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Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes.
Zhile Yang; Kang Li; Yuanjun Guo; Shengzhong Feng; Qun Niu; Yusheng Xue; Aoife Foley. A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles. Energy 2019, 170, 889 -905.
AMA StyleZhile Yang, Kang Li, Yuanjun Guo, Shengzhong Feng, Qun Niu, Yusheng Xue, Aoife Foley. A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles. Energy. 2019; 170 ():889-905.
Chicago/Turabian StyleZhile Yang; Kang Li; Yuanjun Guo; Shengzhong Feng; Qun Niu; Yusheng Xue; Aoife Foley. 2019. "A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles." Energy 170, no. : 889-905.
An improved bare-bones multi-objective particle swarm optimization, namely IMOBBPSO is proposed to optimize the solar-dish Stirling engine systems. A new simple strategy for updating particle’s velocity is developed based on the conventional bare-bones PSO, aiming to enhance the diversity of the solutions and accelerate the convergence rate. In order to test the effectiveness of IMOBBPSO, four benchmarks are used. Compared with the non-dominated sorting genetic algorithm-II (NSGAII) and multi-objective particle swarm optimization algorithm (MOPSO), it is revealed that IMOBBPSO can quickly converge to the true Pareto front and efficiently solve practical problems. IMOBBPSO is then used to solve the design of the solar-dish Stirling engine. It is shown that IMOBBPSO obtains the best optimization results than NSGAII and MOPSO. It further achieves significant improvements 25.6102% to 29.2926% in terms of the output power and entropy generation rate when it is compared with existing results in the literature.
Qun Niu; Ziyuan Sun; Dandan Hua. An Improved Multi-objective Bare-Bones PSO for Optimal Design of Solar Dish Stirling Engine Systems. Programmieren für Ingenieure und Naturwissenschaftler 2017, 167 -177.
AMA StyleQun Niu, Ziyuan Sun, Dandan Hua. An Improved Multi-objective Bare-Bones PSO for Optimal Design of Solar Dish Stirling Engine Systems. Programmieren für Ingenieure und Naturwissenschaftler. 2017; ():167-177.
Chicago/Turabian StyleQun Niu; Ziyuan Sun; Dandan Hua. 2017. "An Improved Multi-objective Bare-Bones PSO for Optimal Design of Solar Dish Stirling Engine Systems." Programmieren für Ingenieure und Naturwissenschaftler , no. : 167-177.
Pumped hydro energy storage (PHES) can relieve the variability and fluctuation of wind energy in power system. Introducing PHES and wind power into unit commitment (UC) has great significance in the control and operation of power systems, which as well as brings great challenge. In this paper, two harmony search methods called ACHS and NPAHS-M are applied to UC-Wind-PHES problems. Numerical experiments have been done on a power system with one PHES station, one wind power unit and ten thermal units. Comparison results show that ACHS and NPAHS-M can provide higher accuracy and better convergence speed, and the utilization of PHES contributes to the improvement of cost saving and system robustness.
Qun Niu; Dandan Hua; Letian Zhang; Chao Wang. Unit Commitment with Wind Power and Pumped Hydro Energy Storage. Communications in Computer and Information Science 2016, 273 -281.
AMA StyleQun Niu, Dandan Hua, Letian Zhang, Chao Wang. Unit Commitment with Wind Power and Pumped Hydro Energy Storage. Communications in Computer and Information Science. 2016; ():273-281.
Chicago/Turabian StyleQun Niu; Dandan Hua; Letian Zhang; Chao Wang. 2016. "Unit Commitment with Wind Power and Pumped Hydro Energy Storage." Communications in Computer and Information Science , no. : 273-281.
This paper proposes a learning-based evolutionary optimization (LBEO) for solving optimal power flow (OPF) problem. The LBEO is a simple and effective algorithm, which simplifies the structure of teaching-learning-based optimization (TLBO) and enhances the convergence speed. The performance of this method is implemented on IEEE 30-bus test system with the minimized fuel cost objective function, and the results show that LBEO is practicable for OPF problem compared with other methods in the literature.
Qun Niu; Wenjun Peng; Letian Zhang. Learning-Based Evolutionary Optimization for Optimal Power Flow. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 34 -45.
AMA StyleQun Niu, Wenjun Peng, Letian Zhang. Learning-Based Evolutionary Optimization for Optimal Power Flow. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():34-45.
