Design and Implementation of Model Predictive Control Strategies for Improved Power Plant Cycling

Design and Implementation of Model Predictive Control Strategies for Improved Power Plant Cycling Xin He With the increasing focus on renewable energy sources, traditional power plants such as coal-fired power plants will have to cycle their load to accommodate the penetration of renewables into the power grid. Significant overshooting and oscillatory performance may occur during cycling operations if classical feedback control strategies are employed for plantwide control. To minimize the impact when power plants are operating away from their designed conditions, model-based optimal control strategies would need to be developed for improved power plant performance during cycling. In this thesis, model predictive control (MPC) strategies are designed and implemented for improved power plant cycling. The MPC strategies addressed correspond to a dynamic matrix control (DMC)-based linear MPC, a classical sequential quadratic programming (SQP)-based nonlinear MPC, a direct transcription-based nonlinear MPC and a proposed modified SQP-based nonlinear MPC. The proposed modified SQP algorithm is based on the backtracking line search framework, which employs a group of relaxed step acceptance conditions for faster convergence. The numerical results for motivating examples, which are selected from literature problem sets, served as proof of concept to verify that the proposed modified SQP has the potential for implementation on high-dimensional systems. To illustrate the tracking performance and computational efficiency of the developed MPC strategies, three processes of different dimensionalities are addressed. The first process is an integrated gasification combined cycling power plant with a water-gas shift membrane reactor (IGCC-MR), which is represented by a first-principles and simplified systems-level nonlinear model in MATLAB. For this application, a setpoint tracking scenario simulating a step increase in power demand, a disturbance rejection scenario simulating a coal feed quality change, and a trajectory tracking scenario simulating a wind power penetration into the power grid are presented. The second application is an aqueous monoethanolamine (MEA)-based carbon capture process as part of a supercritical pulverized coal-fired (SCPC) power plant, whose model is built in Aspen Plus Dynamics. For this system, disturbance rejection scenarios considering a ramp decrease in the flue gas flow rate as well as wind power penetration, and a scenario considering a combination of disturbance rejection and setpoint tracking are addressed. The third process is the entire SCPC power plant with MEA-based carbon capture (SCPC-MEA), which simulation is also built in Aspen Plus Dynamics. Trajectory tracking and disturbance rejection scenarios associated with wind and solar power penetrations are presented for this process. The MPC implementations on the three processes for the different scenarios addressed are successful. The closed-loop results show that the proposed modified SQP-based nonlinear MPC enhances the tracking performance by up to 96% when compared to the DMC-based linear MPC in terms of integral squared error results. The novel approach also improves the MPC computational efficiency by 20% when compared to classical SQP-based and direct transcription-based nonlinear MPCs

Study of supercritical coal-fired power plant dynamic responses and control for grid code compliance

The thesis is concerned with the study of the dynamic responses of a supercritical coal-fired power plant via mathematical modelling and simulation. Supercritical technology leads to much more efficient energy conversion compared with subcritical power generation technology so it is considered to be a viable option from the economic and environmental aspects for replacement of aged thermal power plants in the United Kingdom. However there are concerns for the adoption of this technology as it is unclear whether the dynamic responses of supercritical power plants can meet the Great Britain Grid Code requirement in frequency responses and frequency control. To provide answers to the above concerns, the PhD research project is conducted with the following objectives: to study the dynamic responses of the power plant under different control modes in order to assess its compliance in providing the frequency control services specified by the Great Britain Grid Code; to evaluate and improve the performance of the existing control loops of the power plant simulator and in this regard a controller based on the Dynamic Matrix Control algorithm was designed to regulate the coal flow rate and another controller based on the Generalized Predictive Control algorithm was implemented to regulate the temperature of the superheated steam; to conduct an investigation regarding frequency control at the power plant level followed by an analysis of the frequency control requirements extracted from the Grid Codes of several European and non-European countries. The structure and operation of the supercritical power plant was intensively studied and presented. All the simulation tests presented in this thesis were carried out by the mean of a complex 600 megawatts power plant simulator developed in collaboration with Tsinghua University from Beijing, China. The study of the conducted simulation tests indicate that it is difficult for this type of power plant to comply with the frequency control requirements of the Great Britain Grid Code in its current control method. Therefore, it is essential to investigate more effective control strategies aiming at improving its dynamic responses. In the thesis, new Model Predictive Control power plant control strategies are developed and the performance of the control loops and consequently of the power plant are greatly improved through implementation of Model Predictive Control based controllers

Methodology for ranking controllable parameters to enhance operation of a steam generator with a combined Artificial Neural Network and Design of Experiments approach

