Demand-side contributions to dynamic performance of renewable-rich power systems
Creators: name: Rahman, Mir Toufikur
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400803
subject: Electrical energy generation (incl. renewables, excl. photovoltaics)
classificationCode: 400805
subject: Electrical energy transmission, networks and systems

description: Due to the emission concerns, renewable distributed generations (DGs), particularly solar photovoltaic (PV) and wind, are becoming important power generation sources. The integration of renewable DGs alters some of the electrical characteristics of the power grids, as such the direction of the power flow and the impedance of the power networks. Subsequently, the distribution network is becoming active (i.e. consumer as well as producer of electricity) instead of exhibiting traditional passive (i.e. consumer only) characteristics, which changes the conservation voltage reduction (CVR) or voltage-controlled demand management (VCDM) capabilities. The integration of renewable DGs into the power networks necessitates the development of a framework to assess and quantify the changing capacities of CVR and VCDM. The studies mentioned above need to be performed considering the intermittency and variability of renewable DGs and system loads. Furthermore, prior to VCDM deployment, dynamic power system indices (such as small disturbance stability and voltage stability) need to be evaluated by using system stability studies of an interconnected transmission network.

In support of these objectives, this thesis develops the first research question of ‘how renewable DGs affect the power systems’ steady-state and dynamic characteristics while assessing the demand management capability of power networks.’ This research has developed a mathematical model to prove that the integration of renewable DGs (i.e., solar PV and wind) reduce the network equivalent impedance, increases the static load model parameter, and enhances the CVR or VCDM capabilities. To validate this mathematical relationship, the static load model parameter is determined through the voltage-controlled approach in IEEE 33 bus network, which incorporates an on-load tap changer at the substation transformer. This test network is developed in DIgSILENT PowerFactory, and RMS simulations are carried out to observe how the load responds to the voltage reduction. Various network scenarios, including increased penetration of renewable DGs, varying load compositions, battery energy storage system integration, and network configurations (mesh vs radial), are considered. Year-long hourly renewable DGs and system load profiles are considered to capture the network's seasonal variations of demand management capabilities. The simulation results are also coherent with the developed mathematical formulation, which states that the integration of renewable DGs leads to a higher capability of VCDM.

The second research question of this thesis reveals ‘how the operational uncertainties of renewable DGs and system loads impact the assessment of demand management.’ This enables identifying the probabilistic ranges and operating points of demand management capabilities (as impacted by different probability distributions used for modelling uncertainty) rather than a deterministic value. The mathematical formulation is developed between the probability distribution of renewable DGs (solar PV and wind) and the probability distribution of demand management capabilities. It has been demonstrated mathematically that demand management capabilities' distribution or operating points predominantly depends on the adopted probability distribution of power system parameters. Various levels of uncertainties are considered, and the uncertainty modelling is conducted through the Monte Carlo simulation in MATLAB. Once again, RMS simulations are performed in DIgSILENT PowerFactory with IEEE 33 bus network to quantify the demand management capabilities through the traditional load modelling approach. The simulation results also validate the mathematical model built to depict the asymmetric or irregular distribution of VCDM capabilities in renewable-rich power systems.

The third and final research question in this thesis is concerned with ‘how to efficiently investigate the interconnected power system performance indicators (such as system stability) impacted by the operational uncertainty of renewable DGs.’ The probabilistic clustering approach is used to determine the aggregated model for renewable DGs, particularly a large-scale wind farm throughout the year so that the power system dynamic studies can be carried out more efficiently whenever the system conditions change. To determine the best-performed clustering algorithm, a comparative analysis is performed between K-means, hierarchical, fuzzy c-means (FCM), and density-based spatial clustering of applications with noise (DBSCAN) clustering. Once the aggregated modelling of the wind farm is implemented using the probabilistic clustering in MATLAB, the IEEE 39 bus network connected with a wind farm consisting of one hundred wind turbines is used for modal analysis, RMS simulation, and PV-curve calculation in DIgSILENT PowerFactory to evaluate small disturbance, voltage, and frequency stability, respectively. It has been demonstrated through the simulation that the probabilistically aggregated model can provide similar dynamic performance measures to the detailed model of the wind farm. In addition, the simulation results showed that the aggregated wind farm models (identified by the probabilistic clustering approach) exhibit the same degree of accuracy while performing the simulation 10 times faster (better efficiency).

The findings of this thesis, firstly, inform the distribution network operator and the researchers with the VCDM capabilities of transforming power grids. This thesis will also be helpful for the power system operators in estimating potential energy savings and VCDM patterns considering uncertainty in renewable DGs while ensuring the relevant steady-state and dynamic stability of the network.