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Relevant bibliographies by topics / Unit commitment and economic dispatch

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Academic literature on the topic 'Unit commitment and economic dispatch'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Unit commitment and economic dispatch"

1

Mohamed-Nor, Khalid, and Abdul Halim Abdul Rashid. "Efficient economic dispatch algorithm for thermal unit commitment." IEE Proceedings C Generation, Transmission and Distribution 138, no. 3 (1991): 213. http://dx.doi.org/10.1049/ip-c.1991.0025.

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Ouyang, Z., and S. M. Shahidehpour. "Heuristic multi-area unit commitment with economic dispatch." IEE Proceedings C Generation, Transmission and Distribution 138, no. 3 (1991): 242. http://dx.doi.org/10.1049/ip-c.1991.0030.

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3

Chen, C. L., and S. L. Che. "Short-term unit commitment with simplified economic dispatch." Electric Power Systems Research 21, no. 2 (June 1991): 115–20. http://dx.doi.org/10.1016/0378-7796(91)90025-i.

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Anyaka, Boniface O., J. Felix Manirakiza, Kenneth C. Chike, and Prince A. Okoro. "Optimal unit commitment of a power plant using particle swarm optimization approach." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1135. http://dx.doi.org/10.11591/ijece.v10i2.pp1135-1141.

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Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also three different combinations in the form of 6, 5 and 4 units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.

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Walsh, M. P., and M. J. O'Malley. "Augmented Hopfield network for unit commitment and economic dispatch." IEEE Transactions on Power Systems 12, no. 4 (1997): 1765–74. http://dx.doi.org/10.1109/59.627889.

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Cho, Youngchae, Takayuki Ishizaki, Nacim Ramdani, and Jun-ichi Imura. "Economic Dispatch Cost Reduction in Box-based Robust Unit Commitment." IFAC-PapersOnLine 53, no. 2 (2020): 13248–53. http://dx.doi.org/10.1016/j.ifacol.2020.12.153.

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Xie, Min, Yuxin Du, Peijun Cheng, Wei Wei, and Mingbo Liu. "A Cross-Entropy-Based Hybrid Membrane Computing Method for Power System Unit Commitment Problems." Energies 12, no. 3 (February 2, 2019): 486. http://dx.doi.org/10.3390/en12030486.

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The cross-entropy based hybrid membrane computing method is proposed in this paper to solve the power system unit commitment problem. The traditional unit commitment problem can be usually decomposed into a bi-level optimization problem including unit start-stop scheduling problem and dynamic economic dispatch problem. In this paper, the genetic algorithm-based P system is proposed to schedule the unit start-stop plan, and the biomimetic membrane computing method combined with the cross-entropy is proposed to solve the dynamic economic dispatch problem with a unit start-stop plan given. The simulation results of 10–100 unit systems for 24 h day-ahead dispatching show that the unit commitment problem can be solved effectively by the proposed cross-entropy based hybrid membrane computing method and obtain a good and stable solution.

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Khunkitti, Sirote, Neville R. Watson, Rongrit Chatthaworn, Suttichai Premrudeepreechacharn, and Apirat Siritaratiwat. "An Improved DA-PSO Optimization Approach for Unit Commitment Problem." Energies 12, no. 12 (June 18, 2019): 2335. http://dx.doi.org/10.3390/en12122335.

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Solving the Unit Commitment problem is an important step in optimally dispatching the available generation and involves two stages—deciding which generators to commit, and then deciding their power output (economic dispatch). The Unit Commitment problem is a mixed-integer combinational optimization problem that traditional optimization techniques struggle to solve, and metaheuristic techniques are better suited. Dragonfly algorithm (DA) and particle swarm optimization (PSO) are two such metaheuristic techniques, and recently a hybrid (DA-PSO), to make use of the best features of both, has been proposed. The original DA-PSO optimization is unable to solve the Unit Commitment problem because this is a mixed-integer optimization problem. However, this paper proposes a new and improved DA-PSO optimization (referred to as iDA-PSO) for solving the unit commitment and economic dispatch problems. The iDA-PSO employs a sigmoid function to find the optimal on/off status of units, which is the mixed-integer part of obtaining the Unit Commitment problem. To verify the effectiveness of the iDA-PSO approach, it was tested on four different-sized systems (5-unit, 6-unit, 10-unit, and 26-unit systems). The unit commitment, generation schedule, total generation cost, and time were compared with those obtained by other algorithms in the literature. The simulation results show iDA-PSO is a promising technique and is superior to many other algorithms in the literature.

