How to manage a large and flexible nuclear set in a deregulated electricity market from the point of view of social welfare?

URL des Documents de travail : http://centredeconomiesorbonne.univ-paris1.fr/documents-de-travail/Documents de travail du Centre d'Economie de la Sorbonne 2014.59 - ISSN : 1955-611XIn the case of a large nuclear set (like the French set), nuclear production needs to be flexible to adjust to the predicted evolutions of the energy demmand. Consequently, the dominant position of nuclear in the national energy mix makes it responsible for the overall equilibrium of the electricity system which is directly intertwined with social welfare. In a previous work, we looked at producers own profits (short-term, inter-temporal) considering the equality between supply and demand. Here, we proceed with a full optimization of the social welfare in an identical framework. Theoretically, the optimal production behaviour that maximizes social welfare is characterized by a constant thermal production and a totally flexible nuclear production given that the nuclear capacity is sufficient. Numerically, the significant amount of nuclear capacities compared with thermal capacities in the French electricity market leads to the same “paradoxical” production behaviour. Therefore, we conclude that social optimum is ensured within our model by investing sufficiently in nuclear capacity. The optimal production scheduling determined by the social welfare maximization problem and the optimal inter-temporal production problem are totally opposite

装荷パターン最適化手法を用いたPWR炉心燃料管理の高度化に関する研究

本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである京都大学0048新制・課程博士博士(エネルギー科学)甲第7440号博第3号新制||エネ||1(附属図書館)UT51-98-G369京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻(主査)教授 神田 啓治, 教授 吉川 榮和, 教授 代谷 誠治学位規則第4条第1項該当Doctor of Energy ScienceKyoto UniversityDFA

Small modular reactor full scope core optimization using Cuckoo Optimization Algorithm

Small Modular Reactors (SMRs) with their excellent safety and economic features will be in high demand in the near future. Most SMR designs have longer burn-up cycle length with more fuel enrichment and smaller core size in comparison to the large conventional nuclear reactors. The small size of these reactors causes more neutron leakage (less core radius results in a higher area to volume ratio and more relative leakage). This feature of SMRs causes high values of maximum Power Peaking Factors (PPFs) through the core, so optimizing the safety parameters is of high necessity. Also, long burn-up cycle length needs a high initial excess reactivity, which results into use of some materials and methods to control this high excess reactivity. One of these methods is using a high number of Integral Fuel Burnable Absorber (IFBA) rods.In the present designs of IFBA rods, usually some amounts of fuel with lower enrichment are used at the top and bottom parts of the IFBA rods (known as cutback fuel) to flatten the axial PPFs. The small size of the SMRs (using a lower number of FAs) helps to have much less possible radial loading patterns (in comparison to the large reactors) and provides the possibility to optimize the axial variations in amounts of cutback fuel in IFBA rods simultaneously. Accordingly, the best axial and radial loading pattern according to the objective functions could be achieved. At the present work, the main goal is to optimize radial core loading pattern and axial variations of cutback fuel lengths at the IFBA rods of an SMR simultaneously using a multi-objective neutronic and thermal-hydraulic fitness function. The multi-objective fitness function includes burn-up cycle length, Minimum Departure from Nucleate Boiling (MDNBR), maximum and average radial and axial PPFs during the entire cycle lengths. The Cuckoo Optimization Algorithm (COA) as a new robust metaheuristic algorithm with high convergence speed and global optima achievement has been used. For the thermo-neutronic calculation, DRPACO package consists of the coupling system of DRAGON/ PARCS/COBRA codes have been used. Finally, the results of SMR core axial and radial loading pattern optimization using COA presents a core configuration with improvement in the core safety and economic parameters in comparison to the reference SMR cor

VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D

Improving core performance by reshuffling/reloading the fuel blocks within the core is one of the in-core fuel management methods with two major benefits: a possibility to improve core life and increase core safety. VHTR is a hexagonal annular core reactor with reflectors in the center and outside the fuel rings (3-rings). With the block type fuel assemblies, there is an opportunity for muti-dimensional fuel bocks movement within the core during scheduled reactor refueling operations. As the core is symmetric, by optimizing the shuffle operation of 1/6th of the core, the same process can be repeated through the remaining 5/6th of the core. VHTR has 170 fuel blocks in the core of which 50 are control rod blocks and are not movable to regular fuel block locations. The reshuffling problem now is to find the best combination of 120 fuel blocks that has a minimized power peaking and/or increased core life under safety constraints among the 120! combinations. For evaluating each LP during the shuffling, a fitness function that is developed from the parameters affecting the power peaking and core life is required. Calculating the power peaking at each step using Monte Carlo simulations on a whole core exact geometry model is a time consuming process and not feasible. A parameter is developed from the definitions of reactivity and power peaking factor called the localized reactivity potential that can be estimated for every block movement based on the reaction rates and atom densities of the initial core burnup at the time of shuffling. The algorithm (ReloPSO) is based on Particle Swarm Optimization algorithm the search process by improving towards the optimum from a set of random LPs based on the fitness function developed with the reactivity potential parameter. The algorithm works as expected and the output obtained has a flatter reactivity profile than the input. The core criticality is found to increase when shuffled closer to end of life. Detailed analysis on the burn runs after shuffling at different time of core operation is required to correlate the estimated and actual values of the reactivity parameter and to optimize the time of shuffle

