Optimal short-term thermal unit commitment

Pang, Chee Khiang; Chen, H.C.

doi:10.1109/t-pas.1976.32228

Cited by 191 publications

(48 citation statements)

References 9 publications

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“…These include deterministic, meta-heuristic, and hybrid approaches. Deterministic approaches include the priority list method [4], dynamic programming [5], Lagrangian Relaxation (LR) [6], integer/mixed-integer programming [7], [8], and the branch-andbound methods [9]. Due to the mixed binary and continuous variable nature, of the short term scheduling problem traditional optimization techniques may miss the optimal solution.…”

Section: Unmanaged Short-term Scheduling Of Fcppsmentioning

confidence: 99%

Smart Energy Management of Multiple Full Cell Powered Applications

Alam¹

2007

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DOE Project Officer:Mr. Stephen Waslo Objectives• To demonstrate the most effective design and protection scheme of minigrid power system for multiple fuel cell power plants application • To define the most economical operational schedule for the multiple fuel cell in terms of electrical, thermal and hydrogen production • To lay the bases for generation of hydrogen using the photoelectrochemical solar cells • To develop models for hydrogen production, purification, and storage systems • To demonstrate that smart energy management and control of a fuel cell power sources when subjected to varying demands of electrical and thermal loads together with demand of hydrogen production. Executive SummaryFuel cells, as a distributed energy source, are considered as one of the most promising sources of electrical energy that can meet the increasing demand and environmental constraints. To understand how the fuel cell behaves in a multi-unit generation environment, the research team designed a minigrid system with five fuel cells connected in parallel to feed five different neighborhoods. The thermal power, cogenerated from the fuel cells, is used to feed part of the neighborhood thermal load. In addition, the research team developed smart energy management control software to guarantee that the total power consumption of a typical residential home remains below the available power generated from a fuel cell. Generating hydrogen to feed the fuel cells is one of the main objectives of this project. New materials that help in producing hydrogen from water using sunlight are investigated. Another objective of this research is to investigate the application of small power fuel cells for portable applications. The research team investigated the feasibility of using fuel cells to feed the power to a laptop computer. In addition, using storage devices such as supercapacitor with the fuel cell to meet the sharp change in the laptop computer load is also investigated. Based on the economical analysis of the system and experimental results, we infer that a distributed energy system consisting of multiple fuel cells is a viable and feasible option to supply electrical and thermal power to a minigrid system. OverviewIn this research project the University of South Alabama research team has been investigating smart energy management and control of multiple fuel cell power sources when subjected to varying demands of electrical and thermal loads together with demands of hydrogen production. This research has focused on finding the optimal schedule of the multiple fuel cell power plants in terms of electric, thermal and hydrogen energy. The optimal schedule is expected to yield the lowest operating cost. Our team is also investigating the possibility of generating hydrogen using photoelectrochemical (PEC) solar cells through finding materials for efficient light harvesting photoanodes. The goal is to develop an efficient and cost effective PEC solar cell system for direct electrolysis of water. In addition, models for hydrogen production...

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Section: Unmanaged Short-term Scheduling Of Fcppsmentioning

confidence: 99%

Smart Energy Management of Multiple Full Cell Powered Applications

Alam¹

2007

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“…[7,6,4,5]. These programs also consider power system requirements such as unit maintenance schedules, minimum up and down time requirements, spinning reserve etc.…”

Section: Unit Commitment Constraintsmentioning

confidence: 99%

“…For a larger system, a more heuristic approach that is truncated dynamic programming may be used to replace the current dynamic programming to commit the thermal units [4,5] .…”

Section: Amentioning

confidence: 99%

The application of the ordered list method and the dynamic programming to the unit commitment

Uong¹

2000

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The electric energy that our society has been using changes daily, weekly, etc. It is important to have some way to know when to turn ON or OFF the power generation units in order to supply the energy demand with the cheapest cost.Proper commitment of the available generating units results in significant cost saving.The thesis presents a method of committing generating units in a hydro-thermal power system within practical [4,6,7] . To find the solution for the unit commitment, a number of difficulties are encountered because of many constraints that are to be satisfied. The more constraints applied to the system, the more computer resource and time is consumed. The dynamic programming approach has proved to be one of the very effective methods for the unit commitment [1,4]. This method requires significant computer resources in terms of computer memory to store all the data and time to determine the solution.The purpose of this thesis is to find an efficient method that will result in good unit commitment solution within practical computer time and resource.Almost all the electric energy we use is transformed from thermal and hydro energy. Hence, this study will present the combination of dynamic programming and ordered 3 list methods for the commitment of a power system that includes both thermal and hydro units based upon a specified power schedule. The ordered list method is found to be most suitable for the hydro unit commitment and the dynamic programming to be most suitable to the thermal unit commitment. The commitment is such that the total cost is minimum. The total cost includes both the production cost and costs associated with the start-up which consists of hot-start and cold-start cost and shutdown cost of the units. The spinning reserve requirement is also included. In addition, the operation of the individual units must satisfy the specified minimum up-time, minimum shutdown time and maintenance schedule. UNIT COMMITMENTThe unit commitment determines when to start and shut down units such that the load plus spinning reserve requirement within the specified period is always satisfied.A substantial saving is possible by a proper commitment of the available generating units.The difficulty of getting a good solution for unit commitment is that there are many constraints to be satisfied each time we want to commit each unit in a system.The problem becomes more complex because a system may contain both thermal and hydro units that have different type of constraints. Besides the constraints applied to each individual units, another external factor of spinning reserve requirement is applied to the whole system because of system operating requirements. CONSTRAINTS IN UNIT COMMITMENTMany constraints can be placed on the unit commitment problem. The list given below is by no means exhaustive but it is the most usual in the electric power systems. Each individual power system or power pool may impose different restrictions on the scheduling of units, depending on the generation makeup, load curve character...

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“…There are so many methods are executed to solve static problems. The dynamic problems are solved by the methods like priority list method [4] , dynamic programming methods [5,6] , branch and bound methods, Benders partitioning methods and Lagrangian relaxation (LR) methods [7][8][9][10] . The LR approaches are characterized by their ability to handle various constraints and to estimate the optimality of the solution in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Parallel hybrid enhanced inherited GA based scuc in a distributed cluster

Simon²

2012

AIR

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In the deregulated electricity market, secure operation is an enduring concern of the independent system operator (ISO). For a secure and economical hourly generation schedule of the day ahead market, ISO executes the security constrained unit commitment (SCUC) problem. In this paper, a new formulation of SCUC problem, considering more practical constraints are presented. The proposed SCUC formulation includes constraints, such as hourly power demand, system reserves, ramp up/down limits, minimum ON/OFF duration limits. Unlike the traditional SCUC techniques the proposed method solves the Security Constrained Economic Dispatch (SCED) from the UC. To solve such SCUC model, a hybrid solution method consists of an enhanced inherited genetic algorithm (EIGA) is used for unit commitment master problem and Lambda relaxation method is used for the economic dispatch sub-problem. The message passing interface (MPI) based technique is used to implement the hybrid EIGA in distributed memory model. The time complexity and the solution quality with respect to the number of processors in a cluster are thoroughly analyzed. The effectiveness of the proposed method to solve the SCUC problem is shown on different test systems.

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Optimal short-term thermal unit commitment

Cited by 191 publications

References 9 publications

Smart Energy Management of Multiple Full Cell Powered Applications

Smart Energy Management of Multiple Full Cell Powered Applications

The application of the ordered list method and the dynamic programming to the unit commitment

Parallel hybrid enhanced inherited GA based scuc in a distributed cluster

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