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Prashar, Sushil, Vikram Kumar Kamboj, and Kultaar Deep Singh. "A Cost Effective Solution to Security Constrained Unit Commitment and Dispatch Problem using Hybrid Search Algorithm." E3S Web of Conferences 184 (2020): 01071. http://dx.doi.org/10.1051/e3sconf/202018401071.

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The Security constraints unit commitment (SCUC) is a powerful scheduling technique used in power markets for daily planning. SCUC is a framework that combines two common algorithms in the electricity industry: Unit Commitment (UC) and Economic Dispatch (ED), while adding a new dimension – Security. Load demand is changing continuously due to variation of load of industrial, residential and commercial sectors. Thus, it is very important to decide which generating unit should be kept on and how much power should be dispatched so that time varying load demand can be meet on Hourly basis and there should be no scope of occurrence of loss of load hour (LOLH). In the proposed research, the recently developed hybrid meta-heuristics search algorithm i.e. GWO-RES has been applied to solve the security constrained Unit Commitment and dispatch problem of Electric power system. The efficiency of the proposed hybrid algorithm has been tested for standard IEEE-14 Bus, 30-Bus and 56-bus system and it has been experimentally found that GWO-RES performs much better than hybrid GWO-PS and GWO-RS algorithm.

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Wang, C., and S. M. Shahidehpour. "Effects of ramp-rate limits on unit commitment and economic dispatch." IEEE Transactions on Power Systems 8, no. 3 (1993): 1341–50. http://dx.doi.org/10.1109/59.260859.

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Dissertations / Theses on the topic "Unit commitment and economic dispatch"

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Cheung, Chak H. "A unified approach to unit commitment and economic dispatch in power system control." Thesis, Durham University, 1990. http://etheses.dur.ac.uk/1155/.

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Orero, Shadrack Otieno. "Power systems generation scheduling and optimisation using evolutionary computation techniques." Thesis, Brunel University, 1996. http://bura.brunel.ac.uk/handle/2438/4869.

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Optimal generation scheduling attempts to minimise the cost of power production while satisfying the various operation constraints and physical limitations on the power system components. The thermal generation scheduling problem can be considered as a power system control problem acting over different time frames. The unit commitment phase determines the optimum pattern for starting up and shutting down the generating units over the designated scheduling period, while the economic dispatch phase is concerned with allocation of the load demand among the on-line generators. In a hydrothermal system the optimal scheduling of generation involves the allocation of generation among the hydro electric and thermal plants so as to minimise total operation costs of thermal plants while satisfying the various constraints on the hydraulic and power system network. This thesis reports on the development of genetic algorithm computation techniques for the solution of the short term generation scheduling problem for power systems having both thermal and hydro units. A comprehensive genetic algorithm modelling framework for thermal and hydrothermal scheduling problems using two genetic algorithm models, a canonical genetic algorithm and a deterministic crowding genetic algorithm, is presented. The thermal scheduling modelling framework incorporates unit minimum up and down times, demand and reserve constraints, cooling time dependent start up costs, unit ramp rates, and multiple unit operating states, while constraints such as multiple cascade hydraulic networks, river transport delays and variable head hydro plants, are accounted for in the hydraulic system modelling. These basic genetic algorithm models have been enhanced, using quasi problem decomposition, and hybridisation techniques, resulting in efficient generation scheduling algorithms. The results of the performance of the algorithms on small, medium and large scale power system problems is presented and compared with other conventional scheduling techniques.

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Dahal, Keshav P., S. J. Galloway, G. M. Burt, and J. R. McDonald. "Generation scheduling using genetic algorithm based hybrid techniques." IEEE, 2001. http://hdl.handle.net/10454/2598.

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The solution of generation scheduling (GS) problems involves the determination of the unit commitment (UC) and economic dispatch (ED) for each generator in a power system at each time interval in the scheduling period. The solution procedure requires the simultaneous consideration of these two decisions. In recent years researchers have focused much attention on new solution techniques to GS. This paper proposes the application of a variety of genetic algorithm (GA) based approaches and investigates how these techniques may be improved in order to more quickly obtain the optimum or near optimum solution for the GS problem. The results obtained show that the GA-based hybrid approach offers an effective alternative for solving realistic GS problems within a realistic timeframe.

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Mihailovic, Nemanja. "A Cost Benefit Analysis of Using a Battery Energy Storage System (BESS) Represented by a Unit Commitment Model." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7548.

