Network reinforcement requirements for Scotland and the rest of the UK (RUK) - and possible solutions for this

A novel multi-objective transmission expansion planning (MOTEP) tool has been developed to analyse, on a comprehensive geographical scale, the reinforcements required to a base case electrical transmission network following application of a chosen future energy scenario, and to generate optimal network expansion plans, designed to alleviate these areas of strain, for a range of crucial network planning objectives. Here, we report the application of the MOTEP tool to a base case predicted 2014 GB transmission network (thereby including already planned reinforcements such as the Beauly to Denny line) under heavy strain from three 2020 energy scenarios developed by the two-region UK MARKAL energy system model. Reinforcement requirements for Scotland and the RUK beyond 2014, along with optimal network expansion plan options, are examined

Grid integration of variable renewable energies in Ghana: assessment of the impact on system stability

This research analyses the impact of renewable energies (RE) generation in Ghana’s national interconnected transmission system (NITS) and how its stability is affected. Integrating RE, particularly solar and wind in Ghana’s power system has been discussed at the national level with the intention to diversify the energy mix and reduce the dependency on thermal energy. RE integration introduces operational and infrastructural challenges in Ghana’s network, to which novel measures are required. Using the DIgSILENT PowerFactory simulation tool and MATLAB, simulation scenarios are created to capture diverse network conditions including different RE penetration levels, load demand and infrastructural expansion for three separate years. The ‘optimum’ penetration level of RE in the NITS considering voltage and loading limits is also identified using optimization techniques. The simulation results show that the target scenario is the most prone to both static and dynamic voltage instability. The transient stability analysis however reveals the post-target scenario to be unstable. Furthermore, methods of optimization are used to determine the reactive power deficient nodes in the NITS, which serve as the basis for the stability enhancement measures. The simulations and analysis additionally indicate that implementing the proposed measures indeed enhances the stability of the NITS. Finally, this research shows that RE integration is ‘technically’ feasible in Ghana if the required network reinforcements and operational changes are accordingly considered

Multi-objective network planning for the integration of electric vehicles as responsive demands

The integration of electric vehicles (EVs) into distribution networks presents substantial challenges to Distribution Network Operators (DNOs) internationally. In the 12 months from November 2017, EV registrations in Great Britain have increased by ~22% [A.1], though it is noted that EVs account for only 6% of all UK vehicle registrations [A.1] in 2018. With the UK Government announcement in 2017 [A.2] that "by 2040 there will be an end to the sale of all conventional petrol and diesel cars and vans", the penetration of EVs will require to - unless a new technology emerges - grow exponentially over the next 10 to 20 years towards 100% penetration by 2050. However, the increasing penetration of EVs can provide to the system multiple benefits and assist in mitigating issues; if EV integration is optimally planned using a suitable method. The managed charging of multiple EVs can assist in better utilising power generated by intermittent renewables, which will provide substantial benefits such as peak shifting, deferred reinforcement costs and the reduced requirement for imported energy to support the network at times of need.;Accurately assessing the impact that EVs will have on distribution networks is critical to DNOs [A.3]. In particular, the aim of this thesis is to identify the optimal location, battery size, charger power output and operational envelope for multiple EVs when used as responsive demands in high voltage/low voltage (HV/LV) distribution networks. Societal benefits can include reduced or deferred asset investment costs; reduced technical losses and increasing the utilisation of renewable generation [A.3]. System benefits must be accounted for and can support and inform planning and operational decisions - such as asset investment and network reinforcement. Coordinated smart charging of multiple EVs can assist in managing peaks in the demand curve and increase the utilisation of intermittent renewables. Unmanaged EV charging at times of peak demand would require the DNO to invest in reinforcement solutions to ensure the required additional capacity is made available. However, one approach is to cluster EV charging in periods when the base load would otherwise be low, to lessen the need for asset reinforcement as EV charging during the period of peak demand would be avoided.;Time periods for charging EVs (dependent on the chosen objectives) will be identified and then correlated to times when renewable generation availability is high and when base demand is low. The use of the presented network planning tool will identify EV charging strategies that can be applied to multiple EVs (based on the chosen objectives and with respect to constraints) whilst optimising the type, number and location on a specific modelled network. The planning framework utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2); the use of this algorithm will ensure that the network constraints are not breached and that multiple objectives are included in the analyses. This thesis investigates the impact that the inclusion of multiple EVs (when used as responsive demands); will have on the HV distribution network when the additional EV load is smartly scheduled to meet specific objectives and to correspond with the availability of intermittent renewables. The ultimate aim of this planning approach is to offer DNOs low cost solutions to multiobjective problems relating to EV integration and operation. [References A1-A3 for Abstract available p. XV of thesis.]The integration of electric vehicles (EVs) into distribution networks presents substantial challenges to Distribution Network Operators (DNOs) internationally. In the 12 months from November 2017, EV registrations in Great Britain have increased by ~22% [A.1], though it is noted that EVs account for only 6% of all UK vehicle registrations [A.1] in 2018. With the UK Government announcement in 2017 [A.2] that "by 2040 there will be an end to the sale of all conventional petrol and diesel cars and vans", the penetration of EVs will require to - unless a new technology emerges - grow exponentially over the next 10 to 20 years towards 100% penetration by 2050. However, the increasing penetration of EVs can provide to the system multiple benefits and assist in mitigating issues; if EV integration is optimally planned using a suitable method. The managed charging of multiple EVs can assist in better utilising power generated by intermittent renewables, which will provide substantial benefits such as peak shifting, deferred reinforcement costs and the reduced requirement for imported energy to support the network at times of need.;Accurately assessing the impact that EVs will have on distribution networks is critical to DNOs [A.3]. In particular, the aim of this thesis is to identify the optimal location, battery size, charger power output and operational envelope for multiple EVs when used as responsive demands in high voltage/low voltage (HV/LV) distribution networks. Societal benefits can include reduced or deferred asset investment costs; reduced technical losses and increasing the utilisation of renewable generation [A.3]. System benefits must be accounted for and can support and inform planning and operational decisions - such as asset investment and network reinforcement. Coordinated smart charging of multiple EVs can assist in managing peaks in the demand curve and increase the utilisation of intermittent renewables. Unmanaged EV charging at times of peak demand would require the DNO to invest in reinforcement solutions to ensure the required additional capacity is made available. However, one approach is to cluster EV charging in periods when the base load would otherwise be low, to lessen the need for asset reinforcement as EV charging during the period of peak demand would be avoided.;Time periods for charging EVs (dependent on the chosen objectives) will be identified and then correlated to times when renewable generation availability is high and when base demand is low. The use of the presented network planning tool will identify EV charging strategies that can be applied to multiple EVs (based on the chosen objectives and with respect to constraints) whilst optimising the type, number and location on a specific modelled network. The planning framework utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2); the use of this algorithm will ensure that the network constraints are not breached and that multiple objectives are included in the analyses. This thesis investigates the impact that the inclusion of multiple EVs (when used as responsive demands); will have on the HV distribution network when the additional EV load is smartly scheduled to meet specific objectives and to correspond with the availability of intermittent renewables. The ultimate aim of this planning approach is to offer DNOs low cost solutions to multiobjective problems relating to EV integration and operation. [References A1-A3 for Abstract available p. XV of thesis.