An inter-temporal optimization of flexible nuclear plants operation in market based electricity systems : The case of competition with reservoir

In electricity markets where competition has been established for a long time, a nuclear operator familiar with the operation of such markets could be interested in the optimal long-term management of a flexible nuclear set (like the French) in a competitive market. To obtain a long vision of the optimal management of a nuclear set, we realize a full inter-temporal optimization of the production which results from the maximization of the value of generation over the whole game. Our model takes into consideration the periodical shut-down of nuclear units to reload their fuel, which permits to analyze the nuclear fuel as a stock behaving like a reservoir. A flexible nuclear reservoir permits different allocations of the nuclear fuel during the different demand seasons of the year. Our analysis is realized within a general deterministic dynamic framework where perfect competition is assumed and two flexible types of generation exist : nuclear and thermal non-nuclear. The marginal cost of nuclear production is (significantly) lower that the one of non-thermal production, which induces a discontinuity of producers' profit. In view of this price discontinuity, a "regularization" of the merit order price is achieved within our numerical model which leads to an alternative optimization problem (reglarized problem) that constitutes a good approximation of our initial problem. We also prove that in the absence of binding productions constraints, solutions are fully characterized by a constant nuclear production. However, such solutions do not exist within our numerical model because of production constraints that are active at the optimum. Finally, we study the maximization of social welfare in an identical framework. Similarly, we demonstrate that in the absence of binding production constraints a constant non-nuclear thermal production is a characteristic property of solutions of the social welfare maximization problem.Electricity market, nuclear generation, inter-temporal optimal reservoir operation, competition with reservoir, price discontinuity, social welfare.

Expansion of perturbation theory applied to shim rotation automation of the Advanced Test Reactor

textIn 2007, the Department of Energy (DOE) declared the Advanced Test Reactor (ATR) a National Scientific User Facility (NSUF). This declaration expanded the focus of the ATR to include diversified classes of academic and industrial experiments. An essential part of the new suite of more accurate and flexible codes being deployed to support the NSUF is their ability to predict reactor behavior at startup, particularly the position of the outer shim control cylinders (OSCC). The current method used for calculating the OSCC positions during a cycle startup utilizes a heuristic trial and error approach that is impractical with the computationally intensive reactor physics tools, such as NEWT. It is therefore desirable that shim rotation prediction for startup be automated. Shim rotation prediction with perturbation theory was chosen to be investigated as one method for use with startup calculation automation. A modified form of first order perturbation theory, called phase space interpolated perturbation theory, was developed to more accurately model shim rotation prediction. Shim rotation prediction is just one application for this new modified form of perturbation theory. Phase space interpolated perturbation theory can be used on any application where the range of change to the system is known a priori, but the magnitude of change is not known. A cubic regression method was also developed to automate shim rotation prediction by using only forward solutions to the transport equation.Mechanical Engineerin

Physics-Based 3D Multi-Directional Reloading Algorithm for Deep Burn HTR Prismatic Block Systems

To assure nuclear power sustainability, ongoing efforts on advanced closed-fuel cycle options and adapted open cycles have led to investigations of various strategies involving utilization of Transuranic (TRU) nuclides in nuclear reactors. Due to favorable performance characteristics, multiple studies are focused on transmutation options using High Temperature Gas-cooled Reactors (HTGRs). Prismatic HTGRs allow for 3-Dimensional (3D) fuel shuffling and prior shuffling algorithms were based on experimental block movement and/or manual block shuffle patterns. In this dissertation, a physics based 3D multi-directional reloading algorithm for prismatic deep burn very high temperature reactors (DB-VHTRs) was developed and tested to meet DB-VHTR operation constraints utilizing a high fidelity neutronics model developed for this dissertation. The high fidelity automated neutronics model allows design flexibility and metric tracking in spatial and temporal dimensions. Reduction of TRUs in DB-VHTRs utilizing full vectors of TRUs from light water reactor spent nuclear fuel has been demonstrated for both a single and two-fuel composition cores. Performance of the beginning-of-life and end-of-life (EOL) domains for multi-dimensional permutations were evaluated. Utilizing a two-fuel assembly permutation within the two-fuel system domain for a Single-Fuel vector, the developed shuffling algorithm for this dissertation has successfully been tested to meet performance objectives and operation constraints