Analytics Challenges In A Renewable Energy Paradigm

Demand response, energy flow modelling and situational intelligence are areas where data analytics is making an impact on utility operations.
Published: Thu 10 Mar 2016

The deployment of intermittent renewables and endpoints such as meters and sensors is increasing the need for analytics to provide real-time (or near real-time) insight on the state of the grid and flows of energy.

At the E-world Energy & Water 2016 trade show in Essen, Germany, Engerati sat down with three companies active in the analytics space in Germany and/or more broadly in Europe to discuss the challenges and how they are addressing them.

Demand response for Europe

California-based AutoGrid focusses on demand response and is steadily building up a customer base in Europe as the market develops.

“The European market is quite different from the US,” Thorsten Stechert, Solution Architect, told us. “In the US the flexibility is mainly on the demand side in areas such as HVAC and water heaters. In Europe the distributed generation is stronger and combined with high energy prices this is increasing opportunities to optimize this flexibility and play it against the markets.”

Mr Stechert says each market in Europe is slightly different. The German market is especially interesting with its large number of retailers in the space including areas that have a lot of wind and others have a lot of solar energy. Currently the company is looking at how to make a fast, scaleable virtual power plant (VPP) solution that would also enable smaller retailers without the in-house capabilities to play in the reserve market.

“We see utilities trying to get away from the role of moving electrons from A to B to becoming managers of the flexibilities, and how to interconnect all of those and to make money from it is the challenge we want to help customers with. We also want to take flexibility to the next level – there tends to be one product for residentials and another for industrial customers and the game is about bringing these together in a portfolio management format.”

Modelling energy flows

Dr Bernhard Klaassen from the High Performance Analytics division of the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) reported that his focus is currently on simulating and modelling energy flows in networks and especially on the new Multi-physics Network Simulator (MYNTS) solution.

“With renewable energies there is often a need to combine energy forms,” he says. “For example at a local point with a lot of wind energy but no customers to consume that energy, then the wind energy could be transformed to gas for feeding into the gas network, and software tools are required to ensure these processes will work.”

Dr Klaassen comments that the gas transport solution is the most advanced and is currently being used by the largest gas transporter in Germany, Open Grid Europe. However, the long-term aim is to combine disparate solutions into a single software platform covering all media – electricity, gas, heat and water.

“The solution was developed at the request of the industry and is based on general modelling approaches we developed for industrial applications that we believe provide a high degree of flexibility in terms of both combining all the energy media and in the way modelling can be done by users of their own infrastructures. We look forward to its wider use which will enable us to make it better.”

Situational intelligence on the grid

Partly a legacy of the traditional ‘siloed’ approach within utilities and partly to meet the new requirements of renewables and the smart grid, there is a need to correlate and analyze data from disparate sources in order to provide a full picture of conditions on the network.

“Situational intelligence is key for today’s utility,” says Klaus Kronsbein, Utilities Account Director at SpaceTime, another California company that is making inroads into the European market. “Lots of data is being generated and it needs to be brought together to provide context and insight for decision making.”

Mr Kronsbein points out that there are a growing number of use cases for situational intelligence, of which renewables integration – which is particularly topical in Germany – is one. “Germany is building lots of renewables that are not well integrated into the grid or the markets,” he says. “To manage the rapid variability of renewables production we need to bring together data such as forecasting, sensor data and market prices to better manage the system.”

Failure to do that can lead to overload situations and potentially expensive early grid reinvestments, he warns.

“There are many reasons to grow awareness on the grid,” he concludes. “The situations are very complex and for the right decisions to be made it is necessary to consider the whole scenario.”