Relevant bibliographies by topics / Dynamic stochastic optimal power flow

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Dynamic stochastic optimal power flow'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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

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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.

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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.
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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.

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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.

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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.

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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.
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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.

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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.

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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.

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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.
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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.

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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.

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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.
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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.

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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.
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Dissertations / Theses on the topic "Dynamic stochastic optimal power flow"

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Liang, Jiaqi. "Wind energy and power system interconnection, control, and operation for high penetration of wind power." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47570.

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High penetration of wind energy requires innovations in different areas of power engineering. Methods for improving wind energy and power system interconnection, control, and operation are proposed in this dissertation. A feed-forward transient compensation control scheme is proposed to enhance the low-voltage ride-through capability of wind turbines equipped with doubly fed induction generators. Stator-voltage transient compensation terms are introduced to suppress rotor-current overshoots and torque ripples during grid faults. A dynamic stochastic optimal power flow control scheme is proposed to optimally reroute real-time active and reactive power flow in the presence of high variability and uncertainty. The performance of the proposed power flow control scheme is demonstrated in test power systems with large wind plants. A combined energy-and-reserve wind market scheme is proposed to reduce wind production uncertainty. Variable wind reserve products are created to absorb part of the wind production variation. These fast wind reserve products can then be used to regulate system frequency and improve system security.
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Nasri, Amin. "On the Dynamics and Statics of Power System Operation : Optimal Utilization of FACTS Devicesand Management of Wind Power Uncertainty." Doctoral thesis, KTH, Elektriska energisystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154576.

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Nowadays, power systems are dealing with some new challenges raisedby the major changes that have been taken place since 80’s, e.g., deregu-lation in electricity markets, significant increase of electricity demands andmore recently large-scale integration of renewable energy resources such aswind power. Therefore, system operators must make some adjustments toaccommodate these changes into the future of power systems.One of the main challenges is maintaining the system stability since theextra stress caused by the above changes reduces the stability margin, andmay lead to rise of many undesirable phenomena. The other important chal-lenge is to cope with uncertainty and variability of renewable energy sourceswhich make power systems to become more stochastic in nature, and lesscontrollable.Flexible AC Transmission Systems (FACTS) have emerged as a solutionto help power systems with these new challenges. This thesis aims to ap-propriately utilize such devices in order to increase the transmission capacityand flexibility, improve the dynamic behavior of power systems and integratemore renewable energy into the system. To this end, the most appropriatelocations and settings of these controllable devices need to be determined.This thesis mainly looks at (i) rotor angle stability, i.e., small signal andtransient stability (ii) system operation under wind uncertainty. In the firstpart of this thesis, trajectory sensitivity analysis is used to determine themost suitable placement of FACTS devices for improving rotor angle sta-bility, while in the second part, optimal settings of such devices are foundto maximize the level of wind power integration. As a general conclusion,it was demonstrated that FACTS devices, installed in proper locations andtuned appropriately, are effective means to enhance the system stability andto handle wind uncertainty.The last objective of this thesis work is to propose an efficient solutionapproach based on Benders’ decomposition to solve a network-constrained acunit commitment problem in a wind-integrated power system. The numericalresults show validity, accuracy and efficiency of the proposed approach.

The Doctoral Degrees issued upon completion of the programme are issued by Comillas Pontifical University, Delft University of Technology and KTH Royal Institute of Technology. The invested degrees are official in Spain, the Netherlands and Sweden, respectively.QC 20141028

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Singh, Manish Kumar. "Optimization, Learning, and Control for Energy Networks." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104064.