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This thesis aims to provide a general overview of a cost and benefit analysis of incorporating a battery energy storage system within unit commitment model. The deregulation of the electricity market in the U.S. has only been around for the last two decades. With renewable energy and energy storage systems becoming less expensive, a decentralized market scheme is becoming more popular and plausible. The scope of this work is to provide a fundamental understanding of unit commitment and a cost analysis of applying a battery energy storage system to an already established power system. A battery energy storage system (BESS) was placed within a unit commitment schematic and modeled for a 7 day/168 hour forecast. Three models were generated, two with and one without the battery energy storage device (BESS). The comparison between the three systems was conducted to produce a visual economic justification to the feasibility of a BESS.

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Nemati, Mohsen Shiralizadeh [Verfasser]. "Optimization of Unit Commitment and Economic Dispatch in Microgrids Based on Genetic Algorithm and Mixed Integer Linear Programming / Mohsen Shiralizadeh Nemati." Kassel : Kassel University Press, 2018. http://d-nb.info/1161470972/34.

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Sriyanyong, Pichet. "Particle swarm optimisation with applications in power system generation." Thesis, Brunel University, 2007. http://bura.brunel.ac.uk/handle/2438/4858.

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Today the modern power system is more dynamic and its operation is a subject to a number of constraints that are reflected in various management and planning tools used by system operators. In the case of hourly generation planning, Economic Dispatch (ED) allocates the outputs of all committed generating units, which are previously identified by the solution of the Unit Commitment (UC) problem. Thus, the accurate solutions of the ED and UC problems are essential in order to operate the power system in an economic and efficient manner. A number of computation techniques have progressively been proposed to solve these critical issues. One of them is a Particle Swarm Optimisation (PSO), which belongs to the evolutionary computation techniques, and it has attracted a great attention of the research community since it has been found to be extremely effective in solving a wide range of engineering problems. The attractive characteristics of PSO include: ease of implementation, fast convergence compared with the traditional evolutionary computation techniques and stable convergence characteristic. Although the PSO algorithms can converge very quickly towards the optimal solutions for many optimisation problems, it has been observed that in problems with a large number of suboptimal areas (i.e. multi-modal problems), PSO could get trapped in those local minima, including ED and UC problems. Aiming at enhancing the diversity of the traditional PSO algorithms, this thesis proposes a method of combining the PSO algorithms with a real-valued natural mutation (RVM) operator to enhance the global search capability and investigate the performance of the proposed algorithm compared with the standard PSO algorithms and other algorithms. Prior to applying to ED and UC problems, the proposed method is tested with some selected mathematical functions where the results show that it can avoid being trapped in local minima. The proposed methodology is then applied to ED and UC problems, and the obtained results show that it can provide solutions with good accuracy and stable convergence characteristic with simple implementation and satisfactory calculation time. Furthermore, the sensitivity analysis of PSO parameters has been studied so as to investigate the response of the proposed method to the parameter variations, especially in both ED and UC problems. The outcome of this research shows that the proposed method succeeds in dealing with the PSO' s drawbacks and also shows the superiority over the traditional PSO algorithms and other methods in terms of high quality solutions, stable convergence characteristic, and robustness.

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Hassan, Mohamed Elhafiz. "Power Plant Operation Optimization : Unit Commitment of Combined Cycle Power Plants Using Machine Learning and MILP." Thesis, mohamed-ahmed@siemens.com, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395304.

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In modern days electric power systems, the penetration of renewable resources and the introduction of free market principles have led to new challenges facing the power producers and regulators. Renewable production is intermittent which leads to fluctuations in the grid and requires more control for regulators, and the free market principle raises the challenge for power plant producers to operate their plants in the most profitable way given the fluctuating prices. Those problems are addressed in the literature as the Economic Dispatch, and they have been discussed from both regulator and producer view points. Combined Cycle Power plants have the privileges of being dispatchable very fast and with low cost which put them as a primary solution to power disturbance in grid, this fast dispatch-ability also allows them to exploit price changes very efficiently to maximize their profit, and this sheds the light on the importance of prices forecasting as an input for the profit optimization of power plants. In this project, an integrated solution is introduced to optimize the dispatch of combined cycle power plants that are bidding for electricity markets, the solution is composed of two models, the forecasting model and the optimization model. The forecasting model is flexible enough to forecast electricity and fuel prices for different markets and with different forecasting horizons. Machine learning algorithms were used to build and validate the model, and data from different countries were used to test the model. The optimization model incorporates the forecasting model outputs as inputs parameters, and uses other parameters and constraints from the operating conditions of the power plant as well as the market in which the plant is selling. The power plant in this mode is assumed to satisfy different demands, each of these demands have corresponding electricity price and cost of energy not served. The model decides which units to be dispatched at each time stamp to give out the maximum profit given all these constraints, it also decides whether to satisfy all the demands or not producing part of each of them.

