Wenlei Bai

Bio: Wenlei Bai is an academic researcher from Baylor University. The author has contributed to research in topics: Wind power & Heuristic (computer science). The author has an hindex of 6, co-authored 15 publications receiving 168 citations. Previous affiliations of Wenlei Bai include Hitachi.

Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

Abstract: The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM)—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC) algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.

Modified optimal power flow on storage devices and wind power integrated system

Abstract: The intermittent wind power will impact system operation cost, and voltage profile as well. Voltage profile can be enhanced by implementing energy storage system. Thus the optimal operation of storage devices under wind energy system is of great interest. In this paper, the uncertainty of wind power is tackled by scenario-based approach, and then the optimization problem becomes deterministic under each scenario. Since the problem is highly non-linear and non-convex, a novel heuristic method, artificial bee colony (ABC), is utilized to handle the problem. The objective is to minimize the total fuel cost while enhancing the voltage profile. Modified IEEE-30 bus is used to test the validity and effectiveness of the method.

Orthogonal Arrays Theory And Applications

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A Survey of Learning-Based Intelligent Optimization Algorithms

Abstract: A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a certain learning ability. This is how the traditional intelligent optimization algorithm combines learning operators or specific learning mechanisms to give itself some learning ability, thereby achieving better optimization behavior. We conduct a comprehensive survey of LIOAs in this paper. The research includes the following sections: Statistical analysis about LIOAs, classification of LIOA learning method, application of LIOAs in complex optimization scenarios, and LIOAs in engineering applications. The future insights and development direction of LIOAs are also discussed.

Coordinated Control Strategies for Fuel Cell Power Plant in a Microgrid

Abstract: Solid-oxide fuel cell (SOFC) power plant plays a vital role in a hybrid alternative energy based microgrid due to its reliability and flexibility in power supply However, the control of SOFC is challenging in terms of providing a fast load tracking while maintaining the fuel utilization rate within a safe range To this end, this paper builds two basic coordinated control strategies (CCS) for power management of SOFC-based microgrid The first is “Fuel Cell follows Inverter” scheme, where fast tracking is preferred while SOFC may operate on the edge of the safety range The second is “Inverter follows Fuel Cell” scheme, where high security is guaranteed by sacrificing performance in load tracking To obtain a robust and simple scheme, energy balance principle is used in CCS such that PI controller is sufficient to fulfill the basic duties Moreover, a simple supervisory control strategy is proposed for microgrid to provide a reasonable power reference for SOFC The control system is designed and tuned based on an SOFC plant with inverter's average model The efficiency of the proposed strategies is validated via a grid-connected SOFC/photovoltaic microgrid

Salp swarm optimizer to solve optimal power flow comprising voltage stability analysis

Abstract: A new attempt of employing salp swarm algorithm (SSA) to tackle the optimal power flow (OPF) problem is demonstrated in the current study. This aforementioned problem has four fitness functions to be optimized such as (1) the sum of generating units’ fuel costs, (2) total network real power losses, (3) entire sum of voltage deviation of load buses, and (4) static voltage stability (VS) of electric power systems. At initial stage, these objective are solved one by one, and at a later stage, different vector objective functions are solved simultaneously by the SSA. The VS study based on a modal analysis is taken into consideration as an objective function. In this issue, the eigenvalues and eigenvectors of a reduced Jacobian matrix due to the reactive power change are figured. The smaller magnitude of eigenvalues indicates the vicinity to system voltage instability. As the magnitude of eigenvalues increases, the incremental voltage decreases, which means strong VS. The output active power of generating units, their voltages, transformers tap setting, and capacitor devices represent the search field. Two electric grids such as IEEE 57- and 118-bus electric networks are demonstrated to examine the performance of the SSA. The effectiveness of the SSA–OPF methodology is compared with that obtained by using other competing optimization methods. Furthermore, statistical performance measures comprising parametric and nonparametric tests are made and the simulation results are extensively verified which indicate a competition of the SSA with others algorithms in solving the OPF problem.