Start-up Schedule Research Articles

MCFC 발전시스템용 촉매연소기의 연소 특성에 관한 실험적 연구

In the MCFC power generation system, the combustor supplies a high temperature mixture of gases to the cathode and heat to the reformer by using the off-gas from the anode; the off-gas includes high concentrations of and . Since a combustor needs to be operated in a very lean condition and avoid local heating, a catalytic combustor is usually adopted. Catalytic combustion is also generally accepted as one of the environmentally preferred alternatives for generation of heat and power from fossil fuels because of its complete combustion and low emissions of pollutants such as CO, UHC, and . In this study, experiments were conducted on catalytic combustion behavior in the presence of Pd-based catalysts for the BOP (Balance Of Plant) of 5 kW MCFC (Molten Carbonate Fuel Cell) power generation systems. Extensive investigations were carried out on the catalyst performance with the gaseous fuel by changing such various parameters as addition, inlet temperature, excess air ratio, space velocity, catalyst type, and start-up schedule of the pilot system adopted in the BOP.

火力発電プラント運用の多目的最適化システム(火力発電及び伝熱流動,<特集>第13回動力・エネルギー技術シンポジウム)

This paper proposes an intelligent start-up schedule optimization system for a thermal power plant. This system consists of a neural network, a genetic algorithm, and an interactive multi-objective programming technique. The features of this system are as follows. (1) The start-up schedule can be optimized based on multi-objective evaluation and (2) an optimal and flexible start-up schedule can be determined with a reasonable computing time and calculation accuracy through interaction between human beings and computers.

Application of strategies for sanitation management in wastewater treatment plants in order to control/reduce greenhouse gas emissions

Greenhouse gases (GHG), basically methane (CH 4), carbon dioxide (CO 2) and nitrous oxide (N 2O), occur at atmospheric concentrations of ppbv to ppmv under natural conditions. GHG have long mean lifetimes and are an important factor for the mean temperature of the Earth. However, increasing anthropogenic emissions could produce a scenario of progressive and cumulative effects over time, causing a potential “global climate change”. Biological degradation of the organic matter present in wastewater is considered one of the anthropogenic sources of GHG. In this study, GHG emissions for the period 1990–2027 were estimated considering the sanitation process and the official domestic wastewater treatment startup schedule approved for the Metropolitan Region (MR) of Santiago, Chile. The methodology considers selected models proposed by the Intergovernmental Panel on Climate Change (IPCC) and some others published by different authors; these were modified according to national conditions and different sanitation and temporal scenarios. For the end of the modeled period (2027), results show emissions of about 65 Tg CO 2 equiv./year (as global warming potential), which represent around 50% of national emissions. These values could be reduced if certain sanitation management strategies were introduced in the environmental management by the sanitation company in charge of wastewater treatment.

Intelligent Start-Up Schedule Optimization System for a Thermal Power Plant

This paper proposes an intelligent start-up schedule optimization system for a thermal power plant. This system consists of a dynamic simulation, a neural network, and an interactive multi-objective programming technique. In this study, a new intelligent optimization method using the neural network and a genetic algorithm to realize a satisficing trade-off method, which is an interactive multi-objective programming technique, has been developed and introduced into this system. The features of this system are as follows. (1) The start-up schedule can be optimized based on multi-objective evaluation and (2) an optimal and flexible start-up schedule can be determined with a reasonable computing time and calculation accuracy through human-computer interactions. The system is applied to a simulation for a combined cycle power plant, and to optimize from among multiple objectives, based on varying daily requirements. The application results show that optimal and flexible start-up schedules can be obtained within a reasonable computing time and with acceptable calculation accuracy.