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Bruce, Robert Alasdair Wilson. "Impacts of variable renewable generation on thermal power plant operating regimes." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20387.

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The integration of variable renewable energy sources (VRE) is likely to cause fundamental and structural changes to the operation of future power systems. In the United Kingdom (UK), large amounts of price-insensitive and variable-output wind generation is expected to be deployed to contribute towards renewable energy and carbon dioxide (CO2) emission targets. Wind generation, with near-zero marginal costs, limited predictability, and a limited ability to provide upward dispatch, displaces price-setting thermal power plants, with higher marginal costs, changing flexibility and reserve requirements. New-build, commercial-scale, and low-carbon generation capacity, such as CO2 capture and storage (CCS) and nuclear, may impact power system flexibility and ramping capabilities. Low-carbon generation portfolios with price-sensitive thermal power plants and energy storage are therefore likely to be required to manage increased levels of variability and uncertainty at operational timescales. This work builds on a high-resolution wind reanalysis dataset of UK wind sites. The locations of existing and proposed wind farms are used to produce plausible and internally consistent wind deployment scenarios that represent the spatial distribution of future UK wind capacity. Temporally consistent electricity demand data is used to characterise and assess demand-wind variability and net demand ramp events. A unit commitment and economic dispatch (UCED) model is developed to evaluate the likely operating regimes of thermal power plants and CCS-equipped units across a range of future UK wind scenarios. Security constraints for reserve and power plant operating constraints, such as power output limits, ramp rates, minimum up/down times, and start-up times, ensure the operational feasibility of dispatch schedules. The load factors, time spent at different loads, and the ramping and start-up requirements of thermal power plants are assessed. CO2 duration curves are developed to assess the impacts of increasing wind capacity on the distribution of CO2 emissions. A sensitivity analysis investigates the impacts of part-load efficiency losses, ramp rates, minimum up/down times, and start-up/shut-down costs on power plant operating regimes and flexibility requirements. The interactions between a portfolio of energy storage units and flexible CO2 capture units are then explored. This multi-disciplinary research presents a temporally-explicit and detailed assessment of operational flexibility requirements at full 8760 hour resolution, highlighting the non-linear impacts of increasing wind capacity. The methodological framework presented here uses high spatial-and temporal-resolution wind data but is expected to provide useful insights for other VREbased power systems to mitigate the implications of inadequate flexibility.

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Madaeni, Seyed Hossein. "Challenges in Renewable Energy Integration." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1342628585.

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Leuthold, Florian U. "Economic Engineering Modeling of Liberalized Electricity Markets: Approaches, Algorithms, and Applications in a European Context." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-26135.

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This dissertation focuses on selected issues in regard to the mathematical modeling of electricity markets. In a first step the interrelations of electric power market modeling are highlighted a crossroad between operations research, applied economics, and engineering. In a second step the development of a large-scale continental European economic engineering model named ELMOD is described and the model is applied to the issue of wind integration. It is concluded that enabling the integration of low-carbon technologies appears feasible for wind energy. In a third step algorithmic work is carried out regarding a game theoretic model. Two approaches in order to solve a discretely-constrained mathematical program with equilibrium constraints using disjunctive constraints are presented. The first one reformulates the problem as a mixed-integer linear program and the second one applies the Benders decomposition technique. Selected numerical results are reported.

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Books on the topic "Unit commitment and economic dispatch"

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Bond, Stephen David. Evaluation of unit commitment techniques for the economic scheduling of thermal units. 1985.

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Book chapters on the topic "Unit commitment and economic dispatch"

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Conejo, Antonio J., and Luis Baringo. "Unit Commitment and Economic Dispatch." In Power Electronics and Power Systems, 197–232. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69407-8_7.

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Kumar, Jitendra, Ashu Verma, and T. S. Bhatti. "An Optimization Algorithm for Unit Commitment Economic Emission Dispatch Problem." In Advances in Intelligent Systems and Computing, 113–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1822-1_11.

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Sheblé, Gerald B. "Economic Dispatch, Unit Commitment, and Optimal Power Flow as Auctions." In Computational Auction Mechanisms for Restructured Power Industry Operation, 107–64. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5157-7_4.

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Sriyanyong, P., Y. H. Song, and P. J. Turner. "Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch." In Evolutionary Scheduling, 313–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_12.

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Soliman, Soliman Abdel-Hady, and Abdel-Aal Hassan Mantawy. "Economic Dispatch (ED) and Unit Commitment Problems (UCP): Formulation and Solution Algorithms." In Energy Systems, 185–279. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1752-1_4.

