Online task planning method of anti-ship missile based on rolling optimization

Based on the wave attack task planning method in static complex environment and the rolling optimization framework, an online task planning method in dynamic complex environment based on rolling optimization is proposed. In the process of online task planning in dynamic complex environment,online task planning is based on event triggering including target information update event, new target addition event, target failure event, weapon failure event, etc., and the methods include defense area reanalysis, parameter space update, and mission re-planning. Simulation is conducted for different events and the result shows that the index value of the attack scenario after re-planning is better than that before re-planning and according to the probability distribution of statistical simulation method, the index value distribution after re-planning is obviously in the region of high index value, and the index value gap before and after re-planning is related to the degree of posture change.

Multi-agent-based Rolling Optimization Method for Restoration Scheduling of Distribution Systems with Distributed Generation

Resilience against major disasters is the most essential characteristic of future electrical distribution systems(EDSs).A multi-agent-based rolling optimization method for EDS restoration scheduling is proposed in this paper.When a blackout occurs,considering the risk of losing the centralized authority due to the failure of the common core communication network,the available agents after disasters or cyber-attacks identify the communication-connected parts(CCPs)in the EDS with distributed communication.A multi-time interval optimization model is formulated and solved by the agents for the restoration scheduling of a CCP.A rolling optimization process for the entire EDS restoration is proposed.During the scheduling/rescheduling in the rolling process,CCPs in EDS are re-identified and the restoration schedules for CCPs are updated.Through decentralized decision-making and rolling optimization,EDS restoration scheduling can automatically start and periodically update itself,providing an effective solution for EDS restoration scheduling in a blackout event.A modified IEEE123-bus EDS is utilized to demonstrate the effectiveness of the proposed method.

Emergency Energy Management of Microgrid in Industrial Park Basedon Robust Optimization

Reducing the impact of power outages and maintaining the power supply duration must be considered in implementing emergency energy dispatching in micro-networks.This paper studies a new emergency energy treatment method based on the robust optimal method and the industrial park micro-network with the optical energy storage system.After controlling the load input,a control strategy of adjusting and removing is proposed.Rolling optimal theory is applied to emergency energy scheduling based on a robust optimal mathematical model.A weighting factor is introduced into the optimal model to balance the importance of reducing and retaining the power supply.Uncertainty is designed to adjust the effect of uncertainty on the problem.The example shows that this method can flexibly set the weight coefficient and uncertainty value according to the actual situation so that the input of the control load can be optimized.

Multi-stage Rolling Optimization Model of Project Portfolio Configuration Under Phased Strategy Scenarios

The paper proposes a projected management method to organize,select and rolling configure the projects for multiple strategic stages. We illustrate the definitions of project portfolio rolling benefits,gain of resources and risk accumulation according to the correspondence between project life cycle and phased strategy scenarios. A heuristic-genetic algorithm has been designed to optimize the configuration model at the same time.The rolling configuration model and optimization algorithm are proved effectively by testing the case study of Y enterprise through the Matlab simulation.

Peak Shaving Strategy of Concentrating Solar Power Generation Based on Multi-Time-Scale and Considering Demand Response

According to the multi-time-scale characteristics of power generation and demand-side response(DR)resources,as well as the improvement of prediction accuracy along with the approaching operating point,a rolling peak shaving optimization model consisting of three different time scales has been proposed.The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination,generation power,and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response characteristics of the concentrating solar power(CSP).At the same time,in order to improve the accuracy of the scheduling results,the combination of the day-ahead peak shaving phase with scenario-based stochastic programming can further reduce the influence of wind power prediction errors on scheduling results.The testing results have shown that by optimizing the allocation of scheduling resources in each phase,it can effectively reduce the number of starts and stops of thermal power units and improve the economic efficiency of system operation.The spinning reserve capacity is reduced,and the effectiveness of the peak shaving strategy is verified.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

Trajectory online optimization for unmanned combat aerial vehicle using combined strategy

