Geev Mokryani received the B.Sc. and M.Sc. degrees from Sahand University of Technology, Tabriz, Iran in 2004 and 2007, respectively and the Ph.D. degree from the University of Salerno, Salerno, Italy, in 2012 all in electrical engineering. He was a Visiting Scholar with the Department of Energy Technology, Aalborg University, Aalborg, Denmark, from September 2011 to March 2012. Since November 2013, he is a research associate in Department of Electrical and Engineering, Imperial College London, UK. His current research interests include operations research, planning and control of distribution networks, electricity markets, uncertainty theory, and smart grids.
Understanding the Potential Impacts of High Penetrations of PV within UK Distribution Networks
Geev Mokryani, Paul Westacott, Chiara Candelise and Bikash C. Pal
PV deployment within the UK has seen dramatic uptake in recent years, with installed capacity surpassing 3.5 GWp. This rapid uptake raises the questions: What are the characteristics of this PV ensemble? And how much PV can we integrate into distribution networks? Here we address these questions by firstly looking in detail at how the current PV ensemble is distributed, both spatially and across different market segments, as these factors impact the amount of electricity that is: generated; self-consumed and fed into the grid.
Secondly, we assess the impacts of integrating large quantities of PV into the electricity network, via a worst-case-scenario approach. This scenario is characterized by minimum load demand and maximum non-regulating generation, such as wind and photovoltaic (PV) power. Another key issue which maybe crucial is line congestion, whereby congestion potential on the HV grid limits the capacity of installed PV. To address this, a stochastic method to assess the impact of renewable and non-renewable distributed generators (DGs) integration, such as PV, wind and nuclear, into active distribution networks considering line congestions and network constraints is proposed. The uncertainties related to the stochastic variations of illumination intensity related to PV and load demand are modelled by probability density functions (PDFs). Active network management schemes such as coordinated voltage control and adaptive power factor control are integrated into the method to investigate their impacts on the injected power into the network.
By combining understanding of the specific characteristics of the current UK PV ensemble with a worst-case-scenario modelling approach we are able to build up a method to address the integration of PV with specific focus on UK deployment drivers and distribution networks. This work has broad implications in helping to address the integration of DGs into distribution networks as well as to guide future policy decisions supporting both PV and the wider energy system.