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Journal articles on the topic 'Hydro Unit Commitment'

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Published: 20 July 2024

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1

Chao-An Li, A. J. Svoboda, Chung-Li Tseng, R. B. Johnson, and E. Hsu. "Hydro unit commitment in hydro-thermal optimization." IEEE Transactions on Power Systems 12, no. 2 (May 1997): 764–69. http://dx.doi.org/10.1109/59.589675.

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2

Philpott, A. B., M. Craddock, and H. Waterer. "Hydro-electric unit commitment subject to uncertain demand." European Journal of Operational Research 125, no. 2 (September 2000): 410–24. http://dx.doi.org/10.1016/s0377-2217(99)00172-1.

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3

Bruninx, Kenneth, Yury Dvorkin, Erik Delarue, Hrvoje Pandzic, William Dhaeseleer, and Daniel S. Kirschen. "Coupling Pumped Hydro Energy Storage With Unit Commitment." IEEE Transactions on Sustainable Energy 7, no. 2 (April 2016): 786–96. http://dx.doi.org/10.1109/tste.2015.2498555.

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4

Siu, T. K., G. A. Nash, and Z. K. Shawwash. "A Practical Hydro, Dynamic Unit Commitment and Loading Model." IEEE Power Engineering Review 21, no. 5 (May 2001): 64. http://dx.doi.org/10.1109/mper.2001.4311393.

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5

Chaa-An Li, R. B. Johnson, A. J. Svoboda, Chung-Li Tseng, and E. Hsu. "A robust unit commitment algorithm for hydro-thermal optimization." IEEE Transactions on Power Systems 13, no. 3 (1998): 1051–56. http://dx.doi.org/10.1109/59.709098.

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6

Siu, T. K., G. A. Nash, and Z. K. Shawwash. "A practical hydro, dynamic unit commitment and loading model." IEEE Transactions on Power Systems 16, no. 2 (May 2001): 301–6. http://dx.doi.org/10.1109/59.918302.

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7

Zhou, Boran, Guangchao Geng, and Quanyuan Jiang. "Hydro-Thermal-Wind Coordination_newline in Day-Ahead Unit Commitment." IEEE Transactions on Power Systems 31, no. 6 (November 2016): 4626–37. http://dx.doi.org/10.1109/tpwrs.2016.2530689.

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8

Brito, Brunno H., Erlon C. Finardi, and Fabrício Y. K. Takigawa. "Unit-commitment via logarithmic aggregated convex combination in multi-unit hydro plants." Electric Power Systems Research 189 (December 2020): 106784. http://dx.doi.org/10.1016/j.epsr.2020.106784.

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9

Sutradhar, Suman, Nalin B. Dev Choudhury, and Nidul Sinha. "Modelling of Hydrothermal Unit Commitment Coordination Using Efficient Metaheuristic Algorithm: A Hybridized Approach." Journal of Optimization 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4529836.

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In this paper, a novel approach of hybridization of two efficient metaheuristic algorithms is proposed for energy system analysis and modelling based on a hydro and thermal based power system in both single and multiobjective environment. The scheduling of hydro and thermal power is modelled descriptively including the handling method of various practical nonlinear constraints. The main goal for the proposed modelling is to minimize the total production cost (which is highly nonlinear and nonconvex problem) and emission while satisfying involved hydro and thermal unit commitment limitations. The cascaded hydro reservoirs of hydro subsystem and intertemporal constraints regarding thermal units along with nonlinear nonconvex, mixed-integer mixed-binary objective function make the search space highly complex. To solve such a complicated system, a hybridization of Gray Wolf Optimization and Artificial Bee Colony algorithm, that is, h-ABC/GWO, is used for better exploration and exploitation in the multidimensional search space. Two different test systems are used for modelling and analysis. Experimental results demonstrate the superior performance of the proposed algorithm as compared to other recently reported ones in terms of convergence and better quality of solutions.