This paper presents a combined strategy to solve the trajectory online optimization problem for unmanned combat aerial vehicle (UCAV). Firstly, as trajectory directly optimizing is quite time costing, an online trajectory functional representation method is proposed. Considering the practical requirement of online trajectory, the 4-order polynomial function is used to represent the trajectory, and which can be determined by two independent parameters with the trajectory terminal conditions; thus, the trajectory online optimization problem is converted into the optimization of the two parameters, which largely lowers the complexity of the optimization problem. Furthermore, the scopes of the two parameters have been assessed into small ranges using the golden section ratio method. Secondly, a multi-population rotation strategy differential evolution approach (MPRDE) is designed to optimize the two parameters; in which, 'current-to-best/1/bin', 'current-to-rand/1/bin' and 'rand/2/bin' strategies with fixed parameter settings are designed, these strategies are rotationally used by three subpopulations. Thirdly, the rolling optimization method is applied to model the online trajectory optimization process. Finally, simulation results demonstrate the efficiency and real-time calculation capability of the designed combined strategy for UCAV trajectory online optimizing under dynamic and complicated environments.

Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.

Optimization and perfection of the roughing model for ferritic SUS430

Based on the characteristics of ferritic SUS430 heating and deformation,and combined with the features of the 1780 mm hot-rolling mill,a roughing model was introduced in two aspects:optimizing the rough rolling passes and improving the width control precision.Through reducing the rough rolling passes,the rough rolling time can be shortened,the precision rolling startup temperature can be raised and the yield of the hot-rolled products can be increased.Moreover,on the premise that the slab width fluctuation was great,the precision of the width control can be improved through optimizing the parameters of the hot-rolling width control model.The result shows that the optimization and perfection of the original rolling process of the stainless steel 430 series further improved its capacity and product quality.

Field-weakening control system of induction motor based on model prediction

Considering the actual demand for high-speed operation of induction motors in industrial occasions,the characteristics of induction motors in different regions are analyzed,especially the field weakening characteristics of induction motors in high-speed operation are studied.A field weakening control method of induction motor based on model predictive control(MPC)algorithm is proposed,which can predict the future state of the controlled object,and then obtain the optimal control variables by colling optimization.The simulation results show that the field-weakening control method based on MPC algorithm has faster response speed,stronger robustness and better control performance than the traditional control methods.

Discrete sliding mode prediction control of uncertain switched systems

The robust stabilization problem for a class of uncertain discrete-time switched systems is presented. A predictive sliding mode control strategy is proposed, and a discrete-time reaching law is improved. By applying a predictive sliding surface and a reference trajectory, combining with the state feedback correction and rolling optimization method in the predictive control strategy, a predictive sliding mode controller is synthesized, which guarantees the asymptotic stability for the closed-loop systems. The designed control strategy has stronger robustness and chattering reduction property to conquer with the system uncertainties. In addition, a unique nonswitched sliding surface is designed. The reason is to avoid the repetitive jump of the trajectories of the state components of the closed-loop system between sliding surfaces because it might cause the possible instability. Finally, a numerical example is given to illustrate the effectiveness of the proposed theory.

Rolling horizon optimization for real-time operation of thermostatically controlled load aggregator

Thermostatically controlled loads(TCLs)have great potentials to participate in the demand response programs due to their flexibility in storing thermal energy.The two-way communication infrastructure of smart grids provides opportunities for the smart buildings/houses equipped with TCLs to be aggregated in their participation in the electricity markets.This paper focuses on the realtime scheduling of TCL aggregators in the power market using the structure of the Nordic electricity markets a case study.An International Organization of Standardization(ISO)thermal comfort model is employed to well control the occupants’thermal comfort,while a rolling horizon optimization(RHO)strategy is proposed for the TCL aggregator to maximize its profit in the regulation market and to mitigate the impacts of system uncertainties.The simulations are performed by means of a metaheuristic optimization algorithm,i.e.,natural aggregation algorithm(NAA).A series of simulations are conducted to validate the effectiveness of proposed method.

Robust multi-objective optimization of rolling schedule for tandem cold rolling based on evolutionary direction differential evolution algorithm

According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has some uncertainty during the rolling process,ignoring which will cause poor robustness of rolling schedule.In order to solve this problem,a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evolutionary direction was proposed.The algorithm calculated the horizontal angle of the vector,which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algorithm was verified by two benchmarks.Meanwhile,in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution,a modified Latin Hypercube Sampling process was proposed.Finally,the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the actual rolling schedule.Meanwhile,robustness of solutions was ensured.

The Optimizing Method of Equipment Renewal Opportunity Based on Dynamic Sunk Cost

This article brings forward a new conception of dynamic sunk cost, and then constructs a systematic model that could be used in analyzing equipment renewal opportunity. This model will do much help in solving problems refer to optimizing equipment renewal opportunity in right way.

