Two-stage Optimization Strategy Research Articles

Deployment and Robust Hybrid Beamforming for UAV MmWave Communications

We deploy an unmanned aerial vehicle (UAV) equipped with a large-scale uniform planar array (UPA) to serve multiple ground users in millimeter-wave band. Particularly, the practical UAV jitter is carefully considered, which may affect the beam gains and impact the communication quality. To provide a stable service, we first model the attitude change of the UPA caused by UAV jitter. Then, an optimization problem is formulated to maximize the minimum achievable rate of the users by optimizing the position and robust hybrid beamforming of the UAV. To solve the non-convex problem with highly coupled variables, a two-stage optimization strategy is developed. The first stage aims to decouple the beamforming from the original problem and design the UAV deployment under the assumption of an ideal beam pattern. The second stage aims to design robust hybrid beamforming with the obtained UAV position. Specifically, we first design analog beamforming for wide beams to cover the potential jitter angle range for each user via a chirp sequence-inspired method. Then, with equivalent channel estimation, we design digital beamforming by combining zero-forcing and water-filling power allocation algorithms. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmark algorithms.

Integrated carbon footprint with cutting parameters for production scheduling

A large part of the global carbon footprint comes from manufacturing. Apart from material distribution during product assembly, rational optimization of cutting parameters and production scheduling during processing are the main ways to realize low-carbon manufacturing. The existing researches mainly focus on cutting parameters or production scheduling when reducing manufacturing carbon emissions, but seldom considers the coupling relationship between the two. This limits the reduction of emissions to some extent. On this basis, a collaborative low-carbon optimization system suitable for the actual use of enterprises was developed. The optimization of production scheduling is a discrete problem, while cutting parameters are continuous and processing time brought by different cutting parameters will cause different effects on the scheduling scheme. Therefore, a two-stage optimization strategy is adopted to achieve collaborative optimization of cutting parameters and production scheduling. To achieve multi-objective optimization of the model and speed up convergence, the Crowding Niche mechanism and elite retention strategy are introduced into the selection operator to improve the genetic algorithm based on Pareto Optimality. In addition, different from the traditional that need to manually determine the machine tool and cutting tool according to the demand, a knowledge base module is integrated into the system. The knowledge base module is qualified for the knowledge acquisition and instance matching of the model in order to meet the processing capacity and constraints and enhance the ability of intelligent decision-making during production. Finally, the interactive application interface of the collaborative low-carbon optimization system has been implemented and the effectiveness of the system is verified by taking the manufacturing case of electromechanical enterprises as an example.

Two-stage optimization strategy of multi-objective Volt/Var coordination in electric distribution network considering renewable uncertainties

Renewable energy is connected to the distribution network in the form of distributed generation, and its output is random and fluctuating. The high penetration of renewable energy will weaken the security and economy of the electric distribution network. In view of the uncertainty of renewable power output, Monte Carlo simulation method and simultaneous backward reduction method are used to scenario generation and reduction of wind power and photovoltaic, so as to represent their randomness. On this basis, a two-stage optimization strategy for Volt/Var coordination in distribution network is proposed. In the first stage, a network reconfiguration model with the goal of minimizing the power loss of the system is established, and the particle swarm optimization algorithm is used to solve it. In the second stage, a Volt/Var optimization model with the goal of minimizing the node voltage offset and the system power loss is established, and the multi-objective model is solved by combining the network topology obtained in the first stage with the reactive power adjustment device and voltage regulating equipment such as switching capacitor, static var compensator and on-line tap changer. The distribution network’s Volt/Var is optimized using the non-dominated sorted genetic algorithm-II. The optimization results of the first stage are regarded as the conditions of the second stage. Lastly, the IEEE 33-node extension system is used to validate the effectiveness of the suggested strategy.

Two-stage real-time optimal electricity dispatch strategy for urban residential quarter with electric vehicles’ charging load

Two-stage real-time optimal electricity dispatch strategy for urban residential quarter with electric vehicles’ charging load

A Two-Stage Optimization Approach for Healthcare Facility Location- Allocation Problems With Service Delivering Based on Genetic Algorithm.

Objective: This study assesses a multi-period capacitated maximal-covering location-allocation model for healthcare services, taking interservice referral as well as equity access into account. Methods: A two-stage optimization strategy is used to formulate the model. In the first stage, facilities are located to maximize covered demand, and in the second stage, patients are allocated to capacitated facilities based on their radius of coverage over multiple time periods. The problem, which belongs to the NP-hard class of optimization problems, is solved using a linear mixed-integer programming (MILP) model. Results: A numerical example is presented to evaluate the efficiency of the proposed model. In addition, to identify near-optimal solutions for large instances, a hybrid genetic-sequential quadratic programming approach (GA-SQP) is developed. To examine the performance and efficiency of the GA-SQP, we employed several randomly generated test instances of various sizes and compared them to those obtained using the exact method. Conclusion: The proposed model has demonstrated an excellent ability in locating healthcare facilities and allocating health services while taking shortage and equity into account during each time period.