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10

Colonetti, Bruno, Erlon Finardi, and Lucas Borges Picarelli. "Hydrothermal Unit-Commitment Problem of a Large-Scale System with Representation of Forbidden Zones." Energies 15, no. 1 (December 22, 2021): 39. http://dx.doi.org/10.3390/en15010039.

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As we move towards electrical networks with a growing presence of renewable generation, the representation of the electrical components becomes more important. In hydro-dominated power systems, modelling the forbidden zones of hydro plants becomes increasingly challenging as the number of plants increases. Such zones are ranges of generation that either should be avoided or are altogether unreachable. However, because representing the forbidden zones introduces a substantial computational burden, hydrothermal unit-commitment problems (HTUC) for large systems are usually formulated ignoring the forbidden zones. Nonetheless, this simplification may demand adjustments to the solution of the HTUC, because the generation of the hydro stations may fall in forbidden zones. In practice, the adjustments are usually performed based on the experience of system operators and, then, can be far from an optimal correction. In this paper, we study the impact of explicitly representing the hydro-generation forbidden zones in a large-scale system with more than 7000 buses, 10,000 lines, and 700 hydro units. Our findings show that the simplified model that is current used can deviate significantly from the model with forbidden zones, both in terms of the generation of hydro plants, as well as the generation of thermal plants and the system marginal costs.

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11

Jiang, Ruiwei, Jianhui Wang, and Yongpei Guan. "Robust Unit Commitment With Wind Power and Pumped Storage Hydro." IEEE Transactions on Power Systems 27, no. 2 (May 2012): 800–810. http://dx.doi.org/10.1109/tpwrs.2011.2169817.

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12

Gang, Li, and Cheng Chuntian. "Hybrid PSO Algorithm with Tabu Search for Hydro Unit Commitment." HKIE Transactions 19, no. 2 (January 2012): 18–23. http://dx.doi.org/10.1080/1023697x.2012.10668254.

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13

IGUCHI, Masaru, Guoliang ZHAO, Susumu YAMASHIRO, and Naoyuki MORIYA. "A Weekly Scheduling Method for Hydro and Thermal Unit Commitment." IEEJ Transactions on Power and Energy 122, no. 3 (2002): 375–84. http://dx.doi.org/10.1541/ieejpes1990.122.3_375.

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14

Chen, Yue, Feng Liu, Bin Liu, Wei Wei, and Shengwei Mei. "An Efficient MILP Approximation for the Hydro-Thermal Unit Commitment." IEEE Transactions on Power Systems 31, no. 4 (July 2016): 3318–19. http://dx.doi.org/10.1109/tpwrs.2015.2479397.

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15

van Ackooij, Wim, Claudia D'Ambrosio, Leo Liberti, Raouia Taktak, Dimitri Thomopulos, and Sonia Toubaline. "Shortest Path Problem variants for the Hydro Unit Commitment Problem." Electronic Notes in Discrete Mathematics 69 (August 2018): 309–16. http://dx.doi.org/10.1016/j.endm.2018.07.040.

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16

Parvez, Iram, and Jianjian Shen. "Algorithms of approximate dynamic programming for hydro scheduling." E3S Web of Conferences 144 (2020): 01001. http://dx.doi.org/10.1051/e3sconf/202014401001.

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In hydro scheduling, unit commitment is a complex sub-problem. This paper proposes a new approximate dynamic programming technique to solve unit commitment. A new method called Least Square Policy Iteration (LSPI) algorithm is introduced which is efficient and faster in convergence. This algorithm takes the properties of widely used algorithm least square temporal difference (LSTD), enhance it further and make it useful for optimization problems. First value function is to find a fixed policy by using least square temporal difference Q (LSTDQ) algorithm which is similar to LSTD, then LSPI is introduced for making the policy iteration algorithm by using the results of LSTDQ. It combines the data efficiency of LSTDQ and policy-search efficiency of policy iteration.

