In remote areas, solar hybrid systems play a key role to meet decarbonization commitments, reduce electricity production cost and limit exposure to fuel price volatility. However, replacing diesel or heavy fuel oil (HFO) gensets by solar systems is challenging due to solar variability and intermittency. To ensure grid stability while maximizing solar penetration, storage solutions such as batteries can be used in addition, increasing CAPEX. Solar forecasting is complementary and essential to optimize the management and sizing of hybrid systems.
Since solar production can drop by 80% in a few minutes due to clouds, gensets are continuously running below their optimal power in order to be able to provide sufficient power in a short period of time. In the meantime, solar power is often curtailed to stabilize the grid, meaning that solar energy is not optimally used. Anticipating solar variability a few hours and minutes in advance allows for enhanced genset dispatch schedules and control. Therefore, the required spinning reserve can be reduced, which leads to higher solar penetration and better use of running gensets.
Dealing with solar variability using batteries is effective but costly and not straightforward. By modeling and simulating hybrid systems using a smart energy management system (EMS) powered by historical actual and forecast data, hybrid plant size and control strategies can be optimized so that power supply always satisfies demand at the lowest cost. The more accurate the forecast the more competitive the hybrid system.
CAPEX: up to 15% reduction in storage and/or genset size
OPEX: up to 10% fuel savings
Payback time: below 2 months
By accurately forecasting solar short-term variability.
By optimizing hybrid plant size.
By maximizing solar energy / minimizing spinning reserve.
By lowering fuel consumption.
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Maximize the use of renewable energies while ensuring grid balancing.
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Know the future profitability of your projects, taking into account weather variability.
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Define the best control strategy and maximize self consumption.
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