Design and optimization of the flexible poly-generation process for methanol and formic acid from CO2 hydrogenation under uncertain product prices

During the design of the carbon capture and hydrogen storage process, a static CO2 hydrogenation poly-generation process for methanol and formic acid synthesis was developed, thereby reducing recycling flow and feedstock losses in the single methanol synthesis process and increasing product diversity. Additionally, a novel flexible poly-generation process was proposed as the product prices fluctuated with the market environment throughout the plant's life cycle. For the static poly-generation process, a mixed integer nonlinear programming model was established, followed by the Mesh Adaptive Direct Search algorithm to determine the optimal parameter design. For flexible processes, with the unscented transform tool, a two-stage optimization strategy is proposed for obtaining optimal design and operating parameters that are computationally tractable. With an input CO2 flow rate of 200 kmol/h, the results show (1) the static process with an optimal Return On Investment of 13.05%, a fixed investment cost of $ 34.14 × 106, and an annual net profit of 4.46 × 106 $/year; and (2) the flexible process, in which the flexibility indices of the methanol and formic acid production units are 1.088 and 1.194, respectively, resulting the fixed investment cost increase to $ 39.19 × 106, an increase of 14.79%. Accordingly, the annual net profit increased by 8.97% to 4.86 × 106 $/year, while the Return On Investment decreased by 4.67%–12.44%. For investment decision-makers, the flexible poly-generation process can achieve higher profits and reduce potential market risks when they have adequate financing.

Two-stage topology optimization for a piezo-actuated fast tool servo based on the velocity field level set method

Nowadays, piezo-actuated fast tool servo (FTS) is very promising for assisting the diamond turning of microstructured surfaces. Topology optimization is effective for the structure and dimension integrated design of compliant mechanisms for the FTS. In this study, the velocity field level set (VFLS) method is employed to achieve the desired performance for the FTS. A two-stage optimization strategy is proposed to improve the convergence stability and modeling accuracy for the VFLS, which combines a gradual constraint relaxation in the first stage and the gradual weak material removal in the second stage. Furthermore, a comparative study with and without the two-stage strategy is conducted to optimize two examples with different objectives and multiple constraints. The optimized results with verification by finite element simulations well demonstrate the superiority of the proposed two-stage VFLS-based topology optimization.

MR-Selection: A Meta-Reinforcement Learning Approach for Zero-Shot Hyperspectral Band Selection

Band selection is an effective method to deal with the difficulties in image transmission, storage, and processing caused by redundant and noisy bands in hyperspectral images (HSIs). Existing band selection methods usually need to learn a specific model for each HSI dataset, which ignores the inherent correlation and common knowledge among different band selection tasks. Meanwhile, these methods lead to a huge waste of computation. In this article, a novel zero-shot band selection method, called MR-Selection, is proposed for HSI classification. It formalizes zero-shot band selection as a metalearning problem, where advantage actor–critic algorithm-based reinforcement learning (A2C-RL) is designed to extract the metaknowledge in the band selection tasks of various seen hyperspectral datasets through a shared agent. To learn a consistent representation among different tasks, a dynamic structure-aware graph convolutional network is constructed to build a shared agent in A2C-RL. In A2C-RL, the state is tailored in a feasible way and easy to adapt to various tasks. Meanwhile, the reward is defined according to an efficient evaluation network, which can evaluate each state effectively without any fine-tuning. Furthermore, a two-stage optimization strategy is designed to coordinate optimization directions of a shared agent from different tasks effectively. Once the shared agent is optimized, it can be directly applied to unseen HSI band selection tasks without any available samples. Experimental results demonstrate the effectiveness and efficiency of the MR-Selection on the band selection of unseen HSI datasets.

