Why simulate the integration of systems with the grid?
Before companies and utilities deploy renewable energy generation, storage, and power electronics equipment, it is critical to model the integration of these assets with power grids to ensure grid stability, cost effectiveness, and optimal performance.
Most public utilities do not permit private industries to connect to real power grid infrastructure to test new energy assets at scale, because of the risk to stability. Created to bridge this gap for innovators, the Systems Integration Testbed is a simulated grid environment that is validated on authentic UW Electrical & Computer Engineering research data from the Snohomish Power & Utility District microgrid in Arlington, WA. This unique capability also enables Testbeds users to design clean energy systems based on inputted energy data or a set of specs to fulfill.
Grid stability: Renewables such as solar and wind are variable sources of energy, meaning that the amount of electricity they produce can fluctuate depending on weather conditions and time of day. Simulating the integration of new energy assets can help ensure they can be deployed without causing instability or outages.
Cost-effectiveness: Modeling grid integration allows companies to optimize the design of their renewable energy systems to achieve the most cost-effective solution. This includes determining the optimal size and placement of energy generation and storage systems, which can help to balance energy supply and demand, reduce peak demand charges, and defer power grid upgrades.
Optimal performance: Simulated deployment models can help identify potential issues with the system design or configuration that could impact performance. By addressing these issues before deployment, companies can ensure that their renewable energy systems operate at peak efficiency, optimize their power output, and work correctly with the rest of the grid infrastructure.
Challenges in modeling grid integration
Grid integration modeling can be challenging for companies, utilities, and communities for a variety of reasons.
- Cost: Grid modeling is computationally intensive since the validity of the simulation results depends on computation speeds below the millisecond level. Normal computers cannot operate fast enough to accurately simulate the complexity of an electrical grid. A high-performance, real-time computing capability is required to enable useful simulation environments. These platforms are costly to the point of being unjustifiable purchases for many companies developing renewable energy devices, especially in the early stages.
- Expertise: There are dozens of inputs required to build an accurate electrical grid model and they are grouped into three major categories: system level input data, resource input data, and control level input data. Some of these inputs are best determined by those developing the energy devices being tested, while others are best determined by individuals with broad electrical grid experience. It can be challenging to assemble all required inputs without consulting third-party experts.
- System level input data: These inputs include information about the high-level expectations of the system. Examples include system lifetime, coordinates of the intended deployment location (to forecast weather-dependent power outputs), and total construction budget.
- Resource input data: These inputs capture information about the generation equipment, storage equipment, and expected load profile. Examples include average and maximum power requirements, solar panel size and efficiency, and battery degradation parameters.
- Control level input data: These inputs specify information about the management and coordination of the resources in the system. Examples include simulation time resolution, control time step, specifications and parameters of the converters and inverters in the system.
Expert Advice
To learn more about power systems integration modeling, contact the Washington Clean Energy Testbeds at wcet@uw.edu. The Testbeds offer equipment and expertise on various methods of grid modeling and can help you deploy your equipment safely, quickly, and with minimal investment.