Multi-objective Collaborative Optimization Research Articles

Multi-Objective Collaborative Optimization of Stiffness and Damping Performance of Acoustic Black Hole Plate

This paper uses embedded Acoustic Black Hole (ABH) on the wallboard for structure lightening and improving the performance in broadband vibration reduction. The distribution and size of multiple ABHs affect the generalized static stiffness (GSS) and the upper limit of the structure's maximum kinetic energy (ULMKE) in practical applications, which express the ability to resist static load and vibration reduction performance, respectively. In this research, the structure's GSS and ULMKE are considered optimization objectives due to the trade-off between them. The method based on the Pareto optimal solution is adopted to optimize the layout and the radii of the ABHs. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is used as the multi-objective optimization algorithm. Two 2D-ABHs embedded in a plate are optimized as numerical examples. The optimization generated several groups of Pareto optimal solutions, providing more feasible solutions for different applications faster. The verification further proved the necessity and the effectiveness of multi-objective optimization.

Multivariate multi-objective collaborative optimization of pumped thermal-liquid air energy storage

Pumped thermal-liquid air energy storage (PTLAES) is a novel energy storage system with high efficiency and energy density that eliminates large volumes of cold storage. In this study, three different configurations of PTLAES systems with direct and indirect thermal energy storage were proposed. The “adaptive segmentation-based temperature transfer matrix” method was developed to capture the heat transfer characteristics of multi-stream plate-fin heat exchangers (MPFHEs), which is the key component of the system. To determine the upper limit of the techno-economic performance of the system, single/multi-objective optimization was performed with up to 35 decision variables. Additionally, the “Pareto efficiency” concept was defined to quantify the goodness of a point compared to the Pareto front. The results showed that the irreversible heat transfer and pressure drop in MPFHEs contribute 20.6 %–44.3 % of the total exergy destruction. Therefore, the design of MPFHEs significantly impacts the system performance. Multivariate multi-objective collaborative optimization can significantly improve the optimization results. PTLAES with closed loop indirect thermal energy storage was determined to have the best overall performance, achieving round-trip efficiency of 63.3–70.1 %, levelized cost of storage (LCOS) of 0.162–0.181 $/kWh, and energy density of 122–161 kWh/m3. The optimized PTLAES system can achieve lower LCOS than lithium-ion batteries when the discharge duration exceeds 3.5 h. Furthermore, it was shown that key criteria including round-trip efficiency, LCOS, and energy density can be traded off over a broad design space of different system configurations, parameter combinations, and material choices. This research demonstrated that PTLAES is a promising choice for long-duration energy storage and provided guidance for the design and techno-economic optimization of PTLAES systems.

A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering

To enhance the excavator performance considering the digging force and boom lift force under typical working conditions, this paper aims to solve the complex multiobjective optimization of the excavator by proposing a new knowledge-based method. The digging force at multiple key points is utilized to characterize the excavator’s performance during the working process. Then, a new optimization model is developed to address the imbalanced optimization quality among subobjectives obtained from the ordinary linear weighted model. The new model incorporates the loss degree relative to the optimal solution of each subobjective, aiming to achieve a more balanced optimization. Knowledge engineering is integrated into the optimization process to improve the optimization quality, utilizing a knowledge base incorporating seven different types of knowledge to store and reuse the information related to optimization. Furthermore, a knowledge-based multiobjective algorithm is proposed to perform the knowledge-guided optimization. Experimental results demonstrate that the proposed knowledge-based method outperforms existing methods, resulting in an average increase of 15.1% in subobjective values.

Multi-Objective Co-Operative Game-Based Optimization for Park-Level Integrated Energy System Based on Exergy-Economic Analysis

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme.

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

Most of the conventional multi-objective coordination methods for active distribution network operation are designed based on the principle of weighted random algorithm, which has limited application scope and cannot simplify the algorithm and reduce the complexity of the solution process, resulting in poor stability of active distribution network lines and potential faults. Based on this, this algorithm is introduced to carry out multi-objective collaborative optimization for intelligent distribution network operation. Firstly, the node types for the intelligent distribution network are defined and the power flow of the distribution network is analyzed. Secondly, a multi-objective configuration model for the lower level of the operation is constructed to maximize the distribution network investment and operation benefit index. A multi-objective collaborative optimization algorithm based on an enhanced particle swarm optimization algorithm is designed to reduce the operation cost of the distribution network and enhance the security and stability of the distribution network system during operation. The simulation test results show that after the application of the new method, the intelligent distribution network voltage stability index Vm is always less than 1, and the line stability has been significantly improved.

