Rolling Optimization Algorithm Research Articles

Simulation of motions of a 6DoF unmanned aerial vehicle from the mathematical model in free space

This work prepares a smooth playing ground to modeling and simulation of a Collision Avoidance for Cooperative UAVs with Rolling Optimization Algorithm (ROA) Based on Predictive State Space (PSS) technique using Matlab, Simulink and Aerospace toolboxes of MathsWork. The methodological approach adopted is Computer Aided Software Engineering and to have handle collisions and maneuvers in a mathematical way, normal simulation of the trajectory of both the UAV and obstacles is defined by geometric approach. The goal of the work is to develop a mathematical model of a six degree of freedom (6DoF) of unmanned aerial vehicle (UAV) in free space for further modeling and simulation of a collision avoidance for cooperative UAVs with the required specifications. The motion (modeling of mathematical parameters like velocity, position, body rotational rate and Euler angles) of the 6DoF aircraft was determined by coordinate systems which allow the aircraft’s position and orientation in spaceto be kept tracked. The discrete form of the developed model for simulation was achieved using appropriate transfer functions. The development of the mathematical model will enable the development of an artificial NN model predictive controller (MPC) that can handle the nonlinearities associated with the UAV based on PSS technique.

A Rolling Optimization Algorithm for Real-Time Traffic Control With Delay Minimization

This article presents a rolling optimization algorithm (ROA) for minimizing total vehicle delay for real-time traffic signal control in urban road networks, where the vehicle delay minimization problem is formulated as a quadratically constrained quadratic programming problem, which is NP-hard. The programming problem is relaxed and resolved using the ROA in a distributed manner. In particular, we introduce network partition to a large-scale urban road network and then present a regional ROA to eliminate the low efficiency caused by the large number of decision variables. Numerical experiments are performed on one of Beijing’s district road networks to validate the efficiency of the proposed method in benchmark against several typical techniques.

Multi-time scale energy management of microgrid considering the uncertainties in both supply and demand

The uncertainty of supply and demand have different characteristics, which can adversely affect the stable operation of microgrid. This paper presents a multi-time scale scheduling strategy, which mainly consists of day-ahead scheduling layer and intraday adjustment layer. In the day-ahead scheduling layer, the Monte Carlo method and demand response are used to deal with the uncertainty of both supply and demand, and a stochastic optimal model is established by considering the uncertainty in the microgrid. In the first stage, the unit commitment decisions are derived in a stochastic optimization framework to achieve minimum operating cost. In the second stage, the day-ahead economic power scheduling is achieved by using a deterministic optimization framework. In the intraday adjustment layer, a rolling optimization algorithm is applied to adjust the day-ahead economic power scheduling plan, which does not change the unit commitment decisions. The power fluctuations caused by inaccurate day-ahead forecast data are effectively smoothed with intraday rolling optimization. The simulation results show that the proposed model and strategy can effectively reduce the impact of uncertainty on the optimal scheduling of the microgrid. In addition, comparing the scheduling results for one month, it can be seen that the proposed strategy has better economy in the long-time operation of the microgrid.

Interval optimization based coordinated control for distribution networks with energy storage integrated soft open points

Due to the increasing penetration of distributed generations (DGs) in distribution networks, the randomness and uncertainties of DG outputs can result in bus voltage fluctuations, affecting the power quality and power supply reliabilities of distribution networks. Therefore, this paper proposes an interval optimization (IO) based two-stage optimization method to regulate voltage fluctuations, and to improve the economy of the distribution network that contains energy storage integrated soft open points (E-SOPs) and high penetrations of DGs. The first stage establishes an IO based day-ahead optimization model to optimize the on-load tap changer and the state of charge ranges of the battery energy storage system (BESS) in each E-SOP. The second stage develops an intra-day optimization model based on the rolling optimization algorithm to optimize the active and reactive power at both sides of each E-SOP and the charging and discharging power of the BESS in each E-SOP. Furthermore, numerous simulations and comparisons are carried out on a modified IEEE 33-bus distribution network including high penetrations of DGs, and the results show that the proposed method can not only significantly reduce comprehensive operating costs of distribution networks, but also can effectively control the nodal voltage within the desired range.

