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Accelerated hierarchical optimization method for emergency energy management of microgrids with energy storage systems

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ENERGY SCIENCE & ENGINEERING 2022年第3期10卷 962-972页

作者： Zhou, Weihao Li, Qiang Wu, Kunming Zhang, Leiqi Hassan, Muhammad Arshad Shehzad Chen, Minyou Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China State Grid Zhejiang Elect Power Res Inst Zhejiang Key Lab Distributed Generat & Microgrid Hangzhou Peoples R China Univ Faisalabad Dept Elect Engn Faisalabad Pakistan

When failures occur in microgrids (MGs), the energy management for emergencies is required. To respond to emergencies in MGs rapidly, an accelerated hierarchical optimization method has been proposed, where the outputs of energy storage systems (ESSs) are controlled to provide urgent supports, before the MG reconfiguration starts. However, it is time-consuming to find the optimal schemes for MG reconfiguration. To reduce the time of reconfiguration, an optimization method based on deep neural networks (DNNs) for MG reconfiguration is presented, which consists of three levels. First, the combinational optimization for load shedding amounts is solved to determine the loads that have to be cut off. Second, a DNN is used to find the parameters of reconfiguration instead of the time-consuming calculation of power flow, which is a good way to reduce time of reconfiguration. Third, according to these parameters, the optimal scheme for reconfiguration is selected by a comprehensive evaluation method, where the Delphi method (DM) is employed to adjust the weights of preferences in a comprehensive evaluation function, so it offers the diversity of decisions for the MG reconfiguration. Finally, to test our method, a modified IEEE 33-bus system is built in MATLAB for simulations. Compared to traditional methods, our method can obtain the same reconfiguration scheme under different on/off states of load switches, but the time of reconfiguration is only one-sixty-seventh of that of other methods. Furthermore, in terms of our comprehensive evaluation method, reconfiguration schemes can be selected under different preferences.

关键词： energy management hierarchical optimization method microgrids

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A hierarchical optimization method for Parameter Estimation of Diesel Generators

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IEEE ACCESS 2020年 8卷 176467-176479页

作者： Huang, Chao-Ming Huang, Yann-Chang Chen, Shin-Ju Yang, Sung-Pei Kun Shan Univ Dept Elect Engn Tainan 710 Taiwan Cheng Shiu Univ Dept Elect Engn Kaohsiung 833 Taiwan Kun Shan Univ Green Energy Technol Res Ctr Tainan 710 Taiwan

Diesel generators (DGs) are used to provide electrical power to consumers because their power outputs can be scheduled, and they offer stable operating characteristics in a standalone or microgrid system. The parameters for DGs are set to ensure reliable and accurate simulation for distributed energy resources (DERs), which increases system reliability, maintains power supply quality and reduces operational costs. There are many parameters that must be estimated for a DG. These parameter settings such as system gains and time constants may vary, as facilities are run for a few days. Parameters for a DG must then be re-estimated to ensure accurate simulation in a microgrid system. The proposed method uses a sensitivity analysis to classify parameters into three different categories. A hierarchical optimization method combined with an enhanced whale optimization algorithm (EWOA) is then used to estimate the parameter settings for a DG using actual measurement data. The proposed method is applied to a practical microgrid system, and the results show that the proposed method determines the optimal parameter settings for a DG that enables accurate simulation and robust implementation for a microgrid system.

关键词： Microgrids Transient analysis Voltage control Parameter estimation Generators Sensitivity analysis Microgrid system parameter estimation hierarchical optimization method enhanced whale optimization algorithm

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Extended Control Set Model Predictive Control for Three-Phase Three-Level NPC Converters 9

Extended Control Set Model Predictive Control for Three-Phas...

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9th Southern Power Electronics Conference

作者： Jiang, Wei Zhang, Yonglei Yuan, Xibo Guo, Xiang Zhu, Ruijie Li, Yi China Univ Min & Technol Sch Elect Engn Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9798350351163;9798350351156

Finite control set model predictive control (FCS-MPC) is widely employed in three-phase three-level (3P-3L) converters as a nonlinear multi-objective control strategy. However, variable switching frequencies and poor steady-state performance are serious defects due to the incomplete definition of its control set. To tackle the above problem, this paper proposes to split each control interval into multiple parts to reconstruct a new control set, namely the extended control set model predictive control (xCS-MPC). Moreover, the proposed xCS-MPC is further optimized by introducing a hierarchical optimization method to reduce the computational burden, which can calculate the optimal vector by comprehensively quantifying the current tracking conditions and the capability of the neutral point potential balance. Finally, the proposed MPC method is verified in MATLAB/Simulink. The simulation results shows that the proposed xCS-MPC has superior control performance than PI control and FCS-MPC.

