Multi-objective Optimization Control Strategy Research Articles

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption

A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption

A multi-objective optimization method for enclosed-space lighting design based on MOPSO

With the rapid development of underground spaces and manned cabins, the design of enclose-space has received attention. Humans working in enclosed-space for a long period of time are prone to fatigue, which in serious cases can lead to human errors and major accidents. Therefore, the optimized design of enclosed-space is of great research significance to enhance the efficiency and comfort of workers. The lighting environment is one of the important objectives of the optimized design of enclosed-space, and the current research on lighting design often focuses on single-objective reduction of energy consumption or improvement of visual effects, and lacks attention to non-visual effects. We propose multi-objective optimal control strategy for enclosed-space lighting systems, taking the visual effects, non-visual effects, and energy efficiency of lighting as the optimization objectives and constraints, combined with multi-objective particle swarm optimization algorithm. Firstly, we obtain a set of optimal Pareto front solution for the dimming level by improving the algorithm. Secondly, to select appropriate lighting strategies according to different working scenarios, we introduce the fuzzy integrated evaluation method of experts to find the best compromise. Finally, we used a manned simulated enclosed cabin based on DIALux evo for a case study to compare the optimization results with baseline solutions. The results show that when we employed the improved dimming strategy, the energy efficiency and visual comfort were significantly improved compared with the baseline lighting design. The proposed method can satisfy human non-visual effects requirements, providing a research reference for future enclosed-space lighting optimization.

Multi-Objective Optimization and Stable Operation Control Strategy for Fractional Frequency Transmission System Based on Large Signal Stability Analysis

Multi-Objective Optimization and Stable Operation Control Strategy for Fractional Frequency Transmission System Based on Large Signal Stability Analysis

Pontryagin’s Minimum Principle-Based Power Management of Plug-In Hybrid Electric Vehicles to Enhance the Battery Durability and Thermal Safety

Temperature affects the battery aging and stability, bringing about significant effects on the electric vehicle’s economy, reliability, and safety. Therefore, it is necessary to consider the battery thermal condition when designing the power management scheme. In this paper, an optimal power management strategy for plug-in hybrid electric vehicles is proposed based on Pontryagin’s Minimum Principle (PMP). Two situations, normal operation and cooling system failure, are considered. The impacts of battery aging and temperature rise are characterized based on durability analysis model and thermal model. The temperature variation rate of the battery is incorporated into the Hamiltonian function, and the correlation between the cost optimization and temperature rise is disclosed by shooting method. Then the optimal co-state variables in different cases are determined. Based on above efforts, the PMP-based multi-objective optimal control strategy is proposed and evaluated based on the temperature statistical data in Shenyang, China. The results show that the proposed method can improve the durability of the battery and inhibit the excessive temperature rise. In case of the cooling system failure, it can realize the trade-off between safety and economy to reduce the risk of battery thermal runaway.

Multi-Objective Control Strategy for Switched Reluctance Generators in Small-Scale Wind Power Generations

Switched reluctance generators (SRGs) are widely used in wind power generation. However, due to the natural tendency of SRG, there are always nonnegligible conflicts to achieve high efficiency and low output voltage ripple at the same time. This creates difficulties for the high-performance of SRG. Thus, a multi-objective optimization control strategy is proposed in this paper to improve the static performance of SRG. The proposed control strategy contains following steps. First, in order to gain the maximum output power range at different rotor speeds, the turn-off angle is optimized off-line by simulated annealing algorithm (SAA). The optimized results are fitted as a function of rotor speed for on-line regulating; then, a closed-loop controller is built, and the reference current is regulated according to the difference between actual output power and required output power. Second, a multi-objective function is constructed as the evaluation result of SRG performance, which takes system efficiency, output voltage ripple and power converter loss into consideration. In the end, the turn-on angle is tuned by SAA according to the real-time multi-objective evaluation result. The proposed control strategy can be flexibly applied to SRGs with different structures and avoids the disadvantage of single-objective optimization. The simulation and experiments results show that the overall performance of SRG is improved.

