Two-stage ADMM-based distributed optimal reactive power control method for wind farms considering wake effects

Since the connection of small-scale wind farms to distribution networks,power grid voltage stability has been reduced with increasing wind penetration in recent years,owing to the variable reactive power consumption of wind generators.In this study,a two-stage reactive power optimization method based on the alternating direction method of multipliers(ADMM)algorithm is proposed for achieving optimal reactive power dispatch in wind farm-integrated distribution systems.Unlike existing optimal reactive power control methods,the proposed method enables distributed reactive power flow optimization with a two-stage optimization structure.Furthermore,under the partition concept,the consensus protocol is not needed to solve the optimization problems.In this method,the influence of the wake effect of each wind turbine is also considered in the control design.Simulation results for a mid-voltage distribution system based on MATLAB verified the effectiveness of the proposed method.

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model

In this study,the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated.In the first stage,in order to locate the damage accurately,the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix.In the second stage,to estimate the damage extent,the sensitivity of most used modal properties due to damage,such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy(MNMSE)of suspected damaged elements.Moreover,a modal property change vector is evaluated using the group method of data handling(GMDH)network as a surrogate model during damage extent estimation by optimization algorithm;in this part of methodology,the performance of the three popular optimization algorithms including particle swarm optimization(PSO),bat algorithm(BA),and colliding bodies optimization(CBO)is examined and in this regard,root mean square deviation(RMSD)based on the modal property change vector has been proposed as an objective function.Furthermore,the effect of noise in the measurement of structural responses by the sensors has also been studied.Finally,in order to achieve the most generalized neural network as a surrogate model,GMDH performance is compared with a properly trained cascade feed-forward neural network(CFNN)with log-sigmoid hidden layer transfer function.The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE.Moreover,the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

基于全寿命周期成本的配电网蓄电池储能系统的优化配置

蓄电池储能具有效率高、使用寿命长、对地理条件要求低等优点,其额定功率和额定容量可以独立配置。以配电网中蓄电池储能系统全寿命周期内总的净收益最大为目标,研究配电网中蓄电池的配置和各时段充/放电值的优化,综合考虑了储能套利收入、政府电价补贴收入、减少电能转运费、延缓电网升级以及全寿命周期成本等因素。建立了蓄电池储能系统配置的混合优化模型,提出一种基于差分进化和预测-校正内点法的混合算法并进行求解。最后,算例测试比较了钠硫电池、全钒液流电池、多硫化物/溴液流电池、铅酸电池和锂离子电池的配置和净收益,分析了影响经济效益的指标,为蓄电池的配置规划提出了建议,并验证了所建模型和求解算法的可行性。

Some Results on Two-stage Clinical Trials

Among a variety of adaptive designs, stage-wise design, especially, two-stage design is an important one because patient responses are not available immediately but are available in batches or intermittently in some situations. In this paper, by Bayesian method, the general formula of asymptotical optimal worth is given, meanwhile the length of some optimal designs at first stage concerning two-stage trials in several important cases has been obtained.

A Two-stage Adaptive Robust Model for Residential Micro-CHP Expansion Planning

This paper addresses the planning problem of residential micro combined heat and power (micro-CHP) systems (including micro-generation units, auxiliary boilers, and thermal storage tanks) considering the associated technical and economic factors. Since the accurate values of the thermal and electrical loads of this system cannot be exactly predicted for the planning horizon, the thermal and electrical load uncertainties are modeled using a two-stage adaptive robust optimization method based on a polyhedral uncertainty set. A solution method, which is composed of column-and-constraint generation (C&CG) algorithm and block coordinate descent (BCD) method, is proposed to efficiently solve this adaptive robust optimization model. Numerical results from a practical case study show the effective performance of the proposed adaptive robust model for residential micro-CHP planning and its solution method.

变电站规模设计LCC两阶段优化方法研究

结合实际工程通过理论论证提出了变电站规模设计LCC两阶段优化方法。第一阶段依据LCC最优负载率快速、准确拟定包含变电站的规模和各主变投建时间信息的备选方案,第二阶段对备选方案进行详细的LCC评价来进行决策。该方法为变电站规模设计提供了一套具体的步骤流程和相应规范,并将技术论证和经济论证有效的结合到一起,简单易操作,具有现实意义。该方法在湖南省某220 kV变电站中的应用中验证了其可行性。