Modeling Distributed Energy Systems Can Optimize Grid Development
A study from the University of Surrey introduces an innovative approach to improving grid-connected distributed energy systems by strategically selecting, sizing, and locating small-scale technologies. This process aims to cut carbon emissions and address network imbalances in the evolution of an electric grid.
Small-scale renewable energy systems, or distributed energy systems (DES), are more frequently connected to current electric power networks. This trend is driven by the goal to reduce human-made emissions linked to energy production and use. DES usually includes various other distributed energy resources (DERs), like energy storage and heat production units, placed close to where energy is consumed.
Rooftop solar panels. Image used courtesy of NREL
The increase in renewable energy sources for electricity has led to a greater focus on heat generation technologies like air source heat pumps as eco-friendly alternatives to traditional fossil fuel-based methods. However, current research lacks a thorough investigation into the effects of DES designs, particularly those incorporating electrified heating systems, on the unbalanced low-voltage distribution networks most DES are linked to.
Modeling Optimal Power Flow
A recent study from the University of Surrey breaks ground by presenting an optimization framework that enables precise technology selection and sizing for grid-connected DES while factoring in multiphase optimal power flow (MOPF) constraints.
The team devised an algorithm to simulate the distribution of electricity within an electricity network powered by solar and wind.
Their model considered scenarios where local grids might experience imbalances due to excess heat pumps in one area or an oversupply of electricity beyond the grid’s capacity.
The methodology prioritizes finding applicable solutions that are best suited at a local level while adhering to MOPF constraints. It also avoids pursuing simplified models that might achieve “globally optimal” solutions but would not consider case-by-case needs, potentially breaching MOPF constraints.
Building Local Grids
Ultimately, the modeling showed that producing and utilizing renewable energy locally was more efficient than storing it in costly batteries or exporting it across the grid.
Renewable energy distribution network. Image used courtesy of U.S. Department of Energy
By adding energy prices, available government subsidies, and user demand to their model, the Surrey team could help determine how to design local grids efficiently.
The team acknowledges that electrifying the grid is essential to eliminating reliance on fossil fuel-based energy and a huge challenge, emphasizing that the modeling indicates no single answer to this challenge exists. Instead, every community implementing an electrified grid must consider its unique limitations and needs when designing its networks.
Fluctuations in energy prices, battery expenses, or government financial aid can significantly impact the most suitable solutions for a specific location.
In further efforts to support individual needs, the research team is encouraging the government of the United Kingdom and other governments to seriously consider implementing subsidies or market adjustments, such as altering electricity pricing during off-peak hours, in facilitating communities in transitioning towards achieving net zero emissions.
Addressing Specific Grid Needs
The Surrey team has created a comprehensive and tailored approach to designing and optimizing DES within local grids.
The model incorporating MOPF constraints enables precise technology selection and sizing for grid-connected DES. It assists in avoiding potential grid imbalances caused by excessive heat pumps or oversupply of electricity. The model's capacity to simulate and analyze various scenarios provides insights into the efficiency of utilizing locally produced renewable energy compared to alternatives like storing energy in batteries or exporting it across the grid.
Overall, the model's holistic approach and adaptability to various scenarios offer a pathway toward designing more efficient, sustainable, and tailored energy systems that consider individual communities' unique needs and constraints.