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Miguel A Marino - One of the best experts on this subject based on the ideXlab platform.
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multi reservoir Real Time Operation rules a new genetic programming approach
Proceedings of the Institution of Civil Engineers - Water Management, 2014Co-Authors: Habib Akbarialashti, Elahe Fallahmehdipour, Omid Bozorg Haddad, Miguel A MarinoAbstract:This paper employs non-linear programming, genetic algorithms and fixed-length gene genetic programming (FLGGP) for the Real-Time Operation of a three-reservoir system (Karoon4, Khersan1 and Karoon3) in which dependent and independent approaches are used to forecast the hydroelectric energy generated by the system. A total deficiency function as well as efficiency criteria are used to investigate the results obtained. The latter indicate that the more flexible FLGGP gives the most efficient function for the extraction of reservoir Operation rules in both dependent and independent approaches. By comparing the two approaches, no significant difference was observed. Consequently, due to the simplicity of the application of the forecast-independent approach, it is suggested for application in the extraction of reservoir Operation decision rules. Moreover, the advantages of a three-reservoir system Operation over a single-reservoir system Operation reflect the efficiency of the integrated management of water r...
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evaluation of Real Time Operation rules in reservoir systems Operation
Water Resources Management, 2014Co-Authors: Y Bolouriyazdeli, Bozorg O Haddad, Elahe Fallahmehdipour, Miguel A MarinoAbstract:Reservoir Operation rules are logical or mathematical equations that take into account system variables to calculate water release from a reservoir based on inflow and storage volume values. In fact, previous experiences of the system are used to balance reservoir system parameters in each Operational period. Commonly, reservoir Operation rules have been considered to be linear decision rules (LDRs) and constant coefficients developed by using various optimization procedures. This paper addresses the application of Real-Time Operation rules on a reservoir system whose purpose is to supply total downstream demand. Those rules include standard Operation policy (SOP), stochastic dynamic programming (SDP), LDR, and nonlinear decision rule (NLDR) with various orders of inflow and reservoir storage volume. Also, a multi-attribute decision method, elimination and choice expressing Reality (ELECTRE)-I, with a combination of indices, objective functions, and reservoir performance criteria (reliability, resiliency, and vulnerability) are used to rank the aforementioned rules. The ranking method employs two combinations of indices: (1) performance criteria and (2) objective function and performance criteria by using the same weights for all criteria. Results show that the NLDR gives an appropriate rule for Real-Time Operation. Moreover, NLDR validation is presented by testing predefined curves for dry, normal, and wet years.
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Real Time Operation of reservoir system by genetic programming
Water Resources Management, 2012Co-Authors: Elahe Fallahmehdipour, Bozorg O Haddad, Miguel A MarinoAbstract:Reservoir Operation policy depends on specific values of deterministic variables and predictable actions as well as stochastic variables, in which small differences affect water release and reservoir Operation efficiency. Operational rule curves of reservoir are policies which relate water release to the deterministic and stochastic variables such as storage volume and inflow. To operate a reservoir system in Real Time, a prediction model may be coupled with rule curves to estimate inflow as a stochastic variable. Inappropriate selection of this prediction model increases calculations and impacts the reservoir Operation efficiency. Thus, extraction of an Operational policy simultaneously with inflow prediction helps the operator to make an appropriate decision to calculate how much water to release from the reservoir without employing a prediction model. This paper addresses the use of genetic programming (GP) to develop a reservoir Operation policy simultaneously with inflow prediction. To determine a water release policy, two Operational rule curves are considered in each period by using (1) inflow and storage volume at the beginning of each period and (2) inflow of the 1st, 2nd, 12th previous periods and storage volume at the beginning of each period. The obtained objective functions of those rules have only 4.86 and 0.44 % difference in the training and testing data sets. These results indicate that the proposed rule based on deterministic variables is effective in determining optimal rule curves simultaneously with inflow prediction for reservoirs.