Peer-to-peer energy trading in a community based on deep reinforcement learning

With the massive access to distributed energy resources, an increasing number of users have transformed into prosumers with the functions of producing, storing, and consuming electric energy. Peer-to-peer (P2P) energy trading, as a new way to allow direct energy transactions between prosumers, is becoming increasingly widespread. How to determine the trading strategy of prosumers participating in P2P energy trading while the strategy can satisfy multiple optimization objectives simultaneously is a crucial problem to be solved. To this end, this paper introduces the demand response mechanism and applies the dissatisfaction function to represent the electricity consumption of prosumers. The mid-market rate price is adopted to attract more prosumers to participate in P2P energy trading. The P2P energy trading process among multiple prosumers in the community is constructed as a Markov decision process. We design the method of deep reinforcement learning (DRL) to solve the optimal trading policy of prosumers. DRL, by engaging in continual interactions with the environment, autonomously learns the optimal strategies. Additionally, the deep deterministic policy gradient algorithm is well-suited for handling the continuous and intricate decision problems that arise in the P2P energy trading market. Through the judicious construction of a reinforcement learning environment, this paper achieves multi-objective collaborative optimization. Simulation results show that our proposed algorithm and model reduce costs by 16.5%, compared to the transaction between prosumers and grid, and can effectively decrease the dependence of prosumers on the main grid.

A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction

To address the limitation of existing excavator optimization methods, which primarily focus on the force performance while neglecting energy consumption and fail to realize environmentally friendly and low-carbon designs, this paper proposes a new multi-objective collaborative optimization method for an excavator to reduce energy consumption during the working process while maintaining optimal performance. By formulating a mathematical model that quantifies the energy consumption during the working process, this paper optimizes the working conditions by analyzing the energy consumption characteristics under typical working conditions. To overcome the limitation of existing linear weighting methods in multi-objective optimization, such as imbalanced optimization quality among sub-objectives, this paper proposes a new modeling approach based on the loss degree of sub-objectives. A multi-objective collaborative optimization model for the excavator with reduced energy consumption is established, and a corresponding multi-objective collaborative optimization algorithm is developed and applied to achieve optimal solutions for sub-objectives. The optimization results demonstrate that applying the new multi-objective collaborative optimization method to the excavator achieves better optimization quality than traditional methods. It also provides a more balanced improvement in the optimization values of each sub-objective, resulting in a significant reduction in the energy consumption of the excavator during operation.

Low-carbon Economic Scheduling of Combined Cooling, Heating and Power Microgrid Considering Demand Response and Shared Energy Storage

In order to further promote the new energy consumption of combined cooling, heating, and power (CCHP) microgrid, reduce carbon emissions, and optimize the total operating cost of the microgrid, a low-carbon optimal scheduling model of CCHP microgrid was proposed. It considered demand response (DR) and shared energy storage (SES). Firstly, the concept of SES and its operation mode are applied to the microgrid. Secondly, in order to take advantage of the flexible nature of multiple loads, an integrated demand response (IDR) model with a ladder subsidy mechanism was established. Then, the optimal scheduling model introduces a reward and punishment ladder carbon trading mechanism (RPL-CTM). Finally, taking energy purchase cost, load reduction subsidy cost, interaction cost with energy storage station, carbon trading cost, and wind abandoning penalty cost as optimization objectives, a multi-energy coupling, and multi-objective collaborative optimization scheduling model was established. Numerical analysis indicates that the model can efficiently lower the microgrid’s operating cost and encourage the use of clean energy and carbon emission reduction.

Multiobjective collaborative optimization of novel carbothermal reduction process of stainless steel dust and laterite nickel ore

By using the central composite design based on the response surface method, the multiobjective collaborative optimization of Fe, Cr and Ni metal recovery from stainless steel dust and laterite nickel ore was achieved through carbothermal reduction, and a high metal recovery rate and high-grade Fe-Cr-Ni-C alloy particles were obtained. The addition of laterite nickel ore and reduction temperature exert significant effects on the metal recovery, and a significant interaction is observed among the recoveries of the metals Fe, Cr and Ni in the reduction products. The optimal process parameters obtained through the optimization of the model are listed as follows: the amount of added laterite nickel ore is 5.47%, the reduction temperature is 1428.02 °C, the reduction time is 23.10 min, and the carbon oxygen ratio (FC/O) is 0.85. The predicted results for Fe, Cr and Ni recovery using the model are 93.15%, 91.63% and 92.70%, respectively.

