Log in

Relevant bibliographies by topics / Dynamic stochastic optimal power flow / Journal articles

To see the other types of publications on this topic, follow the link: Dynamic stochastic optimal power flow.

Journal articles on the topic 'Dynamic stochastic optimal power flow'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Dynamic stochastic optimal power flow.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Domyshev, Alexander. "New method of stochastic optimization for dynamic optimal power flow." E3S Web of Conferences 209 (2020): 02010. http://dx.doi.org/10.1051/e3sconf/202020902010.

Full text

Abstract:

An universal algorithm for stochastic optimization is proposed. This algorithm is effective for dynamic optimization of process changing in time with taking into account the time-dependent cost of actions. Proposed algorithm is tested on the model of quite big power system and proved to be effective.

APA, Harvard, Vancouver, ISO, and other styles

2

Bai, Wenlei, Duehee Lee, and Kwang Lee. "Stochastic Dynamic Optimal Power Flow Integrated with Wind Energy Using Generalized Dynamic Factor Model." IFAC-PapersOnLine 49, no. 27 (2016): 129–34. http://dx.doi.org/10.1016/j.ifacol.2016.10.731.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Liang, Jiaqi, Diogenes D. Molina, Ganesh Kumar Venayagamoorthy, and Ronald G. Harley. "Two-Level Dynamic Stochastic Optimal Power Flow Control for Power Systems With Intermittent Renewable Generation." IEEE Transactions on Power Systems 28, no. 3 (August 2013): 2670–78. http://dx.doi.org/10.1109/tpwrs.2013.2237793.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Qin, Zhengfeng, Xiaoqing Bai, and Xiangyang Su. "Robust Stochastic Dynamic Optimal Power Flow Model of Electricity-Gas Integrated Energy System considering Wind Power Uncertainty." Complexity 2020 (October 12, 2020): 1–11. http://dx.doi.org/10.1155/2020/8879906.

Full text

Abstract:

The application of gas turbines and power to gas equipment deepens the coupling relationship between power systems and natural gas systems and provides a new way to absorb the uncertain wind power as well. The traditional stochastic optimization and robust optimization algorithms have some limitations and deficiencies in dealing with the uncertainty of wind power output. Therefore, we propose a robust stochastic optimization (RSO) model to solve the dynamic optimal power flow model for electricity-gas integrated energy systems (IES) considering wind power uncertainty, where the ambiguity set of wind power output is constructed based on Wasserstein distance. Then, the Wasserstein ambiguity set is affined to the eventwise ambiguity set, and the proposed RSO model is transformed into a mixed-integer programming model, which can be solved rapidly and accurately using commercial solvers. Numerical results for EG-4 and EG-118 systems verify the rationality and effectiveness of the proposed model.

APA, Harvard, Vancouver, ISO, and other styles

5

Bai, Wenlei, Duehee Lee, and Kwang Lee. "Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model." Energies 10, no. 12 (December 15, 2017): 2138. http://dx.doi.org/10.3390/en10122138.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Sun, Guoqiang, Yichi Li, Shuang Chen, Zhinong Wei, Sheng Chen, and Haixiang Zang. "Dynamic stochastic optimal power flow of wind power and the electric vehicle integrated power system considering temporal-spatial characteristics." Journal of Renewable and Sustainable Energy 8, no. 5 (September 2016): 053309. http://dx.doi.org/10.1063/1.4966152.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Hutterer, Stephan, and Michael Affenzeller. "Probabilistic Electric Vehicle Charging Optimized With Genetic Algorithms and a Two-Stage Sampling Scheme." International Journal of Energy Optimization and Engineering 2, no. 3 (July 2013): 1–15. http://dx.doi.org/10.4018/ijeoe.2013070101.

Full text

Abstract:

Probabilistic power flow studies represent essential challenges in nowadays power system operation and research. Here, especially the incorporation of intermittent supply plants with optimal control of dispatchable demand like electric vehicle charging power shows nondeterministic aspects. Using simulation-based optimization, such probabilistic and dynamic behavior can be fully integrated within the metaheuristic optimization process, yielding into a generic approach suitable for optimization in uncertain environments. A practical problem scenario is demonstrated that computes optimal charging schedules of a given electrified fleet in order to meet both power flow constraints of the distribution grid while satisfying vehicle-owners’ energy demand and considering stochastic supply of wind power plants. Since solution- evaluation through simulation is computational expensive, a new fitness-based sampling scheme will be proposed, that avoids unnecessary evaluations of less-performant solution candidates.

