Renewables are an increasingly important component of the new energy solution. As a form of distributed generation, a huge amount of data is associated with the operation and management of renewables. With this data comes myriad opportunities and, in turn, challenges.
On 5 June 2018, Osborne Clarke hosted a breakfast bringing together senior industry experts to discuss their views on the future of smart energy and how data analysis and artificial intelligence (AI) could be deployed for the benefit of both businesses and consumers.
Returns: how do you make it work for funders?
Returns are a key driver in any business model and one way they can be generated through data is by the aggregation of marginal gains. In a post-subsidy world and volatile market, optimising resource management will be crucial to getting the most out of energy assets. Data analysis can help identify micro-variations in asset performance which can then be predicted using historic data and acted upon to deliver marginal gains. Equally, using data to guide maintenance schedules can deliver costs savings by pin-pointing where efficiency is being lost and where upgrades are required.
The data itself also has a value. Third parties looking to grow their data set and understand the power of data may wish to purchase it from you – therefore if you are a good steward of your own data, you are cultivating a valuable asset.
Data: what can you do with it legally?
Acquiring data to begin with is not without its challenges, particularly when collecting from customers. Taking the example of smart meters, a homeowner’s consent is required in order for their supplier to take and use half-hourly readings. If this permission is rescinded, the data collected must be deleted and returned to the customer.
Introducing AI complicates the ownership position, since AI learns from the entered data and absorbs some of it into its system, so that data cannot be removed. The issue of data ownership in the context of AI is one of many questions yet to be answered about the fledgling technology.
One way to counter customers’ reluctance to hand over their information is through education; this is vital to secure their trust and willingness to share data. Compliance with data security requirements, including the General Data Protection Regulation and (where applicable) the Information Systems Directive, is also essential for preserving a company’s reputation and avoiding hefty fines and enforcement action.
In terms of sharing data between businesses, the main concern is not data protection or security but competition. There is a tension between a company wanting to hold on to data for its own benefit and uploading the data to an open platform for the benefit of the industry at large. One suggestion is to incentivise large energy companies to share their data in return for a fee for their contribution or a share of the revenue/savings derived.
The tech interplay: understanding what is possible
Recognising how data can be used to increase revenue is one thing, but actioning those possibilities is another. With fully trained AI and industry-wide use of blockchain still some years away, it is important to lay the groundwork for the future and understand the current limitations.
Analysis of data will be constrained by what data you can access. Unfortunately, the UK’s current energy infrastructure does not prioritise ETL real-time data systems, and there is also the need to integrate old legacy systems with new technology, all of which makes the process of collecting and analysing data challenging. Processing the data also requires skill, so hiring and retaining the right personnel is crucial. Where this role is outsourced, companies should consider negotiating change of control and key staff provisions to alleviate the risk of over-reliance on a few key providers.
At this stage, data analytics has proven most successful where the information is isolated, easy to test and is not required to be correlated against a multitude of factors. Where this is the case, the terms of smart contracts must be drafted to facilitate and deliver the benefits identified by the analysis – insight and value must be brought together to fully benefit from the power of data.
Emerging business models: doing things differently
The withdrawal of incentives for renewables has created a greater need for cost efficiencies and new revenue streams, but harnessing of data is not, as yet, widely identified as one of those. Reducing development and connection costs remain the priorities of asset managers, whereas data analytics and AI have yet to make such an impact that projects will not get off the ground without them.
Due to price volatility, many companies are continuing to trade conservatively to lock in stable returns. As a result, there is reluctance to invest heavily in data and AI and to incorporate them into business models. Market education can be used to counter this hesitation to a degree, but ultimately only more data and more processing time will instil confidence in funders and the energy industry. Once data and AI make an impact on the cost of capital, will their true value become clear.