Abstract
The overall emphasis on the development and integration of renewable energy technologies into power networks is founded in widely accepted notions that they will have a substantially positive impact on energy consumption, environment and climate at large. However, there is significant uncertainty about the impact of these Renewable Energy Resources (RERs) on the reliability performance of future electricity networks, i.e. so called Smart Grids. RERs such as solar, wind, hydro, etc. are widely described as being stochastic and intermittent – the consequence of which has various effects on the overall power system performance described by concepts such as power quality, stability or system reliability. In particular, system reliability is quantified by various indices measuring the average duration and frequency of power supply interruptions, energy not supplied to customers, etc. These are very useful indicators for power system analyses as they have direct commercial implications for network planning, operation and reinforcement, as well as customer satisfaction.
Accordingly, the presented research utilises the well-established Monte-Carlo Simulation technique, and further develops it to include the time-variation of failure rates of network components and electricity demand profiles in a realistic test distribution system. This enables a more accurate reproduction of the unpredictability (randomness) of network performance as it accounts for the probabilistic variation of network component behaviour. The test distribution system, which is modelled, scripted, and fully controlled with PSS/E software package, offers the perfect test case to design, deploy and assess different sets of RER schemes.
As a result, the outcomes of this analysis demonstrate that intelligent deployment of RERs such as use of solar in combination with energy storage systems, connected at local level, improves reliability. This is achieved by reducing, among others, the duration of supply interruptions and the energy not supplied to customers. Given that these reductions are commensurate with socio-economic gain, governed by spatial variability, the results of this research are instrumental in the planning and operation of future electricity networks.
Accordingly, the presented research utilises the well-established Monte-Carlo Simulation technique, and further develops it to include the time-variation of failure rates of network components and electricity demand profiles in a realistic test distribution system. This enables a more accurate reproduction of the unpredictability (randomness) of network performance as it accounts for the probabilistic variation of network component behaviour. The test distribution system, which is modelled, scripted, and fully controlled with PSS/E software package, offers the perfect test case to design, deploy and assess different sets of RER schemes.
As a result, the outcomes of this analysis demonstrate that intelligent deployment of RERs such as use of solar in combination with energy storage systems, connected at local level, improves reliability. This is achieved by reducing, among others, the duration of supply interruptions and the energy not supplied to customers. Given that these reductions are commensurate with socio-economic gain, governed by spatial variability, the results of this research are instrumental in the planning and operation of future electricity networks.
| Original language | English |
|---|---|
| Publication status | Acceptance date - 12 Mar 2018 |
| Event | STEM for BRITAIN 2018 Engineering : Poster Competition at Westminster for Early-career Researchers - House of Commons, Westminster, London, UK United Kingdom Duration: 12 Mar 2018 → … http://www.setforbritain.org.uk/2018event.asp |
Conference
| Conference | STEM for BRITAIN 2018 Engineering |
|---|---|
| Country/Territory | UK United Kingdom |
| City | London |
| Period | 12/03/18 → … |
| Internet address |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Energy Engineering and Power Technology
- Safety, Risk, Reliability and Quality