APA, Harvard, Vancouver, ISO, and other styles

8

Liang, Jiaqi, Ganesh K. Venayagamoorthy, and Ronald G. Harley. "Wide-Area Measurement Based Dynamic Stochastic Optimal Power Flow Control for Smart Grids With High Variability and Uncertainty." IEEE Transactions on Smart Grid 3, no. 1 (March 2012): 59–69. http://dx.doi.org/10.1109/tsg.2011.2174068.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Rauh, Andreas. "Kalman Filter-Based Real-Time Implementable Optimization of the Fuel Efficiency of Solid Oxide Fuel Cells." Clean Technologies 3, no. 1 (March 1, 2021): 206–26. http://dx.doi.org/10.3390/cleantechnol3010012.

Full text

Abstract:

The electric power characteristic of solid oxide fuel cells (SOFCs) depends on numerous influencing factors. These are the mass flow of supplied hydrogen, the temperature distribution in the interior of the fuel cell stack, the temperatures of the supplied reaction media at the anode and cathode, and—most importantly—the electric current. Describing all of these dependencies by means of analytic system models is almost impossible. Therefore, it is reasonable to identify these dependencies by means of stochastic filter techniques. One possible option is the use of Kalman filters to find locally valid approximations of the power characteristics. These can then be employed for numerous online purposes of dynamically operated fuel cells such as maximum power point tracking or the maximization of the fuel efficiency. In the latter case, it has to be ensured that the fuel cell operation is restricted to the regime of Ohmic polarization. This aspect is crucial to avoid fuel starvation phenomena which may not only lead to an inefficient system operation but also to accelerated degradation. In this paper, a Kalman filter-based, real-time implementable optimization of the fuel efficiency is proposed for SOFCs which accounts for the aforementioned feasibility constraints. Essentially, the proposed strategy consists of two phases. First, the parameters of an approximation of the electric power characteristic are estimated. The measurable arguments of this function are the hydrogen mass flow and the electric stack current. In a second stage, these inputs are optimized so that a desired stack power is attained in an optimal way. Simulation results are presented which show the robustness of the proposed technique against inaccuracies in the a-priori knowledge about the power characteristics. For a numerical validation, three different models of the electric power characteristic are considered: (i) a static neural network input/output model, (ii) a first-order dynamic system representation and (iii) the combination of a static neural network model with a low-order fractional differential equation model representing transient phases during changes between different electric operating points.

APA, Harvard, Vancouver, ISO, and other styles

10

CECI, CLAUDIA. "UTILITY MAXIMIZATION WITH INTERMEDIATE CONSUMPTION UNDER RESTRICTED INFORMATION FOR JUMP MARKET MODELS." International Journal of Theoretical and Applied Finance 15, no. 06 (September 2012): 1250040. http://dx.doi.org/10.1142/s0219024912500409.

Full text

Abstract:

The contribution of this paper is twofold: we study power utility maximization problems (with and without intermediate consumption) in a partially observed financial market with jumps and we solve by the innovation method the arising filtering problem. We consider a Markovian model where the risky asset dynamics St follows a pure jump process whose local characteristics are not observable by investors. More precisely, the stock price process dynamics depends on an unobservable stochastic factor Xt described by a jump-diffusion process. We assume that agents' decisions are based on the knowledge of an information flow, [Formula: see text], containing the asset price history, [Formula: see text]. Using projection on the filtration [Formula: see text], the partially observable investment-consumption problem is reduced to a full observable stochastic control problem. The homogeneity of the power utility functions leads to a factorization of the associated value process into a part depending on the current wealth and the so called opportunity process Jt. In the case where [Formula: see text], Jt and the optimal investment-consumption strategy are represented in terms of solutions to a backward stochastic differential equation (BSDE) driven by the [Formula: see text]-compensated martingale random measure associated to St, which can be obtained by filtering techniques (Ceci, 2006; Ceci and Gerardi, 2006). Next, we extend the study to the case [Formula: see text], where ηt gives observations of Xt in additional Gaussian noise. This setup can be viewed as an abstract form of "insider information". The opportunity process Jt is now characterized as a solution to a BSDE driven by the [Formula: see text]-compensated martingale random measure and the so called innovation process. Computation of these quantities leads to a filtering problem with mixed type observation and whose solution is discussed via the innovation approach.

