Welcome
This simple website is the presentation of materials relevant for more detailed scrutiny from the thesis, "Optimising the future utilisation of renewable energy resources through smart grid control of electric vehicle charging loads", submitted by Calum Hercus in partial fulfilment for the requirement of the degree: Master of Science - Sustainable Engineering: Renewable Energy Systems and the Environment (2015) at The University of Strathclyde.
Introduction
Various governmental and scientific bodies are now beginning to ponder what future may be painted for an energy sector forecasted in the not so distant future to feature a prominent integration of EVs. Although current forecasts of when the EV boom will strike are somewhat tentative due to difficulty in predicting a number of intrinsically linked variable factors, it is postulated by many to be a question of when, not if. The provisions for such an event therefore need to be considered as of today.
On the utility side there is one particular question which dominates; are current grid electricity distribution network assets capable of satisfying a future scenario of greatly increased peak electrical demand, while minimalising the associated GHG emissions? The answer is probably not, at least not within the current UK grid infrastructure. It is likely that more financially and carbon intensive methods of despatchable electricity generation will bear the brunt, and the EV transition will not be so clean after all.
To facilitate the clean integration of EVs, smarter grid technologies are required. The smart grid is a developing concept, the focus of ongoing research and demonstration projects, which will enable two-directional grid communication between the utility company and its consumers. In theory, this will allow utility companies control over consumers domestic appliances suitable for flexible intervention. As EVs are typically parked in excess of 90% of the time, and do not intuitively require the maximum quantity of electricity which may be delivered in this time, they present an extremely flexible source of demand side management (DMS).
If EV recharging schedules can be autonomously controlled without impacting on the consumer’s range of mobility, they present a hugely dynamic source of demand side flexibility which may be altered on a second by second basis to particularly correlate with the available quantity of renewable electricity supply at that timestep. In a future where high levels of EV market penetration conjoin with the development of a smart grid, we may envision an idealised scenario where national grid supply profiles are no longer ramped up at great expense to follow demand, but instead demand is managed to follow supply. Managing this network across theoretically millions of system nodes distributed across the breadth of the UK is a daunting infrastructural, particularly ICT challenge, but not dissimilar to those comparably overcome by previous generations.
The described thesis investigates how aggregated areas of EV recharging such as workplaces present the opportunity to trial run ideas and technologies of the relatively unknown smart grid, and adjacently develop a preliminary quantity of EV recharging facilities within areas of public infrastructure. Particular focus is on demonstrating how smart control over EV recharging schedules may be implemented to optimise the utilisation of distributed renewable electricity sources and thus improve the overall evaluation of public infrastructure suitability for EV recharging facilities with onsite generation.
On the utility side there is one particular question which dominates; are current grid electricity distribution network assets capable of satisfying a future scenario of greatly increased peak electrical demand, while minimalising the associated GHG emissions? The answer is probably not, at least not within the current UK grid infrastructure. It is likely that more financially and carbon intensive methods of despatchable electricity generation will bear the brunt, and the EV transition will not be so clean after all.
To facilitate the clean integration of EVs, smarter grid technologies are required. The smart grid is a developing concept, the focus of ongoing research and demonstration projects, which will enable two-directional grid communication between the utility company and its consumers. In theory, this will allow utility companies control over consumers domestic appliances suitable for flexible intervention. As EVs are typically parked in excess of 90% of the time, and do not intuitively require the maximum quantity of electricity which may be delivered in this time, they present an extremely flexible source of demand side management (DMS).
If EV recharging schedules can be autonomously controlled without impacting on the consumer’s range of mobility, they present a hugely dynamic source of demand side flexibility which may be altered on a second by second basis to particularly correlate with the available quantity of renewable electricity supply at that timestep. In a future where high levels of EV market penetration conjoin with the development of a smart grid, we may envision an idealised scenario where national grid supply profiles are no longer ramped up at great expense to follow demand, but instead demand is managed to follow supply. Managing this network across theoretically millions of system nodes distributed across the breadth of the UK is a daunting infrastructural, particularly ICT challenge, but not dissimilar to those comparably overcome by previous generations.
The described thesis investigates how aggregated areas of EV recharging such as workplaces present the opportunity to trial run ideas and technologies of the relatively unknown smart grid, and adjacently develop a preliminary quantity of EV recharging facilities within areas of public infrastructure. Particular focus is on demonstrating how smart control over EV recharging schedules may be implemented to optimise the utilisation of distributed renewable electricity sources and thus improve the overall evaluation of public infrastructure suitability for EV recharging facilities with onsite generation.
Key Deliverable
In order to satisfy the thesis objective, software was required that enables unbounded and dynamic analysis of demand profiles from EV recharging which are then matched to a renewable energy supply quantity. Following an extensive review of available software, it was concluded that none currently exists of sufficient modelling capacity. That software is commercially limited in analysis of EV recharging may be indicative of insubstantial demand from researchers, relevant policymakers and grid operators, at least while the level of EV penetration into the road transport sector remains negligibly low. However, if targeted areas of public infrastructure, such as workplaces, are to provide a suitable environment for trialing smart grid technologies fundamental to a revolutionised power industry, modelling capability within a simulated environment is necessary to quantify the impact of smart EV recharging strategies, and encourage real life trial participation.
