Integrating Distributed or Renewable Resources

Permanent URI for this collection


Recent Submissions

Now showing 1 - 5 of 8
  • Item
    A Study of the Impact of Reduced Inertia in Power Systems
    ( 2020-01-07) Agrawal, Urmila ; O'Brien, James ; Somani, Abhishek ; Mosier, Thomas ; Dagle, Jeff
    Inertia in power systems plays an important role in maintaining the stability and reliability of the system by counteracting changes in frequency. However, the traditional sources of synchronous generation are being displaced by renewable resources, which often have no inherent inertia. This paper investigates the impact of reduced system inertia on several aspects of the dynamic stability of power systems, such as angular stability, primary frequency response, and oscillatory modes. This study is performed on a large-scale 2000 bus synthetic Texas model by selectively replacing synchronous generators with inverter-based generation resources. This paper also compares the analysis results obtained by the above-mentioned inertia-reduction approach of renewable integration with another approach in which the inertia constant of all synchronous generators is decreased. This paper demonstrates that only reducing the inertia of all synchronous generators in a system does not provide an accurate analysis of the challenges associated with the reduced system inertia caused by renewable integration.
  • Item
    The Impact of Incorporating Wind Energy in the Electric Grid

    ( 2020-01-07) Carreras, Benjamin ; Reynolds-Barredo, Jose-Miguel ; Newman, David ; Dobson, Ian
    In this paper we investigate the impact of increasing the penetration of wind generation with real variability on the risk to, and robustness of, the power transmission grid using a dynamic model of the power transmission system (OPA). There are three timescales of variability discussed but this paper will focus on the impact of two. It is found that with different fractions and distributions of wind generation and central generation, varied dynamics and risk are possible. One important parameter is the fraction of the total power demand supplied by the wind generation. It is found that the risk has a minimum in fraction of wind power supplied, after which the risk increased as the wind power penetration increases. In the same networks, decreasing the number of central generators without decreasing their power supplied in general increases the risk after a critical minimum number of generators is reached.
  • Item
    Co-optimizing High and Low Voltage Systems: Bi-Level vs. Single-Level Approach
    ( 2020-01-07) Liu, Jialin ; Zephyr, Luckny ; Cardell, Judith
    This paper presents a bi-level optimization framework applied to optimize system performance with (i) increasing presence of distributed energy resources (DER) at the low-voltage level, and (ii) variable wind power generation at the high-voltage level. The paper investigates various system configurations with increasing presence of microgrids, with active devices. System simulations quantify system performance in terms of cost, first using the traditional single-level optimization framework, and second using the proposed bi-level framework. Comparisons between the system with traditional, passive distribution systems and with microgrids are also presented, with results again quantified via the interconnected system operating costs. Results show that at low levels of DER and microgrid penetration, traditional (single-level) system optimization algorithms perform adequately as compared to the proposed bi-level optimization framework. However, as DER and microgrid penetration increase, the traditional single-level framework does not accurately capture the full system benefits of distributed technologies. The results demonstrate that new optimization algorithms, such as the proposed bi-level framework, will be required if the benefits of DER are to be accurately quantified in the evolving power system.
  • Item
    Analytical Method for Energy Storage Sizing and Reliability Assessment for Power Systems with Variable Generation
    ( 2020-01-07) Alamri, Abdullah ; Alowaifeer, Maad ; Meliopoulos, A.P Sakis
    This paper presents a mixed integer linear program (MILP) to optimally size power and energy of energy storage systems (ESSs). The sizing model takes into account conventional generation (CG) operation constraints in addition to seasonal and locational wind speed and solar radiation variations, and variable generation (wind turbine systems (WTSs) and solar cell generators (SCGs)) forced outages. Subsequently, the outcomes of the ESS sizing model are inputted to the probabilistic production method (PCC) to assess the reliability of the integrated system. All aforementioned analyses have been applied to a system with different penetration levels. The method is demonstrated with case studies on a system consisting of 10 CG units and VG penetration levels of 20% and 30%. For each penetration level, ESS sizing is computed and then reliability assessment is performed.
  • Item
    Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast
    ( 2020-01-07) Alqudah, Mohammad ; Djokic, Tatjana ; Kezunovic, Mladen ; Obradovic, Zoran
    A novel method is proposed for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies. The network observed over time is projected to a lower-dimensional representation where a variety of weather measurements are used to train a structured regression model while weather forecast is used at the inference stage. Experiments were conducted at 288 locations in the San Antonio, TX area on obtained from the National Solar Radiation Database. The model predicts solar irradiance with a good accuracy (R2 0.91 for the summer, 0.85 for the winter, and 0.89 for the global model). The best accuracy was obtained by the Random Forest Regressor. Multiple additional experiments were conducted to characterize influence of missing data and different time horizons providing evidence that the new algorithm is robust for data missing not only completely at random but also when the mechanism is spatial, and temporal.