The Experts below are selected from a list of 223368 Experts worldwide ranked by ideXlab platform
Ming Zhou - One of the best experts on this subject based on the ideXlab platform.
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Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation
Energies, 2017Co-Authors: Ming Zhou, Yuehui Huang, Dawei Wang, Zifen HanAbstract:Due to the volatile and correlated nature of wind speed, a high share of wind power penetration poses challenges to power system Production Simulation. Existing power system probabilistic Production Simulation approaches are in short of considering the time-varying characteristics of wind power and load, as well as the correlation between wind speeds at the same time, which brings about some problems in planning and analysis for the power system with high wind power penetration. Based on universal generating function (UGF), this paper proposes a novel probabilistic Production Simulation approach considering wind speed correlation. UGF is utilized to develop the chronological models of wind power that characterizes wind speed correlation simultaneously, as well as the chronological models of conventional generation sources and load. The supply and demand are matched chronologically to not only obtain generation schedules, but also reliability indices both at each Simulation interval and the whole period. The proposed approach has been tested on the improved IEEE-RTS 79 test system and is compared with the Monte Carlo approach and the sequence operation theory approach. The results verified the proposed approach with the merits of computation simplicity and accuracy.
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Universal generating function based probabilistic Production Simulation for wind power integrated power systems
Journal of Modern Power Systems and Clean Energy, 2015Co-Authors: Tingchao Jin, Ming ZhouAbstract:According to the demand of sustainable development and low-carbon electricity, it is important to develop clean resources and optimize scheduling generation mix. Firstly, a novel method for probabilistic Production Simulation for wind power integrated power systems is proposed based on universal generating function (UGF), which completes the Production Simulation with the chronological wind power and load demand. Secondly, multiple-period multiple-state wind power model and multiple-state thermal unit power model are adopted, and both thermal power and wind power are coordinately scheduled by the comprehensive cost including economic cost and environmental cost. Furthermore, the accommodation and curtailment of wind power is synergistically considered according to the available regulation capability of conventional generators in operation. Finally, the proposed method is verified and compared with conventional convolution method in the improved IEEE-RTS 79 system.
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Studies on Impact of Wind Power Using Power System Probabilistic Production Simulation
2012 Asia-Pacific Power and Energy Engineering Conference, 2012Co-Authors: Yuhan Liao, Ming ZhouAbstract:Due to the nature of randomness of wind power, an approach based on multi-state modeling wind power is proposed for power system probabilistic Production Simulation. The traditional probabilistic Production Simulation is used to deal with the optimal allocation and calculation problems of the thermal units, and it's not applicable to the system including wind power any more. The comprehensive model of wind power output is established on the factors of wind speed and the random output of wind generators, and take the wind turbine as a conventional unit in multi-derating states. Considering with the Forced Outage Rate(FOR) of the units, use the improved model to calculate the grid reliablility indicators and the assessment of wind power in probabilistic Production Simulation. Further, numerical Simulations are conducted to illustrate the validity of the methodology. It is shown that the presented model can provide a basis for the further research for evaluating benefits of the wind farm. The proposed approach can play an important role in wind power assessment and planning.
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Calculation of wind-farm capacity credit based on probabilistic Production Simulation and its application
IEEE PES Innovative Smart Grid Technologies, 2012Co-Authors: Junwei Liu, Ming Zhou, Xueyan MaoAbstract:In aspect of capacity, wind power generation and traditional power generation is inner connected. The capacity credit is a convenient and effective way to compare these two different types of power generation. Capacity credit of wind power (CCOWF) is presented in this paper, as well as a calculation method of CCOWF based on probabilistic Production Simulation (PPS), this method is based on sequence operation theory (SOT) and considers both the probabilistic of load and power generation. Considering the loss of load expectation (LOLE) is unchanged with the accessing of wind power and shutting some gas-guzzling thermal power down. The corresponding software is programmed, and the diagrammatic method is developed to calculate and the CCOWF of IEEE-RTS79 test system. In the end, the application of CCOWF in aspect of calculating the ability of peak load regulation and the capacity of spinning reserve is short presented.
Zifen Han - One of the best experts on this subject based on the ideXlab platform.
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Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation
Energies, 2017Co-Authors: Ming Zhou, Yuehui Huang, Dawei Wang, Zifen HanAbstract:Due to the volatile and correlated nature of wind speed, a high share of wind power penetration poses challenges to power system Production Simulation. Existing power system probabilistic Production Simulation approaches are in short of considering the time-varying characteristics of wind power and load, as well as the correlation between wind speeds at the same time, which brings about some problems in planning and analysis for the power system with high wind power penetration. Based on universal generating function (UGF), this paper proposes a novel probabilistic Production Simulation approach considering wind speed correlation. UGF is utilized to develop the chronological models of wind power that characterizes wind speed correlation simultaneously, as well as the chronological models of conventional generation sources and load. The supply and demand are matched chronologically to not only obtain generation schedules, but also reliability indices both at each Simulation interval and the whole period. The proposed approach has been tested on the improved IEEE-RTS 79 test system and is compared with the Monte Carlo approach and the sequence operation theory approach. The results verified the proposed approach with the merits of computation simplicity and accuracy.
Xifan Wang - One of the best experts on this subject based on the ideXlab platform.
