Operational voltage control of future distribution networks

Voltages in distribution networks are subject to variations primarily due to varying demand and the intermittent nature of growing renewable generation. Conventionally voltages are regulated only at certain locations such as distribution substations with some fixed settings for control equipment such as voltage targets or tap changers of under-load tap changing (ULTC) transformers and switching status of shunt capacitors. These settings are normally determined by operators in control rooms and are changed on the condition that power supply cannot be adequately delivered. A fixed constant setting for the controllable devices may not guarantee secured voltage profiles for all load centres at all times during the course of a day and may lead to frequent operations due to fluctuations in generation and demand profiles, which will eventually lead to wear on the control equipment. This thesis investigates the operational control of voltages on distribution networks and proposes a control strategy that manages voltage control devices from an operational planning perspective in order to maintain a desired level of voltage security by applying the most cost-effective control actions. The proposed methodology integrates power system sensitivity analysis and an artificial intelligence (AI) planning approach to schedule voltage control actions for a given electrical system across a specific planning time period based on known generation and demand profiles. The concept of a failsafe mode is incorporated into the proposed control strategy to deal with the potential loss of data communication or ultimate failure of the planned solutions. A typical radial distribution network model was studied under a range of scenarios and the simulation results demonstrated that the proposed methodology was capable of automatically planning control settings for ULTC transformers and MSCs to maintain requisite voltage limits and outperformed the conventional methods by eliminating the number of voltage violations and also reducing the number of control operations. The flexibility of the proposed methodology allows it to be integrated to the existing software platforms used by some of the UK distribution network operators (DNOs).Voltages in distribution networks are subject to variations primarily due to varying demand and the intermittent nature of growing renewable generation. Conventionally voltages are regulated only at certain locations such as distribution substations with some fixed settings for control equipment such as voltage targets or tap changers of under-load tap changing (ULTC) transformers and switching status of shunt capacitors. These settings are normally determined by operators in control rooms and are changed on the condition that power supply cannot be adequately delivered. A fixed constant setting for the controllable devices may not guarantee secured voltage profiles for all load centres at all times during the course of a day and may lead to frequent operations due to fluctuations in generation and demand profiles, which will eventually lead to wear on the control equipment. This thesis investigates the operational control of voltages on distribution networks and proposes a control strategy that manages voltage control devices from an operational planning perspective in order to maintain a desired level of voltage security by applying the most cost-effective control actions. The proposed methodology integrates power system sensitivity analysis and an artificial intelligence (AI) planning approach to schedule voltage control actions for a given electrical system across a specific planning time period based on known generation and demand profiles. The concept of a failsafe mode is incorporated into the proposed control strategy to deal with the potential loss of data communication or ultimate failure of the planned solutions. A typical radial distribution network model was studied under a range of scenarios and the simulation results demonstrated that the proposed methodology was capable of automatically planning control settings for ULTC transformers and MSCs to maintain requisite voltage limits and outperformed the conventional methods by eliminating the number of voltage violations and also reducing the number of control operations. The flexibility of the proposed methodology allows it to be integrated to the existing software platforms used by some of the UK distribution network operators (DNOs)