Rolling Optimization Research Articles

Improved Model Predictive Dynamic Voltage Cooperative Control Technology Based on PMU

With the access to a large number of intermittent and fluctuating new energy sources in the low-voltage distribution network, the complex relationship between producers and consumers makes the node voltage stability and other problems in the power system increasingly prominent. Aiming at the voltage stability problem caused by the intermittent and fluctuation of the new power system, based on the high-precision measurement data of PMU, this article used the model predictive control algorithm to realize regional voltage optimization and improve the stability of node voltage. First, the voltage sensitivity matrix is calculated using PMU measurement data and grid structure state information. Second, active power, reactive power, and voltage were taken as the input of the MPC algorithm, and the optimal compensation voltage instruction was obtained by rolling optimization, and the node voltage was compensated by SVG and SVC. Finally, a simulation experiment was carried out on the MATLAB/Simulink simulation platform, and the experimental results verified the correctness and effectiveness of the proposed control method.

An online dispatch approach for distributed integrated multi‐energy system considering non‐ideal communication conditions

The distributed integrated multi-energy system (DIMS) has many advantages in terms of the utilisation of renewable energy sources and clean energy. Operation strategies for the DIMS based on a real-time profile have been extensively studied. In a DIMS online optimisation problem, besides fluctuations in the renewable energy output and load, inconsistent time scales of the transport dynamics of different energy flows and non-ideal communication (involving communication uncertainty and latency) result in suboptimal operation in dispatch scheduling. An online multi-time-scale optimal operation strategy is proposed for the DIMS to respond to the above challenges, using a hybrid algorithm comprising a model predictive control method and distributed collaborative consensus algorithm (CCA). The approach is based on a hierarchy, comprising rolling optimisation and adjustment. A rolling optimisation is established to schedule operations according to the latest forecast and status information. The rolling dispatch is then adjusted according to the ultrashort-term adjustment using the CCA. Meanwhile, the effect of the information transmission environment on real-time scheduling is considered, and the robust CCA is improved for the implementation of strategies under non-ideal communication conditions. Case studies and results are presented and discussed to show the effectiveness of the proposed approach with the better matching between demand and supply.

A novel model predictive control strategy for multi-time scale optimal scheduling of integrated energy system

Model predictive control is one of the key technologies to realize the multi-time scale optimal scheduling of integrated energy system. However, the traditional centralized model predictive control has the high model order, a large amount of online calculation and is not easy to expand, it is not suitable for the optimal scheduling of an integrated energy system with many distributed units. In this paper, a multi-time scale optimal scheduling method of integrated energy system based on distributed model predictive control is proposed, which realizes the flexible scheduling of the integrated energy system through the coordination and cooperation of various subsystems. Firstly, the detailed models of various generation equipment are established according to the four energy forms of cold, heat, electricity and gas. Then, a multi-time scale optimal scheduling is divided based on three scales: day-ahead long time scale scheduling in 1 h, intra-day predictive control in 15 min and real-time adjustment in 5 min. Next, during the day-ahead scheduling and intra-day rolling optimization, we establish an optimization model based on the best economic operation of the system, the daily operating cost of the system and the minimum penalty cost of start-up and shutdown. During the real-time adjustment, a distributed model predictive control method is proposed to decompose the overall optimization problem of the integrated energy system. Each subsystem estimates the state according to the input sequence of other subsystems at the previous time and optimizes its own performance index. Finally, the case shows that the model predictive control strategy proposed in this paper can increase the speed of optimal operation by about 15% and reduce the cost of optimal operation by about 3.8% compared with the traditional centralized model predictive control method, which not only improves the control performance of system operation, but also improves the economy of system operation.