ICONE15-10855 Schedule Control in Ling Ao Nuclear Power Project

Ling Ao Nuclear Power Station (LANP) is first one built up by self-reliance in China with power capacity 990×2 MWe. The results of quality control, schedule control and cost control are satisfactory. The commercial operation days of Unit 1 and Unit 2 were 28^<th> May 2002 and 8^<th> Jan. 2003 respectively, which were 48 days and 66 days in advance of the project schedule. This paper presents the practices of self-reliance schedule control system in LANP. The paper includes 10 sections: schedule control system; targets of schedule control; schedule control at early stage of project; construction schedule; scheduling practice; Point curves; schedule control of design and procurement; a good practice of construction schedule control on site; commissioning and startup schedule; schedule control culture. Three figures are attached. The main contents of the self-reliance schedule control system are as follows: to draw up reasonable schedules and targets; to setup management mechanism and procedures; to organize powerful project management team; to establish close monitoring system; to provide timely progress reports and statistics information. Five kinds of schedule control targets are introduced, i.e. bar-chart schedule; milestones; Point curves; interface management; hydraulic test schedule of auxiliary piping loops; EMR/EMC/EESR issuance schedules. Six levels of bar-chart schedules were adopted in LANP, but the bar-chart schedules were not satisfactory for complicated erection condition on site, even using six levels of schedules. So a kind of Point curves was developed and their advantages are explained. Scheduling method of three elements: activity, duration, logic, which was adopted in LANP, is introduced. The duration of each piping activities in LANP level 2 project schedule was calculated based on the relevant working Point quantities. The analysis and adjustment of Point curves are illustrated, i.e. balance of monthly quantities; possible production in the peakload month; the necessary rate of a month; the quantities to be completed before half period of the project; the quantities to be achieved before the cold function test. Due to a lot of documents and equipment in LANP, it is impossible to present their supply dates one by one in the level 2 project schedule. The rules, in reference to the corresponding construction starting dates, were given to define the supply dates. The good practices of construction schedule control are presented, including draft of a construction program, performance evaluation, specific coordination meeting and Work Load Point application. The schedule control culture is described, that is one core, two criteria, three control mechanisms, four resources to be secured.

INTELLIGENT START-UP SCHEDULE OPTIMIZATION SYSTEM FOR A THERMAL POWER PLANT

This paper proposes an intelligent start-up schedule optimization system for a thermal power plant. This system consists of a dynamic simulation, a neural network, and an interactive multi-objective programming technique. The features of this system are as follows. (1) The start-up schedule can be optimized based on multi-objective evaluation and (2) the optimal start-up schedule can be determined with a reasonable computing time and calculation accuracy through interaction between human beings and computers.

2215 近似最適化を用いた発電プラント運用の多目的最適化(OS3 近似最適化(II),未来社会を支えるものづくりとひとづくり(設計・システムから))

This paper proposes an intelligent start-up schedule optimization system for a thermal power plant. This system consists of a dynamic simulation, a neural network, and an interactive multi-objective programming technique. The features of this system are as follows. (1) The start-up schedule can be optimized based on multi-objective evaluation and (2) the optimal start-up schedule can be determined with a reasonable computing time and calculation accuracy through interaction between human beings and computers.

4301 対話型多目的計画法を用いた火力発電プラントの起動スケジューリング(S62-1 高効率石炭火力発電(1),S62 高効率石炭火力発電)

4301 対話型多目的計画法を用いた火力発電プラントの起動スケジューリング(S62-1 高効率石炭火力発電(1),S62 高効率石炭火力発電)

Short-term thermal generation scheduling using improved immune algorithm

This paper proposes a new immune algorithm (NIA), which merges the fuzzy system (FS), the annealing immune (AI) method and the immune algorithm (IA) together, to resolve short-term thermal generation unit commitment (UC) problems. This proposed method differs from its counterparts in three main aspects, namely: (1) changing the crossover and mutation ratios from a fixed value to a variable value determined by the fuzzy system method, (2) using the memory cell and (3) adding the annealing immune operator. With these modifications, we can attain three major advantages with the NIA, i.e. (1) the NIA will not fall into a local optimal solution trap; (2) the NIA can quickly and correctly find a full set of global optimal solutions and (3) the NIA can achieve the most economic solution for unit commitment with ease. The UC determines the start-up and shut-down schedules for related generation units to meet the forecasted demand at a minimum cost while satisfying other constraints, such as each unit's generating limit. The NIA is applied to six cases with various numbers of thermal generation units over a 24-h period. The schedule generated by the NIA is compared with that by several other methods, including the dynamic programming (DP), the Lagrangian relaxation (LR), the standard genetic algorithm (GA), the traditional simulated annealing (SA) and the traditional Tabu search (TS). The comparisons verify the validity and superiority in accuracy for the proposed method.