APA, Harvard, Vancouver, ISO, and other styles

11

Pareek, Parikshit, and Hung D. Nguyen. "State-Aware Stochastic Optimal Power Flow." Sustainability 13, no. 14 (July 7, 2021): 7577. http://dx.doi.org/10.3390/su13147577.

Full text

Abstract:

The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase.

APA, Harvard, Vancouver, ISO, and other styles

12

Megel, Olivier, Johanna L. Mathieu, and Goran Andersson. "Hybrid Stochastic-Deterministic Multiperiod DC Optimal Power Flow." IEEE Transactions on Power Systems 32, no. 5 (September 2017): 3934–45. http://dx.doi.org/10.1109/tpwrs.2017.2651409.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Kannan, Rohit, James R. Luedtke, and Line A. Roald. "Stochastic DC optimal power flow with reserve saturation." Electric Power Systems Research 189 (December 2020): 106566. http://dx.doi.org/10.1016/j.epsr.2020.106566.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Biswas, Partha P., P. N. Suganthan, and Gehan A. J. Amaratunga. "Optimal power flow solutions incorporating stochastic wind and solar power." Energy Conversion and Management 148 (September 2017): 1194–207. http://dx.doi.org/10.1016/j.enconman.2017.06.071.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Chen, Gonggui. "Dynamic Optimal Power Flow in FSWGs Integrated Power System." Information Technology Journal 10, no. 2 (January 15, 2011): 385–93. http://dx.doi.org/10.3923/itj.2011.385.393.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Diep-Thanh, Thang, Quang Nguyen-Phung, and Huy Nguyen-Duc. "Stochastic control for optimal power flow in islanded microgrid." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1045. http://dx.doi.org/10.11591/ijece.v9i2.pp1045-1057.

Full text

Abstract:

<p>The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.</p>

APA, Harvard, Vancouver, ISO, and other styles

17

Mezghani, Ilyes, Sidhant Misra, and Deepjyoti Deka. "Stochastic AC optimal power flow: A data-driven approach." Electric Power Systems Research 189 (December 2020): 106567. http://dx.doi.org/10.1016/j.epsr.2020.106567.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Perninge, Magnus. "Stochastic optimal power flow by multi-variate Edgeworth expansions." Electric Power Systems Research 109 (April 2014): 90–100. http://dx.doi.org/10.1016/j.epsr.2013.12.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

OKAWA, Yoshihiro, Yu HARAIKAWA, and Toru NAMERIKAWA. "Distributed Optimal Dynamic Pricing Considering Electric Power Flow." Transactions of the Society of Instrument and Control Engineers 50, no. 3 (2014): 245–52. http://dx.doi.org/10.9746/sicetr.50.245.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Gill, Simon, Ivana Kockar, and Graham W. Ault. "Dynamic Optimal Power Flow for Active Distribution Networks." IEEE Transactions on Power Systems 29, no. 1 (January 2014): 121–31. http://dx.doi.org/10.1109/tpwrs.2013.2279263.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Xie, K., and Y. H. Song. "Dynamic optimal power flow by interior point methods." IEE Proceedings - Generation, Transmission and Distribution 148, no. 1 (2001): 76. http://dx.doi.org/10.1049/ip-gtd:20010026.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Vaahedi, E., Y. Mansour, C. Fuchs, S. Granville, M. D. L. Latore, and H. Hamadanizadeh. "Dynamic security constrained optimal power flow/VAr planning." IEEE Transactions on Power Systems 16, no. 1 (2001): 38–43. http://dx.doi.org/10.1109/59.910779.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Pandya, Sundaram B., and Hitesh R. Jariwala. "Stochastic renewable energy resources integrated multi-objective optimal power flow." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 3 (June 1, 2020): 1582. http://dx.doi.org/10.12928/telkomnika.v18i3.13466.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Xia, Y., and K. W. Chan. "Dynamic Constrained Optimal Power Flow Using Semi-Infinite Programming." IEEE Transactions on Power Systems 21, no. 3 (August 2006): 1455–57. http://dx.doi.org/10.1109/tpwrs.2006.879241.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Schmitz, Magdiel, Daniel Pinheiro Bernardon, Vinicius Jacques Garcia, William Ismael Schmitz, Martin Wolter, and Luciano Lopes Pfitscher. "Price-Based Dynamic Optimal Power Flow With Emergency Repair." IEEE Transactions on Smart Grid 12, no. 1 (January 2021): 324–37. http://dx.doi.org/10.1109/tsg.2020.3018640.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Pu, Guan-Chih, and Nanming Chen. "SENSITIVITY FACTOR BASED SHORT TERM DYNAMIC OPTIMAL POWER FLOW." Journal of the Chinese Institute of Engineers 20, no. 5 (September 1, 1997): 585–92. http://dx.doi.org/10.1080/02533839.1997.9741865.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Taleb, Nadir, Bachir Bentouati, and Saliha Chettih. "Optimal Power Flow Solutions Incorporating Stochastic Solar Power withthe Application Grey Wolf Optimize." Algerian Journal of Renewable Energy and Sustainable Development 03, no. 01 (June 15, 2021): 74–84. http://dx.doi.org/10.46657/ajresd.2021.3.1.8.