Hierarchical Control of Trajectory Planning and Trajectory Tracking for Autonomous Parallel Parking

For the parallel parking problem in narrow space, this paper proposes a trajectory tracking control method with a novel trajectory planning layer for autonomous parallel parking based on a numerical optimization algorithm and model predictive control. In the trajectory planning layer, the vehicle kinematics model suitable for the low-velocity parking scene is established. Considering the vehicle physical constraints, boundary condition constraints, and obstacle avoidance constraints during the parking process, the parking trajectory planning task is described as an optimal control problem, further transformed into a nonlinear programming problem by Gauss pseudo-spectral method. Taking the shortest parking completion time as the optimization objective function, the parking trajectories of the large, medium and small parking spaces are obtained, respectively. A parking trajectory tracking controller based on the model predictive control algorithm is designed in the trajectory tracking control layer. The linear error model is used as the prediction model, and the quadratic programming is adopted as the rolling optimization algorithm in the tracking controller. The velocity and front-wheel swing angle are obtained as control signals for parking trajectory tracking. Through CarSim and Simulink's co-simulation, the feasibility and effectiveness of the proposed parallel parking trajectory planning and tracking control method are verified. The co-simulation results show that the maximum tracking errors of horizontal and longitudinal positions are less than 0.15m. The maximum tracking errors of heading angle are less than 2° under three different parking spaces. Real vehicle tests are carried out to verify the effectiveness of the proposed hierarchical control method. The test results show that the vehicle can park in the parking space safely, quickly and accurately when the actual parking space is detected. The proposed method can plan the parking trajectory with the constraints and the shortest time and control the vehicle to complete the parking operation accurately along the planned trajectory.

Coordinated Voltage Regulation Methods in Active Distribution Networks with Soft Open Points

This paper proposes a coordinated voltage regulation method for active distribution networks (ADNs) to mitigate nodal voltage fluctuations caused by photovoltaic (PV) power fluctuations, where a three-stage optimization scheme is developed to coordinate and optimize the tap position of on-load tap changers (OLTCs), the reactive power of capacitor banks (CBs), and the active and reactive power of soft open points (SOPs). The first stage aims to schedule the OLTC and CBs hourly using the rolling optimization algorithm. In the second stage, a multi-objective optimization model of SOPs is established to periodically (15 min) optimize the active and reactive power of each SOP. Meanwhile, this model is also responsible for optimizing the Q-V droop control parameters of each SOP used in the third stage. The aim of the third stage is to suppress real-time (1 min) voltage fluctuations caused by rapid changes in PV power, where the Q-V droop control is developed to regulate the actual reactive power of SOPs automatically, according to the measured voltage at the SOPs’ connection points. Furthermore, numerous simulations and comparisons are carried out on a modified IEEE 33-bus distribution network to verify the effectiveness and correctness of the proposed voltage regulation method.

Joint Virtual Energy Storage Modeling with Electric Vehicle Participation in Energy Local Area Smart Grid

In this research, the joint virtual energy storage modeling with electric vehicle participation in energy local area Smart Grid is considered. This article first constructs a virtual energy storage model and a joint virtual energy storage model for air conditioning and electric vehicles. Therefore, for the optimization problem of virtual energy storage power, a continuous rolling optimization algorithm to determine the feasible solution of the high-dimensional complex constraint optimization problem is proposed to solve the optimization problem. Finally, the analysis, for example, illustrates the economics of joint virtual energy storage in the Smart Grid. The results prove that air conditioning and electric vehicles have the ability to jointly participate in virtual energy storage, and the comparison proves that joint virtual energy storage can effectively improve the economics of electricity consumption.