Sensor network design for a secure electric energy infrastructure

With the increasing threat of terrorism around the world, more attention has been paid to the security of the electric transmission infrastructure. Events in countries like Colombia, which has experienced as much as 200 terrorist attacks on its transmission infrastructure per year, show the vulnerability of the power system to these kinds of events. Although it is very difficult to avoid or predict when and where these terrorist acts can occur, quick assessment of the situation can help operators to take the optimal actions in order to avoid cascading events and the consequent partial or total blackouts. Wireless sensor networks are becoming the technology of choice for sensing applications mostly due to their ease of installation and associated lower costs. This thesis proposes a novel conceptual design for an application of wireless sensor technology for assessing the structural health of transmission lines and their implementation to improve the observability and reliability of power systems. A two layers model is presented for overcoming the communication range limitations of smart sensors and two operational modes are introduced. The main goal was to obtain a complete physical and electrical picture of the power system in real time, and determine appropriate control measures that could be automatically taken and/or suggested to the system operators once an extreme mechanical condition appears in a transmission line. For evaluating the feasibility of the concept, a dispatcher training simulator (DTS) based on the energy management system (EMS) platform from AREVA T&D was used for simulating the operation of the electric power system in real time as it is monitored at an actual energy control center

Optimal Transmission Investment Strategies for Sustainable Power Systems

Maintaining security and reliability in the electricity supply is fundamental to the functioning of a modern society and drives the need for adequate transmission capacity for both market participants and customers. Planning the investment in transmission has always been a complicated undertaking due to the high development costs and long lead times. Furthermore, to anticipate the future needs of customers is a task as difficult as that of cost-effective planning and construction of new facilities. Trying to find treatments for some of these issues represents a major motivation for this thesis. This thesis investigates the problem of how much reinforcement a transmission system requires when a significant proportion of wind generation is integrated into an existing transmission system. A multi-period transmission planning model is developed for determining optimal transmission capacity by balancing amortised transmission investment costs and annual generation costs subject to network security constraints, The model employs the security-constrained DC optimal power flow formulation and applies a solver (DashXpress) to obtain the results of the remaining linear large-scale optimisation problem. This thesis begins by exploring the impact of wind generation on the determination of appropriate levels of system capacity on the transmission network starting from the premise that it is no longer cost effective to invest in sufficient network capacity to accommodate simultaneous peaks from all generators. As such, a significant finding of this study is that conventional and wind generation should share network capacity. Given the acknowledged increase in uncertainty to security of supply due to difficulties in wind generation forecast this thesis also explores the optimal sourcing of generation reserve, and investigates investment in transmission capacity to exploit the cost benefits offered by standing reserve. Finally, the thesis presents and evaluates an alternative associated with transmission operation and investment level of risk and uncertainty by introducing more flexibility to the way the transmission system is operated. Application of Quadrature Boosters and Demand Side as model of corrective control, brings savings in operating costs without jeopardizing the level of system security, enables better utilisation of existing facilities and reduces the demand for new transmission investment