Wildfire smoke and other particulate matter can substantially inhibit solar photovoltaic (PV) generation production. While solar PV facilities may not be located in areas with a high fire risk, smoke from wildfires can travel hundreds of kilometers impacting a large number of facilities. This paper proposes a geospatial wildfire PV capacity model to quantify the anticipated temporal reduction in PV capacity due to wildfire smoke. A case study using data for two time periods from the 2020 California wildfires and real utility scale solar generation data evidences the model's high accuracy. Results argue that wildfire smoke can cause significant temporal solar generation capacity reductions over wide geographic regions. Application of the proposed model to inform power system resiliency planning is demonstrated for two use cases: generation scheduling and siting. With meteorological service providers beginning to release smoke forecasts, our geospatial wildfire PV capacity model enables balancing authorities to make use of this information to proactively schedule generation to compensate for reductions in PV capacity. The trained model also produces geospatial derate maps that can enable generation developers to consider historical capacity derates due to smoke when making siting or planning decisions.
As the installation of solar-photovoltaic and wind-generation systems continue to grow, the location must be strategically selected to maintain a reliable grid. However, such strategies are commonly subject to system adequacy constraints, while system security constraints (e.g., frequency stability, voltage limits) are vaguely explored. This may lead to inaccuracies in the optimal placement of the renewables, and thus maximum benefits may not be achieved. In this context, this paper proposes an optimization-based mathematical framework to design a robust distributed generation system, able to keep system stability in a desired range under system perturbance. The optimum placement of wind and solar renewable energies that minimizes the impact on system stability in terms of the standard frequency deviation is obtained through particle swarm optimization, which is developed in Python and executed in PowerFactory-DIgSILENT. The results reveal that the proposed approach has the potential to reduce the influence of disturbances, enhancing critical clearance time before frequency collapse and supporting secure power system operation.
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