Two-stage Optimization for Active Distribution Systems Based on Operating Ranges of Soft Open Points and Energy Storage System

Due to the lack of flexible interconnection devices, power imbalances between networks cannot be relieved effectively. Meanwhile, increasing the penetration of distributed generators exacerbates the temporal power imbalances caused by large peak-valley load differences. To improve the operational economy lowered by spatiotemporal power imbalances, this paper proposes a two-stage optimization strategy for active distribution networks (ADNs) interconnected by soft open points (SOPs). The SOPs and energy storage system (ESS) are adopted to transfer power spatially and temporally, respectively. In the day-ahead scheduling stage, massive stochastic scenarios against the uncertainty of wind turbine output are generated first. To improve computational efficiency in massive stochastic scenarios, an equivalent model between networks considering sensitivities of node power to node voltage and branch current is established. The introduction of sensitivities prevents violations of voltage and current. Then, the operating ranges (ORs) of the active power of SOPs and the state of charge (SOC) of ESS are obtained from models between networks and within the networks, respectively. In the intraday corrective control stage, based on day-ahead ORs, a receding-horizon model that minimizes the purchase cost of electricity and voltage deviations is established hour by hour. Case studies on two modified ADNs show that the proposed strategy achieves spatiotemporal power balance with lower cost compared with traditional strategies.

Optimization for IRS-Assisted MIMO-OFDM SWIPT System With Nonlinear EH Model

Simultaneous wireless information and power transfer (SWIPT) has emerged as an appealing solution to prolonging the lifetime of low-power Internet of Things (IoT) networks. Meanwhile, an intelligent reflecting surface (IRS) can reconstruct a favorable wireless propagation environment for IoT terminals to achieve high spectrum and energy efficiencies. To take full advantage of these two technologies, this article studies the optimization of an IRS-assisted multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system with nonlinear energy harvesting (EH) model. In particular, we aim to maximize the achievable data rate by jointly designing the transmit precoding matrices, the IRS matrix, as well as the power splitting (PS) ratio subject to the transmit power and harvested power constraints. Since the formulated problem is highly nonconvex, we develop an alternating optimization (AO)-based algorithm to find a high-quality suboptimal solution. Moreover, we further design a heuristic algorithm based on a two-stage optimization strategy to reduce the computational complexity. Simulation results verify that the proposed AO-based algorithm can significantly improve the achievable data rate compared to conventional benchmarks, and the proposed heuristic low-complexity algorithm can achieve comparable performance to the AO-based algorithm.

Economic-environmental scheduling of microgrid considering V2G-enabled electric vehicles integration

As an important part of the Energy Internet, microgrid (MG) scheduling problem has always attracted great attention, especially under the background that large-scale penetration of electric vehicles (EVs) into buildings poses both opportunity and challenge to the MG energy management. This research presents a two-stage optimization strategy, for improving the economic and environment-friendly operation of MG considering EVs integration. In Stage 1, model of EVs under coordinate charging/discharging stimulated by time-of-use incentive mechanism is established, and the peak–valley hours are dynamically divided to obtain a load curve with minimized peak-to-valley difference (PVD). In Stage 2, aiming for the best tradeoff between the generation cost and pollutant emission, daily optimal scheduling of the MG generators is efficiently calculated according to the varying power demand. For enhancing the convergence, an advanced genetic algorithm with elite preservation strategy is employed. Two cases from a residential MG system is exploited to validate the proposed method, and the results show that firstly the power supply pressure could be obviously relieved owing to the load shifting effect of the coordinated vehicle-to-grid (V2G) service reflected by the decreased PVD (Case 1: from 76.16 to 54.35 kW, Case 2: from 76.25 to 46.19 kW); simultaneously, via applicable power planning of the MG components, cost saving and emission reduction can be both achieved (Case 1: ¥190.12 under V2G management compared to ¥281.66 under basic load, Case 2: ¥177.28 compared to ¥350.24), ensuring the feasibility of the control strategy, which promotes the economical-environmental and reliable operation of the MG system.

A Gramian angular field-based data-driven approach for multiregion and multisource renewable scenario generation

Scenario generation is a pivotal method for providing system operators with a reasonable quantity of power scenarios that are capable of reflecting uncertainties and spatiotemporal processes to make exact and effective decisions for power systems. Aiming at improving the forecasting performance of renewable generation and capturing uncertainty as well as dependency over renewable site groups in different regions, this paper proposes a data-driven approach for parallel scenario generation. To capture the complex spatiotemporal dynamics of renewable energy sources (RESs), the proposed approach utilizes Gramian angular field (GAF) to process time sequences and constructs style-based super-resolution models that correspond with the idea of multi-model ensembles. Thereafter, a two-stage stochastic optimization strategy is adopted to accomplish scenario forecasting using point forecasts and historical error information as input. Based on two real-world datasets from the National Renewable Energy Laboratory (NREL) and the Belgian transmission operator ELIA, the effectiveness of the proposed approach is verified by methods including statistical analysis, spatiotemporal correlations, power system scheduling, and out-of-sample evaluations. Compared with three advanced benchmarks, the proposed approach has superior forecasting performance and spatiotemporal dynamic capture capability. At a 24-h lead time, the proposed model achieves continuous ranked probability scores (CRPSs) of 4–14% over the other models with consistent performance during the economic dispatching of actual power system operations.