Chicago/Turabian StyleQun Niu; Wenjun Peng; Letian Zhang. 2015. "Learning-Based Evolutionary Optimization for Optimal Power Flow." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 34-45.
This paper presents an intelligent economic operation of unit commitment (UC) incorporated with electric vehicles (EV) problem. The model of UC with EV is formulated, which includes constraints of power balance, spinning reserve, minimum up-down time, generation limits and EV limits. An improved harmony search, namely NPAHS-M, is proposed for UC problem with vehicle-to-grid (V2G) technology. This method contains a new pitch adjustment which can enhance the diversity of newly generated harmony and provide a better searching guidance. Simulation results show that EVs can reduce the running cost effectively and NPAHS-M can achieve comparable results compared with the methods in literatures.
Qun Niu; Chao Wang; Letian Zhang. Unit Commitment with Electric Vehicles Based on an Improved Harmony Search Algorithm. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 65 -73.
AMA StyleQun Niu, Chao Wang, Letian Zhang. Unit Commitment with Electric Vehicles Based on an Improved Harmony Search Algorithm. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():65-73.
Chicago/Turabian StyleQun Niu; Chao Wang; Letian Zhang. 2015. "Unit Commitment with Electric Vehicles Based on an Improved Harmony Search Algorithm." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 65-73.
Quantum-behaved particle swarm optimization (QPSO) is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO) and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED) problems with valve-point effects and multiple fuel options. To show the performance of the proposed SQPSO, it is tested on five standard benchmark functions and two ED benchmark problems, including a 40-unit ED problem with valve-point effects and a 10-unit ED problem with multiple fuel options. The results are compared with differential evolution (DE), particle swarm optimization (PSO) and basic QPSO, as well as a number of other methods reported in the literature in terms of solution quality, convergence speed and robustness. The simulation results confirm that the proposed SQPSO is effective and reliable for both function optimization and ED problems.
Qun Niu; Zhuo Zhou; Hong-Yun Zhang; Jing Deng. An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects. Energies 2012, 5, 3655 -3673.
AMA StyleQun Niu, Zhuo Zhou, Hong-Yun Zhang, Jing Deng. An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects. Energies. 2012; 5 (9):3655-3673.
Chicago/Turabian StyleQun Niu; Zhuo Zhou; Hong-Yun Zhang; Jing Deng. 2012. "An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects." Energies 5, no. 9: 3655-3673.
Hybrid flow shop problem (HFSP) can be regarded as a generalized flow shop with multiple processing stages, of which at least one consists of parallel machines. HFSP is fairly common in flexible manufacturing and in process industry. This paper presents an efficient quantum immune algorithm (QIA) for HFSP. The objective is to find an optimal job sequence that minimize the mean flow time. Since HFSP has been proved to be NP-hard in a strong sense even in case of two stages, immune algorithm (IA) and quantum algorithm (QA) are used to solve the problem, respectively. To improve the performance of IA, an effective IA with new adaptive crossover and fractional parts mutation operators is proposed, which is called AIA. A randomly replacing strategy is employed to promote population diversity of QA, namely RRQA. In order to achieve better results, the paper proposes a quantum immune algorithm (QIA), which combines IA with QA to optimize the HFSP. Furthermore, all the improvements are added into QIA to be ARRQIA, which possesses the merits of global exploration, fast convergence, and robustness. The simulation results show that the proposed AIA significantly enhances the performance of IA. RRQA also produces more efficient and more stable results than QA. So far as ARRQIA is concerned, it outperforms the other algorithms in the paper and the average solution quality has increased by 3.37% and 6.82% compared with IA and QA on the total 60 instances.
Qun Niu; Taijin Zhou; Minrui Fei; Bing Wang. An efficient quantum immune algorithm to minimize mean flow time for hybrid flow shop problems. Mathematics and Computers in Simulation 2012, 84, 1 -25.
AMA StyleQun Niu, Taijin Zhou, Minrui Fei, Bing Wang. An efficient quantum immune algorithm to minimize mean flow time for hybrid flow shop problems. Mathematics and Computers in Simulation. 2012; 84 ():1-25.
Chicago/Turabian StyleQun Niu; Taijin Zhou; Minrui Fei; Bing Wang. 2012. "An efficient quantum immune algorithm to minimize mean flow time for hybrid flow shop problems." Mathematics and Computers in Simulation 84, no. : 1-25.