Full text

Abstract:

The present paper aims to validate an electrical network study in consisting of conventional fossil fuel generators with the integration of intermittent generation technologies based on renewable energy resources like wind power or solar photovoltaic (PV) are the stochastic power output. By using an optimal power flow (OPF) problem different frameworks are developed for solving that represent various operating requirements, such as minimization of production fuel cost, and preserving generation emission at the lowest levels... etc. The OPF analysis aims to find the optimal solution and is very important for power system operation with satisfying operational constraints, planning and energy management. However, the intermittent combination of solar exacerbates the complexity of the problem. Within the framework of these criteria, this paper is an overview of the application Grey Wolf Optimizer (GWO) algorithm which solves the OPF problem with renewable energy. The algorithm thus combined and constructed gives optimum results satisfying all network constraints. Give an explanation for findings are based thus need to be with the optimum to effectuate of network constraints.

APA, Harvard, Vancouver, ISO, and other styles

28

Le, Luong Dinh, Jirawadee Polprasert, Weerakorn Ongsakul, Dieu Ngoc Vo, and Dung Anh Le. "Stochastic Weight Trade-Off Particle Swarm Optimization for Optimal Power Flow." Journal of Automation and Control Engineering 2, no. 1 (2014): 31–37. http://dx.doi.org/10.12720/joace.2.1.31-37.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Sharifzadeh, Hossein, and Nima Amjady. "Stochastic security-constrained optimal power flow incorporating preventive and corrective actions." International Transactions on Electrical Energy Systems 26, no. 11 (March 22, 2016): 2337–52. http://dx.doi.org/10.1002/etep.2207.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Alnajjab, Basel, Alberto J. Lamadrid, Lawrence V. Snyder, Shalinee Kishore, and Rick S. Blum. "Stochastic Optimal Power Flow Under Forecast Errors and Failures in Communication." IEEE Transactions on Smart Grid 10, no. 4 (July 2019): 4128–37. http://dx.doi.org/10.1109/tsg.2018.2850342.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Guo, Yi, Kyri Baker, Emiliano Dall'Anese, Zechun Hu, and Tyler Holt Summers. "Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part I: Methodologies." IEEE Transactions on Power Systems 34, no. 2 (March 2019): 1483–92. http://dx.doi.org/10.1109/tpwrs.2018.2878385.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Yang, Haoxiang, and Harsha Nagarajan. "Optimal power flow in distribution networks under stochastic N−1 disruptions." Electric Power Systems Research 189 (December 2020): 106689. http://dx.doi.org/10.1016/j.epsr.2020.106689.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Jadhav, H. T., and Ranjit Roy. "Stochastic optimal power flow incorporating offshore wind farm and electric vehicles." International Journal of Electrical Power & Energy Systems 69 (July 2015): 173–87. http://dx.doi.org/10.1016/j.ijepes.2014.12.060.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Zhu, Xiao Wei, San Ming Liu, Ying Li, Shu Ting Chen, and Zhi Gang Pan. "Study on Optimal Power Flow with Large Scale Wind Power Integration Based on PSO." Applied Mechanics and Materials 687-691 (November 2014): 3332–35. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3332.

Full text

Abstract:

This article is based on the idea that considers the influences of uncertainty, constraint programming model based on stochastic programming opportunities, expressed by means of probability constraints, the establishment of a constraint programming model for the optimal opportunity of wind power uncertainty, stochastic simulation technology development program and the particle swarm algorithm is used to solve this model based on IEEE30, at the end of the node optimization and simulation, to verify the feasibility of the model and algorithm. The whole procedure lays a good foundation for improving the situation of large-scale wind power grid dispatching and operation level access.