Multi-objective optimal droop control of solid oxide fuel cell based integrated energy system

Solid oxide fuel cell (SOFC) based integrated energy system (IES) is promising in the future low-carbon power generation market, due to the high efficiency and flexibility. However, it is challenging for the dynamic control design in dealing with the conflicting objectives in terms of fast power tracking and overall efficiency during the transient process of load response. To this end, this paper develops a multi-objective optimal droop control strategy for the real-time power dispatch of the IES. Firstly, a nonlinear implicit dynamic model consisting of SOFC, lithium-ion battery, photovoltaic array and DC-DC converter is developed. Then, a multi-objective optimization is formulated to balance the power tracking performance and transient efficiency. Non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to search the optimal parameters for droop controller. Simulation results demonstrates that the electricity loss of the proposed method can be reduced by 96.26% with a slight compromise in power tracking performance.

Multi-objective optimal edge-drop control in tandem cold rolling of silicon steel strip

In cold rolling of silicon steel strip, edge drop directly affects the side cutting and yield of silicon steel. To improve the edge drop of silicon steel in the cold rolling process, a multi-stand coordinated control strategy and model based on multi-objective optimization are proposed. The first step is to analyze the influence of each control stand work roll shifting (WRS) on edge drop through the finite element model (FEM), then determine the edge drop control effectiveness of each work roll shifting actuator. Meanwhile, a fitting model is proposed to solve the limitation caused by the discrete change of efficiency factors in solving the optimal adjustments of each WRS. On this basis, the target model for silicon steel edge drop control is formulated, and the optimal adjustment model of WRS of each stand is established. To realize the optimal coordinated control among the rolling mill stands, a multi-objective optimal model of edge drop control has been established by the overall modeling method. The improved penalty function algorithm is developed to calculate the optimal adjustment of WRS for each stand. The experiments and application show that the proposed multi-objective optimal control strategy and model can effectively improve the accuracy of edge drop control in silicon steel continuous cold rolling.

Research on Control Strategy of Light and CO2 in Blueberry Greenhouse Based on Coordinated Optimization Model

As essential environmental parameters in the greenhouse, appropriate light and CO2 will improve agricultural productivity and quality. Although many related studies have been carried out on the intelligent regulation of these environmental factors, the regulation of light and CO2 is usually controlled separately, and energy consumption is rarely considered. This paper proposed a coordinated control strategy for greenhouse light and CO2 based on the multi-objective optimization model. Firstly, the experiments on the net photosynthetic rate of blueberry under different temperatures, photon flux density, and CO2 concentration nesting were carried out to establish a blueberry net photosynthetic rate prediction model based on Support Vector Regression (SVR). Secondly, a model for calculating the energy cost of both light and CO2 was constructed. Thirdly, taking the maximum net photosynthetic rate and the minimum energy cost as the objective functions, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was leveraged to obtain the Pareto optimal solutions of the target regulation values of light and CO2 concentration in different temperature ranges. Then, the optimal values were selected based on two different strategies. Finally, the multi-objective optimal control strategy proposed in this paper was compared with both the classical threshold control strategy and the Gaussian curvature maximization control strategy. The results indicated that the strategy which prioritized energy saving could reduce the energy cost by about 22.33% and 19.08%, respectively, under the premise that the net photosynthetic rate was consistent. Meanwhile, the strategy that prioritized production efficiency could increase the net photosynthetic rate by about 8.40% and 4.42%, respectively, with the same energy cost. In conclusion, the proposed multi-objective optimization control can improve the greenhouse climate control performance and reduce cost compared with other mentioned strategies.