Dynamic power distribution strategy using multi-objective collaborative optimization for hybrid energy storage systems

Dynamic power distribution strategy using multi-objective collaborative optimization for hybrid energy storage systems

Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan

The Train Operation Plan (TOP) of urban rail transit (URT) is a comprehensive plan for the operation of trains, the use of facilities and equipment, and the organization of other operational tasks. The TOP should not only be formulated in terms of time-varying passenger flow periods, but it should also be arranged to consider the substitutability of trains between multiple routes combined with the passenger choice. Based on the principle of “operating by the flow” and the requirement for precise allocation of transport capacity for multiple routes, this article constructs a multiobjective nonlinear integer programming model by taking the minimized generalized travel cost of passengers, total running mileage of trains, fluctuation of trains for each route (as optimization targets), and the combination of requirements of both headways and fully loaded rates as constraints. A multiobjective genetic-based algorithm is designed to simultaneously optimize the TOP and the two-way train stopping time in each period. Finally, the proposed model and algorithm are validated with the real data from the Guangzhou Metro Line 2. The results show that the Pareto optimal TOP and dynamic train stopping time are significantly improved compared to the original values.

Multi-energy collaborative optimization of park integrated energy system considering carbon emission and demand response

Park integrated energy system (PIES) has become a key link of efficient energy conservation and carbon emission reduction. This paper proposes a multi-energy collaborative optimization method of PIES considering carbon emission and demand response (DR). Firstly, the typical structure of the electricity-thermal-gas cogeneration PIES including combined heat and power (CHP), heat pump (HP) and energy storage (ES) is built. Secondly, a ladder carbon trading model for PIES considering carbon quota and actual carbon emission is established. On this basis, a multi-objective collaborative optimization model considering the operation cost, energy utilization efficiency and consumption rate of renewable energy is established, and the multi-objective problem is solved by a multi-objective particle swarm optimization algorithm (MOPSO). Then, taking a typical PIES as an example, the operation conditions of the system before and after DR are analyzed, and the results show that the established model can realize the economic and low-carbon operation and improve renewable energy consumption rate. The numerical results show that when participating in DR, the operation cost of PIES is reduced by 10.18% and the carbon emission is reduced by 3.41%. Finally, the impact of carbon trading price on the operation cost, energy utilization efficiency and consumption rate of renewable energy is analyzed.

Multi-Objective Collaborative Optimization of Distillation Column Group Based on System Identification

In this paper, a multi-objective collaborative optimization (MOCO) strategy is proposed for making decisions on a distillation column group. Firstly, based on data preprocessing, the operating modes of the tower group are determined by use of the fuzzy C-means clustering method. Secondly, based on the proposed concept of a collaborative variable, the discrete state-space model of the main towers are constructed by the subspace identification method. Then, a MOCO optimization model is designed for the ethylene plant. Finally, NSGA-III is used to solve the optimization problem. An analysis of a Pareto-optimal frontier and population is carried out. To illustrate the superiority of the proposed strategy, the results are compared with historical data and the appealing operation area is finally obtained.

A novel collaborative optimization assembly process method for multi-performance of aeroengine rotors

In the assembly process of aeroengine, the aeroengines have been rejected at pass-off test for high vibration of high-pressure rotor under the condition of meeting the geometric accuracy. Repeated disassembly and trial assembly will lead to interface damage, which will affect the safety and reliability of the engine. Therefore, a multi-objective collaborative optimization process method for geometric accuracy and dynamic performance is put forward based on the practical engineering problems. In the proposed method, the primal problem firstly reveals the transfer mechanism of coaxiality and unbalance of rotor system, and the prediction model of the coaxiality and unbalance of the rotor system was established. Then, combined with the high-precision reduced-order model of aeroengine high-pressure combined rotor, the system vibration responses under different assembly states are obtained. Finally, a multi-objective collaborative optimization assembly process method considering coaxiality, unbalance and vibration response is obtained. The results show that the collaborative optimization process method considering assembly accuracy and dynamic performance is the optimal assembly strategy, which can reduce the vibration amplitude of the key nodes of the rotor system while ensuring high assembly accuracy and achieve the low-level vibration of the rotor system.