Two-stage optimization of a virtual power plant incorporating with demand response and energy complementation

The virtual power plant (VPP) breaks the geographical restrictions of environment and resource types by virtue of aggregating the distributed energy resources and dispatchable loads. In view of the impact of large-scale energy consumption by users on the operational safety of VPP, based on the existing demand response (DR) researches, this paper adds a penalty mechanism for the reasonable management of loads. For the difference of resources in multiple VPPs, the current researches ignore the energy interaction between different VPPs, and the external operator is introduced innovatively in this paper to coordinate the real-time complementation of regional energy. Considering the uncertainty from renewable energy and load, a two-stage scheduling strategy for VPP with multi-time scale optimization is proposed, including a long-time scale day-ahead scheduling and a short-time scale real-time scheduling. On the basis of DR and unit constraints, the resource scheduling plan is formulated in the day-ahead stage. In the real-time stage, the energy complementation between VPPs is considered under the framework of rolling optimization for the correction of the previous plan. The scheduling of both stages is aimed at the economic operation of the VPP. Simulation results show that the DR flexibly optimizes the load distribution while bringing economic benefits, and the total profit can be increased by up to 6.65%. Moreover, the results show that the energy interaction under the external operator coordination significantly reduces the total correction cost by up to 8.23%, and the correction cost is decreased with the extension of the rolling optimization scheduling period.

Multi-time-scale energy management for microgrid using expected-scenario-oriented stochastic optimization

The uncertainty of renewable distributed energy sources, load demand and electric vehicles bring great difficulties to the optimal scheduling of the grid-connected microgrid, this paper proposes a multi-time scale stochastic optimization model for microgrid with low operation cost and high reliability against these uncertainties. In the day-ahead scheduling stage, the Monte Carlo method is used to generate stochastic scenarios and simulate the uncertainty of the microgrid. The day-ahead stochastic optimization model is established considering the energy balance in the expected-scenario, the operation cost of distributed energy sources, the charging and discharging characteristics of electric vehicles, and the stable operation under multiple stochastic scenarios. Then the hourly robust scheduling decisions are obtained by solving the day-ahead stochastic optimization model to achieve the optimization objective of expected optimal and stochastic feasibility. In the intraday ultra-short-term scheduling stage, according to the updated minute forecast information, a rolling optimization strategy is carried out and the intraday optimal power allocation is obtained to minimize the operation cost in the prediction time domain. Even if the prediction error of the ultra-short-term forecast data is very small, it still adversely affects the stability of the system in the real-time operation stage. To eliminate the random fluctuations of renewable energy output and load demand in the real-time operation, the power deviation is smoothed through interacting with the power grid. Compared to the deterministic scheduling model, simulation results demonstrate that the proposed stochastic scheduling model and multi-time-scale optimization strategy are superior in minimizing the operation cost and reducing the negative impact of uncertainty on the economic operation.

Model Prediction Control-Based Energy Management Combining Self-Trending Prediction and Subset-Searching Algorithm for Hydrogen Electric Multiple Unit Train

To apply the actual fuel cell hybrid system, improve the efficiency of fuel cells, and reduce the operating cost, the model predictive control (MPC)-based energy management strategy (EMS) combining self-trending prediction and the subset-searching algorithm is proposed in this article. Compared with the traditional MPC, the proposed EMS reduces the error of speed prediction and simplifies the process of solving the optimal control trajectory, which increases the potential for practical engineering applications. Moreover, the proposed EMS considers the degradation of the power sources, which would lead to low operating costs from a long-term scale perspective. Finally, this article carries out a hardware-in-the-loop experiment to verify the feasibility and superiority of the proposed EMS. The results show that compared with the optimal benchmark, the operating cost is only increased by 7.70%, and the fuel cells operate in the high-efficiency range. Otherwise, compared with the traditional Markov chain and dynamic programming, the root-mean-square error and single time of each rolling optimization, respectively, reduce 27.79% and 64.9% at least. In addition, this article tests the adaptability of the proposed EMS on other track sections. The results show that in any track section, the proposed EMS can maintain the state of charge (SOC) and reduce the operating cost.

Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming (SQP) for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control. The designed control system includes four parts, namely a predictive model, rolling optimization, online correction, and feedback correction. Considering the strong nonlinearity of engine, a predictive model is established by Back Propagation (BP) neural network for the entire flight envelope, whose input and output are determined with random forest algorithm and actual situation analysis. Rolling optimization typically uses SQP as the optimization algorithm, but SQP algorithm is easy to trap into local optimization. Therefore, the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP. In addition to the traditional three parts of model predictive control, an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain. Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy, and the proposed control system can achieve good tracking performance with the limited parameters within the safe range.

