Showing 6 results for Wind Power
H. Rajabi Mashhadi, M. A. Armin,
Volume 11, Issue 3 (9-2015)
Abstract
Utilization of wind turbines as economic and green production units, poses new challenges to the power system planners, mainly due to the stochastic nature of the wind, adding a new source of uncertainty to the power system. Different types of distribution and correlation between this random variable and the system load makes conventional method inappropriate for modeling such a correlation. In this paper, the correlation between the wind speed and system load is modeled using Copula, a mathematical tool recently used in the field of the applied science. As the effect of the correlation coefficient is the main concern, the copula modeling technique allows simulating various scenarios with different correlations. The conducted simulations in this paper reveals that the wind speed correlation with the load has significant effect on the system reliability indices, such as expected energy not served (EENS) and loss of load probability (LOLP). Moreover, in this paper the effect of the correlation coefficient on the effective load carrying capability (ELCC) of the wind turbines is analyzed, too. To perform the aforementioned simulations and analyses, the modified RBTS with an additional wind farm is used.
M. E. Moazzen, S. A. Gholamian, M. Jafari-Nokandi,
Volume 13, Issue 2 (6-2017)
Abstract
Permanent magnet synchronous generators (PMSG) have a huge potential for direct-drive wind power applications. Therefore, optimal design of these generators is necessary to maximize their efficiency and to reduce their manufacturing cost and total volume. In this paper, an optimal design of a six-phase 3.5 KW direct-drive PMSG to generate electricity for domestic needs is performed. The aim of optimal design is to reduce the manufacturing cost, losses and total volume of PMSG. To find the best design, single/multi-objective design optimization is carried out. Cuckoo optimization algorithm (COA) is adopted to solve the optimization problem. Comparison between the results of the single-objective and multi-objective models shows that simultaneous optimization of manufacturing cost, losses and total volume leads to more suitable design for PMSG. Finally, finite-element method (FEM) is employed to validate the optimal design, which show a good agreement between the theoretical work and simulation results.
F. Misaghi, T. Barforoushi, M. Jafari-Nokandi,
Volume 13, Issue 2 (6-2017)
Abstract
In this paper, a novel framework is proposed to study impacts of regulatory incentive on distributed generation (DG) investment in sub-transmission substations, as well as upgrading of upstream transmission substations. Both conventional and wind power technologies are considered here. Investment incentives are fuel cost, firm contracts, capacity payment and investment subsidy relating to wind power. The problem is modelled as a bi-level stochastic optimization problem, where the upper level consists of investor's decisions maximizing its own profit. Both market clearing and decision on upgrading of transmission substation aiming at minimizing the total cost are considered in the lower level. Due to non-convexity of the lower level and impossibility of converting to single level problem (i.e. mathematical programming with equilibrium constraints (MPEC)), an algorithm combing enumeration and mathematical optimization is used to tackle with the non-convexity. For each upgrading strategy of substations, a stochastic MPEC, converted to a mixed integer linear programming (MILP) is solved. The proposed model is examined on a six-bus and an actual network. Numerical studies confirm that the proposed model can be used for analysing investment behaviour of DGs and substation expansion.
S. Sivasakthi, R. K. Santhi, N. Murali Krishnan, S. Ganesan, S. Subramanian,
Volume 13, Issue 2 (6-2017)
Abstract
The increasing concern of global climate changes, the promotion of renewable energy sources, primarily wind generation, is a welcome move to reduce the pollutant emissions from conventional power plants. Integration of wind power generation with the existing power network is an emerging research field. This paper presents a meta-heuristic algorithm based approach to determine the feasible dispatch solution for wind integrated thermal power system. The Unit Commitment (UC) process aims to identify the best feasible generation scheme of the committed units such that the overall generation cost is reduced, when subjected to a variety of constraints at each time interval. As the UC formulation involves many variables and system and operational constraints, identifying the best solution is still a research task. Nowadays, it is inevitable to include power system reliability issues in operation strategy. The generator failure and malfunction are the prime influencing factor for reliability issues hence they have considered in UC formulation of wind integrated thermal power system. The modern evolutionary algorithm known as Grey Wolf Optimization (GWO) algorithm is applied to solve the intended UC problem. The potential of the GWO algorithm is validated by the standard test systems. Besides, the ramp rate limits are also incorporated in the UC formulation. The simulation results reveal that the GWO algorithm has the capability of obtaining economical resolutions with good solution quality.
B. Mamipour Matanag, N. Rostami, S. Tohidi,
Volume 17, Issue 2 (6-2021)
Abstract
This paper proposes a new method for direct control of active power and stator flux of permanent magnet synchronous generator (PMSG) used in the wind power generation system. Active power and stator flux are controlled by the proposed discrete time algorithm. Despite the commonly used vector control methods, there is no need for inner current control loops. To decrease the errors between reference and measured values of active power and stator flux, the space vector modulation (SVM) is used, which results in a constant switching frequency. Compared to vector control, the proposed direct control method has advantages such as higher dynamic response due to elimination of inner current control loops and no need to coordinate system transformation blocks as well as the PI controllers and their adjustment. Moreover, permanent magnet flux vector and several machine parameters such as stator inductances are not required which can improve the robustness of the control system. The proposed method can be used in both types of surface-mounted and interior PMSGs. The effectiveness of the proposed method in comparison to the vector control method with optimized PI coefficients by the particle swarm algorithm is evaluated. Simulation results performed in MATLAB/Simulink software show that higher dynamic response with lower active power and the stator flux ripple are achieved with the proposed method.
T. Barforoushi, R. Heydari,
Volume 18, Issue 2 (6-2022)
Abstract
Curtailment of the production of wind resources due to uncertainty can affect the expansion of the transmission networks. The issue that needs to be addressed is how to expand the transmission network, which is accompanied by increasing wind energy utilization. In this paper, a new framework is proposed to solve the transmission expansion planning (TEP) problem in the presence of wind farms, considering wind curtailment cost. The proposed model is a risk-constrained stochastic bi-level problem that, the difference between the expected social welfare and investment cost is maximized at the upper level where optimal decisions on expansion plans are adopted by the independent system operator (ISO). To make the best use of wind generation resources, a new term called wind power curtailment cost is added to the upper level. Also, the risk index is included in expansion decisions. The market-clearing is considered at the lower level, aiming at maximizing social welfare. Uncertainties relating to wind power and the forecasted demand are modeled by sets of scenarios. Using duality theory, the proposed framework is modeled as mixed-integer linear programming (MILP) problem. The model is examined using the classical Garver’s six-bus test system and the IEEE 24-bus reliability test system (RTS). The results show that by considering the wind curtailment cost, the transmission network is expanded in a way that increases the wind energy utilization factor from 92.05% to 95.17%.