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Massive infrastructure networks such as electric power, natural gas, or water systems play a pivotal role in everyday human lives. Development and operation of these networks is extremely capital-intensive. Moreover, security and reliability of these networks is critical. This work identifies and addresses a diverse class of computationally challenging and time-critical problems pertaining to these networks. This dissertation extends the state of the art on three fronts. First, general proofs of uniqueness for network flow problems are presented, thus addressing open problems. Efficient network flow solvers based on energy function minimizations, convex relaxations, and mixed-integer programming are proposed with performance guarantees. Second, a novel approach is developed for sample-efficient training of deep neural networks (DNN) aimed at solving optimal network dispatch problems. The novel feature here is that the DNNs are trained to match not only the minimizers, but also their sensitivities with respect to the optimization problem parameters. Third, control mechanisms are designed that ensure resilient and stable network operation. These novel solutions are bolstered by mathematical guarantees and extensive simulations on benchmark power, water, and natural gas networks.
Doctor of Philosophy
Massive infrastructure networks play a pivotal role in everyday human lives. A minor service disruption occurring locally in electric power, natural gas, or water networks is considered a significant loss. Uncertain demands, equipment failures, regulatory stipulations, and most importantly complicated physical laws render managing these networks an arduous task. Oftentimes, the first principle mathematical models for these networks are well known. Nevertheless, the computations needed in real-time to make spontaneous decisions frequently surpass the available resources. Explicitly identifying such problems, this dissertation extends the state of the art on three fronts: First, efficient models enabling the operators to tractably solve some routinely encountered problems are developed using fundamental and diverse mathematical tools; Second, quickly trainable machine learning based solutions are developed that enable spontaneous decision making while learning offline from sophisticated mathematical programs; and Third, control mechanisms are designed that ensure a safe and autonomous network operation without human intervention. These novel solutions are bolstered by mathematical guarantees and extensive simulations on benchmark power, water, and natural gas networks.
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Campbell, Angela Mari. "Architecting aircraft power distribution systems via redundancy allocation." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53087.

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Recently, the environmental impact of aircraft and rising fuel prices have become an increasing concern in the aviation industry. To address these problems, organizations such as NASA have set demanding goals for reducing aircraft emissions, fuel burn, and noise. In an effort to reach the goals, a movement toward more-electric aircraft and electric propulsion has emerged. With this movement, the number of critical electrical loads on an aircraft is increasing causing power system reliability to be a point of concern. Currently, power system reliability is maintained through the use of back-up power supplies such as batteries and ram-air-turbines (RATs). However, the increasing power requirements for critical loads will quickly outgrow the capacity of the emergency devices. Therefore, reliability needs to be addressed when designing the primary power distribution system. Power system reliability is a function of component reliability and redundancy. Component reliability is often not determined until detailed component design has occurred; however, the amount of redundancy in the system is often set during the system architecting phase. In order to meet the capacity and reliability requirements of future power distribution systems, a method for redundancy allocation during the system architecting phase is needed. This thesis presents an aircraft power system design methodology that is based upon the engineering decision process. The methodology provides a redundancy allocation strategy and quantitative trade-off environment to compare architecture and technology combinations based upon system capacity, weight, and reliability criteria. The methodology is demonstrated by architecting the power distribution system of an aircraft using turboelectric propulsion. The first step in the process is determining the design criteria which includes a 40 MW capacity requirement, a 20 MW capacity requirement for the an engine-out scenario, and a maximum catastrophic failure rate of one failure per billion flight hours. The next step is determining gaps between the performance of current power distribution systems and the requirements of the turboelectric system. A baseline architecture is analyzed by sizing the system using the turboelectric system power requirements and by calculating reliability using a stochastic flow network. To overcome the deficiencies discovered, new technologies and architectures are considered. Global optimization methods are used to find technology and architecture combinations that meet the system objectives and requirements. Lastly, a dynamic modeling environment is constructed to study the performance and stability of the candidate architectures. The combination of the optimization process and dynamic modeling facilitates the selection of a power system architecture that meets the system requirements and objectives.
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Huang, Renke. "Seamless design of energy management systems." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53518.