Research on process optimization and rapid prediction method of thermal vibration stress relief for 2219 aluminum alloy rings

Abstract 2219 aluminum alloy rings are important part of liquid cryogenic rocket fuel tanks. Residual stress is inevitably introduced in the forming process of the rings due to the nonlinear thermomechanical coupling conditions, which will affect its mechanical properties, fatigue properties, corrosion resistance, and dimensional stability. Thermal vibratory stress relief (TVSR) has great potential in reducing residual stress, and process optimization of TVSR is necessary to further improve its application, but it is rarely reported. In this study, process optimization of roll formed 2219 aluminum alloy rings is conducted. The influence of vibration amplitude, vibration time, vibration frequency, heating time, holding time, and cooling time on TVSR treatment are investigated. Results show that the maximum equivalent residual stress of 2219 aluminum alloy rings can be reduced by 93.6% after optimized TVSR treatment. With the increase in vibration time, heating time, holding time, and cooling time, the maximum equivalent stress decreases. However, the increase in the vibration amplitude results in an increase in the maximum equivalent stress. Further, a genetically optimized artificial neural network intelligent optimization algorithm is applied to quickly predict the TVSR effect of 2219 aluminum alloy rings.

Control Algorithm for Trajectory Tracking of an Underactuated USV under Multiple Constraints

A trajectory tracking control method based on the cascade of optimal guidance and adaptive control laws was proposed for trajectory tracking of an unmanned surface vehicle under multiple motion constraints including an ocean current disturbance environment. Considering the velocity and acceleration constraints, angular velocity, and angular acceleration constraints in guidance law design, motion planning was conducted with three degrees of freedom using a motion model and predictive control, while optimal guidance law was designed through a rolling optimization strategy to stabilize the position error under multiple constraints. Moreover, the stability of the guidance law was proven. In control law design, an adaptive observer was introduced to estimate and compensate for ocean current disturbance, which affected the precision of control. The course and velocity control laws were designed with the adaptive sliding mode control technology to stabilize the course angle and longitudinal velocity errors; the stability of these control laws was further proven. Based on the system design, the stability of the entire closed-loop control system was proven with the cascade system and Lyapunov theory. Theoretical analysis and simulation experiments demonstrated the effectiveness and advancement of the proposed algorithm.

Tri-Level Integrated Optimization Design Method of a CCHP Microgrid with Composite Energy Storage

Combined cooling, heating, and power (CCHP) microgrids are important means of solving the energy crisis and environmental problems. Multidimensional composite energy storage systems (CESSs) are vital to promoting the absorption of distributed renewable energy using CCHP microgrids and improving the level of energy cascade utilization. In this context, this paper proposes a multi-energy coupling structure that includes a multidimensional CESS with a compressed air energy storage (CAES) connected to a CCHP microgrid. Dividing design and operation causes some problems, such as low operating efficiency and difficult energy matching of CESSs. To solve the existing problems, an integrated design method is proposed that considers the capacity configuration of the equipment and the optimal operation of the system on a multi-timescale. The optimization result of the capacity configuration level is used as the constraint of the operational control level, and the equipment output plan of the operational control level is used as the optimized operation strategy and parameters of the system. The C-NSGA-II algorithm is adopted at the capacity configuration level and day-ahead scheduling level. Rolling optimization is solved using the PSO algorithm. The final result that satisfied the output design was obtained after several iterations. The average daily cost and CO2 emission reduction rate (CO2ERR) of capacity configuration levels are $2241 and 45.02%. The best CO2ERRs of day-ahead scheduling optimization levels are 39.9% and 45.9% in summer and winter, where the operating cost saving rate (OCSR) are 30.5% and 38.3% separately. Examples show that the integrated design method presented in this paper has significant advantages in enhancing energy-grade matching and improving the economy and environmental protection of the system.

Three-Level Active Power Filter Based on Model Predictive Control

The model predictive control (MPC) algorithm is used in the harmonic compensation of active power filter (APF), which has a fast dynamic response and does not require a PWM modulation model. However, this method has some shortcomings, such as the mass computing and difficult selections of weight factors. To solve these problems, this paper proposes a single objective function improved MPC algorithm based on sector judgment, which only takes the reference current and feedback current as the objective function, omits the weight coefficient setting process, and reduces the number of rolling optimization from 27 to 7, thus reducing the computing time and control complexity. The improved model predictive control is applied to APF. Finally, the simulations and experiments show that the improved MPC algorithm is accurate and efficient.

