Creating additional network capacity on constrained medium voltage networks utilizing distributed generation (specifically PV technology) - Ramdhin, Avinash (Department of Electrical Engineering, 2025)
Medium voltage (MV) networks are designed for forward power flow and they radially distribute power to various types of electrical loads. With electrical load growth driven by economic growth, aging networks have limited network capacity to supply the increase in load demand and non-technical losses, such as theft, further exacerbate the problem. This incremental load growth results in the network feeder having unacceptable voltage regulation and/or thermal limitations and such a network is defined as a constrained network where no new load or increase of existing load can be connected. Short-term and long-term mitigation solutions are implemented on these constrained networks to create more electrical capacity to meet the rising load demand. These solutions and investment thereof are also influenced by strategic load forecasting and may include the installation of voltage regulators, shunt compensation, new substations and/or various other network strengthening solutions. Long-term solutions are generally quite costly and the timeline for implementation is extremely long (>3 years). Short-term solutions are limited by equipment ratings and as such the network capacity is improved but by a relatively lower percentage. Due to the fixed and limited output of these devices, an alternate, additional un-constraining mechanism is required. Integrating distributed generation (DG) to medium voltage (MV) networks can improve or worsen the operating level of the network. However, linking this balance of network improvement to the amount of generation would improve the operating level. This research therefore utilizes the integration of DG, specifically solar photovoltaic (PV) installations, as an alternate approach to improve the capacity of constrained electricity networks. Solar PV technology is becoming practically feasible in its installation and cost; and is being supported by industrial and residential load types. The literature review compiled in this thesis highlights the various network improvement solutions utilized to assist MV networks in operating within their grid code regulations. The research then develops a coded method to be utilized for network analysis in DIgSILENT Powerfactory supported by a data analytic interface in Microsoft Excel. This analysis optimally places PV to the network to maximize network capacity and is quantified by defining an objective function that relates network capacity improvement to DG power generation. Technical guides, policies, distribution standards and grid codes that govern the integration of DG to MV and LV (low voltage) networks determine the mathematical constraints of the objective function. The non-linear solutions to the function results in optimizing the amount and allocation of distributed DG along the MV feeder hence creating additional capacity on constrained networks. Particle Swarm Optimization (PSO) was found to be best suited for this research due to its efficiency to solving non-linear optimization problems and is proposed as the appropriate method of optimally integrating DG to un- constrain MV networks. Suitable applications of the assessment tool are placing microgrids and electric vehicle charging infrastructure. Two realistic and practical electrical networks (11 kV and 22 kV) supplying rural, commercial and industrial type loads were chosen as test networks. These networks were modelled in DIgSILENT Powerfactory by firstly using the manual method of connecting DG to the network and then secondly by applying the developed DPL script to the same network. Then results were compared to investigate what optimal PV magnitude and point of connection led to what increase in network capacity for both methods. These results are summarized below. For Network 1, a very interesting relationship was seen when calculating the objective function to achieve the percentage improvement per MW of PV generation added to the network. Different scenarios were used such as, a constrained feeder during a light load scenario was modelled using the above methodology. The network capacity to PV generation ratio (objective function) indicates that the network capacity improvement of 67% could be achieved per MW of generation added. Similarly, for the normal feeder peak load scenario, the objective function (%/MW) was approximated around 40%/MW. The developed tool results correlated closely with the results from the manual method of placing PV on MV networks to maximize network capacity. The applicability of the derived results can be, for example interpreted as such, if one is to install 400 kW of PV on Network 1, the objective function for the peak times is 39%/MW so for 0.4 MW, the network capacity improvement is calculated by 39% x 0.4 = 15.6%. A similar approach was applied to Network 2 and similar results were derived albeit Network 2 having a voltage regulator as the voltage controlling device on the network. It was concluded from the analysis that there is no linear relationship on the amount of generation and position on a network to which it may be installed. The many tee-off points on the backbone and the variation of load with respect to its location, speaks to the uniqueness of every network and no general rule can be made for PV integration. However, the ratio of the network capacity increase to the amount of generation is more or less consistent for every scenario.