<p>The CANDU-900 reactor design is an improvement on the current CANDU-6 reactor in the areas of economics, safety of operation and fuel cycle flexibility. As power grids start to rely more heavily on nuclear, it will be imperative for future nuclear generating station designs to be able to adjust their output to suit the fluctuating demands of the grid. Additionally, the need to reduce global nuclear waste has motivated research into mixed oxide fuel with the goal of maximizing spent fuel repository capacity and reducing decay heat via transmutation of transuranic actinides. The objective of this thesis is to provide insight into the load following capabilities of the CANDU-900 reactor design for a transuranic mixed oxide (TRUMOX) fueled core.</p> <p>The three-dimensional fuel management code, RFSP-IST, was used to simulate a reactor operating history for week long load following operations in a generic CANDU-900 reactor. Daily refuelling operations as well as reactivity device movements supplementary to RFSP were performed using the RECORD RRS emulator program. Core snapshots were taken at periodic intervals using the SIMULATE module to observe and track various reactor parameters. Average liquid zone controller fills as well as core reactivity and channel power values were used to determine the controllability of the reactor for various load following depths.</p> <p>The results of the load following simulations show that TRUMOX fuel has superior load following capabilities to that of conventional NU fuel for practical operational scenarios in a CANDU-900 reactor. Load following operations could be performed for TRUMOX fuel down to 85% full power in a safe and controllable manner using only the liquid zone controllers to account for the xenon transient reactivity as compared to NU which could only be done down to 90% full power. For load following simulations that both fuel types were capable of performing in a controllable manner, the TRUMOX fuelled core maintained on average a larger safety margin between the average liquid zone controller fills and the established safety limits.</p> / Master of Applied Science (MASc)

This thesis is a follow up on "SPARC fast reactor design: Design of two passively metal-fuelled sodium-cooled pool-type small modular fast reactors with Autonomous Reactivity Control" by Tobias Lindström (2015). In this thesis the two reactors designed by Lindström in said thesis were evaluated. The goal was to determine the reactors ability to load follow as well as the burnup of the neutron absorber used in the passive control system. To be able to determine the dynamic behaviour of the reactors the reactivity feedbacks of the cores were modelled using Serpent, a Monte Carlo simulation software for 3D neutron transport calculations. These feedbacks were then implemented into a dynamic simulation of the core, primary and secondary circulation and steam generator. The secondary circulation and feedwater flow were used to regulate steam temperature and turbine power. The core was left at constant coolant flow and no control rods were used. The simulations showed that the reactor was able to load follow between 100 % and 40 % of rated power at a speed of 6 % per minute. It was also shown that the reactor could safely adjust its power between 100 % and 10 % of rated power suggesting that load following is possible below 40 % of rated power but at a lower speed. Finally the reactors were allowed compensate for the variations in a week of the Latvian wind power production in order to show one possible application of the reactor.

Conventionally, to support varying power demand, the utility company must prepare to supply more electricity than actually needed, which causes inefficiency and waste. With the increasing penetration of renewable energy which is intermittent and stochastic, how to balance the power generation and demand becomes even more challenging. Demand response, which reschedules part of the elastic load in users' side, is a promising technology to increase power generation efficiency and reduce costs. However, how to coordinate all the distributed heterogeneous elastic loads efficiently is a major challenge and sparks numerous research efforts. In this thesis, we investigate different methods to provide demand response and improve power grid efficiency. First, we consider how to schedule the charging process of all the Plugged-in Hybrid Electrical Vehicles (PHEVs) so that demand peaks caused by PHEV charging are flattened. Existing solutions are either centralized which may not be scalable, or decentralized based on real-time pricing (RTP) which may not be applicable immediately for many markets. Our proposed PHEV charging approach does not need complicated, centralized control and can be executed online in a distributed manner. In addition, we extend our approach and apply it to the distribution grid to solve the bus congestion and voltage drop problems by controlling the access probability of PHEVs. One of the advantages of our algorithm is that it does not need accurate predictions on base load and future users' behaviors. Furthermore, it is deployable even when the grid size is large. Different from PHEVs, whose future arrivals are hard to predict, there is another category of elastic load, such as Heating Ventilation and Air-Conditioning (HVAC) systems, whose future status can be predicted based on the current status and control actions. How to minimize the power generation cost using this kind of elastic load is also an interesting topic to the power companies. Existing work usually used HVAC to do the load following or load shaping based on given control signals or objectives. However, optimal external control signals may not always be available. Without such control signals, how to make a tradeoff between the fluctuation of non-renewable power generation and the limited demand response potential of the elastic load, and to guarantee user comfort level, is still an open problem. To solve this problem, we first model the temperature evolution process of a room and propose an approach to estimate the key parameters of the model. Then, based on the model predictive control, a centralized and a distributed algorithm are proposed to minimize the fluctuation and maximize the user comfort level. In addition, we propose a dynamic water level adjustment algorithm to make the demand response always available in two directions. Extensive simulations based on practical data sets show that the proposed algorithms can effectively reduce the load fluctuation. Both randomized PHEV charging and HVAC control algorithms discussed above belong to direct or centralized load shaping, which has been heavily investigated. However, it is usually not clear how the users are compensated by providing load shaping services. In the last part of this thesis, we investigate indirect load shaping in a distributed manner. On one hand, we aim to reduce the users' energy cost by investigating how to fully utilize the battery pack and the water tank for the Combined Heat and Power (CHP) systems. We first formulate the queueing models for the CHP systems, and then propose an algorithm based on the Lyapunov optimization technique which does not need any statistical information about the system dynamics. The optimal control actions can be obtained by solving a non-convex optimization problem. We then discuss when it can be converted into a convex optimization problem. On the other hand, based on the users' reaction model, we propose an algorithm, with a time complexity of O(log n), to determine the RTP for the power company to effectively coordinate all the CHP systems and provide distributed load shaping services. / Graduate