Deep reinforcement learning optimal control strategy for temperature setpoint real-time reset in multi-zone building HVAC system

Determining a proper trade-off between energy consumption and indoor thermal comfort is important for HVAC system control. Deep Q-learning (DQN) based multi-objective optimal control strategy is designed for temperature setpoint real-time reset to balance the energy consumption and indoor air temperature. In addition, this study develops an EnergyPlus-Python co-simulation testbed to evaluate DQN control strategy in a simulation environment. A case study experiment is conducted to evaluate the performance of DQN control strategy for real-time reset of supply air temperature and chilled supply water temperature setpoint in a multi-zone building VAV system. The developed EnergyPlus-Python co-simulation testbed is used to train and test the DQN control strategy for performance analysis. The applied DQN strategy leans to update control actions (i.e. temperature setpoint) through interaction with the simulation environment. Simulation results show that the DQN control strategy is effective in finding a proper trade-off between the energy consumption of HVAC system and indoor air temperature. Meanwhile, the DQN control strategy can find a proper temperature setpoint reset sequence in smaller training episodes, and the control actions can be stable after ten DQN training episodes. This study provides a preliminary direction of deep reinforcement learning control strategy for temperature setpoint real-time reset in multi-zone building HVAC systems.

A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory

A multi-objective power flow optimization control strategy for a power split plug-in hybrid electric vehicle using game theory

Multi-Objective Unified Optimal Control Strategy for DAB Converters with Triple-Phase-Shift Control

To solve the problems of large current stress, difficult soft-switching of all switches, and slow dynamic response of dual active bridge converters, a multi-objective unified optimal control strategy based on triple-phase-shift control was proposed. The forward power flow global modes of triple-phase-shift control were analyzed, and three high-efficiency modes were selected to establish the analytical models of current stress and soft-switching. Combined with these models, the optimal solutions in different modes were derived by using the cost function-optimization equation to overcome the limitation of the Lagrange multiplier method, such that the DAB converter achieved the minimum current stress, and all switches operated in the soft-switching state over the entire power range. At the same time, the virtual power component was introduced in the phase-shift ratio combination, which improved the dynamic response of output voltage under the input voltage or load steps changed by power control. The theoretical analysis and experimental results show that the proposed control strategy can optimize the performance of the DAB converter from three aspects, such as current stress, soft-switching, and dynamic response, which achieves multi-objective optimization of the steady-state and dynamic performance of DAB converters.

Aging Affects Multi-Objective Optimal Control Strategies during Obstacle Crossing

Obstacle crossing challenges balance and increases the risk of falls in older people. Knowledge of the control strategies adopted by older people will be helpful for the study of the mechanisms of falls during obstacle crossing and the development of fall-prevention approaches. A mechanical model of the body combined with measured gait data was used to study the control strategies adopted by 17 healthy older and 17 young adults when crossing obstacles of different heights, in terms of the best-compromise weighting sets for the objectives of minimizing energy expenditure and maximizing the toe-obstacle and heel-obstacle clearances. The older group showed increased leading toe-obstacle clearance and trailing toe-obstacle distance, but decreased leading heel-obstacle distance. Compared with the young, the crossing strategy of older people emphasized the foot-obstacle clearance to reduce the risk of tripping, at the expense of energy expenditure. It appears that the multi-objective optimal control strategy relies on the muscular strength of the lower extremities and precise end-point control. Therefore, maintaining or improving the muscle strength and the ability of limb position control is critical for safe and successful obstacle-crossing in the older population.

Single and Multiobjective optimal control of epidemic models involving vaccination and treatment

Introduction: A rigorous multiobjective optimal control strategy (that does not require the use of weighting functions) of the epidemic models that consider vaccination and treatment strategies is presented. Modifications of the standard susceptible-infectious-removed, susceptible-exposed-infectious-removed, and the modified susceptible-infectious-removed models are dynamically optimized to minimize the number of infected individuals while, controlling the rate at which the individuals are vaccinated and treated.
 Method:The optimization program, Pyomo , where the differential equations are automatically converted to a Nonlinear Program using the orthogonal collocation method is used for performing the dynamic optimization calculations. The Lagrange-Radau quadrature with three collocation points and 10 finite elements are chosen. The resulting nonlinear optimization problem was solved using the solver BARON 19.3, accessed through the Pyomo-GAMS27.2 interface.
 Results: The computational results how that the multiobjective optimal control profiles generated by this strategy are very similar to those produced when weighting functions are used.
 Conclusion: The main conclusion of this work is to demonstrate that one can perform a rigorous dynamic optimization of epidemic models without the use of weighting functions that have the potential to produce some uncertainty and doubt in the results, in addition to dealing with unnecessary additional variables.

