Abstract

The main outcome of this research is the development of a Probabilistic Impact Assessment methodology to comprehensively understand the effects of low carbon technologies (LCTs) in low voltage (LV) distribution networks and the potential solutions available to increase their adoption.The adoption of LCTs by domestic customers is an alternative to decreasing carbon emissions. Given that these customers are connected to LV distribution networks, these assets are likely to face the first impacts of LCTs. Thus, to quantify these problems a Monte Carlo-based Probabilistic Impact Assessment methodology is proposed in this Thesis. This methodology embeds the uncertainties related to four LCTs (PV, EHPs, µCHP and EVs). Penetration levels as a percentage of houses with a particular LCT, ranging from 0 to 100% in steps of 10%, are investigated. Five minute time-series profiles and three-phase four-wire LV networks are adopted. Performance metrics related to voltage and congestion are computed for each of the 100 simulations per penetration level. Given the probabilistic nature of the approach, results can be used by decision makers to determine the occurrence of problems according to an acceptable probability of technical issues.To implement the proposed methodology, electrical models of real LV networks and high resolution profiles for loads and LCTs are also developed. Due to the historic passive nature of LV circuits, many Distribution Network Operators (DNOs) have no model for them. In most cases, the information is limited to Geographic Information Systems (GIS) typically produced for asset management purposes and sometimes with connectivity issues. Hence, this Thesis develops a methodology to transform GIS data into suitable computer-based models. In addition, thousands of residential load, PV, µCHP, EHP and EV profiles are created. These daily profiles have a resolution of five minutes. To understand the average behaviour of LCTs and their relationship with load profiles, the average peak demand is calculated for different numbers of loads with and without each LCT.The Probabilistic Impact Assessment methodology is applied over 25 UK LV networks (i.e., 128 feeders) for the four LCTs under analysis. Findings show that about half of the studied feeders are capable of having 100% of the houses with a given LCT. A regression analysis is carried out per LCT, to identify the relationships between the first occurrence of problems and key feeder parameters (length, number of customers, etc.). These results can be translated into lookup tables that can help DNOs produce preliminary and quick estimates of the LCT impacts on a particular feeder without performing detailed studies. To increase the adoption of LCTs in the feeders with problems, four solutions are investigated: feeder reinforcement, three-phase connection of LCTs, loop connection of LV feeders and implementation of OLTCs (on-load tap changers) in LV networks. All these solutions are embedded in the Probabilistic Impact Assessment. The technical and economic benefits of each of the solutions are quantified for the 25 networks implemented.

Abstract

International audience; Simultaneous development of photovoltaic generation and electric vehicles strengthens the solicitations on the electric power system. This paper investigates the possible synergy between these players to jointly improve the production predictability while ensuring a low carbon mobility. It stands for a step towards a quantification of its economic and environmental fallout. First a context is described for a PV-EV collaboration. Then this is gathered into an optimization problem. Grid commitment constraints, battery aging and mobility needs are here considered from the environmental point of view of equivalent primary energy. Finally, a resolution method is presented which achieve an time-efficient optimization of the power flow for each vehicle, based on upstream computed charging policies. It relies on a stochastic modeling of both vehicles availability and forecast error of the PV production. The resolution framework is the stochastic dynamic programming, coupled with on-line minimization so as to avoid the curse of dimensionality. The proposed resolution enables to compute optimal power flow for each vehicle, even among large fleets. The emphasis is here set on a versatile resolution method which could take over many detailed objective functions.