Deep Learning Based Forecasting-Aided State Estimation in Active Distribution Networks

Operating an active distribution network (ADN) in the absence of enough measurements, the presence of distributed energy resources, and poor knowledge of responsive demand behaviour is a huge challenge. This paper introduces systematic modelling of demand response behaviour which is then included in Forecasting Aided State Estimation (FASE) for better control of the network. There are several innovative elements in tuning parameters of FASE-based, demand profiling, and aggregation. The comprehensive case studies for three UK representative demand scenarios in 2023, 2035, and 2050 demonstrated the effectiveness of the proposed approach

Stochastic Volt/VAr control of power distribution systems

The increasing stochasticity in the distribution networks is rendering the traditional volt/VAr control (VVC) techniques more and more ineffective because of their basic premise that the nodal net power injections are constant throughout the optimization horizon. This results in voltage magnitude excursions outside the prescribed limits when the uncertain nodal injections exhibit an appreciable variance. This thesis acknowledges the random nature of these nodal injections, load and DG outputs, and seeks to achieve VVC solutions which are feasible for most of the scenarios in real time. Thus, two novel VVC routines are proposed viz a centralized algorithm based on chance constrained programming and a distributed algorithm based on stochastic programming. The centralized chance constrained VVC problem is solved through the scenario-based approach which requires to collect large number of scenarios, making the problem computationally intractable, from the probability spaces of the random variables and ensure that the basic constraints of the original problem are satisfied for all of these scenarios. Therefore, an algorithm is proposed which only considers very small, but properly chosen, subset of the original scenarios for optimization, and at the same time guarantees that the basic constraints for every scenario are satisfied in the original scenario set. The distributed stochastic VVC problem is solved by the alternating direction method of multipliers. This method allows the problem to be solved in a decentralized fashion by dividing the whole network into various zones and equipping each zone with a local controller. A proper coordination of the local controllers ensures the final solutions are globally optimal. The developed algorithms are tested on the 95-bus UKGDS, IEEE 123-bus system and a 190- bus system. The results show the superior performance of these methods by highly reducing the voltage violations as compared to the traditional deterministic VVC routines.Open Acces

Solar integration in the UK and India: technical barriers and future directions

The technology of solar photovoltaic (PV) systems has seen remarkable boom the past decade and remains central in the decarbonization and post-COVID19 recovery plan of many countries worldwide. However, global experience shows that PV installations start to level off in networks with certain solar integration levels. Among the various reasons, such as financing, regulations, markets etc., this report focuses and takes a deep look on technical challenges that act as barriers against further solar integration and develops a roadmap with technology innovations and transformations that will allow a high-solar future. The UK and India are the focal points of this investigation, examining closely how the challenges and future directions relate or differ between the two countries. This report arises out of the UK-India Joint Virtual Clean Energy Centre (JVCEC), which is a research consortium that involves ten UK universities and thirteen Indian institutes within three virtual centres namely, the Joint UK-India Clean Energy Centre (JUICE), the India-UK Centre for Education and Research in Clean Energy (IUCERCE) and the UK India Clean Energy Research Institute (UKICERI). </p