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The contributions of the research are (a) an infrastructure of data acquisition systems that provides the necessary information for an automated EMS system enabling autonomous distributed state estimation, model validation, simplified protection, and seamless integration of other EMS applications, (b) an object-oriented, interoperable, and unified component model that can be seamlessly integrated with a variety of applications of the EMS, (c) a distributed dynamic state estimator (DDSE) based on the proposed data acquisition system and the object-oriented, interoperable, and unified component model, (d) a physically-based synchronous machine model, which is expressed in terms of the actual self and mutual inductances of the synchronous machine windings as a function of rotor position, for the purpose of synchronous machine parameters identification, and (e) a robust and highly efficient algorithm for the optimal power flow (OPF) problem, one of the most important applications of the EMS, based on the validated states and models of the power system provided by the proposed DDSE.
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Yamaguti, Lucas do Carmo. "Despacho ótimo de geração e controle de potência reativa no sistema elétrico de potência /." Ilha Solteira, 2019. http://hdl.handle.net/11449/183534.

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Orientador: Jose Roberto Sanches Mantovani
Resumo: Neste trabalho são propostos modelos matemáticos determinístico e estocástico de programação cônica de segunda ordem em coordenadas retangulares para o problema de fluxo de potência ótimo de geração e controle de potência reativa no sistemas elétricos de potência, considerando as minimização dos custos de geração de energia, perdas ativas da rede e emissão de poluentes no meio ambiente. Os modelos contemplam as principais características físicas e econômicas do problema estudado, assim como os limites operacionais do sistema elétrico. Os modelos são programados em linguagem AMPL e suas soluções são obtidas através do solver comercial CPLEX. Os sistemas testes IEEE30, IEEE118 e ACTIVSg200 são utilizados nas simulações computacionais dos modelos propostos. Os resultados obtidos pelo modelo determinístico desenvolvido são validados através de comparações com os resultados fornecidos pelo software MATPOWER , onde ambos consideram apenas a existência de gerações termoelétricas. No modelo estocástico utiliza-se a técnica de geração de cenários e considera-se um período de um ano (8760 horas), e geradores que utilizam fontes de geração renováveis e não renováveis.
Abstract: In this work we propose deterministic and stochastic mathematical models of second order conical programming in rectangular coordinates for the optimal power flow problem of reactive power generation and control in electric power systems, considering the minimization of energy generation costs, losses networks and emission of pollutants into the environment. The models contemplate the main physical and economic characteristics of the studied problem, as well as the operational limits of the electric system. The models are programmed in AMPL language and their solutions are obtained through the commercial solver CPLEX. The IEEE30, IEEE118 and ACTIVSg200 test systems are used in the computer simulations of the proposed models. The results obtained by the deterministic model developed are validated through comparisons with the results provided by the software MATPOWERR , where both consider only the existence of thermoelectric generations. The stochastic model uses the scenario generation technique and considers a period of one year (8760 hours), and generators using renewable and non-renewable generation sources.
Mestre
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Haessig, Pierre. "Dimensionnement et gestion d’un stockage d’énergie pour l'atténuation des incertitudes de production éolienne." Thesis, Cachan, Ecole normale supérieure, 2014. http://www.theses.fr/2014DENS0030/document.

