Unlocking grid flexibility - Enable faster and safer renewable integration

Let’s take a closer look at flexibility and what it actually means:
In 2019, the IEA defined power system flexibility as “the ability of a power system to reliably and cost-effectively manage the variability and uncertainty of demand and supply across all relevant timescales, from ensuring instantaneous stability of the power system to supporting long-term security of supply”.
In the context of power networks, flexibility touches many areas of both business practice and operation. Traditionally, power system flexibility aimed to deal with variability and uncertainty in baseload generation. Operators have used ancillary services to deal with loss of generation and circuits or errors in demand forecast. Electricity demand changes depend on seasonal weather conditions, industrialization, and indeed on social and cultural factors.

The increasing share of the variable renewable energy resources has an impact on both power system flexibility and operation. It has increased variability and uncertainty and the net load (total demand minus VRE production) is balanced by dispatchable generation and BESS. All these factors make balancing very challenging and influence electricity pricing.

Flexibility touches the ability to on-board distributed energy resources – predicting, absorbing, and managing their contribution to the overall function of the grid – from frequency stability to operating reserves and dispatching inflexible units.
Similarly, it touches the ability to manage the production and power flow of baseload generation as demand and circumstances change, shifting demand profiles for a better fit with generation.

Grid flexibility is data-driven

The new power landscape is more volatile, and indeed more versatile, than power companies have traditionally been used to. But in moving away from the inflexibility and relative predictability of centralized power systems, utilities become critically dependent on the timeliness, accuracy, and scope of data.
To take just one example, think about the weather. For wind turbines, solar arrays and, indeed, hydro, reliance on quality meteorological forecasts is essential. No surprise there. But what is changing, is the ability of forecasters to accurately predict at a far more granular level than previously possible. And that means precision and a better ability to predict performance down to the level of an individual installation. This granularity increases in importance as forecast errors become a main source of net load uncertainty, as described in the 2018 IRENA Power System Flexibility for the energy transition report.
Beyond the improved meteorological forecasting made possible through AI and high-performance computing, digital now impacts many other areas of operation including:

• Local load balancing with edge computing,
• Forecasting built on machine-learning and local intelligence,
• Intelligent battery storage management systems,
• Aggregation platforms as seen in Virtual Power Plants for flexibility in demand/response management.

In parallel, this increase in data richness gives the utility the opportunity to be more detailed in their demand forecast – especially when one considers the degree of integration with third-party information sources. With more and more people working remotely, for example, what is the dual impact of both home office and business premises power usage?

The data platform imperative

For the utilities, there is an explosion of data at machine level, with a corresponding shift in intelligence to the point of local generation and management. This was highlighted in 2018 in the Universal Smart Energy Framework (USEF) which defined the flexibility platform as “an IT platform where the coordination, trading, dispatch or support services for flexibility markets take place.”

There is a similar explosion in data from beyond the immediate perimeter of the utility: every smart fridge and social network can feed into the utilities’ understanding of operation and opportunity.
But are there barriers to fulfilling the promise of this new wealth of data?

Well, of course – some technical, some cultural and some regulatory. The most fundamental is perhaps technical. Unless we can establish a common data platform, or at least a means of simulating one, we cannot benefit from the full potential of data analytics, machine learning and AI. Without a common data platform, our intelligence will inevitably remain siloed.

Without the analytics and automation, there can be no actionable intelligence, and without that we cannot achieve the agility needed to fully integrate the realm of renewables.

Topics

Industry insights
Data and artificial intelligence
IoT and edge
Digital Transformation