关键词： computational burden extended control set(xCS) FCS-MPC hierarchical optimization method predictive control 3P-3L NPC converters

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hierarchical Real-Time Optimal Planning of Collision-Free Trajectories of Collaborative Robots

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2023年第4期107卷 57-57页

作者： Lukas, Dalibor Kot, Tomas VSB Tech Univ Ostrava 17 Listopadu 15 Ostrava 70800 Czech Republic

In collaborative robotics the manipulator trajectory has to be planned to avoid collisions, yet in real-time. In this paper we pose the problem as minimization of a quadratic functional among piecewise linear trajectories in the angular (joint) space. The minimization is subjected to novel nonlinear inequality constraints that simplify the original non-penetration constraints to become cheap to evaluate in real time while still preserving collision-avoidance. The very first and most critical step of the computation is to find an initial trajectory that is free of collisions. To that goal we minimize a weighted sum of the violated constraints until they become feasible or a maximal number of steps is reached. Sometimes an incremental growing of the obstacle helps. By incremental growing we mean that we sequentially solve auxiliary subproblems with obstacles growing from ground or falling from top and use as the initial trajectory the one optimized in the previous step. The initial trajectory is then optimized while preserving feasibility at each step. We solve a sequence of simple-bound constrained quadratic programming problems formulated in the dual space of Lagrange multipliers, which are related to the original linearized inequality constraints that are active or close-to-active. Finally, we refine the trajectory parameterization and repeat the optimization, which we refer to as an hierarchical approach, until an overall prescribed time limit, being well below a second, is reached.

关键词： Real-time Collision-free Path planning hierarchical optimization method Steepest-descent Active-set Inverse kinematics

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Prediction method of permissible error ranges of control parameters for underwater gliders under given operation accuracy

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APPLIED OCEAN RESEARCH 2020年 103卷 1页

作者： Wu, Hongyu Niu, Wendong Wang, Shuxin Yan, Shaoze Tsinghua Univ Dept Mech Engn State Key Lab Tribol Beijing 100084 Peoples R China Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300072 Peoples R China

For electromechanical systems, on the premise that operation accuracy satisfies requirements, a relatively larger error range of control parameter generally means a less manufacturing cost. Here a prediction method of permissible error ranges of control parameters is studied for underwater gliders under given operation accuracy. First, the dynamic model of eight-degree of freedom for an underwater glider is established, which is validated by experimental data measured in the South China Sea. On this basis, the glider steady-state motion errors are quantitatively analyzed within the specific ranges of both control parameters and their errors. Then a hierarchical optimization method is proposed to determine the permissible error ranges of control parameters. To improve iteration calculation speed, we establish response surface models (RSMs) to calculate the approximate steady-state motion errors. The hierarchical optimization method consists of sub-level optimization and system level optimization. At the first step of sub-level optimization, the genetic algorithm and RSMs are used for seeking the control parameters values and their error values that correspond to the approximate maximum steady-state motion errors. At the second step of sub-level optimization, the values obtained at the first step are substituted into the dynamic model to calculate the corresponding steady-state motion errors. In the system-level optimization, the non-dominated sorting genetic algorithm II is used for seeking the maximum error ranges of control parameters, on the premise that the steady-state motion errors obtained by the sub-level optimization satisfy constraints. Finally, the optimization results are validated by the dynamic model. The research results can provide significant guidance for the accuracy design of underwater gliders.

关键词： Underwater glider Dynamic analysis Steady-state motion errors Error ranges of control parameters hierarchical optimization method

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Combined Oligopoly and Oligopsony Bilateral Electricity Market Model Using CV Equilibria

Combined Oligopoly and Oligopsony Bilateral Electricity Mark...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Alikhanzadeh, AmirHessam Irving, Malcolm Brunel Univ BIPS London England

ISBN: (纸本)9781467327299

In a liberalized electricity market, all the participants are responsible for their decisions;therefore various methods such as equilibrium models are introduced to manage the risks of participating in the electricity market. Especially in those electricity markets with specific structures, such as BETTA (British Electricity Transmission and Trading Agreement), based on bilateral trading, the usage of this modeling becomes more significant. This paper proposes a novel Conjectural Variation (CV) equilibrium model for bilateral electricity markets, such as BETTA, to reduce the market participants' exposure to risk. Through an iterative co-ordination algorithm, consisting of a conjectural variations equilibrium model of an oligopolistic set of generators with a corresponding oligopsonistic equilibrium model of a set of supply companies, the 'match' of both quantity and price between these two models can be obtained. This match can be found by a hierarchical optimization approach, using the Matlab Pattern-Search optimization method.

关键词： BETTA Bilateral Electricity Market Conjectural Variations Equilibrium Models Game Theory hierarchical optimization method Nash Equilibrium Pattern-Search Algorithm

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Combined Oligopoly and Oligopsony Bilateral Electricity Market Model Using CV Equilibria

Combined Oligopoly and Oligopsony Bilateral Electricity Mark...

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IEEE Power and Energy Society General Meeting

作者： AmirHessam Alikhanzadeh Malcolm Irving Brunel Institute of Power Systems BIPS Brunel University London UK

ISBN: (纸本)9781467327275

关键词： Index Terms-- BETTA Bilateral Electricity Market Conjectural Variations Equilibrium Models Game Theory hierarchical optimization method Nash Equilibrium Pattern- Search Algorithm power markets Generating sets oligopsony oligopoly CATACLYSMIC VARIABLE STARS

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