APA, Harvard, Vancouver, ISO, and other styles

35

Inoue, Masaki, Tomonori Sadamoto, Mitsuru Arahata, and Aranya Chakrabortty. "Optimal Power Flow Design for Enhancing Dynamic Performance: Potentials of Reactive Power." IEEE Transactions on Smart Grid 12, no. 1 (January 2021): 599–611. http://dx.doi.org/10.1109/tsg.2020.3019417.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Lodoen, Ole P., and Henning Omre. "Scale-Corrected Ensemble Kalman Filtering Applied to Production-History Conditioning in Reservoir Evaluation." SPE Journal 13, no. 02 (June 1, 2008): 177–94. http://dx.doi.org/10.2118/111374-pa.

Full text

Abstract:

Summary In reservoir evaluation problems, the reservoir properties are largely unknown. To infer these properties from observations of the reservoir production is referred to as history matching or production history conditioning. Traditionally, this is done by repeated fluid-flow simulations, where all the available production data are used simultaneously to arrive at a set of history-matched reservoir models. In recent years, the amount of data continuously collected from a reservoir under production has been on the increase. Hence, the need for automatic, continuous model updating is apparent. The ensemble Kalman filter has been shown to be suitable for this purpose. However, large reservoir evaluation problems require upscaling reservoir properties to perform the necessary number of fluid-flow simulations. Traditional ensemble Kalman filtering is shown to give bias in the production history conditioned reservoir representations. The loss in accuracy and precision introduced by performing fluid-flow simulations on a coarser scale should be accounted for, but this is rarely or never done. We introduce the scale-corrected ensemble Kalman filter approach in order to quantify the loss in accuracy and precision. A reference scale is defined and all uncertainty quantifications are made relative to this scale, although the fluid flow simulations are made on a coarser scale. The production history conditioned reservoir representation will be accurate with realistic precision measures on this reference scale. The methodology is demonstrated on a large case study inspired by the characteristics of the Troll field in the North Sea. Introduction One of the objectives of reservoir evaluation is to find the optimal well configuration and well-operating conditions for a given reservoir. Forecasts of hydrocarbon production for a given recovery strategy can be used to determine this. Quantification of the uncertainty both in the prediction of the reservoir properties and in the forecast of the production properties should be an integral part of the evaluation process. The assessment of the uncertainty in the production forecasts requires repeated fluid-flow simulations. This is done by using a reservoir production simulator. The reservoir conditions needed as input to this simulator are in practice largely unknown, however. Therefore, the uncertainty in the reservoir properties must be described by a stochastic reservoir model, taking all the available data into account. Prior to starting production, the available data are static data. After the reservoir has been in production for a while, an observed production history is also available. The observed production history should be used to update the reservoir model, and thereby improve the production forecasts. In petroleum-related literature this is referred to as "history matching." Traditionally, production history conditioning is performed through repeated fluid-flow simulations, where the reservoir properties are tuned to the production history, either manually or automatically by minimizing an objective function involving the mismatch between simulated and observed production. There are two problems with this methodology. The first problem is the computational cost of repeated fluid-flow simulations, which severely restricts the size of the reservoir models to which the production history conditioning can be applied. The second problem is that the reservoir models are updated using all the available production data simultaneously. This means that when new production data become available, the entire production history conditioning process must be repeated. In recent years, the use of permanent sensors for monitoring dynamic production properties has increased, requiring more frequent updating of the reservoir models. Ideally, the observations should be included in the model sequentially as they become available. This approach requires continuous or sequential production history-conditioning techniques. The Kalman filter has been widely used for this type of time series problem. However, the Kalman filter is most appropriate when the number of variables in the model is low and the observations are linearly related to the model. This is not the case in spatio-temporal reservoir evaluation problems, where the number of model parameters is typically very high, and the relation between the reservoir model and the production observations, represented by a fluid-flow simulator, is highly nonlinear. Several extensions to the Kalman filter techniques have been suggested, among these the ensemble Kalman filter, developed by Evensen (1994). The ensemble Kalman filter is used to update both the reservoir properties and the production properties. The computations are based on an ensemble of realizations of the reservoir and production properties, from which relevant statistics concerning the model uncertainty can be estimated. At times where new observations become available, all ensemble members are updated to honor these observations. Consequently, the realizations are always kept up to date with the latest observations. The ensemble Kalman filter methodology has been applied to numerous cases in various fields, such as weather forecasting (Evensen 1994; Houtekamer and Mitchell 1998), ground water hydrology (Reichle et al. 2002), and petroleum engineering (Nævdal et al. 2002, 2005; Gu and Oliver 2005; Wen and Chen 2006; Haugen et al. 2006). For a review of recent progress see Evensen (2007). The ensemble Kalman filter is shown to perform well with an ensemble size of around 100 members. In practice, however, the computational demands by fluid-flow simulation on large reservoir models prohibit ensembles of this size. This problem is typically overcome by performing fluid-flow simulations on a coarser-scale representation of the reservoir variables. This upscaling is known to introduce bias in the production forecasts, however, which should be accounted for. For notational convenience, we will refer to this as coarse-scale fluid flow simulation contrary to fine-scale fluid flow simulation on the preferable fine-scale representation of the reservoir variables. Let us emphasize that the same fluid-flow simulator is used; it is only the gridding of the input variables which vary. In this paper we use the general ensemble Kalman filtering framework of Evensen (1994), and extend it to correct for the effect of using coarse-scale fluid flow simulators, using the approach of Omre and Lødøen (2004). The basic idea of Omre and Lødøen is to use coarse-scale fluid flow simulation to predict the results from fine-scale fluid-flow simulation, and to assess the associated prediction uncertainty. The fine-scale representation is termed the reference scale. This correction is feasible if the coarse-scale fluid flow simulations capture the most important features of the fine-scale fluid-flow simulations. We coin our approach scale-corrected ensemble Kalman filter. This paper proceeds as follows: We start by defining the notation and describing the ensemble Kalman filter methodology. Then we motivate and present our model extensions. We proceed by presenting a case study, which is inspired by the characteristics of the Troll field in the North Sea. Further, we present and discuss the results from our simulation studies, and finally we draw some conclusions.