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17

Arsenov, Arsen, and Georgi M. Dimirovski. "Optimizing Unit Commitment Technique for Operating Automated Hydro-Electric Power Plants." IFAC Proceedings Volumes 37, no. 19 (October 2004): 189–94. http://dx.doi.org/10.1016/s1474-6670(17)30681-x.

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18

Guerrero, Victoria, Agustín Sánchez de la Nieta, Javier Contreras, and Pedro F. Correia. "Unit Commitment with Wind Generation and Reversible-Hydro System in Islands." IFAC Proceedings Volumes 47, no. 3 (2014): 4050–55. http://dx.doi.org/10.3182/20140824-6-za-1003.00686.

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19

Jain, Achala, and Anupama P. Huddar. "Multi-objective-based robust unit commitment using hydro-thermal-wind: a hybrid technique." International Journal of Energy Sector Management 13, no. 4 (November 4, 2019): 804–27. http://dx.doi.org/10.1108/ijesm-07-2018-0015.

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Purpose The purpose of this paper is to solve economic emission dispatch problem in connection of wind with hydro-thermal units. Design/methodology/approach The proposed hybrid methodology is the joined execution of both the modified salp swarm optimization algorithm (MSSA) with artificial intelligence technique aided with particle swarm optimization (PSO) technique. Findings The proposed approach is introduced to figure out the optimal power generated power from the thermal, wind farms and hydro units by minimizing the emission level and cost of generation simultaneously. The best compromise solution of the generation power outputs and related gas emission are subject to the equality and inequality constraints of the system. Here, MSSA is used to generate the optimal combination of thermal generator with the objective of minimum fuel and emission objective function. The proposed method also considers wind speed probability factor via PSO-artificial neural network (ANN) technique and hydro power generation at peak load demand condition to ensure economic utilization. Originality/value To validate the advantage of the proposed approach, six- and ten-units thermal systems are studied with fuel and emission cost. For minimizing the fuel and emission cost of the thermal system with the predicted wind speed factor, the proposed approach is used. The proposed approach is actualized in MATLAB/Simulink, and the results are examined with considering generation units and compared with various solution techniques. The comparison reveals the closeness of the proposed approach and proclaims its capability for handling multi-objective optimization problems of power systems.

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20

Shaaban, Mohamed Abdel Moneim, Hossein Zeynal, and Khalid Nor. "MILP-Based Short-Term Thermal Unit Commitment and Hydrothermal Scheduling Including Cascaded Reservoirs and Fuel Constraints." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2732. http://dx.doi.org/10.11591/ijece.v9i4.pp2732-2742.

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<span>Reservoirs are often built in cascade on the same river system, introducing inexorable constraints. It is therefore strategically important to scheme out an efficient commitment of thermal generation units along with the scheduling of hydro generation units for better operational efficiency, considering practical system conditions. This paper develops a comprehensive, unit-wise hydraulic model with reservoir and river system constraints, as well as gas constraints, with head effects, to commit thermal generation units and schedule hydro ones in the short-term. A mixed integer linear programming (MILP) methodology, using the branch and bound & cut (BB&C) algorithm, is employed to solve the resultant problem. Due to the detailed modelling of individual hydro units and cascaded dependent reservoirs, the problem size is substantially swollen. Multithread computing is invoked to accelerate the solution process. Simulation results, conducted on various test systems, reiterate that the developed MILP-based hydrothermal scheduling approach outperforms other techniques in terms of cost efficiency.</span>

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21

Yoshikawa, Motonobu, Hiroshi Nakajima, Yoshiyuki Kurebayashi, Toshiyuki Sawa, Mitsuo Kinoshita, and Yuuji Nakata. "Method for Unit Commitment of Thermal and Pumped-storage Hydro Power Plants." IEEJ Transactions on Power and Energy 114, no. 12 (1994): 1220–26. http://dx.doi.org/10.1541/ieejpes1990.114.12_1220.