Using immune genetic algorithm based hybrid techniques for short-term unit commitment problem

This paper presents a hybrid chaos search (CS), immune algorithm (IA)/genetic algorithm (GA), and fuzzy system (FS) method (CIGAFS) for solving short-term thermal generating unit commitment (UC) problems. The UC problem involves determining the start-up and shut-down schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve, and individual units. First, we combined the IA and GA, then we added the CS and the FS approach. This hybrid system was then used to solve the UC problems. Numerical simulations were carried out using three cases: 10, 20, and 30 thermal unit power systems over a 24 h period. The produced schedule was compared with several other methods, such as dynamic programming (DP), Lagrangian relaxation (LR), standard genetic algorithm (SGA), traditional simulated annealing (TSA), and traditional Tabu search (TTS). A comparison with an immune genetic algorithm (IGA) combined with the CS and FS was carried out. The results show that the CS and FS all make substantial contributions to the IGA. The result demonstrated the accuracy of the proposed CIGAFS approach.

Start-Up Schedule Optimizing System of a Combined Cycle Power Plant

This paper presents a method to determine the optimal operational parameters of a combined cycle power plant. The proposed method combines a dynamic simulation with an optimization calculation based on the nonlinear programming method. The reason for the optimization of the plant start-up scheduling is being able to reduce the start-up time by keeping the thermal stresses in the thick parts of the heat recovery steam generator and in the steam turbine under their allowable values.

A study of a unit commitment problem with transmission constraints

AbstractUnit commitment problem is an optimization problem to determine the start‐up and shut‐down schedule of thermal units while satisfying various constraints, for example, generation‐demand balance, unit minimum up/down time, system reserve, and so on. Since this problem involves a large number of 0–1 type variables that represent up/down status of the unit and continuous variables expressing generation output, it is a difficult combinatorial optimization problem to solve. The study at present concerns the method for requiring the suboptimum solution efficiently.Unit commitment method widely used solves the problem without consideration of voltage, reactive power, and transmission constraints. In this paper, we will propose a solution of unit commitment with voltage and transmission constraints, based on the unit decommitment procedure (UDP) method, heuristic method, and optimal power flow (OPF). In this method, initial unit status will be determined from random numbers and the feasibility will be checked for minimum start‐up/shut‐down time and demand‐generation balance. If the solution is infeasible, the initial solution will be regenerated until a feasible solution can be found. Next, OPF is applied for each time period with the temporary unit status. Then, the units that have less contribution to the cost are detected and will be shut down based on the unit decommitment rules. This process will be repeated until suboptimal solution is obtained.The proposed method has been applied to the IEEE 118‐bus test system with 36 generating units with successful result. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(3): 36–45, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10187

Theoretical proof of edge search strategy applied to power plant start-up scheduling

Power plant start-up scheduling is aimed at minimizing the start-up time while limiting maximum turbine rotor stresses. This scheduling problem is highly nonlinear and has a number of local optima. In our previous research, we proposed an efficient search model: genetic algorithms (GAs) with enforcement operation to focus the search along the edge of the feasible space where the optimal schedule is supposed to stay. Based on a nonlinear dynamic simulation and a linear inverse calculation with the iteration method, the enforcement operation is applied to move schedules generated by GA toward the edge. We prove that the optimal schedule lies on the edge, ensuring that searching along the edge instead of the entire space can improve the search efficiency significantly without missing the optimum. Furthermore, we provide a theoretical setting equation for the inverse enforcement gains of the linear inverse calculation, intended to move schedules closer to the edge at each iteration of the enforcement operation. The theoretical setting equation is verified and discussed with the test results. We propose the theoretical setting equation with the test results as a guideline for the use of our proposed search model: GA with enforcement operation.