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Le contexte de nos travaux de thèse est l'intégration de l'énergie éolienne sur les réseaux insulaires. Ces travaux sont soutenus par EDF SEI, l'opérateur électrique des îles françaises. Nous étudions un système éolien-stockage où un système de stockage d'énergie doit aider un producteur éolien à tenir, vis-à-vis du réseau, un engagement de production pris un jour à l'avance. Dans ce contexte, nous proposons une démarche pour l'optimisation du dimensionnement et du contrôle du système de stockage (gestion d'énergie). Comme les erreurs de prévision J+1 de production éolienne sont fortement incertaines, la gestion d'énergie du stockage est un problème d'optimisation stochastique (contrôle optimal stochastique). Pour le résoudre, nous étudions tout d'abord la modélisation des composants du système (modélisation énergétique du stockage par batterie Li-ion ou Sodium-Soufre) ainsi que des entrées (modélisation temporelle stochastique des entrées incertaines). Nous discutons également de la modélisation du vieillissement du stockage, sous une forme adaptée à l'optimisation de la gestion. Ces modèles nous permettent d'optimiser la gestion de l'énergie par la méthode de la programmation dynamique stochastique (SDP). Nous discutons à la fois de l'algorithme et de ses résultats, en particulier de l'effet de la forme des pénalisations sur la loi de gestion. Nous présentons également l'application de la SDP sur des problèmes complémentaires de gestion d'énergie (lissage de la production d'un houlogénérateur, limitation des rampes de production éolienne). Cette étude de l'optimisation de la gestion permet d'aborder l'optimisation du dimensionnement (choix de la capacité énergétique). Des simulations temporelles stochastiques mettent en évidence le fort impact de la structure temporelle (autocorrélation) des erreurs de prévision sur le besoin en capacité de stockage pour atteindre un niveau de performance donné. La prise en compte de paramètres de coût permet ensuite l'optimisation du dimensionnement d'un point de vue économique, en considérant les coûts de l'investissement, des pertes ainsi que du vieillissement. Nous étudions également le dimensionnement du stockage lorsque la pénalisation des écarts à l'engagement comporte un seuil de tolérance. Nous terminons ce manuscrit en abordant la question structurelle de l'interaction entre l'optimisation du dimensionnement et celle du contrôle d'un système de stockage, car ces deux problèmes d'optimisation sont couplés
The context of this PhD thesis is the integration of wind power into the electricity grid of small islands. This work is supported by EDF SEI, the system operator for French islands. We study a wind-storage system where an energy storage is meant to help a wind farm operator fulfill a day-ahead production commitment to the grid. Within this context, we propose an approach for the optimization of the sizing and the control of the energy storage system (energy management). Because day-ahead wind power forecast errors are a major source of uncertainty, the energy management of the storage is a stochastic optimization problem (stochastic optimal control). To solve this problem, we first study the modeling of the components of the system. This include energy-based models of the storage system, with a focus on Lithium-ion and Sodium-Sulfur battery technologies. We then model the system inputs and in particular the stochastic time series like day-ahead forecast errors. We also discuss the modeling of storage aging, using a formulation which is adapted to the control optimization. Assembling all these models enables us to optimize the energy management of the storage system using the stochastic dynamic programming (SDP) method. We introduce the SDP algorithms and present our optimization results, with a special interest for the effect of the shape of the penalty function on the energy control law. We also present additional energy management applications with SDP (mitigation of wind power ramps and smoothing of ocean wave power). Having optimized the storage energy management, we address the optimization of the storage sizing (choice of the rated energy). Stochastic time series simulations show that the temporal structure (autocorrelation) of wind power forecast errors have a major impact on the need for storage capacity to reach a given performance level. Then we combine simulation results with cost parameters, including investment, losses and aging costs, to build a economic cost function for sizing. We also study storage sizing when the penalization of commitment deviations includes a tolerance threshold. We finish this manuscript with a structural study of the interaction between the optimizations of the sizing and the control of an energy storage system, because these two optimization problems are coupled
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Oriondo, Marco Alonso Meneses [UNESP]. "Resolução do problema de fluxo de potência ótimo pela meta-heurística algoritmo dos fogos de artifício de busca dinâmica com mutação de covariância." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/138202.

