Texas Tech University
Center for Pulsed Power & Power Electronics

Authors: I. Zaman; M. He

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9458408

Abstract: The recent spike in microgeneration of renewable energy is demanding a smart, reliable, secured and efficient technology to enable Peer to Peer (P2P) energy trading. Due to the inherent characteristics, blockchain has been a preferred technology for realizing P2P energy trading. However, blockchain implementations for P2P energy trading so far are suffering from critical challenges such as security, privacy and scalability. In this paper, we introduce a P2P energy trading platform that leverages the popular blockchain technology and addresses these concerns. In particular, a multilayered semi-permissioned blockchain based platform along with a Quality of Transaction (QoT) module is proposed as a trading platform that can be used for transaction of energy. A two stage blockchain architecture, backed by QoT, ensures proper verification and validation of the participants and transactions. We present two use cases that demonstrate two different attack scenarios to highlight the resiliency of the proposed framework. Finally, a qualitative analysis shows the effectiveness of the system with respect to security, privacy and scalability.

IEEE Conferences

Authors: X. Chen; J. Zhao; M. He

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9458556

Abstract: Numerical weather predictions used for wind power forecasting might not be updated in a timely manner in practice, due to its high computational complexity and complicated postprocessing. Thus, the accuracy of wind power forecasts could be significantly compromised especially during wind ramp events. This paper presents an innovative method for improving regional wind power ramp forecasting through ensemble learning of numerical weather prediction models, by using real-time weather measurements as the supervisory data. The numerical weather prediction models are combined to minimize the discrepancy between the forecast values and the real-time measurements in the trend of wind ramps, and the weights of the linear combination are calculated through gradient boosting. The proposed method is non-intrusive and could be efficiently carried out online. The proposed method is evaluated on historical ERCOT wind power ramp events, and compared with existing ensemble aggregation method using simple averaging. The results reveal the effectiveness of the proposed method for improving wind power forecasting during wind ramp events.

IEEE Conferences

Authors: X. Chen; J. Zhao; M. He

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9289816

Abstract: Wind power ramps are the abrupt yet significant change in wind power productions. The information on the ordinal levels of impending wind power ramp could help power system operator to arm operation or ramping reserves in a timely manner. This paper presents novel approaches for regional wind power ramp level forecasting using real-time meso-scale wind speed measurements. Motivated by the correlation of the meso-scale wind speed measurements with the regional wind power data, the proposed approach utilizes multinomial logistic regression for wind power ramp forecasting. An approach that combines the probabilistic output of individual regressive models in a weighted manner is proposed, with the weights calculated by minimizing the Brier skill score of the combined model. The proposed methods are tested by using real-world data, and is compared with benchmark methods. The results reveal the effectiveness of the proposed approaches.

IEEE Conferences

Authors: X. Chen; J. Zhao; M. He

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8662198

Abstract: This paper present a new conceptual framework of PV power generation credit sharing by leveraging social tie in residential community to maximize the financial benefit of community solar programs. Social tie-driven credit sharing schemes for residential community is designed to manage the dynamic allocation of solar PV power production ratio and credit among community members, so as to avoid unnecessary devaluation of solar PV power production by electric utilities. Along this avenue, a community solar management system that incorporates and integrates social tie network and credit sharing schemes is then developed.

IEEE Conferences

Authors: S. Dinkhah; C. A. Negri; M. He; S. B. Bayne

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8790615

Abstract: In this paper, we propose a stable electrical grid model, in which a home with a Photo-Voltaic (PV) system and Vehicle to Grid (V2G) capable Electric Vehicle (EV) can operate in both grid-connected and islanded modes. The model is used for studying load transients, power-sharing, and fault analysis. The implemented control system overcomes challenging situations such as load changing and transient conditions by managing the power of the battery and PV and regulating the voltage and frequency in the islanded mode. The Maximum Power Point Tracking (MPPT) is modified to include a feature for limiting the power in case of islanded mode and fully charged EV battery. Furthermore, the droop control and virtual inertia is utilized in a unified control manner. The model is implemented in MATLAB/Simulink and deployed to a real-time simulation by using an OPAL-RT simulator to validate the feasibility of the proposed model. The results for the real-time simulations are presented, showing the capabilities for voltage and frequency regulation of the controller, in load variations and fault condition.

IEEE Conferences

Authors: S. Abedi; M. He; D. Obadina

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8352818

Abstract: Large-scale and ubiquitous penetration of wind power generation to power systems necessitates more conservative provision of system reliability by ensuring adequately committed reserve and observance of transmission constraints. In addition, wind power curtailment due to the technical limitations of system operations, such as transmission congestion, should be efficiently mitigated. To this aim, this paper presents a congestion risk-aware unit commitment formulation in a two-settlement market environment. The uncertainty impact of multicorrelated wind power and contingencies on the risk of transmission congestion for each line, called the Line Transfer Margins (LTM), is incorporated using basic statistical data on the nodal wind power forecast and probability of credible line-outages across the system. The LTMs, formulated free of any distributional assumptions, collectively provide a measure for transmission reserves, which effectively mitigate the likelihood of transmission congestion, reserve undeliverability, and wind power curtailment in the real-time economic dispatch. The effectiveness of the proposed approach is verified through comparative case studies on IEEE RTS-96 for various wind power and LTM scenarios.

IEEE Journals

Authors: J. Zhao; S. Abedi; M. He; P. Du; S. Sharma; B. Blevins

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7872512

Abstract: Hourly wind power ramps in ERCOT are studied by applying extreme value theory. Mean excess plot reveals that the tail behavior of large hourly wind power ramps indeed follows a generalized Pareto distribution. The location, shape, and scale parameters of generalized Pareto distribution are then determined by using mean excess plot and the least square technique, from which risk measures including α quantile value at risk and conditional value at risk are calculated.

