Decomposition Coordination Research Articles

A Fast Dynamic Simulation Method of an Active Distribution Network with Distributed Generations Based on Decomposition and Coordination

With the penetration of distributed resources into power distribution networks, power distribution networks are transforming into active distribution networks with a high proportion of distributed generations and power electronic equipment. Efficient modeling and simulation methods are essential to perform dynamic response analysis. In order to satisfy the fast/steady/slow multiple time-scale simulation requirements of active distribution networks, a fast/medium/slow time partition model and a network decoupling method for short line characteristic lines is proposed in this paper. Through the decomposition coordination simulation method, the network is decomposed into multiple regions that can be simulated in parallel. Based on the interconnection of fiber optic network cards, a multi-rate parallel simulation and synchronization strategy is proposed, which significantly improves the simulation speed of active distribution networks while ensuring simulation accuracy. The numerical experiments have been conducted based on a modified IEEE 33-bus and a PG&E 69-bus, and simulation results show the feasibility of the proposed method. The verification results of the example show that using adaptive variable-step-size multi-rate parallel simulation technology can increase the subnet computation-time balance rate and simulation acceleration ratio to 119.90% and 121.31% in the same rate-parallel mode.

Effective numerical computation of p(x)–Laplace equations in 2D

In this article we implement a method for the computation of a nonlinear elliptic problem with nonstandard growth driven by the Laplacian operator. Our implementation is based in the decomposition–coordination method that allows us, via an iterative process, to solve in each step a linear differential equation and a nonlinear algebraic equation. Our code is implemented in MatLab in two dimensions and turns out to be extremely efficient from the computational point of view.

Reliability analysis of flap deflection angle based on collaborative intelligent moving Kriging model

To effectively monitor the reliability of civil aircraft flap deflection angle, combined with the quick access recorder(QAR), the collaborative intelligent moving Kriging(CIMK) method is proposed by absorbing the Kriging model, decomposition and co-ordination strategy, equilibrium optimizer(EO), and moving least square(MLS). Among them, the decomposition coordination strategy is used to deal with the relationship between the flaps left and right deflection angles. MLS is employed to select effective modeling samples and solve the undetermined coefficients of Kriging model. EO method is applied to determine optimizing the local compact support region radius of MLS. Firstly, the fault reason for flap left-right asymmetry is analyzed to clarify the main characteristic parameters in QAR data. Secondly, combined with the QAR data of relevant influencing parameters, the civil aircraft flap deflection model(limit state function) is constructed by using CIMK. Then, the reliability and influence of civil aircraft flap deflection angle are analyzed by Monte Carlo(MC) sampling method. The results show that when the flap deflection angle is 3°, the reliability is 0.450 2, and the important factors affecting the flap deflection angle are Mach number, left angle of attack, right angle of attack, etc. Compared with the response surface method(RSM), Kriging, support vector machine(SVM), and back-propagation-artificial neural network(BP-ANN), the average absolute error accuracy of the proposed method is relative improved by 53.02%, 51.43%, 49.03%, and 44.04%, the average relative error accuracy is relative improved by 68.36%, 66.76%, 64.41%, and 62.64%, and the modeling efficiency is relative improved by 50.62%, 26.35%, and 43.01% respectively compared with Kriging, SVM and BP-ANN. When the number of simulations is 103, the analysis accuracy is relative improved by 8.82%, 7.25%, 6.22%, and 3.98% respectively.