Ismail Kayahan - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Model Predictive Control-based Real-Time Operation of a Transmission Constrained Joint Wind-PHS System
2018 6th International Conference on Control Engineering & Information Technology (CEIT), 2018Co-Authors: Ismail Kayahan, Ugur YildiranAbstract:Trading wind energy in deregulated markets is a challenging task due to uncertainties involved. To cope with this complication, a significant body of work is devoted to the development of day-ahead bidding strategies based on stochastic programming. However, the problem of Real-Time Operation, which can be defined as the management of the system in balancing markets after day-ahead bidding phase is completed, is not studied well in the literature. Motivated by this fact, in the present work, a stochastic model predictive control (SMPC) based Real-Time Operation method is developed for a transmission-constrained joint wind-PHS system. It is assumed that the generation company participates in the day-ahead market and balancing market as a price taker player. Since Real- Time Operation depends on contracts made a priori, day-ahead bidding is also considered as an integral part and modeled as mixed-integer linear programming (MILP) based stochastic program. Main features of the proposed framework, which distinguish it from the previous studies, are the application of an SMPC strategy for Real-Time Operation and inclusion of transmission constraints in bidding and Operation phases.
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Risk-averse stochastic model predictive control-based Real-Time Operation method for a wind energy generation system supported by a pumped hydro storage unit
Applied Energy, 2018Co-Authors: Ugur Yildiran, Ismail KayahanAbstract:Abstract A wind energy producer participating in deregulated markets needs to make contracts on the energy it will supply in the next day. Deviations from the contracts, which could occur due to wind uncertainties, are compensated in Real-Time balancing markets at a considerable cost. Therefore, developing advanced day-ahead bidding and Real-Time Operation strategies minimizing such imbalance costs constitutes an important problem. There are several works on finding optimal day-ahead bids but the Real-Time Operation problem is not studied well. Motivated by this fact, we propose a new strategy in which the day-ahead bids are computed by solving a risk-averse stochastic program, and Real-Time Operation is performed by a stochastic model predictive control-based algorithm with a risk control capability. The algorithm is applied to a Realistic system composed of wind farms and a pumped hydro storage plant. Its performance is compared to a number of approaches appearing in the literature. Because the problem considered has two conflicting objectives of profit maximization and risk minimization, a Pareto optimality analysis is also conducted. Finally, the validity of a common practice followed in the literature, which is estimating the economic performance by bidding optimization, is investigated by comparing the estimate with the actual performance achieved by Real-Time Operation methods.
Chihchiang Wei - One of the best experts on this subject based on the ideXlab platform.
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intelligent Real Time Operation of a pumping station for an urban drainage system
Journal of Hydrology, 2013Co-Authors: Niensheng Hsu, Chienlin Huang, Chihchiang WeiAbstract:In this study, we apply artificial intelligence techniques to the development of two Real-Time pumping station Operation models, namely, a historical and an optimized adaptive network-based fuzzy inference system (ANFIS-His and ANFIS-Opt, respectively). The functions of these two models are the determination of the Real-Time Operation criteria of various pumping machines for controlling flood in an urban drainage system during periods when the drainage gate is closed. The ANFIS-His is constructed from an adaptive network-based fuzzy inference system (ANFIS) using historical Operation records. The ANFIS-Opt is constructed from an ANFIS using the best Operation series, which are optimized by a tabu search of historical flood events. We use the Chung-Kong drainage basin, New Taipei City, Taiwan, as the study area. The Operational comparison variables are the highest water level (WL) and the absolute difference between the final WL and target WL of a pumping front-pool. The results show that the ANFIS-Opt is better than the ANFIS-His and historical Operation models, based on the Operation simulations of two flood events using the two Operation models.
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a multipurpose reservoir Real Time Operation model for flood control during typhoon invasion
Journal of Hydrology, 2007Co-Authors: Niensheng Hsu, Chihchiang WeiAbstract:To address the problem of a multipurpose reservoir flood control in Taiwan, this paper develops a reservoir Real-Time Operation (RES-RT) model for determining the optimal Real-Time release during a typhoon. The RES-RT model consists of three sub-models: a quantitative precipitation forecast (QPF) model, which is developed to predict rainfall; a streamflow prediction (RTRL) model, which is developed to predict reservoir inflow; and a reservoir Operation optimization (RESOP) model, which is developed to forecast the optimal reservoir release hydrograph. The RESOP model objectives are (1) maximizing the peak flow reduction at selected downstream control points; and (2) optimizing reservoir storage at the end of the flood. The constraints incorporate the three-flood-stage Operation guidelines that are suitably applied in Taiwan. The developed RES-RT model has been applied to Shihmen reservoir system in Taiwan. Comparing the three scenarios of historical Operations, current rules, and RES-RT model running, results show that the RES-RT model produces much better performance for all scenarios in terms of reducing the peak flow at the downstream control point as well as meeting the target reservoir storage at the flood ending. Consequently, RES-RT model demonstrates its effectiveness for estimating the optimal Real-Time releases.
