Demand for digital services is growing rapidly. Since 2010, the number of internet users worldwide has more than doubled, while global internet traffic has expanded 20-fold. The data centres and data transmission networks that underpin digitalisation have led to rising energy use. Rapid improvements in energy efficiency have helped limit energy demand growth from data centres and data transmission networks, which each account for about 1-1.5% of global electricity use. Nevertheless, strong government and industry efforts on energy efficiency, renewables procurement and RD&D will be essential to curb energy demand and emissions growth over the next decade. Moreover, to get on track with the Net Zero Scenario, emissions must drop by half by 2030.
Digital tools will help keep distributed solar PV growing strongly, highlights the current status of distributed solar PV deployment and associated challenges. It also provides options for policy makers to leverage digitalisation in order to improve the management of growth in distributed PV and unlock its full potential in support of the clean energy transition.
Deployment of distributed solar PV is rising rapidly. In 2022, distributed PV – or small solar PV installations that generate electricity for residential, commercial, industrial and off-grid applications – represented 48% of global solar PV capacity additions, and its annual growth was the highest in history. Annual growth of distributed PV is expected to be even stronger the next two years. In 2024, it is set to reach 140 gigawatts, an increase of more than 30% compared with 2022 levels.
Effective distributed PV deployment and integration at scale thus requires modern, digitalised grids and digital tools. These innovations will alleviate the challenges of managing increasing distributed PV capacity while fostering greater system efficiency. Digitalisation is already supported by the imperative to reduce technical and commercial losses, optimise commercial operations, and lower costs. The tools to address these issues, however, may not be well suited to easing the integration of distributed PV into the supply mix. PV-specific approaches are essential, such as matching excess solar PV generation during the day with EVs through smart charging or pairing distributed PV with battery storage. These solutions can avoid curtailment of PV generation, reduce peak loads and optimise spending to reinforce electricity grids.
Available tools also include digitally enabled distributed PV registries, which users can access through online portals and apps. These registries provide the information needed to better deploy distributed PV and manage the broader power system. Smart inverters convert direct current from PV panels to the alternating current electricity grids need and can automatically adjust output to maintain grid stability. These inverters can support voltage and frequency control, reduce energy losses, enable granular management of resources, and enable more effective identification of faults and subsequent service restoration. A study in California estimated smart inverters could create up to USD 1.4 billion in annual savings by increasing reliability, power quality and system efficiency. Another study in Australia estimated coupling smart inverters with optimally sized battery storage could reduce power curtailment by 47%.
Digital tools to analyse data from bi-directional smart meters (which measure both electricity flows from the grid to consumers and from distributed PV to the grid) can help detect the location of distributed PV installations and provide visibility on customers’ generation and consumption patterns. This can better support the allocation of network tariffs and charges and help distribution companies and transmission system operators improve forecasting and system efficiency. These tools also allow distributed PV owners to respond to incentives in real time, provide services to the grid and engage in peer-to-peer trading. A pilot project in Australia showed the generation forecasted using traditional methods was 200 MW higher than near real-time generation forecasted using granular smart meter data. When coupled with real-time messaging, it resulted in a 35% reduction of energy bills.