Experimental investigation on effects of fuel injection and intake parameters on combustion and performance of a turbocharged diesel engine at different altitudes

Matching characteristics between fuel injection and intake control parameters significantly affect overall performances in diesel engine at variable altitudes. Prior to optimizing the control parameters of diesel engines operating at high altitudes areas, there is a necessity to identify the influence law of fuel injection and intake parameters on engine performance. This study focused on the effects of main injection timing (MIT), fuel injection pressure (FIP), EGR rate, and variable nozzle turbocharger (VNT) opening on the combustion, performances and NOx and smoke emissions in a turbocharged diesel engine at different altitudes (2000, 1,000, and 0 m). Strategies for optimization of engine performance and the coupling relationship between these parameters were analyzed. The results showed that as the altitude increased from 0 m to 2000 m, the engine torque dropped by 2.9%, the BSFC increased by 2.6%, the NOx emissions reduced by 11.8%, and the opacity smoke increased by 26.2%. The effects of MIT, FIP, EGR rate and VNT opening on engine performances were more significant at high altitudes. As the MIT was advanced at 2000 m altitude, the engine torque increased by 5.6%, the BSFC reduced by 5.9%, the opacity smoke decreased by 55%, while the NOx emissions increased by 54%%. Advance the injection timing properly can overcome the altitude effect on engine power. With the FIP increased from 105 to 130 MPa at 2000 m, the engine torque reduced by 1.7%, the BSFC increased by 1.6%, the opacity smoke dropped by 44.5%, and the NOx emissions increased by 17.5%. When the EGR rate increases by 5%, the NOx emission reduces by 16.4%,17.9 and 21.9 respectively at 0, 1,000 and 2000 m altitude. The EGR rate should be properly reduced to obtain the recovery of engine power at plateau. As the VNT vane opening decreased from 40% to 20%, the NOx emission increase by 9.4% and 9.9% at 0 m and 1,000 m respectively, and it reduces by 3.9% at 2000 m, while the opacity smoke reduces by 38.9% at different altitudes. The optimization of VNT vane opening requires a balance between combustion performance and pumping losses. The multi-objective collaborative optimization technique should be applied to optimize these parameters to help improve engine efficiency and emissions at high altitudes.

Parameters optimization of electro-hydraulic power steering system based on multi-objective collaborative method

Electro-hydraulic power steering (EHPS) systems are widely used in commercial vehicles due to their adjustable power assist and energy-saving advantages. In this paper, a dynamic model of the EHPS system is developed, and quantitative expressions for three evaluation indexes, steering road feel, steering sensibility and steering energy loss, are derived for the first time. A multi-objective collaborative optimization model of the EHPS system is then established, which consists of one total system and three parallel subsystems, based on collaborative optimization theory. Considering the coupled variables of each subsystem, the total system is optimized by a multi-objective algorithm, while the subsystems are optimized by a single-objective algorithm. The optimization results demonstrate that the average frequency domain energy of the steering road feel is increased by 69.1%, the average frequency domain energy of steering sensitivity is reduced by 19.2%, and steering energy consumption is reduced by 10.8% compared to the initial value. The non-dominated sorting genetic algorithm-II (NSGA-II) shows superior comprehensive performance compared to the other two multi-objective algorithms, and the optimization performance can be further improved by setting appropriate algorithm parameters.

Cooperative Multi-Objective Control of Heterogeneous Vehicle Platoons on Highway with Varying Slopes

Stability, vehicle safety, energy saving, and passenger comfort are the major objectives of vehicle platooning control. These objectives are coupled, interrelated, and even conflicting, so integrated optimization of multiple objectives is quite challenging. Particularly for heterogeneous platoons, the difficulties are intensified for the differences in vehicle dynamics. In this paper, the concept of symmetry is utilized in the platooning control, that is, the design method of each vehicle’s controller is the same. For each controller, it is to solve the optimal solution of multi-objective collaborative optimization. The concept of asymmetry is meanwhile embodied in the parameter setting of each controller, for the vehicle heterogeneity. The contents of this study are as follows. First, a mathematical model is established, in which the differences in vehicle dynamic characteristics of heterogeneous platoon, road slope, and aerodynamics are all taken into account. Then, based on distributed nonlinear model predictive control (DNMPC) method, multi-objective control strategies are proposed for the leader and followers, cooperatively. Furthermore, a weight coefficient optimization method is presented, to further improve the platoon’s multi-objective synthesis performance. Finally, comparative experiments are carried out. Results demonstrate that, compared with the classic cruise control method of vehicle platoons, the proposed approach can reduce energy consumption by more than 5% and improve tracking performance on the premise of passenger comfort. Real-road experiments verify that the proposed control system can function effectively and satisfy the computational requirements in real applications.