Decomposition–Coordination of Double-Layer MPC for Constrained Systems

Large-scale industrial processes usually adopt centralized control and optimization methods. However, with the growth of the scale of industrial processes leading to increasing computational complexity, the online optimization capability of the double-layer model predictive control algorithm is challenged, exacerbating the difficulty of the widespread implementation of this algorithm in the industry. This paper proposes a distributed double-layer model predictive control algorithm based on dual decomposition for multivariate constrained systems to reduce the computational complexity of process control. Firstly, to solve the problem that the original dual decomposition method does not apply to constrained systems, two improved dual decomposition model prediction control methods are proposed: the dual decomposition method based on the quadratic programming in the subsystem and the dual decomposition method based on constraint zones, respectively. It is proved that the latter will certainly converge to the constraint boundaries with appropriate convergence factors for the controlled variables. The online optimization ability of the proposed two methods is compared in discussion and simulation, concluding that the dual decomposition method based on the constraint zones exhibits superior online optimization ability. Further, a distributed double-layer model predictive control algorithm with dual decomposition based on constraint zones is proposed. Different from the objective function of the original dual decomposition model predictive control, the proposed algorithm's dynamic control-layer objective function simultaneously tracks the steady-state optimization values of the controlled and manipulated variables, giving the optimal solution formulation of the optimization problem consisting of this objective function and the constraints. The algorithm proposed in this paper achieves the control goals while significantly reducing the computational complexity and has research significance for promoting the industrial implementation of double-layer model predictive control.

Multimedia Urban Road Path Optimization Based on Genetic Algorithm

In order to study multimedia urban road path optimization based on genetic algorithm, a dynamic path optimization based on genetic algorithm is proposed. Firstly, for the current situation of traffic congestion, time constraints are strictly considered based on the traditional hard time window logistics distribution vehicle scheduling problem model. Then, the mathematical model is established, and the optimal solution is solved by the combination of decomposition coordination algorithm and genetic algorithm. We divide multiple customers into different customer groups and determine the service object order of each express car in each customer group, so as to obtain the most valuable scheduling scheme. Finally, in the process of solving the model, the relevant and reliable distribution basis for enterprise distribution is collected, including customer geographical coordinates, demand, delivery time window, unit cost required for loading and unloading, loading and unloading time, and penalty cost to be borne by distribution enterprises after early arrival and late arrival. Using the improved genetic algorithm, the optimal solution of each objective function is actually obtained in about 140 generations, which is faster than that before the improvement. Using the genetic algorithm based on sequence coding, a hybrid genetic algorithm is constructed to solve the model problem. Through the comparative analysis of experimental data, it is known that the algorithm has good performance, is a feasible algorithm to solve the VSP problem with time window, and can quickly obtain the vehicle routing scheduling scheme with reference value.

Mobile Robot Autonomous Navigation: A Path Planning Approach

This work proposes a novel approach for solving the problem of safe autonomous navigation and path planning for a four-wheel robot. To formulate and state the optimization problem, we first model the robot kinematics, then associate the objective functions and constraints to the model. The multi-objective optimization problem we formulated is then solved using the Decomposition-Coordination (DC) technique. The DC method consists of subdividing the global complex problem into smaller sub-problems that are considerably easier to solve and can be processed in parallel, which minimizes the computational time and allows the algorithm to generate the optimal path in near real-time. The four-wheel robot starts executing the generated path, except if an obstruction comes across, then it will correct its path with the assistance of an obstacle avoidance algorithm. This same process of obstacle avoidance continues until the robot reaches its desired goal. In this paper, we present a brief proof of the convergence and stability of the DC method. We also explain the principle of the collision avoidance algorithm, and finally, we present the results of our simulations.

Effective and Safe Trajectory Planning for an Autonomous UAV Using a Decomposition-Coordination Method.