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Rodríguez del Nozal, A., A. Tapia, L. Alvarado-Barrios, and D. G. Reina. "Application of Genetic Algorithms for Unit Commitment and Economic Dispatch Problems in Microgrids." In Nature Inspired Computing for Data Science, 139–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33820-6_6.

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Phan, Dzung T., and Ali Koc. "Optimization Approaches to Security-Constrained Unit Commitment and Economic Dispatch with Uncertainty Analysis." In Energy Systems, 1–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38134-8_1.

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Kannan, Nithiyananthan, Youssef Mobarak, and Fahd Alharbi. "Application of Cloud Computing for Economic Load Dispatch and Unit Commitment Computations of the Power System Network." In Computational Vision and Bio-Inspired Computing, 1179–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37218-7_124.

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Shayeghi, Hossein, and Masoud Alilou. "Optimal Dispatch and Unit Commitment in Microgrids." In Microgrids, 97–126. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59750-4_4.

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Al-Saadi, Mohammed K., Patrick C. K. Luk, and John Economou. "Unit Commitment Dynamic Unified Active and Reactive Power Dispatch of Microgrids with Integration of Electric Vehicles." In Communications in Computer and Information Science, 665–76. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6364-0_67.

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Conference papers on the topic "Unit commitment and economic dispatch"

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Bragin, Mikhail A., and Peter B. Luh. "Distributed and asynchronous unit commitment and economic dispatch." In 2017 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2017. http://dx.doi.org/10.1109/pesgm.2017.8273960.

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Tuffaha, Mutaz, and Jan Tommy Gravdahl. "Dynamic formulation of the unit commitment and economic dispatch problems." In 2015 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2015. http://dx.doi.org/10.1109/icit.2015.7125276.

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Kit Po Wong. "Computational intelligence applications in unit commitment, economic dispatch and loadflow." In APSCOM-97. International Conference on Advances in Power System Control, Operation and Management. IEE, 1997. http://dx.doi.org/10.1049/cp:19971804.

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Alamri, Abdullah, Maad AlOwaifeer, and A. P. Sakis Meliopoulos. "Multi-Objective Unit Commitment Economic Dispatch for Power Systems Reliability Assessment." In 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2020. http://dx.doi.org/10.1109/pmaps47429.2020.9183684.

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Cvijic, Sanja, and Jinjun Xiong. "Security constrained unit commitment and economic dispatch through benders decomposition: A comparative study." In 2011 IEEE Power & Energy Society General Meeting. IEEE, 2011. http://dx.doi.org/10.1109/pes.2011.6039643.

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Panda, Rajesh, and Prashant Kumar Tiwari. "Security Constrained Unit Commitment Economic Dispatch based Optimal Bidding Strategy in Risky Environment." In 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia). IEEE, 2019. http://dx.doi.org/10.1109/gtdasia.2019.8715992.

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Farsadi, Murtaza, Hadi Hosseinnejad, and Tohid Sattarpour Dizaji. "Solving unit commitment and economic dispatch simultaneously considering generator constraints by using nested PSO." In 2015 9th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2015. http://dx.doi.org/10.1109/eleco.2015.7394478.

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Kumar, S. Senthil, and V. Palanisamy. "A New Dynamic Programming Based Hopfield Neural Network to Unit Commitment and Economic Dispatch." In 2006 IEEE International Conference on Industrial Technology. IEEE, 2006. http://dx.doi.org/10.1109/icit.2006.372253.

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Botterud, Audun, Zhi Zhou, Jianhui Wang, Jean Sumaili, Hrvoje Keko, Joana Mendes, Ricardo Bessa, and Vladimiro Miranda. "Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: A case study of Illinois." In 2013 IEEE Power & Energy Society General Meeting. IEEE, 2013. http://dx.doi.org/10.1109/pesmg.2013.6672914.

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Bragin, Mikhail A., Peter B. Luh, Joseph H. Yan, and Gary A. Stern. "An efficient approach for Unit Commitment and Economic Dispatch with combined cycle units and AC Power Flow." In 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE, 2016. http://dx.doi.org/10.1109/pesgm.2016.7741156.

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Reports on the topic "Unit commitment and economic dispatch"

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Meyers, C., Y. Yao, T. Edmunds, and S. Smith. Large-Scale Stochastic Optimization for Unit Commitment and Economic Dispatch. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1091373.

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Yao, Y., C. Meyers, S. Smith, and T. Edmunds. Large-scale Stochastic Optimization for Unit Commitment and Economic Dispatch. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1055842.

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