Smart modelling to shape future power systems
7 min read
01 Oct 2019
7 min read
01 Oct 2019
South Africa ranks second last amongst 115 countries in its readiness to transition into a more inclusive, sustainable, affordable and secure global energy system. That’s according to the World Economic Forum’s ‘Fostering Effective Energy Transition’ report released in March this year. State-run Eskom generates, transports and distributes nearly 95% of South Africa’s electricity. Coal, which is abundantly available and can be sourced at lower prices, is South Africa’s biggest source for electricity (59% in 2018) and accounted for more than 80% of the power generated by Eskom in 2017. But it has been a matter of concern due to climate change implications.
In the past 2 decades, South Africa has been working to increase the share of renewables and clean energy sources like natural gas in its energy mix via programs like Renewable Energy Independent Power Producers Procurement Programme (REIPPPP), the Integrated Energy Plan (IEP) and the Integrated Resource Plan (IRP). To South Africa’s advantage, renewable energy (RE) prices have fallen substantially in the past few years. Even so, an energy transition like the one in South Africa demands huge capital outlays, superior technical expertise, and a drastic change in the social and political mindset.
In 2015, the South African government received guidance from Wärtsilä to examine and understand the gas flexibility requirements for South Africa’s power system and how these requirements link to the complexities apparent in the LNG supply chain. Through rigorous analysis and complex power system modelling (using Plexos), it was determined that a 3 GW LNG-fuelled power plant would generate system savings in excess of USD 250 million per year.
This was considering the high degree of LNG supply flexibility (which comes at a premium), the variable energy and reserve load factors that are required to support the intermittency associated with renewable energy. In essence, the modelling revealed that the system-level benefits of having a flexible solution far outweighed the project-level cost savings with a cost-optimised LNG supply agreement that traditionally favours high volume, stable offtakes. That is the clear benefit of using such modelling systems.
The power of Advanced System Modelling
“Advanced Modelling involves optimisation of the power system in a way where power plants’ system flexibility and the ability to balance intermittent wind and solar is considered truthfully. In contrast, the traditional approach uses approximations, which have worked ok in the past when power was generated mainly from large centralised power plants like coal and nuclear and no intermittent renewables were in the systems on a large scale. As renewables increase in a power system, the results tend to go even more wrong which leads to wrong decisions and hence wrong investments,” explains Jyrki Leino, Senior Manager, Business Development at Wärtsilä Energy Business.
Let’s say, country ‘A’ decides to add renewables and sets a target of 50% or 100% energy coming from renewable sources in some year in future i.e. 2030. It can quite easily calculate how much installed wind and solar it needs to meet this target. But how will it evaluate what the rest of the system (power plants) will look like in the same year in the future?
This and several other questions need to be answered. How flexible should the system be? How can country ‘A’ provide for it? And most importantly how would optimal system look like and how much will all of it cost? The country has to consider what its requirement in the future will be based on where it is today to ensure it does not make wrong investment decisions. For instance, it could install inflexible power plants today, which may fit well in its system for a while but may become redundant in 5 or 10 years because its choice of asset hinders renewable penetration due to its inflexibility. The stranded asset would mean that its assessment, decision and investments have gone wrong. Advanced System Modelling can prevent this as it helps find the most optimal solution (least cost) while at the same time considering all the technical constraints.
Building smart systems
“We have conducted modelling and system studies for 80 systems since 2011 and growing. At that time people didn’t see a need for advanced power system modelling because power systems were not yet facing any severe lack of flexibility issues,” says Leino.
He adds, “While we saw that there is an increasing need for flexibility in future, widely used traditional modelling approaches did not reveal the need for it at that time. Today advanced modelling approach has been very well taken and has helped countries like South Africa realise that previous master plan was not capable to integrate that much renewable they had planned.”
Advanced Systems Modelling takes different factors into account, such as real-life systems operation along with balancing renewable generation or system reserve provisions. It considers all technical constraints of the system and power plants like maximum capacity, minimum stable level, maximum ramp-up, variable production profiles of wind and solar etc., cost-related parameters like fuel costs and heat rates, variable operation costs, ramping costs etc. Once the software knows all these parameters, it is operated (by running powers plants) in an optimised way where the demand every hour of a year is met in the most cost-efficient manner. In this system, modelling is done precisely and chronologically versus other approaches like the duration-curve approach that uses approximations.
As most countries rush in to add renewables to their portfolio to lower carbon emissions, they need to make investments that will remain meaningful 20 to 30 years from now. There is no doubt that Advanced System Modelling will play a crucial role in shaping their future power systems. After all, it is the only system that takes real-life system challenges into account and provides a fundamental understanding into multiple future scenarios while identifying most sensitivities. South Africa realised this early. Many more nations are likely to follow.