AbstractClimate change is one of the biggest crises in the twenty-first century. The world has come together to deal with this global crisis. Recently, the United Nations (UN) called a 26th Climate Change conference (COP26), encouraging participants to accelerate their actions toward the goals defined under the Paris agreement and UN Framework Convention on climate change. Just like all other sectors, the electrical power sector is also becoming more sustainable by adopting renewable energy resources (RERs) from small scale to large scale. Solar PVs and wind turbines are becoming a very common sources of electricity these days. But both technologies are not capable of completely replacing large fossil fuel-fired thermal power plants because of their stochastic nature. Also, it will be challenging for the electrical grid to meet ancillary services requirements using these RERs.
Concentrated solar power (CSP) plant is a renewable energy technology which extracts the heat from solar radiation using reflecting mirrors and uses it to produce steam to run the steam turbine for electricity generation. The large thermal energy storage (TES) capability of such power plant makes it a dispatchable source of energy which can easily vary its output and run power plant for several hours with maximum capacity without the availability of sun. This makes CSP a promising renewable source of energy to replace large coal or gas power plants to achieve net zero by 2050. Many research and pilot projects are trying to explore the way to meet all electricity needs with RERs using the latest analytical and control technologies. However, little work has been done to analyse the operational planning of CSP for providing ancillary services utilising state-of-the-art technologies.
In this research work, a comprehensive dynamic model of a CSP technology, called parabolic trough power plant (PTPP), is developed for the applications of ancillary services to the grid. The PTPP simulation model consists of a solar field, TES, heat exchanger, power generation block and control unit. The developed PTPP model is tested and validated in MATLAB Simulink platform under various weather and loading conditions. The day-ahead operational planning for PTPP is performed using an artificial neural-network based long short-term memory model to forecast day-ahead solar radiation and load demand, which are the inputs to the PTPP simulation model. To analyse the accuracy of day-ahead planning of PTPP, the results are compared for day-ahead planned operation and actual operation. The total day-ahead PTPP generation was estimated to be 13777.7 MWe; however, the observed generation was 12516.4 MWe which is 9.15% lower than predicted, proving the accuracy of scheduling to be 90.85%.
|Date of Award
|21 Oct 2022
|Nigel Schofield (Main Supervisor) & Sid-Ali Amamra (Co-Supervisor)