Today’s energy industry has been experiencing a dramatic transformation like never before. The current business models of distributing energy are no longer efficient in light of the rapid growth of solar and storage deployments. The traditional model is linear and one-way with distinct phases like production, generation, and consumption. But today, energy consumers are becoming “prosumers”, so the energy distribution model is giving rise to a new class of two-way, many-to-many models, i.e., decentralized models. “The drivers for these changes are so called Distributed Energy Resources. The enablers for these changes are the infrastructure investments utilities have made in smart grid technologies. The net effect is seismic—in California, for example, over the next few years between 20 to 50 percent of energy consumers are going to buy energy from sources other than three investor-owned utilities but still pay these utilities to ship the electricity to their home—just to name a few,” says Jian Zhang, Founder and CEO, GridX. All of this is disrupting the way energy utilities have operated for a very long time and unfortunately, their traditional billing and financial analytical tools are not capable of handling the myriad methods of buying and selling energy and the volume of transactions. Responding to this challenge, GridX has developed a set of financial tools to help utilities and their partners to operate in the emerging decentralized energy distribution models.

According to Zhang, the trend of decentralization is global and can be seen in the U.S., Europe, Germany, Australia, Japan and China. Legacy utility billing systems based on mainframes and those provided by big software vendors like Oracle and SAP have been the norm. But these systems were developed for traditional business models and are not flexible enough to address the complexity of today’s energy transactions and scalable enough to process micro-transactions in the form of energy bought and sold per hour or 15 minutes. GridX’s solution, based on Big Data technology, can process millions of actual and what-if complex transactions per second.

GridX’s solution is an integrated suite of big data billing, settlement and billing analytics applications that enable utilities to develop pricing and business models, implement them quickly, market to their customers effectively, clear energy transactions on an interval-by-interval basis, bill customers, and settle with trading partners.

Ultimately, we are providing a suite of integrated financial and billing applications to help the energy industry transform itself from the traditional distribution models to a decentralized distribution model

“These are traditionally silo’ed business processes, with disjoined financial support tools, addressing various aspects of utility ‘product lifecycle’. We are the first one to integrate them and offer them as turnkey ‘financial operating system’ for utilities,” notes Zhang.

GridX technology is increasingly becoming a cornerstone of industry’s best practices for those utilities transitioning to new products and business models. An example of GridX solution in action is its use by SMUD, which used GridX to evaluate the bill impacts of its new rate designs for every customer before rolling them out. Another example is PG&E, which is transitioning to Time-of-Use tariffs. GridX is used as a shadow billing engine to calculate actual and what-if bills under the current rate plans as well as alternative rate plans for every one of more than 5 million customers including residential, SMB, large C&I, and agricultural customers. This effectively creates a “what if” tool for the utility and its consumers. Since the services went into production in 2016, GridX has calculated more than 1.2 billion actual and hypothetical bills, with penny level accuracy, so that PG&E can help their customers choose the best rate plans based on their personalized energy consumption behaviors. “Ultimately, we are providing a suite of integrated financial and billing applications to help the energy industry transform itself from the traditional distribution models to a decentralized distribution model,” Zhang concludes.