resulting in more accurate representations of the joint stiffness behavior.Comparative analysis shows that the proposed stiffness model achieves more than at least 11% improved accuracy compared with existing models.Furthermore,case verification shows that the proposed optimization model can improve stiffness while effectively reducing mass,and it is applied in boom optimization design.

A review of optimization modeling and solution methods in renewable energy systems

The advancement of renewable energy(RE)represents a pivotal strategy in mitigating climate change and advancing energy transition efforts.A current of research pertains to strategies for fostering RE growth.Among the frequently proposed approaches,employing optimization models to facilitate decision-making stands out prominently.Drawing from an extensive dataset comprising 32806 literature entries encompassing the optimization of renewable energy systems(RES)from 1990 to 2023 within the Web of Science database,this study reviews the decision-making optimization problems,models,and solution methods thereof throughout the renewable energy development and utilization chain(REDUC)process.This review also endeavors to structure and assess the contextual landscape of RES optimization modeling research.As evidenced by the literature review,optimization modeling effectively resolves decisionmaking predicaments spanning RE investment,construction,operation and maintenance,and scheduling.Predominantly,a hybrid model that combines prediction,optimization,simulation,and assessment methodologies emerges as the favored approach for optimizing RES-related decisions.The primary framework prevalent in extant research solutions entails the dissection and linearization of established models,in combination with hybrid analytical strategies and artificial intelligence algorithms.Noteworthy advancements within modeling encompass domains such as uncertainty,multienergy carrier considerations,and the refinement of spatiotemporal resolution.In the realm of algorithmic solutions for RES optimization models,a pronounced focus is anticipated on the convergence of analytical techniques with artificial intelligence-driven optimization.Furthermore,this study serves to facilitate a comprehensive understanding of research trajectories and existing gaps,expediting the identification of pertinent optimization models conducive to enhancing the efficiency of REDUC development endeavors.

Modeling and Optimization of Solar Collector Design for the Improvement of Solar-Air Source Heat Pump Building Heating System

To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an improvement in the system’s heat generation coefficient,overall efficiency,and stability.In this study,we focus on a residential building located in Lhasa as the target for heating purposes.Initially,we simulate and analyze a solar-air source heat pump combined heating system.Subsequently,while ensuring the system meets user requirements,we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system.Through this analysis,we determine the optimal installation angle and collector area to optimize system performance.