Neural network prediction model to achieve intelligent control of unbalanced drilling’s underpressure value

In underbalanced drilling, accidents like well leakage and overflow not only damage the reservoir but cause great safety risks to drilling operations. Therefore, it is of great engineering significance to maintain a reasonable under-pressure state by controlling a reasonable underpressure value. Data mining is an advanced method for retrieving and creating corresponding models in massive data. During the drilling process, there are a large number of real-time monitoring data and historical data. Therefore, a neural network prediction control model based on improved rolling optimization algorithm has been proposed. Combined with control principle of underpressure value, a set of online rolling optimization neural network control model for achieving underpressure value intelligent control of underbalanced drilling is formed. The control model optimizes the neural network prediction control model through the rolling optimization algorithm, realizing advanced prediction of reasonable underpressure value, and performs fast and stable self-feedback control of the output prediction results. By using field data for optimization analysis, the analysis results show that using the neural network prediction control model of online rolling optimization can effectively conduct accurate prediction and real-time control for the reasonable underpressure value.

Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations

For the train timetabling problem (TTP) in a metro system, the operator-oriented and passenger-oriented objectives are both important but partly conflicting. This paper aims to minimize both objectives by considering frequency (in the line planning stage) and train cost (in the vehicle scheduling stage). Time-varying passenger demand and train capacity are considered in a nonsmooth, nonconvex programming model, which is transformed into a mixed integer programming model with a discrete time-space graph (DTSG). A novel dwell time determining process considering congestion at stations is proposed, which turns the dwell times into dependent variables. In the solution approach, we decompose the TTP into a subproblem for optimizing segment travel times (OST) and a subproblem for optimizing departure headways from the shunting yard (OH). Branch-and-bound and frequency determining algorithms are designed to solve OST. A novel rolling optimization algorithm is designed to solve OH. The numerical experiments include case studies on a short metro line and Beijing Metro Line 4, as well as sensitivity analyses. The results demonstrate the predictive ability of the model, verify the effectiveness and efficiency of the proposed approach, and provide practical insights for different scenarios, which can be used for decision-making support in daily operations.

Optimal Interactive Operation of Microgrid Under Demand Response Based on Rolling Optimization Algorithm

As one of the ways to integrate of distributed sources, energy storage and flexible loads, micro grid technology is closely related to demand side response and has become the important area of energy research. In this paper, the micro grid optimization model is established considering the operation characteristics of different types of micro sources and the demand for different test modes of demand response. Then the model and adjustment strategy of flexible loads in rolling algorithm are proposed. Finally, by means of simulation analysis, it shows the demand side response management system model not only improves the electricity consumption economy and energy efficiency, but also plays a role of peak load shifting, which demonstrates the effectiveness of the rolling algorithm and model.

Rolling optimization formation control for multi-agent systems under unknown prior desired shapes

This paper deals with the optimal formation control problem of multi-agent systems under a prior unknown desired shapes. The objective of this paper is to accomplish a rolling optimal formation of multi-agent systems by incorporating trajectory tracking and energy saving control into a unified framework. First, according to the real-time desired position offset between each follower and the leader, the rolling optimization performance indexes are introduced. Then, a distributed rolling optimization algorithm is proposed to realize the desired formation of multi-agent systems under the minimum energy consumption of agents. Finally, several simulation examples are conducted to illustrate the effectiveness of the proposed design algorithm.

Collision Avoidance for Cooperative UAVs with Rolling Optimization Algorithm Based on Predictive State Space

Unmanned Aerial Vehicles (UAVs) have recently received notable attention because of their wide range of applications in urban civilian use and in warfare. With air traffic densities increasing, it is more and more important for UAVs to be able to predict and avoid collisions. The main goal of this research effort is to adjust real-time trajectories for cooperative UAVs to avoid collisions in three-dimensional airspace. To explore potential collisions, predictive state space is utilized to present the waypoints of UAVs in the upcoming situations, which makes the proposed method generate the initial collision-free trajectories satisfying the necessary constraints in a short time. Further, a rolling optimization algorithm (ROA) can improve the initial waypoints, minimizing its total distance. Several scenarios are illustrated to verify the proposed algorithm, and the results show that our algorithm can generate initial collision-free trajectories more efficiently than other methods in the common airspace.