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22

Ploussard, Quentin, Thomas D. Veselka, Konstantinos Oikonomou, and Nathalie Voisin. "Hydro-economics tradeoff surfaces to guide unit commitment in production cost models." Applied Energy 324 (October 2022): 119728. http://dx.doi.org/10.1016/j.apenergy.2022.119728.

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23

Reza Norouzi, Mohammad, Abdollah Ahmadi, Ali Esmaeel Nezhad, and Amir Ghaedi. "Mixed integer programming of multi-objective security-constrained hydro/thermal unit commitment." Renewable and Sustainable Energy Reviews 29 (January 2014): 911–23. http://dx.doi.org/10.1016/j.rser.2013.09.020.

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24

Yuan, Xiaohui, Bin Ji, Yanbin Yuan, Rana M. Ikram, Xiaopan Zhang, and Yuehua Huang. "An efficient chaos embedded hybrid approach for hydro-thermal unit commitment problem." Energy Conversion and Management 91 (February 2015): 225–37. http://dx.doi.org/10.1016/j.enconman.2014.12.021.

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25

Paredes, M., L. S. A. Martins, and S. Soares. "Using Semidefinite Relaxation to Solve the Day-Ahead Hydro Unit Commitment Problem." IEEE Transactions on Power Systems 30, no. 5 (September 2015): 2695–705. http://dx.doi.org/10.1109/tpwrs.2014.2359803.

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26

Asir Rajan, C. Christober. "Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach." International Journal of Electrical Power & Energy Systems 33, no. 4 (May 2011): 939–46. http://dx.doi.org/10.1016/j.ijepes.2011.01.002.

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27

Cristian Finardi, Erlon, and Murilo Reolon Scuzziato. "Hydro unit commitment and loading problem for day-ahead operation planning problem." International Journal of Electrical Power & Energy Systems 44, no. 1 (January 2013): 7–16. http://dx.doi.org/10.1016/j.ijepes.2012.07.023.

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28

Postolov, Borče. "SECURITY CONSTRAINED HYDRO-THERMAL UNIT COMMITMENT FOR DIFFERENT HYDROLOGICAL SCENARIOS USING GENETIC ALGORITHM." Journal of Electrical Engineering and Information Technologies 6, no. 1 (2021): 15–28. http://dx.doi.org/10.51466/jeeit2161182015p.

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29

Finardi, E. C., and E. L. daSilva. "Solving the Hydro Unit Commitment Problem via Dual Decomposition and Sequential Quadratic Programming." IEEE Transactions on Power Systems 21, no. 2 (May 2006): 835–44. http://dx.doi.org/10.1109/tpwrs.2006.873121.

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30

Finardi, E. C., R. D. Lobato, V. L. de Matos, C. Sagastizábal, and A. Tomasgard. "Stochastic hydro-thermal unit commitment via multi-level scenario trees and bundle regularization." Optimization and Engineering 21, no. 2 (July 3, 2019): 393–426. http://dx.doi.org/10.1007/s11081-019-09448-z.

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31

Sahraoui, Youcef, Pascale Bendotti, and Claudia D'Ambrosio. "Real-world hydro-power unit-commitment: Dealing with numerical errors and feasibility issues." Energy 184 (October 2019): 91–104. http://dx.doi.org/10.1016/j.energy.2017.11.064.

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32

Ming, Zeng, Zhang Kun, and Wang Liang. "Study on unit commitment problem considering wind power and pumped hydro energy storage." International Journal of Electrical Power & Energy Systems 63 (December 2014): 91–96. http://dx.doi.org/10.1016/j.ijepes.2014.05.047.

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33

Cheng, Xianliang, Suzhen Feng, Hao Zheng, Jinwen Wang, and Shuangquan Liu. "A hierarchical model in short-term hydro scheduling with unit commitment and head-dependency." Energy 251 (July 2022): 123908. http://dx.doi.org/10.1016/j.energy.2022.123908.

