Internet of Things is increasingly gaining ground with advances in technology of internet, data and communication. This has spurred a lot of innovative business practices and applications across sectors, especially in Industrial automation, healthcare, transportation & logistics, energy. One such industry where IoT will play a significant role in coming years is Solar. India is a signatory of Paris climate accord and committed to reduce carbon emissions. It had set an ambitious target of achieving over 50% of power production from renewable sources by 2027.
Utilities house enormous datasets that defy traditional analysis, for which machine-learning could be of great benefit. When machine-learning is applied to IoT data, utility companies are able to realise the next generation power grid that can eventually handle billions of endpoints on utility networks autonomously. Pacific Gas and Electric's (PG&E) emerging technologies leader Tom Martin and Paul Doherty, corporate relations, discuss how machine learning and data science is being leveraged for asset maintenance and the integration of distributed energy resources (DER). MSEI: What does machine-learning mean to PG&E? What is your definition of machine-learning?
A new energy demand response start-up is preparing to launch within weeks which will use machine learning and artificial intelligence to manage a portfolio of storage assets and provide real-time energy reserves to the grid. Upside Energy's Virtual Energy Store aims to relieve stress on the grid by using predictive algorithms to manage a number of distributed storage resources to reduce reliance on the spinning reserve capacity provided by traditional power stations. The energy start-up's cloud service currently coordinates batteries and other devices at around 40 sites but has the potential to manage thousands more across a broad portfolio of technologies, including small batteries within uninterruptible power supplies (UPS), electric vehicles and solar PV. The company signed a firm frequency response (FFR) bridging contract with the National Grid in November and is currently qualifying its assets before officially launching its commercial service by early March. Upside will begin by providing initial service to large batteries at Sheffield and Manchester Universities before seeking to take advantage of National Grid's decision to lower the minimum entry point for its main frequency response tendering market from 10MW to 1MW from April.
The U.S. solar revolution has been a terrific boon to customer choice, the economy and climate policy planning. But solar panels alone can't achieve the full value of solar generation or the aggressive goals of greenhouse gas reductions. Moreover, solar developers face a wave of changes that is challenging their continued growth. Energy markets are shifting, supply chains are becoming more competitive, electric and solar rates are changing and customers' interest in controlling their energy destiny is increasing. The falling costs of energy storage can give developers and customers the flexibility to deal with all those changes and further grow their business.
For investors, deciding whether to invest money into renewable-energy projects can be difficult. The issue is volatility: Wind-powered energy production, for instance, changes annually -- and even weekly or daily -- which creates uncertainty and investment risks. With limited options to accurately quantify that volatility, today's investors tend to act conservatively. But MIT spinout EverVest has built a data-analytics platform that aims to give investors rapid, accurate cash-flow models and financial risk analyses for renewable-energy projects. Recently acquired by asset-management firm Ultra Capital, EverVest's platform could help boost investment in sustainable-infrastructure projects, including wind and solar power.