An integrated dynamic voltage control strategy in active distribution network based on improved model predictive control

AbstractThe distribution networks have more frequent and fast voltage fluctuations with the large‐scale access of distributed energy resources and electric vehicles. Based on remote terminal unit (RTU) and distribution terminal unit (DTU), traditional hierarchical control methods are difficult to achieve rapid global coordinated voltage regulation due to static modeling. For this purpose, this paper proposes an integrated dynamic voltage control strategy which integrates the objectives of local voltage control and secondary voltage control. First, simplified dynamic models of controllable equipment in active distribution network are established instead of the static models. On this basis, the state‐space prediction model of the control system is established. Then, based on the improved model predictive control (IMPC), the integrated dynamic voltage control strategy realizes rapid and global coordinated control through state prediction, rolling optimization and real‐time feedback correction. Numerical results show that the proposed strategy can achieve fast global coordinated voltage control in the time scale of seconds.

Rolling optimization of transmission network recovery and load restoration considering hybrid wind-storage system and cold load pickup

A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.

Energy Optimal Dispatch of the Data Center Microgrid Based on Stochastic Model Predictive Control

Renewable energy outputs such as wind turbines and photovoltaics, as well as data center workloads are both random and uncertain. In order to enhance the stability and economy of the data center in actual operation effectively, a multi-time scale optimal dispatch method for the data center microgrid based on stochastic model predictive control is proposed in this paper. In the day-ahead scheduling stage, the characteristic of the data center that batch workloads are allowed to be served delayed is considered. And the scenario analysis method is applied to describe the uncertainty of loads and renewable energy outputs, based on which an economic optimization scheduling model is established to minimize the system operating cost. The intra-day scheduling utilizes the rolling optimization and feedback correction of model predictive control to correct the deviation of loads and renewable energy outputs and adjust the day-ahead dispatch plan in real time, which ensures the effectiveness of day-ahead plan and the stability of system operating. Through the simulation results of a typical data center microgrid, the effectiveness of the proposed method is verified.

Optimization strategy of combined thermal-storage-photovoltaic economic operation considering deep peak load regulation demand

Due to the randomness and uncertainty of renewable energy output and the increasing capacity of its access to power system, the deep peak load regulation of power system has been greatly challenged. The application of energy storage unit is a measure to reduce the peak load regulation pressure of thermal power units. In this paper, a joint optimal scheduling model of photovoltaic, energy storage units and thermal power units is established. The impacts of energy storage system on operation economy and photovoltaic abandonment are studied. The comprehensive operation cost measuring the operation economy considers the fuel cost that changes with the power in a quadratic function relationship, the startup-shutdown cost that changes linearly according to the number of start-shut actions, the compensation cost of deep peak load regulation according to the power block where the output locates in and the photovoltaic abandonment cost. The optimal scheduling model takes the minimization of comprehensive cost as the objective function, and the operation constraints of photovoltaic, energy storage units and thermal power units as the feasible range. The rolling optimization strategy is applied to solve the multi-time scale results. A real prefecture-level urban power system with seventy-four thermal power units is taken as an example. Different amounts of energy storage units are set for analysis. In the case assumed in this paper, the results show that enough energy storage configuration can improve the operation economy and reduce the load loss and photovoltaic abandonment to almost zero.

Air Quality Prediction Based on Quadratic Prediction Model

In order to improve the performance of the prediction model of air quality prediction,a secondary prediction mathematical 
 model is established in this paper.The first is to clean the data and find the potential model relationship between variables through 
 data mining and correlation methods,so as to establish the limit learning machine model.The model needs to be able to explain 
 the influence of meteorological index variables on pollutant concentration diffusion to a certain extent.Then,the EML model is 
 optimized by genetic algorithm,rolling optimization and other methods to reduce noise and make the data as accurate as possible.