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Neste trabalho apresenta-se uma versão especializada da meta-heurística Algoritmo dos Fogos de Artifício de Busca Dinâmica com Mutação de Covariância (AFABDMC) para resolver o problema de Fluxo de Potência Ótimo (FPO) em sistemas de transmissão. No algoritmo proposto consideram-se como variáveis contínuas de controle a magnitude da tensão e a geração de potência ativa nas barras de geração e como variáveis de controle discretas o estado de operação dos shunts e a posição do comutador de taps em transformadores. Assim, o modelo para o problema é de Programação Não-Linear Inteira Mista (PNLIM). A estratégia de resolução adotada consiste em controlar, em cada iteração, os valores das variáveis de controle discretas utilizando-se a meta-heurística AFABDMC e a partir desses valores escolhidos pela meta-heurística, resolver um problema de Programação Não-Linear (PNL) que fornece os valores das variáveis de controle contínuas junto com o estado de operação do sistema. A meta-heurística AFABDMC foi escrita em linguagem MATLAB e o modelo do problema em AMPL. Os subproblemas de PNL foram resolvidos utilizando-se o solver KNITRO, sendo que a interface entre o MATLAB e o AMPL foi feita utilizando-se o AMPL API. Foram realizados testes com os sistemas IEEE de 14, 30, 57 e 118 barras e os resultados indicam que a metodologia proposta é capaz de encontrar soluções de muito boa qualidade para o problema.
In this work, a new specialized metaheuristic based on the Dynamic Search Fireworks Algorithm with Covariance Mutation (DSFWACM) is applied on the Optimal Power Flow (OPF) problem in transmission systems. In the proposed method, generator bus voltage magnitudes and active power generation are considered as continuous variables and the operating state of the shunts and transformer taps settings are considered as discrete variables. Thus, the model is a Mixed-Integer Nonlinear Programming (MINLP) problem. The adopted resolution strategy is to control, in each iteration, the value of the discrete control variables using the DSFWACM metaheuristic and from the metaheuristic’s chosen values, solve the Nonlinear Programming (NLP) problem that provides the values of the continuous control variables along with the system’s operation state. The DSFWACM metaheuristic was written in MATLAB and the problem model in AMPL. The NLP sub-problems were solved using the KNITRO solver, and the interface between MATLAB and AMPL was implemented using the AMPL API. Tests were conducted with the IEEE 14, 30, 57 and 118-bus systems and the results show that the proposed method is able to find high quality solutions to the problem.
CNPq: 132374/2011-0
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Oriondo, Marco Alonso Meneses. "Resolução do problema de fluxo de potência ótimo pela meta-heurística algoritmo dos fogos de artifício de busca dinâmica com mutação de covariância /." Ilha Solteira, 2016. http://hdl.handle.net/11449/138202.

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Orientador: Rubén Romero Lázaro
Resumo: Neste trabalho apresenta-se uma versão especializada da meta-heurística Algoritmo dos Fogos de Artifício de Busca Dinâmica com Mutação de Covariância (AFABDMC) para resolver o problema de Fluxo de Potência Ótimo (FPO) em sistemas de transmissão. No algoritmo proposto consideram-se como variáveis contínuas de controle a magnitude da tensão e a geração de potência ativa nas barras de geração e como variáveis de controle discretas o estado de operação dos shunts e a posição do comutador de taps em transformadores. Assim, o modelo para o problema é de Programação Não-Linear Inteira Mista (PNLIM). A estratégia de resolução adotada consiste em controlar, em cada iteração, os valores das variáveis de controle discretas utilizando-se a meta-heurística AFABDMC e a partir desses valores escolhidos pela meta-heurística, resolver um problema de Programação Não-Linear (PNL) que fornece os valores das variáveis de controle contínuas junto com o estado de operação do sistema. A meta-heurística AFABDMC foi escrita em linguagem MATLAB e o modelo do problema em AMPL. Os subproblemas de PNL foram resolvidos utilizando-se o solver KNITRO, sendo que a interface entre o MATLAB e o AMPL foi feita utilizando-se o AMPL API. Foram realizados testes com os sistemas IEEE de 14, 30, 57 e 118 barras e os resultados indicam que a metodologia proposta é capaz de encontrar soluções de muito boa qualidade para o problema.
Abstract: In this work, a new specialized metaheuristic based on the Dynamic Search Fireworks Algorithm with Covariance Mutation (DSFWACM) is applied on the Optimal Power Flow (OPF) problem in transmission systems. In the proposed method, generator bus voltage magnitudes and active power generation are considered as continuous variables and the operating state of the shunts and transformer taps settings are considered as discrete variables. Thus, the model is a Mixed-Integer Nonlinear Programming (MINLP) problem. The adopted resolution strategy is to control, in each iteration, the value of the discrete control variables using the DSFWACM metaheuristic and from the metaheuristic’s chosen values, solve the Nonlinear Programming (NLP) problem that provides the values of the continuous control variables along with the system’s operation state. The DSFWACM metaheuristic was written in MATLAB and the problem model in AMPL. The NLP sub-problems were solved using the KNITRO solver, and the interface between MATLAB and AMPL was implemented using the AMPL API. Tests were conducted with the IEEE 14, 30, 57 and 118-bus systems and the results show that the proposed method is able to find high quality solutions to the problem.
Mestre
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Yong, Taiyou. "Study of stochastic optimal power flow /." 2001. http://www.library.wisc.edu/databases/connect/dissertations.html.