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ISGT Europe - A new framework of probabilistic Production Simulation of power systems with wind energy resources
2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), 2012Co-Authors: Tianyu Ding, Zhaohong Bie, Can Sun, Xiuli Wang, Xifan WangAbstract:The fossil energy crisis and environment concerns have brought a dramatic development of renewable energy resources such as wind energy resources and solar energy sources over the past decade. The high uncertainty of renewable energy resources renders the existing probabilistic Production Simulation approach less applicable. This paper proposes a new framework of probabilistic Production Simulation which gives better consideration of the variability/intermittency effects of renewable energy, especially the wind energy. This new framework uses Monte Carlo methods to realize the combination of probabilistic Production Simulation and stochastic process sampling. In this paper, we model the wind speed in a certain wind farm as a stochastic process using a stochastic differential equation which can fit the marginal distribution and the sequential correlation structure of the true wind speed. According to the power characteristic curve of the wind turbine, we could generate the wind power series from the wind speed series.
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Studies on models and algorithms of the power system probabilistic Production Simulation integrated with wind farm
2009 IEEE Power & Energy Society General Meeting, 2009Co-Authors: Zhaohong Bie, Xin Zou, Zijing Wang, Xifan WangAbstract:In order to analysis the effect of the integrated wind farm in the power system probabilistic Production Simulation, a new model of the wind farm output power considering multi-wake effect is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power. Compared with the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, a new wind farm model — Time Sequential multi-state Generator Model for the power system probabilistic Production Simulation involved integrating wind farm is presented. In this model, the fluctuation and sequential characteristics of the wind power are fully taken into account and the wind power is simulated practically. The calculation and analysis have been executed on the IEEE RTS test system integrated wind farms. The results indicate that the models and algorithms presented can be considered the integrated wind farm well and truly in the power system probabilistic Production Simulation. The above research can also provide a basis for the further study for evaluating benefits of the wind farm.
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Generation planning using Lagrangian relaxation and probabilistic Production Simulation
International Journal of Electrical Power & Energy Systems, 2004Co-Authors: Haoyong Chen, Xifan Wang, Xinyu ZhaoAbstract:Abstract Generation planning has been extensively investigated and applied to practical power industry. This paper presents the generation planning model of Jiaotong Automatic System Planning Package (JASP) of Xi'an Jiaotong University, China. JASP decomposes the generation planning problem into a high-level power plant investment decision problem and a low-level operation planning problem and solves them by a decomposition-coordination method. Lagrangian Relaxation is used to solve the power plant investment decision problem and probabilistic Production Simulation is used to solve the operation planning problem. The generation planning model of JASP can be easily extended to the context of market liberalization. Simulation results show that JASP can not only overcome the ‘curse of dimensionality’ but also find economical and technically sound generation planning scheme.
G Gross - One of the best experts on this subject based on the ideXlab platform.
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a Production Simulation tool for systems with integrated wind energy resources
IEEE Transactions on Power Systems, 2011Co-Authors: Nicolas Maisonneuve, G GrossAbstract:The rapid increase in wind power capacity over the past decade has resulted from the adoption of policies that encourage the wider use of renewable energy sources in order to reduce CO2 and the dramatic cost reductions due to technology advancements. The high variability in wind speeds poses major difficulties in power system planning and operations, leading to an acute need for practical planning and operations tools to study the effects of the integration of wind resources into the grid. This paper addresses the need in the planning domain through the development of a computationally efficient probabilistic Production Simulation approach with the capability to quantify the variable effects of systems for varying levels of wind penetration with the uncertainty in the variability/intermittency effects of wind generation at multiple sites together with the other sources of uncertainty explicitly represented. The Simulation approach is based on the identification of the prevailing wind regimes in the regions where wind resources are located and the judicious application of conditional probability concepts in incorporating the wind regime representation. The regimes-based approach described in the paper effectively captures both the seasonal and the diurnal variations of renewable resources and their correlation with the load seasonal and diurnal changes. Additionally, the proposed approach explicitly quantifies the impacts of wind on the additional reserve requirements on the controllable resources. The paper illustrates the effectiveness of the approach by its application to large-scale test systems using historical data.
Hans F. Ravn - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Production Simulation including combined heat and power plants
Electric Power Systems Research, 1998Co-Authors: Helge V. Larsen, Halldór Pálsson, Hans F. RavnAbstract:Expansion planning of combined heat and power systems requires Simulation tools for estimating key indicators for decision making. For power systems, so-called probability Production Simulation techniques have been used extensively. These techniques have been extended to include combined heat and power (CHP) systems with a single heat area and back-pressure type CHP units. In this paper, existing Simulation methods for CHP are extended to include extraction power plants. Furthermore, the case of multiple heat areas is addressed and three different Simulation strategies are presented for such systems. The results show that an assumption of perfectly correlated heat demands in all heat areas gives results that are very similar to a general case, whereas working with a system with all heat areas aggregated into one gives rather poor results. It is demonstrated that it is possible to use the traditional concepts and methods for power-only analysis on a CHP system. Additional concepts are presented, depending on the heat criterion applied. It is concluded that probabilistic Production Simulation including CHP units can be performed with reasonable effort and accuracy.
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A method to perform probabilistic Production Simulation involving combined heat and power units
IEEE Transactions on Power Systems, 1996Co-Authors: C. Sondergren, Hans F. RavnAbstract:The problem considered concerns expansion planning for combined heat and power (CHP) systems. The purpose is to develop a new method for pet-forming probabilistic Production Simulation. This is done by extending the probabilistic approach for power generation Simulation to include heat generation as well. The probabilistic approach is based on two-dimensional probability load density functions. By convolution of the combined heat and power units, the equivalent load functions are obtained and the expected energy generation of the units, the expected unserved energy, and the expected overflow are determined. It is shown that in the CHP system a trade off between unserved energy and overflow energy is inherent and therefore the analysis is qualitatively different from analysis of systems with only power units. The corresponding theory is developed and case studies demonstrate the feasibility of the proposed method.