A multi-objective operation strategy optimization for ice storage systems based on decentralized control structure

Improving the operational efficiency of chillers and science-based planning the cooling load distribution between the chillers and ice tank are core issues to achieve low-cost and energy-saving operations of ice storage air-conditioning systems. In view of the problems existing in centralized control architecture applied in heating, ventilation, and air conditioning, a distributed multi-objective particle swarm optimization improved by differential evolution algorithm based on a decentralized control structure was proposed. The energy consumption, operating cost, and energy loss were taken as the objectives to solve the chiller’s hourly partial load ratio and the cooling ratio of ice tank. A large-scale shopping mall in Xi’an was used as a case study. The results show that the proposed algorithm was efficient and provided significantly higher energy-savings than the traditional control strategy and particle swarm optimization algorithm, which has the advantages of good convergence, high stability, strong robustness, and high accuracy. Practical application: The end equipment of the electromechanical system is the basic component through the building operation. Based on this characteristic, taken electromechanical equipment as the computing unit, this paper proposes a distributed multi-objective optimization control strategy. In order to fully explore the economic and energy-saving effect of ice storage system, the optimization algorithm solves the chillers operation status and the load distribution. The improved optimization algorithm ensures the diversity of particles, gains fast optimization speed and higher accuracy, and also provides a better economic and energy-saving operation strategy for ice storage air-conditioning projects.

Multi-objective non-simultaneous dynamic optimal control for an ejector expansion heat pump with thermal storages

This study is to enhance the overall performances of a transcritical CO2 heat pump coupled with hot and cold thermal energy storages (HPTES) during energy charging by using a controllable ejector to replace an expansion valve (ExV) in heat pump and a new multi-objective non-simultaneous dynamic optimization strategy. A Modelica-based dynamic simulation model was developed and validated for an ejector expansion CO2 heat pump with thermal storages during charging process, and a model-based multi-objective non-simultaneous dynamic optimization strategy was proposed. Under a single-objective dynamic optimal control strategy, the overall coefficient of performance (COP) can be increased 22.0%, energy consumption can be reduced 17.3%, and charging time can be reduced significantly during charging process of thermal storages by using an ejector expansion heat pump compared to an ExV heat pump. Charging performances of the ejector HPTES system can be enhanced further by using a multi-objective non-simultaneous dynamic optimal control strategy, in which multiple transient performance parameters of the HPTES system, instead of energy consumption and cost as in the literature, were used as multi-objective functions. The mechanisms of dynamic performance enhancements of an ejector HPTES were revealed. This study would be useful for a dynamic optimal control of other coupled energy systems under unsteady conditions.

An Efficient Energy Management Scheme for a Hybrid FC-SC-Battery Electric Vehicle using Model Predictive Control and Multi-Objective Particle Swarm Optimization

This paper presents a new Design for an efficient Energy Management System (EMS) for a Plug-in Hybrid Electric Vehicle (PHEV) which is supplied by Fuel Cell/Super-capacitor/Battery sources to attain maximum energy savings and minimize the amount of the fossil fuel utilized by the transportation sector. Model Predictive Control (MPC) combined with Multi-Objective Particle Swarm Optimization (MOPSO) control strategy was proposed to manage the battery and the SC State of Charge (SOC) and decide the optimal power distribution between the Fuel Cell (FC), the Battery and the Super Capacitor (SC). The main target of the proposed EMS calculation technique is to achieve maximum energy efficiency, minimum battery current variation, minimum variation of the state of charge of the SC and minimum cumulative hydrogen consumption during the driving cycle. The proposed MOPSO based EMS methodology has been checked by the High Speed (US06) Driving Cycle utilizing the MATLAB/Simulink. Five diverse driving patterns are utilized to assess the speculation capacity of the developed technique. Simulation results and the real-time experiment demonstrate that the proposed MOPSO-Model Predictive Control based EMS technique can accomplish better energy effectiveness compared with Fuzzy Logic Rule-based EMS and GA-based EMS.