In this paper, we present a Decomposition Coordination (DC) method applied to solve the problem of safe trajectory planning for autonomous Unmanned Aerial Vehicle (UAV) in a dynamic environment. The purpose of this study is to make the UAV more reactive in the environment and ensure the safety and optimality of the computed trajectory. In this implementation, we begin by selecting a dynamic model of a fixed-arms quadrotor UAV. Then, we define our multi-objective optimization problem, which we convert afterward into a scalar optimization problem (SOP). The SOP is subdivided after that into smaller sub-problems, which will be treated in parallel and in a reasonable time. The DC principle employed in our method allows us to treat non-linearity at the local level. The coordination between the two levels is achieved after that through the Lagrange multipliers. Making use of the DC method, we can compute the optimal trajectory from the UAV’s current position to a final target practically in real-time. In this approach, we suppose that the environment is totally supervised by a Ground Control Unit (GCU). To ensure the safety of the trajectory, we consider a wireless communication network over which the UAV may communicate with the GCU and get the necessary information about environmental changes, allowing for successful collision avoidance during the flight until the intended goal is safely attained. The analysis of the DC algorithm’s stability and convergence, as well as the simulation results, are provided to demonstrate the advantages of our method and validate its potential.

Preparation of Y3+ and transition metal ions codoped-BiFeO3 with enhanced magnetism and photocatalytic properties

Nanosized particles of Bi 0.95 Y 0.05 Fe 0.97 M 0.03 O 3 (M ​= ​Mn,Co,Ni,Cu,Zn) were successfully synthesized through thermal decomposition coordination precursors with diethylenetriaminepentaacetic acid (H 5 dtpa) as ligand. All prepared samples were characterized by XRD, Uv–vis DRS, SEM, PL, EIS, VSM and so on. From XRD results, the main phase of the samples was rhombohedral R3c (161) BiFeO 3 , and contained a small amount of soft iron ore Bi 25 FeO 40 . The phase and morphology of prepared samples were affected by doping ions. The average grain size of the samples was about 20 ​nm.T.The Uv–vis demonstrated the band gap of all samples was less than 2 ​eV and the response to light of all samples extended from ultraviolet region to visible region (~650 ​nm).The BFO–Y–Mn exhibited the minimum band gap of 1.77 ​eV. At the same time, photocatalytic activities of all samples prepared were evaluated through the photocatalytic degradation of methyl orange (MO). BiFeO 3 co-doping with 5%Y 3+ and 3%Mn 2+ exhibited excellent photocatalytic activity for MO solution. In the present of BFO–Y–Mn, the degradation rate of MO under light irradiation of 3 ​h was about 98%. Y 3+ ion and transition metal ions co-doped bismuth ferrite were successfully prepared by low-temperature thermal decomposition of coordination precursor method. The effects of different transition metal ions on the phase, magnetism, band structure, and photocatalytic properties of BiFeO 3 have been investigated. According to the energy band potential, it can be inferred that •OH − is the main active species in the photodegradation reaction of MO. • Y 3+ and M 2+ co-doped BiFeO 3 photocatalysts were successfully synthesized by a facile method. • Y 3+ and M 2+ co-doped BiFeO 3 photocatalysts demonstrated residual magnetism at room temperature. • The phase, band structure, and photocatalytic properties of co-doped BiFeO 3 ​have been investigated.

An optimization model for joint power generation dispatching of the cascade hydropower stations on the lower reaches of Jinsha River and the Yangtze River

The Xiluodu, Xiangjiaba, Three Gorges and Gezhouba cascade hydropower stations, which located at the lower reaches of Jinsha River and the upper reaches of Yangtze River, plays a very important role in the electrical power system in China. To optimize and solve the joint power generation problem of the cascade system with highly complex constraints, hydraulic relationship and multiple dispatching objectives, this research focus on: (1) a mid-and-long term power generation optimization model for the cascade system is established, considering hydraulic and electricty connections, as well as the flood control, water supply and ecology objectives; (2) an improved LSSDC-DDDP hybrid optimization framework, based on the theory of large-scale system decomposition coordination (LSSDC) and the discrete differential dynamic programming (DDDP), is designed to well solving the proposed optimization model. In the optimization framework, a relative coefficient is used to calculate the coordination factor among subsystems (a single hydropower station); meanwhile, an adaptive corridor and a bias corridor are adopted to enhance convergence speed of DDDP. The optimization model is applied in the dispatching calculation of the cascade system using the 2015-2017 actual runoff conditions, respectively. The results indicate that joint optimal dispatching of the cascade system can theoretically increase its yearly total power generation benefit about 2.73 billion yuan on average, simultaneously satisfying the requirements of downstream water supply and improving the total firm output guarantee rate of the system.