A two-stage rolling optimization strategy for park-level integrated energy system considering multi-energy flexibility

Due to the advantages, such as being environment-friendly, the penetration of renewable energy has increased rapidly. However, the uncertainty and randomness of renewable energy source output severely impact economic dispatch, especially for those park-level integrated energy systems. To improve the economy of system operation and the accuracy of scheduling results, this paper presents a two-stage rolling dispatch strategy, in which a refined model of power flow, natural gas pipelines, and heat flow are established. The multi-energy flow model is linearized by the piecewise linearization method and second-order cone relaxation method. Moreover, a virtual energy storage model considering the thermal inertia in district heat systems and building enclosures is constructed. Further, to quantify the capacity of adjustable resources in park-level integrated energy systems, a multi-energy flexible source model is set up and integrated. Ultimately, to even out the prediction errors, a two-stage rolling dispatch is carried out, which corrects the results of day-ahead schedule plans. Based on the aforementioned works, case results demonstrate the effectiveness of the presented approach, both in renewable energy accommodation rate (with a promotion of 13.3%) and system operation economy (with a reduction of 14.55% in operation costs). It is recognized that the constructed models play important roles in improving the system flexibility and the presented operation strategy can effectively enhance the accuracy of the dispatch plans in real-time operation.

Multi-dimensions analysis of solar hybrid CCHP systems with redundant design

The combination of photovoltaic cells and cooling, heating, and power (PV–CCHP) systems has the characteristics of high energy efficiency, low pollutant emissions, and good economic benefits. PV-CCHP systems contain multiple types of equipment, and every kind of equipment may malfunction during operation. Redundant design can effectively improve the reliability and availability of systems. This paper compares and analyzes the redundantly designed PV-CCHP (RPV-CCHP) system, the multi-device parallel operation without redundancy design PV-CCHP (MPV-CCHP) system, and the single device operation PV-CCHP (SPV–CCHP) system performance in economy, environment, and energy. A two-stage optimization strategy is proposed on the traditional operation mode, and an office building in Beijing takes as the case study. The result shows that the integrated performance of the RPV-CCHP system is the best, followed by the MPV-CCHP system and, finally, the SPV-CCHP system. The primary energy saving ratio, carbon dioxide emission reduction ratio, and annual total cost saving ratio of the RPV-CCHP system are significantly superior to the MPV-CCHP and SPV-CCHP. Furthermore, a sensitivity analysis of the three systems was carried out to show how the integrated performance will change when electricity and natural gas prices change.

An optimized kinetic model for H[formula omitted]/CO combustion in CO[formula omitted] diluent at elevated pressures

Oxy-fuel combustion is a promising strategy for carbon capture. Existing syngas combustion models overpredicted recently measured ignition delay time data in high-level CO2 diluent and elevated pressures (40, 80, 100 and 200 atm). A trial model was constructed by updating the Kéromnès mechanism (Combust. Flame, 2013, 160, 995–1011) with some recent reaction rate studies and the addition of O+OH+M = HO2+M. Among all 33 reactions, 23 influential reactions were selected for optimization based on sensitivity results. 787 syngas shock-tube ignition delay time data and 1900 laminar flame speed measurements were considered. The optimized model was computed from the trial model using an efficient two-stage optimization strategy. Temperature-dependent initial uncertainty was implemented and all Arrhenius parameters were optimization simultaneously. The optimized model significantly improves the prediction accuracy for the ignition delay time targets, especially those in CO2 diluent, and produces comparable prediction performance for the laminar flame speed targets to the other models. Most of the reaction rate adjustments were well within the corresponding uncertainty (≤30%). Five reactions with relatively large rate adjustment were discussed. Sensitivity and reaction path analysis were conducted to examine the kinetic difference between the optimized model and the Kéromnès model at relevant conditions. Chemical and physical effects of CO2 as a diluent were investigated.