A fuzzy logic approach to accommodate thermal stress and improve the start-up phase in combined cycle power plants

Use of combined cycle power generation plant has increased dramatically over the last decade. A supervisory control approach based on a dynamic model is developed, which makes use of proportional-integral-derivative (PID), fuzzy logic and fuzzy PID schemes. The aim is to minimize the steam turbine plant start-up time, without violating maximum thermal stress limits. An existing start-up schedule provides the benchmark by which the performance of candidate controllers is assessed. Improvements regarding possible reduced start-up times and satisfaction of maximum thermal stress restrictions have been realized using the proposed control scheme.

Dynamic Optimization Method for Start-up Scheduling of a Combined Cycle Power Plant.

This paper presents a method to determine the optimal operational parameters of a combined cycle power plant, using a dynamic simulation program and nonlinear optimization technique. This proposal method combines the optimization calculation based on the nonlinear programming method with the dynamic simulation. The operational design is optimized in consideration with the plant dynamic characteristics, i.e. machines thermal stress, start-up time and energy loss, etc. are predicted by the dynamic simulation. An application for the optimization of plant start-up scheduling is able to reduce the start-up time and energy loss with keeping the machines thermal stress under the allowable values. The result shows the start-up time and energy loss are reduced by 21.7% and 19.5% during the gas turbine loading process, compared with a conventional heuristic method.

Synthesis, optimal planning and scheduling for process plant utility systems

A systematic methodology is presented for the synthesis, optimal planning and scheduling of plant utility systems. The methodology first addresses long-term synthesis and planning problems and continues with short-term maintenance scheduling applications. The synthesis approach determines new units, their layout, their number and capacities, as well as the operating strategies appropriate in view of process variations. The scheduling problem methodology determines maintenance, along with shut-down and start-up schedules for uncertain operational scenarios. The optimisation technology is applied in the form of natural decomposition schemes.

Adaptive-edge search for power plant start-up scheduling

Power plant start-up scheduling is aimed at minimizing the start-up time while limiting maximum turbine-rotor stresses. A shorter start-up time not only reduces fuel and electricity consumption during the start-up process, but also increases its capability of adapting to changes in electricity demand. The start-up scheduling problem can be formulated as a function optimization problem with constraints. We have constructed an efficient and robust search model-a genetic algorithm (GA) with an enforcement operation-which forces the search along the edge of the feasible space, where the optimal schedule is supposed to exist. However, this model has to perform a prior Monte Carlo test to obtain the enforcement gains used for the implementation of the enforcement operation. In this paper, we attempt to eliminate the Monte Carlo test by proposing a self-reliant search model by introducing a GA with an adaptive enforcement operation that can generate and adapt enforcement gains during the search process. The test results of this proposed model show that the overall number of time-consuming dynamic simulations for the constraints calculation can be reduced further, thus increasing the overall efficiency of finding the optimal or near-optimal schedules.

Dynamic simulation and its applications to optimum operation support for advanced combined cycle plants

The object system of this paper is an advanced combined cycle (ACC) plant which is composed of gas-turbines, heat recovery steam generators and a steam turbine. In this paper, the subjects related to the total engineering simulation model for the ACC plant dynamics, the developed general simulation tools, the application for real time approval of the operation support system and the application for the ACC optimum start-up scheduling are presented.

Startup optimization of a combined cycle power plant based on cooperative fuzzy reasoning and a neural network

A startup optimization control system for a gas and steam turbine combined cycle power plant is developed. The system can minimize startup time of the plant through cooperative fuzzy reasoning and a neural network autonomously adapting to varying process dynamics due to varying operational conditions, i.e. the ambient temperature and humidity. The operational conditions are taken into consideration by the neural network with a learning mechanism to optimize the schedule. The system is applied to a simulation for a plant with a three pressure staged reheat type 235.7 MW rated capacity, and the following points are seen. (1) The system can harmonize machines operations making good use of the operational margins, i.e. machine thermal stress and NO/sub x/ emission. (2) Startup time and energy loss are reduced by 35.6% and 26.3%, respectively, compared with the conventional off-line startup scheduling method.

適応的探索による火力発電プラント起動スケジューリング

適応的探索による火力発電プラント起動スケジューリング