Ugur Yildiran - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Model Predictive Control-based Real-Time Operation of a Transmission Constrained Joint Wind-PHS System
2018 6th International Conference on Control Engineering & Information Technology (CEIT), 2018Co-Authors: Ismail Kayahan, Ugur YildiranAbstract:Trading wind energy in deregulated markets is a challenging task due to uncertainties involved. To cope with this complication, a significant body of work is devoted to the development of day-ahead bidding strategies based on stochastic programming. However, the problem of Real-Time Operation, which can be defined as the management of the system in balancing markets after day-ahead bidding phase is completed, is not studied well in the literature. Motivated by this fact, in the present work, a stochastic model predictive control (SMPC) based Real-Time Operation method is developed for a transmission-constrained joint wind-PHS system. It is assumed that the generation company participates in the day-ahead market and balancing market as a price taker player. Since Real- Time Operation depends on contracts made a priori, day-ahead bidding is also considered as an integral part and modeled as mixed-integer linear programming (MILP) based stochastic program. Main features of the proposed framework, which distinguish it from the previous studies, are the application of an SMPC strategy for Real-Time Operation and inclusion of transmission constraints in bidding and Operation phases.
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Risk-averse stochastic model predictive control-based Real-Time Operation method for a wind energy generation system supported by a pumped hydro storage unit
Applied Energy, 2018Co-Authors: Ugur Yildiran, Ismail KayahanAbstract:Abstract A wind energy producer participating in deregulated markets needs to make contracts on the energy it will supply in the next day. Deviations from the contracts, which could occur due to wind uncertainties, are compensated in Real-Time balancing markets at a considerable cost. Therefore, developing advanced day-ahead bidding and Real-Time Operation strategies minimizing such imbalance costs constitutes an important problem. There are several works on finding optimal day-ahead bids but the Real-Time Operation problem is not studied well. Motivated by this fact, we propose a new strategy in which the day-ahead bids are computed by solving a risk-averse stochastic program, and Real-Time Operation is performed by a stochastic model predictive control-based algorithm with a risk control capability. The algorithm is applied to a Realistic system composed of wind farms and a pumped hydro storage plant. Its performance is compared to a number of approaches appearing in the literature. Because the problem considered has two conflicting objectives of profit maximization and risk minimization, a Pareto optimality analysis is also conducted. Finally, the validity of a common practice followed in the literature, which is estimating the economic performance by bidding optimization, is investigated by comparing the estimate with the actual performance achieved by Real-Time Operation methods.
Xin Tian - One of the best experts on this subject based on the ideXlab platform.
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Generalizing fuzzy SARSA learning for Real-Time Operation of irrigation canals
Water, 2020Co-Authors: Kazem Shahverdi, José María Maestre, Farinaz Alamiyan-harandi, Xin TianAbstract:Recently, a continuous reinforcement learning model called fuzzy SARSA (state, action, reward, state, action) learning (FSL) was proposed for irrigation canals. The main problem related to FSL is its convergence and generalization in environments with many variables such as large irrigation canals and situations beyond training. Furthermore, due to its architecture, FSL may require high computation demands during its learning process. To deal with these issues, this work proposes a computationally lighter generalizing learned Q-function (GLQ) model, which benefits from the FSL-learned Q-function, to provide operators with a faster and simpler mechanism to obtain Operational instructions. The proposed approach is tested for different water requests in the East Aghili Canal, located in the southwest of Iran. Several performance indicators are used for evaluating the GLQ model results, showing convergence in all the investigated cases and the ability to estimate Operational instructions (actions) in situations beyond training, delivering water with high accuracy regarding several performance indicators. Hence, the use of the GLQ model is recommended for determining the Operational patterns in irrigation canals.
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Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System
Water, 2020Co-Authors: Nay Myo Lin, Xin Tian, Martine Rutten, Edo Abraham, José M. Maestre, N.c. Van De GiesenAbstract:This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for Real-Time Operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir Operation problem in Real Time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal Operation is required to compromise the three Operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in Real-Time Operation of a multi-reservoir system.
