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Abstract

With the integration of renewable energy into power systems, traditional power systems face new challenges. Due to their inherent fluctuations and variability, the introduction of renewable energy in power systems poses new challenges in modelling uncertainty. Controlling and optimising the operation cost by adjusting the output generation of renewable energy resources makes the operation more reliable and secure.

We first formulate the optimal power flow (OPF) problems for both the transmission and distribution systems and investigate the variables that greatly affect the outcome.

Solving the power system optimal operation problem, we realise the importance of uncertainties involved with renewable energy due to the inherent variability of weather data. Accurate forecasting mechanisms that address their inherent intermittency and variability enable the smooth integration of such resources in power system operations. To solve this problem, in the next step, we propose a novel probabilistic framework to predict short-term PV output taking into account the variability of weather data over different days and seasons. We go beyond existing prediction methods, building a pipeline of processes, i.e., feature selection, clustering and Gaussian Process Regression (GPR). We make use of datasets that comprise power output and meteorological data such as irradiance, temperature, zenith, and azimuth. First, a correlation study is performed to select the weather features which affect solar output to a greater extent. Next, we categorise the data into four groups based on solar output and time using k-means clustering. Finally, we determine a function that relates the selected features with solar output using GPR and Matérn 5/2 as a kernel function. We validate our method with five solar generation plants in different locations and compare with the existing methodologies. More specifically, to test the proposed model, two different methods are used: (i) 5-fold cross-validation; and (ii) holding out 30 random days as test data. To confirm the model accuracy, we apply our framework 30 independent times on each of the four clusters. The average error follows a normal distribution, and with a 95% confidence level, it takes values between −1.6% to 1.4%. The proposed framework decreases the normalised root mean square error and mean absolute error by 54.6% and 55.5%, respectively, compared with other relevant works.

Although we address the integration of a Microgrid into the distribution power network in the first research question, we yet need to address the transmission system constraints, as the incorporation of renewable energy into power systems poses serious challenges to the transmission and distribution power system operators (TSOs and DSOs). To fully leverage these resources, there is a need for a new market design with improved coordination between TSOs and DSOs. To answer the last research question, we propose two coordination schemes between TSOs and DSOs: one centralised and another decentralised that facilitate the integration of distributed based generation; minimise operational cost; relieve congestion; promote a sustainable system. To this end, we approximate the power equations with linearised equations so that the resulting OPFs in both the TSO and DSO become convex optimisation problems. In the resulting decentralised scheme, the TSO and DSO collaborate to allocate all resources in the system optimally. In particular, we propose an iterative bi-level optimisation technique where the upper level is the TSO that solves its own OPF and determines the locational marginal prices at substations. We demonstrate numerically that the algorithm converges to a near-optimal solution. We study the interaction of TSOs and DSOs and the existence of any conflicting objectives with the centralised scheme. More specifically, we approximate the Pareto front of the multi-objective optimal power flow problem where the entire system, i.e., transmission and distribution systems, is modelled. The proposed ideas are illustrated through a five-bus transmission system connected with distribution systems, represented by the IEEE 33- and 69-bus feeders.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Doctoral Theses School of Science & Technology > School of Science & Technology Doctoral Theses School of Science & Technology > Computer Science

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