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Book chapters on the topic "Dynamic stochastic optimal power flow"

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Christensen, G. S., M. E. El-Hawary, and S. A. Soliman. "Dynamic Optimal Load Flow." In Optimal Control Applications in Electric Power Systems, 21–53. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4899-2085-0_3.

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Shafiq, Sundas, Nadeem Javaid, Sikandar Asif, Farwa Ali, Nasir Hussain Chughtai, and Nouman Khurshid. "An Optimal Power Flow Approach for Stochastic Wind and Solar Energy Integrated Power Systems." In Advances in Intelligent Systems and Computing, 292–304. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93659-8_26.

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Capitanescu, Florin. "Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems." In Dynamic Vulnerability Assessment and Intelligent Control for Sustainable Power Systems, 149–76. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119214984.ch7.

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Schween, Nils, Philipp Gerstner, Nico Meyer-Hübner, Viktor Slednev, Thomas Leibfried, Wolf Fichtner, Valentin Bertsch, and Vincent Heuveline. "A Domain Decomposition Approach to Solve Dynamic Optimal Power Flow Problems in Parallel." In Trends in Mathematics, 41–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32157-4_4.

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Huebner, Nico, Nils Schween, Michael Suriyah, Vincent Heuveline, and Thomas Leibfried. "Multi-area Coordination of Security-Constrained Dynamic Optimal Power Flow in AC-DC Grids with Energy Storage." In Trends in Mathematics, 27–40. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32157-4_3.

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"Toward Dynamic Stochastic Optimal Power Flow." In Handbook of Learning and Approximate Dynamic Programming. IEEE, 2009. http://dx.doi.org/10.1109/9780470544785.ch22.

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Momoh, James A. "Optimal Power Flow." In Adaptive Stochastic Optimization Techniques with Applications, 197–246. CRC Press, 2015. http://dx.doi.org/10.1201/b19256-11.

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Glavitsch, Hans, and Rainer Bacher. "Optimal Power Flow Algorithms." In Control and Dynamic Systems, 135–205. Elsevier, 1991. http://dx.doi.org/10.1016/b978-0-12-012741-2.50008-7.

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Jabari, Farkhondeh, Heresh Seyedia, Sajad Najafi Ravadanegh, and Behnam Mohammadi Ivatloo. "Stochastic Contingency Analysis Based on Voltage Stability Assessment in Islanded Power System Considering Load Uncertainty Using MCS and k-PEM." In Advances in Computer and Electrical Engineering, 12–36. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9911-3.ch002.