In response to the software review, it was decided that a new modelling tool is necessary to simulate smart EV recharging strategies and analyse how they enhance the utilisation of renewable electricity generation. The development of this modelling tool constituted a key deliverable of the thesis. Although tailored with relevance to the primary thesis objective, this modelling tool was intended to plug gaps in existing software. Therefore, its development was with intention of providing general applicability for use in future, related research. The modelling tool was developed using Microsoft Excel.
The development and operating principles of this modelling tool is described in tool within the corresponding thesis. This thesis may be freely downloaded from http://www.esru.strath.ac.uk/Documents/MSc_2015/Hercus.pdf or using the file provided.
In response to the software review, it was decided that a new modelling tool is necessary to simulate smart EV recharging strategies and analyse how they enhance the utilisation of renewable electricity generation. The development of this modelling tool constituted a key deliverable of the thesis. Although tailored with relevance to the primary thesis objective, this modelling tool was intended to plug gaps in existing software. Therefore, its development was with intention of providing general applicability for use in future, related research. The modelling tool was developed using Microsoft Excel.
The development and operating principles of this modelling tool is described in tool within the corresponding thesis. This thesis may be freely downloaded from http://www.esru.strath.ac.uk/Documents/MSc_2015/Hercus.pdf or using the file provided.
Thesis - Optimising the future utilisation of renewable energy resources through smart grid control of electric vehicle charging loads.pdf | |
File Size: | 1502 kb |
File Type: |
Modelling Tool - Demonstration Version
The first stage of development was to create a demonstration version with all the basic functionality which may be expanded for detailed analysis. This demonstration version was, however, uniquely designed as an educational tool to provide introduction to some core concepts of EV recharging. This demonstration version can only model a single recharge event for the maximum specification of two vehicles within a domestic setting. To replicate the most likely domestic scenario of overnight EV recharging, this single recharge event was not confined to a rigid 24 hour time period, and can traverse from one day into the next.
For the demonstration tool and further use in analysis, it was important from the outset to define basic operation. The modelling tool would enable the user to; (1) specify any EV model popularly available on the UK market; (2) select a model compatible charge point type/recharge speed; (3) specify a time period for the recharge event; (4) and then design a distributed renewable energy system consisting any combination of solar PV paneling, with optional battery storage, and wind power. The power output from these distributed renewable energy sources would be specific to the specified calendar month, and to meteorological conditions at defined locations within close proximity. The tool would then autonomously simulate this event and generate clear results for key system, environmental and financial performance parameters.
Available for free download:
For the demonstration tool and further use in analysis, it was important from the outset to define basic operation. The modelling tool would enable the user to; (1) specify any EV model popularly available on the UK market; (2) select a model compatible charge point type/recharge speed; (3) specify a time period for the recharge event; (4) and then design a distributed renewable energy system consisting any combination of solar PV paneling, with optional battery storage, and wind power. The power output from these distributed renewable energy sources would be specific to the specified calendar month, and to meteorological conditions at defined locations within close proximity. The tool would then autonomously simulate this event and generate clear results for key system, environmental and financial performance parameters.
Available for free download:
Modelling Tool Demonstration Version.xlsx | |
File Size: | 1144 kb |
File Type: | xlsx |
Modelling Tool - Technical Analysis Version
The EV recharging modelling tool described in preceding discussion is a demonstration version of general applicability. It possesses all basic functionality for detailed analysis of smart EV recharging strategies, and thus the capacity for generating results relevant to the primary thesis objective. However, the functionality is restricted to only a single recharging event over 48 hours and the maximum specification of two EVs, while the available renewable supply is only calculated for average meterological values over a 24 hour period. This was modified to enable more extensive analysis representative of real life scenarios.
Available for free download:
Available for free download:
Modelling Tool Technical Version.xlsx | |
File Size: | 4074 kb |
File Type: | xlsx |
Technical Analysis - Optimising EV Recharging Schedules
The described thesis defines a method by which the developed modelling tool may be manipulated to optimise EV recharging schedules for a number of suitably pre-defined scenarios. These EV recharging schedules are provided below both in their original format, and optimised to quantities of intermittent renewable energy supply, quantifying the impact of the thesis objective.
Available for free download:
Available for free download:
EV Recharging Schedules Solar PV 25.xlsx | |
File Size: | 310 kb |
File Type: | xlsx |
EV Recharging Schedules Solar PV 75.xlsx | |
File Size: | 505 kb |
File Type: | xlsx |
EV Recharging Schedules Wind 25.xlsx | |
File Size: | 312 kb |
File Type: | xlsx |
EV Recharging Schedules Wind 75.xlsx | |
File Size: | 307 kb |
File Type: | xlsx |
About the Author
My name is Calum Hercus. In November 2015 I graduated from the University of Strathclyde with the degree MSc (Distinction): Sustainable Engineering: Renewable Energy Systems and the Environment, having previously graduated in July 2014 from the University of Glasgow with the degree BSc (Hons) (2nd class lower): Astronomy and Physics.