The operation of complex systems can drift away from the initial design conditions, due to environmental conditions, equipment wear or specific restrictions. Steam generators are complex equipment and their proper operation relies on the identification of their most relevant parameters. An approach to rank the operational parameters of a subcritical steam generator of an actual 360 MW power plant is presented. An Artificial Neural Network - ANN delivers a model to estimate the steam generator efficiency, electric power generation and flue gas outlet temperature as a function of seven input parameters. The ANN is trained with a two-year long database, with training errors of 0.2015 and 0.2741 (mean absolute and square error) and validation errors of 0.32% and 2.350 (mean percent and square error). That ANN model is explored by means of a combination of situations proposed by a Design of Experiment - DoE approach. All seven controlled parameters showed to be relevant to express both steam generator efficiency and electric power generation, while primary air flow rate and speed of the dynamic classifier can be neglected to calculate flue gas temperature as they are not statistically significant. DoE also shows the prominence of the primary air pressure in respect to the steam generator efficiency, electric power generation and the coal mass flow rate for the calculation of the flue gas outlet temperature. The ANN and DoE combined methodology shows to be promising to enhance complex system efficiency and helpful whenever a biased behavior must be brought back to stable operation

Transitioning to Affordable and Clean Energy

Transitioning to Affordable and Clean Energy is a collective volume which combines original contributions and review papers that address the question how the transition to clean and affordable energy can be governed. It will cover both general analyses of the governance of transition, including policy instruments, comparative studies of countries or policies, and papers setting out scientifically sound visions of a clean and just energy system. In particular, the following aspects are foregrounded: • Governing the supply and demand side transformation • Geographical and cultural differences and their consequences for the governance of energy transitions • Sustainability and justice related to energy transitions (e.g., approaches for addressing energy poverty) Transitioning to Affordable and Clean Energy is part of MDPI's new Open Access book series Transitioning to Sustainability. With this series, MDPI pursues environmentally and socially relevant research which contributes to efforts toward a sustainable world. Transitioning to Sustainability aims to add to the conversation about regional and global sustainable development according to the 17 SDGs. The book series is intended to reach beyond disciplinary, even academic boundaries

Impact on heat rate and subsequent emissions due to varying operation of coal fired power plants

Energy mix modellers often use a constant emissions factor model, which more or less implies a constant heat rate, when trying to show the emissions reduction benefits of integrating renewable power generation system on the grid. This approach does not consider the fact that there is a deterioration in the heat rate with load for the Coal Fired Power Plants that need to accommodate the additional renewable supply. If varying heat rate were to be included in a study, it is often limited to plant specific cases. This PhD presents a novel Variable Turbine Cycle Heat Rate (V-TCHR) model for predicting the part load Turbine cycle heat rate (TCHR) response of various Coal Fired Power Plant (CFPP) architectures, without detail knowledge of the entire steam cycle parameters. A total of 192 process models of representative CFPP architectures were developed using a Virtual Plant software. The models had different combinations of the degree of reheat; the throttle temperature; throttle pressure; and condenser cooling technology. The part load response of all the models were simulated using the software

Design Optimization and Dynamic Simulation of Steam Cycle Power Plants: A Review

After more than one century from its first use for electric power production, steam cycles are still the object of continuous research and development efforts worldwide. Indeed, owing to its favorable thermodynamic properties, steam cycles are not only used in coal-fired power plants but in a large variety of applications such as combined cycles, concentrated solar power plants and polygeneration plants. On the other hand, to cope with the efficiency and flexibility requirements set by today’s energy markets, the design and the operation of steam cycles must be carefully optimized. A key rule is played by the simulation and optimization codes developed in the last 30 years. This paper provides an introduction to the main types of simulation and optimization problems (design, off-design operation and dynamic), an overview of the mathematical background (possible solution approaches, numerical methods and available software), and a review of the main scientific contributions

Energy efficiency improvement in coal fired power plant through operational optimisation

Energy consumption is a significant cost to all business with the countries large industrial plants consuming 75 % of all energy produced in Australia. This cost is not only a financial burden but it has an environmental cost. The energy consumption within coal-fired power stations that is directly associated with generation is called auxiliary power. Approximately 10 % of all power produced is used to drive power stations internal auxiliary power needs. It is the auxiliary power consumption at Tarong Power Station that is the focus of this dissertation. This dissertation first seeks to understand the stations energy consumption through a comprehensive review of auxiliary power issues worldwide and the creation of control system tracking logic. The next stage of the dissertation then models that consumption in MATLAB and finally proposes ways in which to reduce that consumption without capital investment. The auxiliary power consumption within Tarong Power Station is recorded by two energy meters per unit on the main high voltage unit transformer feeds. The energy consumed is then reported each week as a percentage of unit generation. It is at this high level that the consumption is currently understood. This project has created energy tracking logic in the unit control system, a Siemens T3000 installation, to provide additional usage knowledge. A number of MATLAB models have been produced. The first of these reproduces the energy usage map of a running unit. The final Simulink model allows modification of the major component loading to trial energy reduction options. Using this model a reduction of 10 % at low loads has been achieved. The accuracy of the energy tracking logic and models created is proven to be within 2 % of the field energy metering. This dissertation has concluded that meaningful energy efficiency improvement can be obtained through operational improvement at Tarong Power Station