Hydraulically coupled power-plants commitment within short-term operation planning in mixed hydro-thermal power systems

2007 ◽  
Vol 7 (5) ◽  
pp. 323-330 ◽  
Author(s):  
M. V. Rakic ◽  
Z. M. Markovic
2014 ◽  
Vol 953-954 ◽  
pp. 537-542
Author(s):  
Hai Feng Zhang ◽  
Zhao Jun Zhang ◽  
Jun Zhou ◽  
Yuan Chao Yang

In view of the uncertainty and intermittency of wind power, this paper presents a bi-objective short-term operation model to manage wind-thermal power systems. This model takes into account both the offer cost and emission. Wind power is regarded as a random variable and is assumed to follow the beta distribution. The bi-objective particle swarm optimization (BOPSO) approach is applied to solve the bi-objective model and Pareto front is obtained. The model and the solution method are tested on a generic system. The validity of the model and the method has been approved.


2008 ◽  
Vol 78 (5) ◽  
pp. 835-848 ◽  
Author(s):  
Fabricio Salgado ◽  
Pedro Pedrero

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Ning ◽  
Guanghao Lu ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Hualei Wang

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.


2020 ◽  
Vol 209 ◽  
pp. 07014
Author(s):  
Tulkin Gayibov ◽  
Bekzod Pulatov

Optimal planning of short-term modes of power systems is a complex nonlinear programming problem with many simple, functional and integral constraints in the form of equalities and inequalities. Especially, the presence of integral constraints causes significant difficulties in solving of such problem. Since, under such constraints, the modes of power system in separate time intervals of the considered planning period become dependent on the values of the parameters in other intervals. Accordingly, it becomes impossible to obtain the optimal mode plan as the results of separate optimization for individual time intervals of the period under consideration. And the simultaneous solution of the problem for all time intervals of the planning period in the conditions of large power systems is associated with additional difficulties in ensuring the reliability of convergence of the iterative computational process. In this regard, the issues of improving the methods and algorithms for optimization of short-term modes of power systems containing thermal and large hydroelectric power plants with reservoirs, in which water consumption is regulated in the short-term planning period, remains as an important task. In this paper, we propose the effective algorithm for solving the problem under consideration, which makes it possible to quickly and reliably determine the optimal operating modes of the power system for the planned period. The results of research of effectiveness of this algorithm are presented on the example of optimal planning of daily mode of the power system, which contains two thermal and three hydraulic power plants..


2012 ◽  
Vol 236-237 ◽  
pp. 714-719
Author(s):  
Wei Lan ◽  
Bin Wang ◽  
Yi Ming Feng

Nowadays, the high-speed economic development has caused significant consumption of energy. While the circumstance is getting severer, solar energy is taken as a kind of clean, environmental friendly resource with infinite storage that has aroused a wide public concern. Photovoltaic and solar thermal are two main categories of solar applications. Because of its high conversion efficiency, low emission and flexible installation, dish Stirling solar power technology is more preferable to be used among the solar thermal area. From the view of practical engineering application, this paper illustrates multiple focusing methods of the current dish Stirling solar power systems in detail, and the comparison of these methods are given to analyze their advantages, disadvantages and their application scenarios. It can be used for the future development of dish Stirling solar power technology and applied as a reference for large dish solar thermal power plants’ installations and tests.


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