APA, Harvard, Vancouver, ISO, and other styles

37

Tang, W. J., M. S. Li, Q. H. Wu, and J. R. Saunders. "Bacterial Foraging Algorithm for Optimal Power Flow in Dynamic Environments." IEEE Transactions on Circuits and Systems I: Regular Papers 55, no. 8 (September 2008): 2433–42. http://dx.doi.org/10.1109/tcsi.2008.918131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Tu, Xiaoping, Louis-A. Dessaint, and Huy Nguyen-Duc. "Transient stability constrained optimal power flow using independent dynamic simulation." IET Generation, Transmission & Distribution 7, no. 3 (March 1, 2013): 244–53. http://dx.doi.org/10.1049/iet-gtd.2012.0539.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Costa, A. L., and A. Simões Costa. "Energy and ancillary service dispatch through dynamic optimal power flow." Electric Power Systems Research 77, no. 8 (June 2007): 1047–55. http://dx.doi.org/10.1016/j.epsr.2006.09.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Mohagheghi, Erfan, Mansour Alramlawi, Aouss Gabash, and Pu Li. "A Survey of Real-Time Optimal Power Flow." Energies 11, no. 11 (November 13, 2018): 3142. http://dx.doi.org/10.3390/en11113142.

Full text

Abstract:

There has been a strong increase of penetration of renewable energies into power systems. However, the renewables pose new challenges for the operation of the networks. Particularly, wind power is intermittently fluctuating, and, therefore, the network operator has to fast update the operations correspondingly. This task should be performed by an online optimization. Therefore, real-time optimal power flow (RT-OPF) has become an attractive topic in recent years. This paper presents an overview of recent studies on RT-OPF under wind energy penetration, offering a critical review of the major advancements in RT-OPF. It describes the challenges in the realization of the RT-OPF and presents available approaches to address these challenges. The paper focuses on a number of topics which are reviewed in chronological order of appearance: offline energy management systems (EMSs) (deterministic and stochastic approaches) and real-time EMSs (constraint satisfaction-based and OPF-based methods). The particular challenges associated with the incorporation of battery storage systems in the networks are explored, and it is concluded that the current research on RT-OPF is not sufficient, and new solution approaches are needed.

