Yazar "Bai, Wenlei" seçeneğine göre listele
Listeleniyor 1 - 5 / 5
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Heuristic Optimization for Wind Energy Integrated Optimal Power Flow(Ieee, 2015) Bai, Wenlei; Eke, İbrahim; Lee, Kwang Y.Wind energy has been playing a critical role in modern electric power system due to the fact that wind is free of cost and environment-friendly. However the inherent intermittency of wind has complicated system operation such as optimal power flow (OPF). In this paper, the authors present a stochastic OPF model integrated with wind power (WOPF). Since WOPF is a highly non-linear and non-convex problem, a heuristic method, artificial bee colony (ABC), is utilized to handle the problem. Heuristic methods are credited for their simplicity to solve complicated non-linear optimization problem without approximating the system and the ability to find better global optimum. Two IEEE systems (30 and 118 bus) are used to test the validity and effectiveness of the method.Öğe Improved Artificial Bee Colony Based on Orthognal Learning for Optimal Power Flow(Ieee, 2015) Bai, Wenlei; Eke, İbrahim; Lee, Kwang Y.Optimal power flow (OPF) problem is to optimize an objective function (usually total cost of generation), while satisfying system constraints. The OPF is a non-linear and non-convex problem, and an artificial bee colony (ABC) algorithm is utilized to handle the problem. Heuristic methods are credited for their simplicity to solve complex non-linear optimization problem without simplifying approximation of the system. However, the original ABC has poor efficiency on exploitation search, thus in order to find better global optimum, this paper proposes an improved ABC (IABC) based on orthogonal learning. The IABC implements the idea of orthogonal experiment design (OED) based on the orthogonal learning. The validity and effectiveness of the method are tested in the IEEE-30 bus system.Öğe An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem(Pergamon-Elsevier Science Ltd, 2017) Bai, Wenlei; Eke, İbrahim; Lee, Kwang Y.The increasing fuel price has led to high operational cost and therefore, advanced optimal dispatch schemes need to be developed to reduce the operational cost while maintaining the stability of grid. This study applies an improved heuristic approach, the improved Artificial Bee Colony (IABC) to optimal power flow (OPF) problem in electric power grids. Although original ABC has provided robust solutions for a range of problems, such as the university timetabling, training neural networks and optimal distributed generation allocation, its poor exploitation often causes solutions to be trapped in local minima. Therefore, in order to adjust the exploitation and exploration of ABC, the IABC based on the orthogonal learning is proposed. Orthogonal learning is a strategy to predict the best combination of two solution vectors based on limited trials instead of exhaustive trials, and to conduct deep search in the solution space. To assess the proposed method, two fuel cost objective functions with high non-linearity and non-convexity are selected for the OPF problem. The proposed IABC is verified by IEEE-30 and 118 bus test systems. In all case studies, the IABC has shown to consistently achieve a lower cost with smaller deviation over multiple runs than other modern heuristic optimization techniques. For example, the quadratic fuel cost with valve effect found by IABC for 30 bus system is 919.567 $/hour, saving 4.2% of original cost, with 0.666 standard deviation. Therefore, IABC can efficiently generate high quality solutions to nonlinear, nonconvex and mixed integer problems.Öğe Optimal Power Flow Considering Global Voltage Stability Based on a Hybrid Modern Heuristic Technique(Elsevier, 2022) Bai, Wenlei; Lee, Kwang Y.; Eke, İbrahimIntegrating large renewable energy to grid complicates power systems' steady-state operation which leads to the high risk of voltage instability due to its intermittent and stochastic properties. The steady-state voltage stability margin (VSM) can be measured by the smallest singular value (SSV) of the load flow Jacobian matrix. Unlike other voltage stability indices such as L -index which shows the probability of voltage collapse for a particular bus, namely, local stability index, SSV represents the global voltage stability and the smaller the value is, the riskier the whole system's stability is. Voltage stability constrained -optimal power flow (VSC-OPF) is an effective tool to stabilize system voltage. Yet VSC-OPF is a highly non-linear and non-convex problem because of AC power flow and implicit SSV constraints. Such challenge is tackled, in this work, by a novel hybrid heuristic method, differential evolutionary particle swarm optimization (DEEPSO) which can easily formulate explicit constraints and objective functions to obtain near-optimal solutions efficiently. Several case studies are conducted on the IEEE 30 bus system, and they demonstrate the effectiveness of the hybrid technique and presents some insights on voltage profiles under various system operating condition. Copyright (C) 2022 The Authors.Öğe Optimal Scheduling of Distributed Energy Resources by Modern Heuristic Optimization Technique(Ieee, 2017) Bai, Wenlei; Eke, Ibrahim; Lee, Kwang Y.The increasing number and types of energy resources and prosumers has complicated the operation in microgrid greatly. Such problem becomes a hard-to-solve or even impossible-to-solve for traditional mathematical algorithms without necessary approximation. However, modern heuristic optimization techniques have proven their efficiency and robustness in complex non-linear, non-convex and large-size problems. In this paper, we propose a comprehensive microgrid which consists of renewables, distributed generators, demand response, marketplace, energy storage system and prosumers, and investigate the behaviors of such system. A novel heuristic method, artificial bee colony, is proposed to solve the day-ahead optimal scheduling of the microgrid. Case studies have shown that such algorithm is able to solve the problem fast, reliable with satisfactory solutions. For the first case, the computational time is 9 minutes compared with 19 hours by a traditional methodical tool which has not taken necessary approximation of the original problem.