An ecological stability-oriented model for the conjunctive allocation of surface water and groundwater in oases in arid inland river basins

Abstract With the continuous development of the population and social economy, the spatial and temporal distribution of water resources in arid inland river basins is severely uneven, and there is a sharp contradiction between agricultural water use and ecological water use. Irrational development and utilization of water resources has led to many problems, such as shrinking oases and drying lakes. To solve this problem, this study proposes a multiobjective, multiwater-source, ecological stability-oriented double-layer model for optimal allocation of water resources based on the large-scale system decomposition–coordination principle, the water balance principle, and a water supply and demand forecasting model. This model can resolve the contradiction between agricultural water use and ecological water use by optimizing and adjusting the crop planting structure, industrial structure, and the amount of water allocated to and groundwater level in each region and thereby achieve ecological stability and restoration of oases. The developed model was applied to the Heihe River Basin in an inland region of Northwest China. The long-term time series data of 2000–2016 were used to construct and calibrate the model. Finally, the practical ecological stability-oriented plan for conjunctive allocation of surface water and groundwater in different plan years was proposed. This model enriches the research results related to the conjunctive allocation of surface water and groundwater and provides a reference for the ecological restoration of oases in arid inland river basins.

Distributed regularized stochastic configuration networks via the elastic net

Stochastic configuration network (SCN) has great potential in developing fast learning model with sound generalization capability and can be easily extended to the distributed computing framework. This paper aims to develop a distributed regularized stochastic configuration network to solve the limitations of traditional centralized learning on the scalability and efficiency in computing and storage resources for massive datasets. The local models are constructed using a classical stochastic configuration network, and the global unified model is built by the alternating direction method of multipliers (ADMM). Elastic net regularization term combining the LASSO and ridge methods is added into loss function of the ADMM optimization to prevent the model from overfitting when the data has high-dimensional collinearity. Each layer of the local regularized SCN model of a node in the topology network is constructed incrementally; its input weights and biases are broadcast to all other nodes under the inequality constraints. Output weights and the Lagrange multipliers of each node are calculated alternately through the decomposition–coordination procedure of the ADMM optimization algorithm until it finally converges to a unified model. A comprehensive study on five benchmark datasets and the ball mill experimental data has been carried out to evaluate the proposed method. The experiment results show that the proposed distributed regularized stochastic configuration network has relative advantages in terms of accuracy and stability compared with the distributed random vector functional link network.

Demand Side Response Participation in Reserve Configuration Optimization Based on Decomposition and Coordination

As the proportion of new energy sources in the grid increases and the structure of the ultra-high-voltage grid becomes more complicated, the variability of the external environment and the complexity of the grid itself bring many new challenges to the power system reserve configuration problem. The multilevel and multitime scale characteristics of the grid reserve are analyzed in this paper, and the reserve is classified according to the response time characteristics combined with the system function. We establish a demand-side response participation system reserve configuration optimization model, including synchronous generator generation and reserve models, demand-side scheduling and reserve models, grid power generation planning and reserve optimization mathematical models. Based on the idea of decomposition coordination, the current power generation plan and reserve optimization problem are decomposed into a finite fault state safety constraint unit combination subproblem, Monte Carlo secondary negative reserve constraint check subproblem, frequency safety constraint checking subproblem based on large-disturbance long-process time domain simulation analysis, and fault state static safety constraint check subproblem based on minimum load shedding. Considering the demand side response to the reserve configuration of the system, combined with the problem of previous unit association, the optimal allocation of the reserve coal consumption units is realized by determining the start and stop status of the unit and the unit output.