Online Optimal Power Control of an Offshore Oil-Platform Power System

Offshore oil-platform power systems are important infrastructure for the exploitation of maritime oil and gas. However, its current energy management system, with relatively simple control scheme and low-level automation, can hardly operate the system in a secure and economic manner to match the rapid progress of offshore oil and gas exploitation. To address this issue, an online multi-objective optimal power control strategy is proposed and implemented based on the existing SCADA system. By incorporating network loss, gas consumption and voltage deviation into a synthesized objective function and taking the various operational constraints into account, the power-control task is formulated into a mixed-integer nonlinear optimized problem. And then an efficient procedure that combines the interior point method and the fast branch and bound method is developed to solve the problem. Consequently the optimal control strategy can be obtained online and either system security or operational efficiency is achieved. The developed control system has been put into practical use in the Weixinan offshore oil-platform power system (China’s first of its kind). Field test results show that it can coordinate active and reactive power for online optimal control as per the changes of operating conditions. The improved safety, efficiency and power quality of the power system will definitely promote exploitation of offshore oil and gas in the respect of both security and efficiency.

Multiobjective optimal control for wastewater treatment process using adaptive MOEA/D

Through the analysis of the biological wastewater treatment process (WWTP), a multiobjective optimal control strategy is developed with the usage of energy consumption (EC) and effluent quality (EQ) as objectives to be optimized. To effectively handle the multiobjective optimization problem (MOP) with complex Pareto-optimal front (POF), an adaptive multiobjective evolutionary algorithm based on decomposition (AMOEA/D) is proposed in this paper. Since the efficiency of the multiple reference points and two-phase optimization strategies in solving MOPs with complex POFs has been proved. In the proposed AMOEA/D, an auto-switching strategy based on the aggregation function enhancement is designed to automatically make the algorithm switch from the first phase to the second phase. Besides, an adaptive differential evolution strategy is introduced into AMOEA/D to balance exploration and exploitation during the evolutionary process. Finally, the dynamic optimization, intelligent decision and bottom tracking control of the set-points of the dissolved oxygen and nitrate nitrogen in the WWTP are achieved via the combination of AMOEA/D with the self-organizing fuzzy neural network approximator and the self-organizing fuzzy neural network controller. The international benchmark simulation model No. 1 (BSM1) is utilized for experimental verification. Simulation results demonstrate that the proposed AMOEA/D can effectively reduce the EC of the WWTP under the premise of ensuring effluent parameters to meet the effluent discharge standards.

Model-based Dynamic Optimal Control of an Ejector Expansion CO2 Heat Pump Coupled with Thermal Storages

This study presents a model-based dynamic optimization strategy for charging of hot and cold thermal storages with a dual-mode CO2 ejector expansion heat pump, which could be used as a high-efficiency smart grid enabling option in heating and cooling services for buildings or industry. The dynamic model of this coupled system was developed by Modelica. The outlet water temperatures of hot and cold tanks are used as indicators in dynamic optimal control strategy. To optimize the overall performances during energy process, the transient performances are optimized by genetic algorithm based on dynamic model of system. Single-objective and multi-objective dynamic optimal control strategies were developed and applied for this system. Study results show that these two developed model-based dynamic optimal control strategies can optimize the overall performances of such coupled systems during energy charging. Compared with the control strategy with constant control parameters, these two dynamic optimal control strategies can be useful in the coordinative optimization of multiple control parameters, and multi-objective dynamic optimal control strategy is superior to the single-objective one.