A Review of the Intelligent Optimization and Decision in Plastic Forming.

The plastic forming process involves many influencing factors and has some inevitable disturbance factors, rendering the multi-objective collaborative optimization difficult. With the rapid development of big data and artificial intelligence (AI) technology, intelligent process optimization has become one of the critical technologies for plastic forming. This paper elaborated on the research progress on the intelligent optimization of plastic forming and the data-driven process planning and decision-making system in plastic forming process optimization. The development trend in intelligent optimization of the plastic forming process was researched. This review showed that the intelligent optimization algorithm has great potential in controlling forming quality, microstructure, and performance in plastic forming. It is a general trend to develop an intelligent optimization model of the plastic forming process with high integration, versatility, and high performance. Future research will take the data-driven expert system and digital twin system as the carrier, integrate the optimization algorithm and model, and realize the multi-scale, high-precision, high-efficiency, and real-time optimization of the plastic forming process.

Active energy interaction between multiple distributed energy systems with flexible decentralization operation: Energy bank

• A novel platform of energy bank is designed for multiple DESs active interaction. • An elastic and scalable DESs coordination framework is developed. • The energy storage and energy interaction are integrated to form device sharing. • The emission and cost decrease by 5% and 1% through redistributing 3% demand. • The storage resources of DESs improved by 784% and saved 14% battery degeneration. This work proposed a DES aggregator of energy bank (EB) to unite distributed energy resources in the local energy community, which is an energy exchange and storage platform, and profits from service fees. In the business between DESs and EB, DESs actively report their energy interaction willingnesses to EB according to their system structures and operating environments. Then, the EB unified coordinates energy demander and supplier. The DES only focuses on its production, and the EB does not interfere with DES operation. Thereby, the privacy and autonomy of DES are guaranteed, and the EB can continuously aggregate DES in an elastic and scalable manner. Finally, a multi-objective collaborative optimization is formulated to improve the economic and environmental performances of DESs. Different application scenarios of EB are compared in the case study to verify the proposed EB. The collaborative optimization between DESs and EB has the best performance, and using EB to aggregate independent optimized DESs also significantly improves performance, in which the interests of all DESs are protected. Moreover, the charge and discharge complementarity of shared energy storage in EB increases the dispatchable storage capacity and power of DES by up to 295% and 784%, respectively, and prolongs the battery life by up to 2.74 years.

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Global issues such as global warming, rising sea level, melting Arctic and Antarctic glaciers, deterioration of the natural environment, and depletion of fossil energy have attracted increasing attention. As the concept of environmental protection has become increasingly popular, the concept of new energy automobiles has been proposed to keep the heat up. Especially in recent years, with the advantages of energy conservation, environmental protection, and low noise, new energy automobiles have changed from concept to industry and have been generally accepted and recognized by consumers in the consumer market. The increasingly stringent energy crisis and emission regulations have put forward more stringent requirements for modern automobiles. The thermal management of the new generation of intelligent automobiles is not only limited to simply solving the problem of engine heat dissipation but also involves reliability, power, economy, emissions, and comfort. It is an important vehicle development technology with many performances such as performance. The integrated thermal management of new energy vehicles includes engine cooling, oil cooling, air conditioning refrigeration, HVAC heating, supercharged intercooling, low cycle thermal fatigue, and thermal damage. For new energy vehicles such as hybrid and pure electric vehicles, it also includes motor cooling, motor controller cooling, and power battery temperature control. For the purpose of vehicle thermal management optimization design, this paper innovatively proposes IVTM technical solutions, relying on multidimensional numerical calculation coupling and multiobjective collaborative optimization control to integrate system design, scheme evaluation, performance analysis, dynamic control, and collaborative optimization. Through the integrated thermal management collaborative control strategy based on the full operating conditions of the new energy vehicle, the comprehensive improvement of multiple evaluation indicators such as system design performance, thermal management, and control performance and economy is achieved.