Study on Multi-Scale Coupled Ecological Dispatching Model Based on the Decomposition-Coordination Principle

Studies on environmental flow have developed into a flow management strategy that includes flow magnitude, duration, frequency, and timing from a flat line minimum flow requirement. Furthermore, it has been suggested that the degree of hydrologic alteration be employed as an evaluation method of river ecological health. However, few studies have used it as an objective function of the deterministic reservoir optimal dispatching model. In this work, a multi-scale coupled ecological dispatching model was built, based on the decomposition-coordination principle, and considers multi-scale features of ecological water demand. It is composed of both small-scale model and large-scale model components. The small-scale model uses a daily scale and is formulated to minimize the degree of hydrologic alteration. The large-scale model uses a monthly scale and is formulated to minimize the uneven distribution of water resources. In order to avoid dimensionality, the decomposition coordination algorithm is utilized for the coordination among subsystems; and the adaptive genetic algorithm (AGA) is utilized for the solution of subsystems. The entire model—which is in effect a large, complex system—was divided into several subsystems by time and space. The subsystems, which include large-scale and small-scale subsystems, were correlated by coordinating variables. The lower reaches of the Yellow River were selected as the study area. The calculation results show that the degree of hydrologic alteration of small-scale ecological flow regimes and the daily stream flow can be obtained by the model. Furthermore, the model demonstrates the impact of considering the degree of hydrologic alteration on the reliability of water supply. Thus, we conclude that the operation rules extracted from the calculation results of the model contain more serviceable information than that provided by other models thus far. However, model optimization results were compared with results from the POF approach and current scheduling. The comparison shows that further reduction in hydrologic alteration is possible and there are still inherent limitations within the model that need to be resolved.

An Optimal Allocation Model for Large Complex Water Resources System Considering Water supply and Ecological Needs

Water resources are very important to support the socio-economic development and maintain environmental health, which is a typical issue in water resources management. In this study, we developed an optimal allocation model for a large complex system of water resources by considering both water supply and river ecological benefits. The water supply benefit is defined as the minimum water deficit for different water users, while the ecological benefit involves making the reservoir release as close as possible to the natural streamflow. To solve this problem, the combination of decomposition-coordination (DC) and discrete differential dynamic programming (DDDP) methods were proposed. The proposed methods first decomposed a large system with multi-objective programming into subsystems, and the optimal solution of each subsystem was accomplished by the DDDP method to solve the system efficiently. Then the subsystems’ solutions were coordinated to figure out the near global optimal solution. The proposed models were tested in the Lingui and Yongfu County, Guilin City in China. Results show that the optimal reservoir release is close to the natural flow regime and there is a slight water deficit ratio in both level years. The water supply objective is more sensitive to the system model compared with the ecological objective, and the result of water allocation is optimized when the reservoir release is as close as possible to the natural flow based on the minimum water deficit. The proposed system model could facilitate sustainable water use and provide technical support for water resources management in economic development.

A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination

Multi-objective unit commitment (MOUC) considers concurrently both economic and environmental objectives, then finds the best trade-off with respect to these objectives. This paper proposes a novel model for MOUC, and a decomposition coordination approach is presented to solve the model. The economic objective is to reduce the fuel cost while the environmental objective is to reduce the CO 2 emission. The MOUC model considers these objectives by minimizing the distance to the Utopian point, which avoids generating Pareto optimal solutions. The model is solved by a decomposition coordination approach, which decomposes the whole system into subsystems and performs an iterative process. During each iteration step, the tie-line power flow is updated based on the margin price in connected subsystems, then, each subsystem is solved by branch and bound method, and the result is improved during iterations as shown in case studies. Besides, as the process does not require uploading units parameters, it protects the privacy of generating companies. Numerical case studies conducted using the proposed multi-objective model are applied to illustrate the performance of the approach.

