Day-Ahead Solar Forecasting Based on Multi-level Solar Measurements

The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and achieve better forecasts. The proposed model consists of four stages of data preparation, establishment of fitting model, model training, and forecasting. The model is tested under different weather conditions. Numerical simulations exhibit the acceptable performance of the model when compared to forecasting results obtained from two-level and single-level studies

Distribution market as a ramping aggregator for grid flexibility support

The growing proliferation of microgrids and distributed energy resources in distribution networks has resulted in the development of Distribution Market Operator (DMO). This new entity will facilitate the management of the distributed resources and their interactions with upstream network and the wholesale market. At the same time, DMOs can tap into the flexibility potential of these distributed resources to address many of the challenges that system operators are facing. This paper investigates this opportunity and develops a distribution market scheduling model based on upstream network ramping flexibility requirements. That is, the distribution network will play the role of a flexibility resource in the system, with a relatively large size and potential, to help bulk system operators to address emerging ramping concerns. Numerical simulations demonstrate the effectiveness of the proposed model on when tested on a distribution system with several microgrids.Comment: IEEE PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, 16-19 Apr. 201

Machine Learning Applications in Estimating Transformer Loss of Life

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.Comment: IEEE Power and Energy Society General Meeting, 201

Leveraging Sensory Data in Estimating Transformer Lifetime

Transformer lifetime assessments plays a vital role in reliable operation of power systems. In this paper, leveraging sensory data, an approach in estimating transformer lifetime is presented. The winding hottest-spot temperature, which is the pivotal driver that impacts transformer aging, is measured hourly via a temperature sensor, then transformer loss of life is calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average (CMA) model is subsequently applied to the data stream of the transformer loss of life to provide hourly estimates until convergence. Numerical examples demonstrate the effectiveness of the proposed approach for the transformer lifetime estimation, and explores its efficiency and practical merits.Comment: 2017 North American Power Symposium (NAPS), Morgantown, WV, 17-19 Sep. 201

Data Fusion and Machine Learning Integration for Transformer Loss of Life Estimation

Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, machine learning and data fusion techniques are integrated to estimate transformer loss of life. Using IEEE Std. C57.91-2011, a data synthesis process is proposed based on hourly transformer loading and ambient temperature values. This synthesized data is employed to estimate transformer loss of life by using Adaptive Network-Based Fuzzy Inference System (ANFIS) and Radial Basis Function (RBF) network, which are further fused together with the objective of improving the estimation accuracy. Among various data fusion techniques, Ordered Weighted Averaging (OWA) and sequential Kalman filter are selected to fuse the output results of the estimated ANFIS and RBF. Simulation results demonstrate the merit and the effectiveness of the proposed method

Aggregated DER Management in Advanced Distribution Grids

Evolution of modern power systems are more distinct in distribution grids, where the growing integration of microgrids as well as distributed energy resources (DERs), including renewable energy resources, electric vehicles (EVs), and energy storage, poses new challenges and opportunities to grid management and operation. Rapid growth of distribution automation as well as equipment monitoring technologies in the distribution grids further offer new opportunities for distribution asset management. The idea of aggregated DERs is proposed as a remedy to streamline management and operation of advanced distribution grids, as discussed under three subjects in this dissertation. The first subject matter focuses on DER aggregation in microgrid for distribution transformer asset management, while the second one stresses on aggregated DER for developing a spinning reserve-based optimal scheduling model of integrated microgrids. The aggregation of EV batteries in a battery swapping stations (BSS) for enhancing grid operation is investigated in the third subject. Distribution transformer, as the most critical component in the distribution grids, is selected as the component of the choice for asset management practices, where three asset management studies are proposed. First, an approach in estimating transformer lifetime is presented based on the IEEE Std. C57.91-2011 and using sensory data. Second, a methodology to obtain a low-error estimate of transformer loss-of-life is investigated, leveraging an integrated machine learning and data fusion technique. Finally, a microgrid-based distribution transformer asset management model is developed to prolong the transformer lifetime. The resulting model aims at reshaping the distribution transformer loading via aggregating microgrid DERs in an efficient and asset management-aware manner. The increasing penetration of microgrids in distribution grids sets the stage for the formation of multiple microgrids in an integrated fashion. Accordingly, a spinning reserved based optimal scheduling model for integrated microgrids is proposed to minimize not only the operation cost associated with all microgrids in the grid-connected operation, but also the costs of power deficiency and spinning reserve in the islanded operation mode. The resulting model aims at determining an optimal configuration of the system in the islanded operation, i.e., optimal super-holons combination, which plays a key role in minimizing the system-aggregated operation cost and improving the overall system reliability. The evolving distribution grids introduce the concept of the BSS, which is emerging as a viable means for fast energy refill of EVs, to offer energy and ancillary services to the distribution grids through DER aggregation. Using a mixed-integer linear programming method, an uncertainty-constrained BSS optimal operation model is presented that not only covers the random customer demands of fully charged batteries, but also focuses on aggregating the available distributed batteries in the BSS to reduce its operation cost. Furthermore, the BSS is introduced as an energy storage for mitigating solar photovoltaic (PV) output fluctuations, where the distributed batteries in the BSS are modeled as an aggregated energy storage to capture solar generation variability. Numerical simulations demonstrate the effectiveness of the proposed models as well as their respective viability in achieving the predefined operational objectives

Two-Stage Hybrid Day-Ahead Solar Forecasting

Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. This paper proposes a two-stage day-ahead solar forecasting method that breaks down the forecasting into linear and nonlinear parts, determines subsequent forecasts, and accordingly, improves accuracy of the obtained results. To further reduce the error resulted from nonstationarity of the historical solar radiation data, a data processing approach, including pre-process and post-process levels, is integrated with the proposed method. Numerical simulations on three test days with different weather conditions exhibit the effectiveness of the proposed two-stage model