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34

Nayak, Nimain Charan. "A Hybrid EP-SA-TS Method To Solve The Hydro � Thermal Unit Commitment Problem." i-manager's Journal on Embedded Systems 2, no. 4 (January 15, 2014): 1–11. http://dx.doi.org/10.26634/jes.2.4.2801.

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35

Cheng, Chuntian, Jiayang Wang, and Xinyu Wu. "Hydro Unit Commitment With a Head-Sensitive Reservoir and Multiple Vibration Zones Using MILP." IEEE Transactions on Power Systems 31, no. 6 (November 2016): 4842–52. http://dx.doi.org/10.1109/tpwrs.2016.2522469.

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36

Padmini, S., R. Jegatheesan, and K. Rubini. "A Novel Technique for Solving Hydrothermal Scheduling and Unit Commitment Using Artificial Immune System." Applied Mechanics and Materials 573 (June 2014): 679–83. http://dx.doi.org/10.4028/www.scientific.net/amm.573.679.

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This paper presents an artificial immune system algorithm to the short-term hydrothermal scheduling and unit commitment problem. This technique is applied to maximize the profit of Generating Companies (GENCO) which consider the softer demand constraint. A novel method is proposed for solving Hydrothermal Scheduling (HTS) using Artificial Immune System (AIS) in a competitive electricity market. The proposed algorithm is tested on four-hydro and three-thermal system for 12 hours. It is observed from the numerical results that the proposed algorithm provides better profit as compared to the conventional method and hence can be adopted by GENCO. Keywords: Hydrothermal Scheduling (HTS), Artificial Immune System (AIS)

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37

Sediqi, Mohammad Masih, Mohammed Elsayed Lotfy, Abdul Matin Ibrahimi, Tomonobu Senjyu, and Narayanan K. "Stochastic Unit Commitment and Optimal Power Trading Incorporating PV Uncertainty." Sustainability 11, no. 16 (August 20, 2019): 4504. http://dx.doi.org/10.3390/su11164504.

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This paper focuses on the optimal unit commitment (UC) scheme along with optimal power trading for the Northeast Power System (NEPS) of Afghanistan with a penetration of 230 MW of PV power energy. The NEPS is the biggest power system of Afghanistan fed from three main sources; 1. Afghanistan’s own power generation units (three thermal units and three hydro units); 2. imported power from Tajikistan; 3. imported power from Uzbekistan. PV power forecasting fluctuations have been handled by means of 50 scenarios generated by Latin-hypercube sampling (LHS) after getting the point solar radiation forecast through the neural network (NN) toolbox. To carry out the analysis, we consider three deterministic UC and two stochastic UC cases with a two-stage programming model that indicates the day-ahead UC as the first stage and the intra-day operation of the system as the second stage. A binary-real genetic algorithm is coded in MATLAB software to optimize the proposed cases in terms of thermal units’ operation costs, import power tariffs, as well as from the perspective of the system reliability risks expressed as the reserve and load not served costs. The results indicate that in the deterministic UC models, the risk of reserve and load curtailment does exist. The stochastic UC approaches including the optimal power trading are superior to the deterministic ones. Moreover, the scheduled UC costs and reserves are different from the actual ones.

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38

Marcelino, Carolina Gil, Carlos Camacho-Gómez, Silvia Jiménez-Fernández, and Sancho Salcedo-Sanz. "Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm." Energies 14, no. 9 (April 25, 2021): 2443. http://dx.doi.org/10.3390/en14092443.

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Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem. This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.

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39

Gomes e Souza, Henderson, Erlon Cristian Finardi, Brunno Henrique Brito, and Fabrício Yutaka Kuwabata Takigawa. "Partitioning approach based on convex hull and multiple choice for solving hydro unit-commitment problems." Electric Power Systems Research 211 (October 2022): 108285. http://dx.doi.org/10.1016/j.epsr.2022.108285.