APA, Harvard, Vancouver, ISO, and other styles

41

Phan, Dzung, and Soumyadip Ghosh. "Two-stage stochastic optimization for optimal power flow under renewable generation uncertainty." ACM Transactions on Modeling and Computer Simulation 24, no. 1 (January 2014): 1–22. http://dx.doi.org/10.1145/2553084.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Faulwasser, Timm, Alexander Engelmann, Tillmann Mühlpfordt, and Veit Hagenmeyer. "Optimal power flow: an introduction to predictive, distributed and stochastic control challenges." at - Automatisierungstechnik 66, no. 7 (July 26, 2018): 573–89. http://dx.doi.org/10.1515/auto-2018-0040.

Full text

Abstract:

Abstract The Energiewende is a paradigm change that can be witnessed at latest since the political decision to step out of nuclear energy. Moreover, despite common roots in Electrical Engineering, the control community and the power systems community face a lack of common vocabulary. In this context, this paper aims at providing a systems-and-control specific introduction to optimal power flow problems which are pivotal in the operation of energy systems. Based on a concise problem statement, we introduce a common description of optimal power flow variants including multi-stage problems and predictive control, stochastic uncertainties, and issues of distributed optimization. Moreover, we sketch open questions that might be of interest for the systems and control community.

APA, Harvard, Vancouver, ISO, and other styles

43

Khan, Inam Ullah, Nadeem Javaid, Kelum A. A. Gamage, C. James Taylor, Sobia Baig, and Xiandong Ma. "Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources." IEEE Access 8 (2020): 148622–43. http://dx.doi.org/10.1109/access.2020.3015473.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Guo, Yi, Kyri Baker, Emiliano Dall'Anese, Zechun Hu, and Tyler Holt Summers. "Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part II: Case Studies." IEEE Transactions on Power Systems 34, no. 2 (March 2019): 1493–503. http://dx.doi.org/10.1109/tpwrs.2018.2878380.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Ekwue, A. O., and R. N. Adams. "Optimal power rescheduling for system security using stochastic second-order load flow." International Journal of Electrical Power & Energy Systems 11, no. 4 (October 1989): 277–82. http://dx.doi.org/10.1016/0142-0615(89)90038-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Mühlpfordt, Tillmann, Timm Faulwasser, Veit Hagenmeyer, Line Roald, and Sidhant Misra. "On polynomial real-time control policies in stochastic AC optimal power flow." Electric Power Systems Research 189 (December 2020): 106792. http://dx.doi.org/10.1016/j.epsr.2020.106792.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Grover-Silva, Etta, Miguel Heleno, Salman Mashayekh, Gonçalo Cardoso, Robin Girard, and George Kariniotakis. "A stochastic optimal power flow for scheduling flexible resources in microgrids operation." Applied Energy 229 (November 2018): 201–8. http://dx.doi.org/10.1016/j.apenergy.2018.07.114.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Gabriel, Steven A., Prawat Sahakij, and Swaminathan Balakrishnan. "Optimal Retailer Load Estimates Using Stochastic Dynamic Programming." Journal of Energy Engineering 130, no. 1 (April 2004): 1–17. http://dx.doi.org/10.1061/(asce)0733-9402(2004)130:1(1).

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Abdollahi, Arsalan, Ali Ghadimi, Mohammad Miveh, Fazel Mohammadi, and Francisco Jurado. "Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm." Electronics 9, no. 6 (June 24, 2020): 1043. http://dx.doi.org/10.3390/electronics9061043.

Full text

Abstract:

This paper deals with investigating the Optimal Power Flow (OPF) solution of power systems considering Flexible AC Transmission Systems (FACTS) devices and wind power generation under uncertainty. The Krill Herd Algorithm (KHA), as a new meta-heuristic approach, is employed to cope with the OPF problem of power systems, incorporating FACTS devices and stochastic wind power generation. The wind power uncertainty is included in the optimization problem using Weibull probability density function modeling to determine the optimal values of decision variables. Various objective functions, including minimization of fuel cost, active power losses across transmission lines, emission, and Combined Economic and Environmental Costs (CEEC), are separately formulated to solve the OPF considering FACTS devices and stochastic wind power generation. The effectiveness of the KHA approach is investigated on modified IEEE-30 bus and IEEE-57 bus test systems and compared with other conventional methods available in the literature.

APA, Harvard, Vancouver, ISO, and other styles

50

Elattar, Ehab E. "Optimal Power Flow of a Power System Incorporating Stochastic Wind Power Based on Modified Moth Swarm Algorithm." IEEE Access 7 (2019): 89581–93. http://dx.doi.org/10.1109/access.2019.2927193.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!