A P-Q Coordination Based Model Predictive Control for DFIG High-Voltage Ride Through

This paper presents a P-Q coordination based model predictive control (MPC) for doubly fed induction generator (DFIG) to handle high-voltage ride through (HVRT) considering bipolar blocking of high-voltage direct current (HVDC) system. The overvoltage at the point of common coupling (PCC) after DC bipolar blocking and its sensitivity to current injection of DFIG is firstly analyzed based on a typical sending-end AC grid equivalent model. Then, the current feasible region of DFIG operation during overvoltage process is explicitly revealed considering generator and converter constrains. In the proposed HVRT control, a model predictive control (MPC) based method is developed where the minimum voltage tracking error is taken as the main objective function and the constrains are updated every control horizon based on real-time current feasible region. Through the rolling optimization and feedback correction framework, the rotor active and reactive currents of DFIG are coordinated and PCC voltage can be finally controlled to its target value to achieve HVRT. Further, the key MPC parameters are determined by solving an optimal problem to achieve better system robust stability and dynamic performances. Finally, the validity of the proposed control is verified by simulations in MATLAB/Simulink.

Optimal Control Method of Path Tracking for Four-Wheel Steering Vehicles

Path tracking is a key technique for intelligent electric vehicles, while four-wheel steering (4WS) technology is of great significance to improve its accuracy and flexibility. However, the control methods commonly used in path tracking for a 4WS vehicle cannot take full advantage of the additional steering freedom of the 4WS vehicle, because of restricting the relationship between the front and rear wheels steering angle. To address this issue, we derive a kinematic model without the restriction based on the small-angle assumption. Then, the objective function and constraints of system control quantity optimization are designed based on the tracking error model. After the optimization problem is solved in the form of quadratic programming with constraints, the control sequence with the smallest performance index is obtained through rolling optimization. The proposed method is tested on a high-fidelity Carsim/Simulink co-simulation platform and an experimental vehicle. The results show that the standard deviation of the lateral error and the yaw angle error of the algorithm is less than 0.1 m and 3.0°, respectively. Compared with the other two algorithms, the control of the front and rear wheels angle of this method is more flexible and the tracking accuracy is higher.

Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints

In this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the control signals generated by distributed secondary control algorithm are used to solve the problems of voltage and frequency recovery and power allocation for each distributed generators (DGs). Therefore, the model predictive control (MPC) with the mechanism of rolling optimization is adopted in the second control layer to achieve the above control objectives and solve the physical state constraint problem at the same time. Meanwhile, in order to reduce the communication cost, we designed the self-triggered control based on the prediction mechanism of MPC. In addition, the proposed algorithm of self-triggered MPC does not need sampling and detection at any time, thus avoiding the design of observer and reducing the control complexity. In addition, the Zeno behavior is excluded through detailed analysis. Furthermore, the stability of the algorithm is verified by theoretical derivation of Lyapunov. Finally, the effectiveness of the algorithm is proved by simulation.

Multi-time scale dynamic robust optimal scheduling of CCHP microgrid based on rolling optimization

Combined cooling, heating and power (CCHP) microgrid can effectively improve the efficiency of renewable energy and reduce the operating cost of microgrids. However, the uncertainties of renewable energy sources and variety load demands make the optimal scheduling of CCHP microgrid complex and difficult to achieve. In this paper, a novel multi-time scale dynamic robust optimal scheduling strategy is proposed for the coordinated operation of CCHP microgrid, which includes two time scales: the day-ahead scheduling scale and the intraday adjustment scale. In the day-ahead scheduling scale, the uncertainties of CCHP microgrid are introduced by describing the day-ahead forecast data as interval numbers with upper and lower bounds. The scheduling decision-making variables and the interaction power with power grid are obtained by considering the regulation cost in the worst-case scenario. In the intraday scheduling operation scale, the rolling optimization strategy is applied to obtain intraday optimal power allocation by minimizing the scheduling cost at each prediction horizon based on the intraday ultra-short-term forecast data, which preserves the day-ahead scheduling decision. Finally, the power deviation of the intraday ultra-short-term optimal power allocation is smoothed based on real-time operating data. The simulation results demonstrate that the proposed strategy can effectively reduce the operating cost and improve the robustness for the coordinated operation of CCHP microgrid.