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Abstract:
Increased electricity demands and economic operation of large power systems in a deregulated environment with a limited investment in transmission expansion planning causes interconnected power grids to be operated closer to their stability limits. Meanwhile, the loads uncertainty will affect the static and dynamic stabilities. Therefore, if there is no emergency corrective control in time, occurrence of wide area contingency may lead to the catastrophic cascading outages. Studies show that many wide area blackouts which led to massive economic losses may have been prevented by a fast feasible controlled islanding decision making. This chapter introduces a novel computationally efficient approach for separating of bulk power system into several stable sections following a severe disturbance. The splitting strategy reduces the large initial search space to an interface boundary network considering coherency of synchronous generators and network graph simplification. Then, a novel islanding scenario generator algorithm denoted as BEM (Backward Elimination Method) based on PMEAs (Primary Maximum Expansion Areas) has been applied to generate all proper islanding solutions in the simplified network graph. The PPF (Probabilistic Power Flow) based on Newton-Raphson method and Q-V modal analysis has been used to evaluate the steady-state stability of created islands in each generated scenario. BICA (Binary Imperialistic Competitive Algorithm) has then been applied to minimize total load-generation mismatch considering integrity, voltage permitted range and steady-state voltage stability constraints. The best solution has then been applied to split the entire power network. A novel stochastic contingency analysis of islands based on PSVI (Probability of Static Voltage Instability) using MCS (Monte Carlo Simulation) and k-PEM (k-Point Estimate Method) have been proposed to identify the critical PQ buses and severe contingencies. In these approaches, the ITM (Inverse Transform Method) has been used to model uncertain loads with normal probability distribution function in optimal islanded power system. The robustness, effectiveness and capability of the proposed approaches have been validated on the New England 39-bus standard power system.
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Sasaki, H., J. Kubokawa, N. Yorino, and R. Yokoyama. "DEVELOPMENT OF OPTIMAL POWER FLOW AND APPLICATION TO DYNAMIC ECONOMIC LOAD DISPATCH." In Power Systems and Power Plant Control 1989, 117–22. Elsevier, 1990. http://dx.doi.org/10.1016/b978-0-08-037039-2.50025-x.

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Conference papers on the topic "Dynamic stochastic optimal power flow"

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Hutterer, Stephan, Stefan Vonolfen, and Michael Affenzeller. "Genetic programming enabled evolution of control policies for dynamic stochastic optimal power flow." In Proceeding of the fifteenth annual conference companion. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2482732.

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Liang, Jiaqi, Ganesh Kumar Venayagamoorthy, and Ronald Harley. "Two-level dynamic stochastic optimal power flow control for power systems with intermittent renewable generation." In 2014 IEEE Power & Energy Society General Meeting. IEEE, 2014. http://dx.doi.org/10.1109/pesgm.2014.6939430.

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Jiaqi Liang, G. K. Venayagamoorthy, and R. G. Harley. "Dynamic stochastic optimal power flow control for intelligent coordination of grid-connected energy systems." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6344983.

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Luo, Jinshan, Yuan Tian, Keyou Wang, Guojie Li, Ying Wang, and Haoyang Yu. "Dynamic stochastic optimal power flow considering spatial correlation of wind speed based on simplified pair copula." In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2017. http://dx.doi.org/10.1109/ei2.2017.8245518.

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Hamon, Camille, Magnus Perninge, and Lennart Soder. "Applying stochastic optimal power flow to power systems with large amounts of wind power and detailed stability limits." In 2013 IREP Symposium - Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP). IEEE, 2013. http://dx.doi.org/10.1109/irep.2013.6629407.

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Jiaqi Liang, G. Venayagamoorthy, and R. Harley. "Wide-area measurement based dynamic stochastic optimal power flow control for smart grids with high variability and uncertainty." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6344989.

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Hamon, Camille, Magnus Perninge, and Lennart Soder. "Closure of “applying stochastic optimal power flow to power systems with large amounts of wind power and detailed stability limits”." In 2013 IREP Symposium - Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP). IEEE, 2013. http://dx.doi.org/10.1109/irep.2013.6629416.

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Yong, T., R. Entriken, and Pei Zhang. "Reserve Determination with Stochastic Optimal Power Flow." In 2009 Asia-Pacific Power and Energy Engineering Conference. IEEE, 2009. http://dx.doi.org/10.1109/appeec.2009.4918725.

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Sharif, S. S., and J. H. Taylor. "Dynamic optimal reactive power flow." In Proceedings of the 1998 American Control Conference (ACC). IEEE, 1998. http://dx.doi.org/10.1109/acc.1998.703223.

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Entriken, Robert, Aidan Tuohy, and Daniel Brooks. "Stochastic optimal power flow in systems with wind power." In 2011 IEEE Power & Energy Society General Meeting. IEEE, 2011. http://dx.doi.org/10.1109/pes.2011.6039581.

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