A novel approach for solving multi-objective unit commitment based on decompositioncoordination

Multi-objective unit commitment (MOUC) considers simultaneously both economic and environmental objectives, then finds the best trade off with respect to these objectives. This paper proposes a novel model for MOUC, and a decomposition coordination approach is presented to solve the model. The MOUC model considers environmental objective by introducing a novel penalty term, and it's a quantized term for preference of environmental objective, which could be a basis for carbon tax makers. The model is solved by a decomposition coordination approach, which decomposes the whole system into subsystems and performs an iterative process. During each iteration step, the tie-line is updated based on the margin price in connected subsystems, then, each subsystem is solved by Lagrangian relaxation (LR), and the result is improved during iterations as shown in case studies. Besides, as LR does not require uploading units' parameters, it protects the privacy of generating companies. Numerical case studies considering different scenarios, conducted using the proposed multi-objective model, are applied to illustrate the robustness as well as the performance of the approach.

State filtering‐based least squares parameter estimation for bilinear systems using the hierarchical identification principle

This study presents a combined parameter and state estimation algorithm for a bilinear system described by its observer canonical state-space model based on the hierarchical identification principle. The Kalman filter is known as the best state filter for linear systems, but not applicable for bilinear systems. Thus, a bilinear state observer (BSO) is designed to give the state estimates using the extremum principle. Then a BSO-based recursive least squares (BSO-RLS) algorithm is developed. For comparison with the BSO-RLS algorithm, by dividing the system into three fictitious subsystems on the basis of the decomposition–coordination principle, a BSO-based hierarchical least squares algorithm is proposed to reduce the computation burden. Moreover, a BSO-based forgetting factor recursive least squares algorithm is presented to improve the parameter tracking capability. Finally, a numerical example illustrates the effectiveness of the proposed algorithms.

Combined heat and power scheduling: Utilizing building‐level thermal inertia for short‐term thermal energy storage in district heat system

A major limitation to integrating wind power in electric power system (EPS) is the massive generation capacity of the high‐penetration inflexible combined heat and power (CHP) units in district heating system (DHS). A heat and power optimal dispatch is established in this study to exploit building‐level thermal inertia as a form of short‐term thermal energy storage (TES) to unlock the flexibility of CHP units. This proposed short‐term building TES can provide storage potential by utilizing small variations in indoor temperature under the proposed dispatch control model to control the storage capacity in the dispatch process. A linear optimization operational model is run to integrate energy systems by balancing heat and power demands over multiple time periods with CHP units, wind power, short‐term building TES, and district‐level heating network (DHN) storage capacity. A two‐stage decomposition–coordination iteration algorithm is proposed to solve this optimization program. The simulation results are analyzed to determine the potential benefits of this short‐term TES in terms of economics, wind power integration, and the flexibility of CHP units. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Autonomous Navigation for an Unmanned Aerial Vehicle by the Decomposition Coordination Method

This paper introduces a new approach for solving the navigation problem of Unmanned Aerial Vehicles (UAV) by studying its rotational and translational dynamics and then solving the nonlinear model by the Decomposition Coordination method. The objective is to reach a destination goal by the mean of an autonomous computed optimal path calculated through optimal control sequence. Solving such complex systems often requires a great amount of computation. However, the approach considered herein is based on the Decomposition Coordination principle, which allows the nonlinearity to be treated at a local level, thus offering a low computing time. The stability of the method is discussed with sufficient conditions for convergence. A numerical application is given in consolidation the theoretical results.

A Reactive Path Planning Approach for a Four-wheel Robot by the Decomposition Coordination Method

In this paper, we discuss the problem of safe navigation by solving a non-linear model for a four-wheel robot while avoiding the upcoming obstacles that may cross its path using the Decomposition Coordination Method (DC). The method consists of first, choosing a non-linear system with the associated objective functions to optimize. Then we carry on the resolution of the model using the Decomposition Coordination Method, which allows the non-linearity of the model to be handled locally and ensures coordination through the use of the Lagrange multipliers. An obstacle-avoidance algorithm is presented thus offering a collision-free solution. A numerical application is given to concert the efficiency of the method employed herein along with the simulation results.