The increasing awareness on the effect of carbon emissions in our planet has led to several countries to adopt targets for their reduction. One way of contributing to this aim is to use and distribute electricity more efficiently. In this context, Conservation Voltage Reduction (CVR), a well-known technique that takes advantage of the positive correlation between voltage and demand to reduce energy consumption, is gaining renewed interest. This technique saves energy by only reducing customer voltages, without relying on customer actions and, therefore, can be controlled by the Distribution Network Operator (DNO). CVR not only brings benefits to the electricity system by reducing generation requirements (fewer fossil fuel burning and carbon emissions), but also to customers, as energy bill reductions. The extent to which CVR can bring benefits mainly depends on the customers load composition and their voltages. While the former dictates the voltage-demand correlation, the latter constraints the voltage reduction that can be applied without violating statutory limits. Although CVR has been studied for many years, most of the studies neglect the time-varying voltage-demand characteristic of loads and/or do not assess end customer voltages. While these simplifications could be used to estimate CVR benefits for fixed and limited voltage reductions, realistic load and network models are needed to assess the performance of active CVR schemes, where voltages are actively managed to be close to the minimum limit. Moreover, distribution networks have been traditionally designed with limited monitoring and controllability. Therefore, CVR has been typically implemented by adopting conservative voltage reductions from primary substations, for both American and European-style networks. However, as new infrastructure is deployed in European-style LV networks (focus of this work), such as monitoring and on-load tap changers (OLTCs), the opportunity arises to actively manage voltages closer to end customer (unlocking further energy savings). Although these technologies have shown to effectively control voltages in LV networks, their potential for CVR has not been assessed before. Additionally, most CVR studies were performed in a context where distributed generation (DG) was not common. However, this has changed in many countries, with residential photovoltaic (PV) systems becoming popular. As this is likely to continue, the interactions of residential PV and CVR need to be studied. This thesis contributes to address the aforementioned literature gaps by: (i) proposing a simulation framework to characterise the time-varying voltage-demand correlation of individual end customers; (ii) developing a process to model real distribution networks (MV and LV) from DNO data; (iii) adopting a Monte Carlo-based quantification process to cater for the uncertainties related to individual customer demand; (iv) assessing the CVR benefits that can be unlocked with new LV infrastructure and different PV conditions. To accomplish (iv), first, a simple yet effective rule-based scheme is proposed to actively control voltages in OLTC-enabled LV networks without PV and using limited monitoring. It is demonstrated that by controlling voltages closer to customers, annual energy savings can increase significantly, compared to primary substation voltage reductions. Also, to understand the effect of PV on CVR, a centralized, three-phase AC OPF-based CVR scheme is proposed. This control, using monitoring, OLTCs and capacitors across MV and LV networks, actively manages voltages to minimize energy consumption in high PV penetration scenarios whilst considering MV-LV constraints. Results demonstrate that without CVR, PV systems lead to higher energy imports for customers without PV, due to higher voltages. Conversely, the OPF-based CVR scheme can effectively manage voltages throughout the day, minimising energy imports for all customers. Moreover, if OLTCs at secondary substations are available (and managed in coordination with the primary substation OLTC), these tend to regulate customer voltages close to the minimum statutory limit (lower tap positions), while the primary OLTC delivers higher voltages to the MV network to also reduce MV energy losses.

Voltage instability is a major concern in operation of power systems and it is well known that voltage instability and collapse have led to blackout or abnormally low voltages in a significant part of the power system. Consequently, tracking the proximity of the power system to an insecure voltage condition has become an important element of any protection and control scheme. The expected time until instability is a critical aspect. There are a few energy management systems including voltage stability analysis function in the real-time environment of control centres, these are based on assumptions (such as off-line models of the system loads) that may lead the system to an insecure operation and/or poor utilization of the resources. Voltage instability is driven by the load dynamics, and investigations have shown that load restoration due to the on-load tap changer (OLTC) action is the main cause of the voltage instability. However, the aggregate loads seen from bulk power delivery transformers are still the most uncertain power system components, due to the uncertainty of the participation of individual loads and shortcomings of the present approaches in the load modeling. In order to develop and implement a true on-line voltage stability analysis method, the on-line accurate modeling of the higher voltage (supply system) and the lower voltage level (aggregate load) based on the local measurements is required. In this research, using the changes in the load bus measured voltage and current, novel methods are developed to estimate the supply system equivalent and to identify load parameters. Random changes in the load voltage and current are processed to estimate the supply system Thevenin impedance and the composite load components are identified in a peeling process using the load bus data changes during a large disturbance in the system. The results are then used to anticipate a possible long-term voltage instability caused by the on-load tap changer operation following the disturbance. Work on the standard test system is provided to validate the proposed methods. The findings in this research are expected to provide a better understanding of the load dynamics role in the voltage stability, and improve the reliability and economy of the system operation by making it possible to decrease uncertainty in security margins and determine accurately the transfer limits.