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40

Borghetti, A., C. D'Ambrosio, A. Lodi, and S. Martello. "An MILP Approach for Short-Term Hydro Scheduling and Unit Commitment With Head-Dependent Reservoir." IEEE Transactions on Power Systems 23, no. 3 (August 2008): 1115–24. http://dx.doi.org/10.1109/tpwrs.2008.926704.

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41

van Ackooij, Wim. "Decomposition approaches for block-structured chance-constrained programs with application to hydro-thermal unit commitment." Mathematical Methods of Operations Research 80, no. 3 (August 23, 2014): 227–53. http://dx.doi.org/10.1007/s00186-014-0478-5.

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42

Rudolf, A., and R. Bayrleithner. "A genetic algorithm for solving the unit commitment problem of a hydro-thermal power system." IEEE Transactions on Power Systems 14, no. 4 (1999): 1460–68. http://dx.doi.org/10.1109/59.801929.

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43

Taktak, Raouia, and Claudia D’Ambrosio. "An overview on mathematical programming approaches for the deterministic unit commitment problem in hydro valleys." Energy Systems 8, no. 1 (January 13, 2016): 57–79. http://dx.doi.org/10.1007/s12667-015-0189-x.

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44

Gea-Bermúdez, Juan, Kaushik Das, Hardi Koduvere, and Matti Juhani Koivisto. "Day-Ahead Market Modelling of Large-Scale Highly-Renewable Multi-Energy Systems: Analysis of the North Sea Region towards 2050." Energies 14, no. 1 (December 25, 2020): 88. http://dx.doi.org/10.3390/en14010088.

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This paper proposes a mathematical model in order to simulate Day-ahead markets of large-scale multi-energy systems with a high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources, such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of considerably increasing the computational time. Relaxing integer variables significantly reduces the computational time, without highly compromising the accuracy of the results. The proposed model, together with the insights from the study case, can be especially useful for system operators for optimal operational planning.

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45

Parvez, Iram, JianJian Shen, Mehran Khan, and Chuntian Cheng. "Modeling and Solution Techniques Used for Hydro Generation Scheduling." Water 11, no. 7 (July 6, 2019): 1392. http://dx.doi.org/10.3390/w11071392.

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The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

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46

Nieva, R., A. Inda, and J. Frausto. "CHT: A Digital Computer Package for Solving Short Term Hydro-Thermal Coordination and Unit Commitment Problems." IEEE Power Engineering Review PER-6, no. 8 (August 1986): 42–43. http://dx.doi.org/10.1109/mper.1986.5527793.

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47

Aoki, K., T. Satoh, M. Itoh, T. Ichimori, and K. Masegi. "Unit Commitment in a Large-Scale Power System Including Fuel Constrained Thermal and Pumped-Storage Hydro." IEEE Power Engineering Review PER-7, no. 11 (November 1987): 51–52. http://dx.doi.org/10.1109/mper.1987.5526916.

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48

Vieira, Bruno, Ana Viana, Manuel Matos, and João Pedro Pedroso. "A multiple criteria utility-based approach for unit commitment with wind power and pumped storage hydro." Electric Power Systems Research 131 (February 2016): 244–54. http://dx.doi.org/10.1016/j.epsr.2015.10.024.

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49

Nieva, R., A. Inda, and J. Frausto. "CHT: A Digital Computer Package for Solving Short Term Hydro-Thermal Coordination and Unit Commitment Problems." IEEE Transactions on Power Systems 1, no. 3 (1986): 168–74. http://dx.doi.org/10.1109/tpwrs.1986.4334977.

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50

Aoki, K., T. Satoh, M. Itoh, T. Ichimori, and K. Masegi. "Unit Commitment in a Large-Scale Power System including Fuel Constrained Thermal and Pumped-Storage Hydro." IEEE Transactions on Power Systems 2, no. 4 (1987): 1077–84. http://dx.doi.org/10.1109/tpwrs.1987.4335304.

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