A two-stage benefit optimization and multi-participant benefit-sharing strategy for hybrid renewable energy systems in rural areas under carbon trading

Hybrid renewable energy systems (HRESs) are a promising tool for reducing emissions in rural areas. Suitable transaction mechanisms and impartial benefit-sharing strategies are the basis for sustainable HRESs. An HRES that focuses on biomass energy comprising three participants, is proposed and a two-stage benefit optimization and sharing model that is based on a reward-punishment-tiered carbon trading (CT) policy is established in this study. To ensure that maximum benefits are gained from HRESs, impartial benefit-sharing strategies such as minimum cost-retaining savings, Nash negotiation, and an improved Shapley value are selected. The results of the first stage show that the benefits of an alliance between the three participants increased by $22621.08 and $11497.49, respectively, under CT. The results of the second stage show that a suitable benefit-sharing strategy can be selected according to the needs of different cooperation models, with the contribution of all participants fully reflected under the improved Shapley value method. The use of a two-stage optimization model improved the willingness of participants to cooperate, based on the premise of maximum benefits. Additionally, new ideas for promoting the development of rural energy can further be provided.

Model Updating Using Bridge Influence Lines Based on an Adaptive Metamodel Global Optimization Method

Model updating seeks to update a finite-element model to reduce discrepancies between predicted and measured data. Metamodel-based model updating using bridge influence lines is a very promising method. However, the accuracy of traditional metamodel methods is not high due to the traditional metamodels being fitted in the entire parameter space using limited sample data. To address this problem, a model updating procedure based on the adaptive metamodel global optimization method is proposed. First, a systematic model updating theory using influence lines is developed, including three different objective functions and a selection of model updating parameters. Then, the adaptive metamodel-based global optimization method is proposed. The adaptive metamodel is iteratively fitted using the automatically added sample data by the mode-pursuing sampling method in the process of optimization. To improve the efficiency of finding the global optimal solution, a two-stage optimization strategy is presented. Finally, the proposed procedure is applied to numerical and practical examples of a long-span suspension bridge. In the numerical example, the global optimal solution is found successfully compared with the traditional metamodels. Excellent agreements are observed between the updated influence lines and measured influence lines in both numerical and practical examples. Based on the proposed theory, the accuracy and efficiency of model updating can well meet engineering requirements.

HLocalExp-CM: confidence map by hierarchical local expansion moves for accurate stereo matching

We present a stereo matching approach referred to as HLocalExp-CM by exploiting the hierarchical local contextual information and a confidence map based on a new grid structure. The proposed approach preserves fine depth edges and extracts accurate disparities in weak texture, textureless, and repeated texture regions. The proposed approach adopts a two-stage optimization strategy. In the framework of first stage, a multiresolution cost aggregation is minimized to reduce the search space of the disparity plane of each pixel. The second stage iteratively optimizes the confidence map and a global energy function to progressively improve the disparity accuracy for each pixel. The confidence map is estimated through classifying the pixels into distinctive and ambiguous ones by computing the decreasing rate of the multiresolution cost aggregation and then performs a spatial propagation and plane refinement for the update of the disparity of each pixel, thereby successfully eliminating the ambiguity of nondistinctive pixels. The global energy function based on a pairwise Markov random field uses cross-scale cost aggregation for taking advantage of context information of objects in different scenarios on local grid regions, which is different from the deep learning technique uses convolution layers extracting the context information. The proposed approach is evaluated on Middlebury benchmark V3, and is ranked first based on “bad 2.0 all metric,” a widely used criterion for the evaluation of stereo images, while the eighth place on “bad 2.0 nonocc metric” (recorded on July 24, 2021).

A Precisely‐Controlled Multichannel Phononic Crystal Resonant Cavity (Adv. Theory Simul. 12/2021)

A Precisely-Controlled Multichannel Phononic Crystal Resonant Cavity A novel multichannel PnC based energy-harvesting conceptual device with high localization effect and high frequency resolution is reported in article number 2100250 by Luo and co-workers. The configurations of PnC cavities are designed by the proposed two-stage topology optimization strategy using the material-field series-expansion model. The cover depicts a resonant cavity device with five-parallel-channels where each channel can accurately capture the energy of incident wave at the respective frequency.

Optimal Designs of Phononic Crystal Microstructures Considering Point and Line Defects

In this paper, a two-stage optimization strategy for designing defective unit cells of phononic crystal (PnC) to explore the localization and waveguide states for target frequencies is proposed. In the optimization model, the PnC microstructures are parametrically described by a series of hyperelliptic curves, and the optimal designs can be obtained by systematically changing the designable parameters of hyperellipse. The optimization contains two individual processes. We obtain the configurations of a perfect unit cell for different orders of band gap maximization. Subsequently, by taking advantage of the supercell technique, the defective unit cells are designed based on the unit cell configuration for different orders of band gap maximization. The finite element models show the localization and waveguide phenomenon for target frequencies and validate the effectiveness of the optimal designs numerically.