Optimal Offering Strategy of Virtual Power Plant with Hybrid Renewable Ocean Energy Portfolio

This paper proposes a hybrid ocean energy sys-tem to form a virtual power plant(VPP)for participating in electricity markets in order to promote the renewable ocean energy utilization and accommodation.In the proposed system,solar thermal energy is integrated with the closed-cycle ocean thermal energy conversion(OTEC)to boost the temperature differences between the surface and deep seawater for efficiency and flexibility improvements,and the thermodynamic effects of seawater mass flow rates on the output of solar-boosted OTEC(SOTEC)are exploited for deploying SOTEC as a renewable dispatchable unit.An optimal tidal-storage operation model is also developed to make use of subsea pumped storage(SPS)with hydrostatic pressures at ocean depths for mitigating the intermittent tidal range energy in order to make the arbitrage in the electricity market.Furthermore,a two-stage coordinated scheduling strategy is presented to optimally control seawater mass flow rates of SOTEC and hydraulic reversible pump-turbines of SPS for enhancing the daily VPP profit.Comparative studies have been investigated to confirm the superiority of the developed methodology in various renewable ocean energy and electricity market price scenarios.

Resilience Enhancement of Urban Energy Systems via Coordinated Vehicle-to-grid Control Strategies

Coordinated vehicle-to-grid(V2G)control strategies can sustain essential loads of an energy system during islanding,thereby increasing resilience.In this context,this paper investigates the resilience enhancement benefits of smart V2G control,the value of electric vehicle(EV)owner cooperation on system resilience,as well as the complementary effects of PV and EV interaction in an urban multi-energy microgrid(MEMG).By using a rolling horizon approach to optimize day-ahead operation of the MEMG and subsequently dispatching EVs,uncertainties in outage start time,EV arrival/departure times,and initial state of charge(SOC)are mitigated.Results show that smart V2G control can provide a substantial essential load curtailment reduction compared to a non-EV scenario,meanwhile,non-coordinated grid-to-vehicle(G2V)operation was shown to slightly burden the system with a slight increase in non-essential load curtailment.Investigations into the influence of EV cooperation on resilience showed that a high percentage of system-prioritized(SP)EVs could help greatly further reduce essential load curtailment compared to individual-prioritized(IP)EVs.Finally,the complementary benefits of smart V2G control and PV were demonstrated,showing a reduction in both PV and essential load curtailments with increasing numbers of EVs.Overall,the application of smart V2G control,especially with cooperation of EV owners,can drive significant resilience enhancement during islanding,while further benefits can be obtained through having a sufficient number of EVs to utilize high PV penetration.

Stochastic coordinated operation of wind and battery energy storage system considering battery degradation

Grid-scale battery energy storage systems(BESSs)are promising to solve multiple problems for future power systems.Due to the limited lifespan and high cost of BESS,there is a cost-benefit trade-off between battery effort and operational performance.Thus,we develop a battery degradation model to accurately represent the battery degradation and related cost during battery operation and cycling.A linearization method is proposed to transform the developed battery degradation model into the mixed integer linear programming(MILP)optimization problems.The battery degradation model is incorporated with a hybrid deterministic/stochastic look-ahead rolling optimization model of windBESS bidding and operation in the real-time electricity market.Simulation results show that the developed battery degradation model is able to effectively help to extend the battery cycle life and make more profits for wind-BESS.Moreover,the proposed rolling look-ahead operational optimization strategy can utilize the updated wind power forecast,thereby also increase the wind-BESS profit.

Optimal air-conditioning load control in distribution network with intermittent renewables

The coordinated operation of controllable loads,such as air-conditioning load, and distributed generation sources in a smart grid environment has drawn significant attention in recent years. To improve the wind power utilization level in the distribution network and minimize the total system operation costs, this paper proposes a MILP(mixed integer linear programming) based approach to schedule the interruptible air-conditioning loads. In order to mitigate the uncertainties of the stochastic variables including wind power generation, ambient temperature change, and electricity retail price, the rolling horizon optimization(RHO) strategy is employed to continuously update the real-time information and proceed the control window. Moreover, to ensure the thermal comfort of customers, a novel two-parameter thermal model is introduced to calculate the indoor temperature variation more precisely. Simulations on a five node radial distribution network validate the efficiency of the proposed method.