Generation Forecasting Optimizes Storage In Solar-Diesel Hybrid Microgrids

In recent years, solar-diesel hybrid applications have become more and more interesting, as the technology allowing for integrating photovoltaic (PV) energy into diesel power plants has improved considerably and PV investment costs have decreased to a great extent. While the first big wave of solar power plants occurred in a subsidized environment, these incentives are now running out or have been cut largely. Solar power has to compete with conventional forms of energy, while existing energy costs are a natural benchmark for renewable energy investments. Traditional power from diesel generation is expensive, as the fuel has to be transported to remote locations, while diesel generators are relatively small and, thus, less efficient than large-scale conventional coal, natural gas or nuclear power plants. In this regard, we can observe that solar and wind energy are highly competitive in comparison to power from diesel.

Many companies that traditionally built or financed large-scale, grid-connected solar power plants have identified the integration of solar power solutions into remote diesel power plants as a “sweet spot” for the near future. More and more hybrid projects are being built or are under development. Typical applications are rural electrification, the telecommunication sector, the mining industry and remote hotels and resorts.

The situation is similar regarding energy storage. In many grid environments, legislation for investing and operating storage assets is not in place or not sufficient regarding volume or price. In solar-diesel hybrid microgrids with a potentially higher penetration of PV, the objective is to fully use solar power during periods of high irradiation. Under the traditional approach with high-penetration solar-diesel hybrid microgrids, diesel generators are switched off and storage is configured as a “bridge to backup.” This means that energy storage systems provide additional power in case of sudden drops of the solar generation or of major load increases until the diesel gensets are ramped-up again. In low-penetration approaches, the diesel gensets are never switched off and provide the so-called spinning reserve. They are run at a rather inefficient minimum load point, typically at around 30-40% of their maximum load.

Solar-diesel hybrid systems are, by nature, rather small in comparison to large power grids, while the intermittency of the solar component often poses a considerable challenge for the system. PV production forecasting can improve the levelized cost of electricity (LCOE) through an optimized system design with lower investment costs. In basic PV-diesel hybrid systems, forecasting has the potential to crowd out a battery storage system by predicting sudden shadings of the PV array, which allows for the ramping up of gensets before potential power losses occur.

In more sophisticated systems, PV production forecasting can improve LCOE by optimizing the storage size and the diesel genset configuration. Investment costs can be reduced by using smaller storage systems and avoiding and postponing replacement investments in the battery system and the diesel gensets. In addition, the operations and maintenance costs of the gensets can be improved by running them at more efficient loading levels and avoiding unnecessary start/stop cycles.

The forecasting is typically based on data from a sky imager, a camera that takes hemispherical pictures from the sky, and a forecasting algorithm. Sky imagers are already proven in real applications and appear to be very reliable if configured correctly according to the specific framework conditions.

Taking into account the relatively low costs of PV prediction solutions, it is obvious that considerable value is created in almost any kind of PV-diesel hybrid system. For solar-diesel hybrid systems, this means that they are becoming more competitive because overall LCOE is reduced and the investment case for solar-diesel hybrid solutions turns out to be more favorable.

Software simulation tools that include sky imagers are also being developed. They support functions for dimensioning and designing the optimal mode of operation of a hybrid system based on various framework conditions, such as different needs in regard to a secure supply of energy, electricity costs and various load and irradiation patterns.

Beyond their application in off-grid solar-diesel hybrid systems, similar advantages are inherent to small insular microgrids with solar power, rural electrification in weak grid areas and smart grids in an urban environment where a network of sky imagers can be deployed.

Dr. Thomas Hillig is the founder of Germany-based renewable energy consultancy THEnergy. The photo of a sky imager is courtesy of Steadysun.


  1. “Solar power has to compete with conventional forms of energy, while existing energy costs are a natural benchmark for renewable energy investments. ”
    That could read “…has to compete with conventional forms of SUBSIDIZED energy….

  2. As an energy consultant I helped the navy build several solar diesel battery hybrid systems for remote sites. I strongly suport the continued development / deployment of this cost effective approach.


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