IEEE Journals

Authors: S. Abedi; M. He; M. Giesselmann

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7747960

Abstract: Real-time actuation of scheduled reserve capacity in power system operations with high penetration of wind power is prone to failure on account of unexpected shortcomings in network transfer capability. In this paper, a graph partitioning-based reserve zoning method is incorporated into the security-constrained unit commitment to improve the deliverability of operating reserves in a reserve zone and mitigate possible congestions caused by uncertain wind power. A graph representation of power system is proposed in which the edge weights are quantified by the likelihood of secure transmission utilization for each line. The probability distribution of line flows are characterized by the uncertainty of multiple correlated wind farm output forecasts as well as credible line outage contingencies reflected on the line flows using distribution factors. The minimum k-cut problem using the Gomory-Hu equivalent tree is addressed as a simple and efficient method to solve the NP-complete partitioning problem. The resultant zones can assure reduced risk of congested operating conditions and thus, provide a new approach to efficient management of intra-zonal congestions.

IEEE Conferences

Authors: L. Yang; M. He; V. Vittal; J. Zhang

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7152977

Abstract: In this paper, stochastic optimization of economic dispatch (ED) and interruptible load management is investigated using short-term distributional forecast of wind farm generation. Specifically, using the statistical information of wind farm generation extracted from historical data, a Markov chain-based distributional forecast model for wind farm generation is developed in a rigorous optimization framework, in which the diurnal nonstationarity and the seasonality of wind generation are accounted for by constructing multiple finite-state Markov chains for each epoch of 3 h and for each individual month. Based on this distributional forecast model, the joint optimization of ED and interruptible load management is cast as a stochastic optimization problem. Additionally, a robust ED is formulated using an uncertainty set constructed based on the proposed distributional forecast, aiming to minimize the system cost for worst cases. The proposed stochastic ED is compared with four other ED schemes: 1) the robust ED; 2) deterministic ED using the persistence wind generation forecast model; 3) scenario-based stochastic ED; and 4) deterministic ED, in which perfect wind generation forecasts are used. Numerical studies, using the IEEE Reliability Test System-1996 and realistic wind measurement data from an actual wind farm, demonstrate the significant benefits obtained by leveraging the Markov chain-based distributional forecast and the interruptible load management.

IEEE Journals

Authors: M. He; V. Vittal; J. Zhang

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7285972

Abstract: Short-term wind farm power forecasting is studied by exploiting the spatio-temporal correlation between individual turbine's power output. A multivariate time series model for wind farm power generation is developed by using vector autoregression (VAR). In order to avoid the possible over-fitting issues caused by a large number of autoregressive coefficients and the impact on the forecasting performance of VAR models, a sparsified autoregressive coefficient matrix is constructed by utilizing the information on wind direction, wind speed and wind farm's layout. Then, the VAR model parameters are obtained through maximum likelihood estimation of real-time measurement data, by taking into account the sparse structure of the autoregressive coefficient matrix. The proposed approach is compared with univariate autoregressive models through numerical experiments, resulting in significant improvement, which is attributed to the turbine-level correlation captured by the developed VAR model.

IEEE Conferences

Authors: S. Abedi; M. He; S. M. Fatemi

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7131779

Abstract: Penetration of Plug-in Electric Vehicles (PEVs) parking lots into distribution systems and microgrids offers useful decision tools for improving energy management and operations of power systems. In this paper, with an objective to minimize the operation cost while subject to the system, unit and security constraints, a microgrid energy management scheme is proposed, by taking into account PEVs throughout the grid as distributed storage responsive to the nodal prices. The presented method comprises the ex-ante optimal dispatch to minimize the anticipated operation cost, followed by a near real-time dispatch to manage the violation of security constraints regarding any congestion and voltage limit in the microgrid. The second dispatch, as recourse to the ex-ante dispatch, aims to minimize the operation cost and the deviation from the ex-ante dispatch decisions, while any possible binding constraint occurred in the ex-ante dispatch is mitigated. Simulation results on a test microgrid system demonstrate that the presented scheme can effectively ensure secure and economic operations of the microgrid as well as the risk reduction of the ex-ante decisions.

IEEE Conferences

Authors: M. He; M. Giesselmann

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7131804

Abstract: Microgrids, as individual controllable entities that can operate either islanded from or interconnected to main power grid, have emerged as a promising solution to improving energy efficiency and resilience to disturbance. When linked together in a self-organized manner, a cluster of microgrids can significantly enhance the reliability and power quality for critical load. With this insight, we study the self-organization and decentralized energy management of a microgrid cluster islanded from main grid after a disruptive event. In the self-organization stage, depending on the available generation resources, each microgrid decides on whether to connect to the cluster; and the microgrid energy management systems then “negotiate” on the optimal power exchange with each other in the cluster. Once the power exchange is determined, the generation and storage resources of each microgrid are managed to guarantee the energy reliability of critical loads and overall energy efficiency, through a scheduling procedure followed by a dispatch procedure. The effectiveness of the proposed method is revealed via case studies.

IEEE Conferences

Authors: M. He; S. Nimmagadda; S. Bayne; M. Giesselmann

PDF: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7285973

Abstract: In this paper, a novel scheme for subsynchronous oscillation detection and modal parameter estimation is proposed, by leveraging the rich information contained in high-rate phasor measurements as well as the effectiveness of synchrosqueezing transform for multimodal signal analysis. Specifically, an instantaneous time-frequency representation of a voltage/current signal is first obtained by applying synchrosqueezing transform to the real-time data collected by a phasor measurement unit. The non-zero synchrosqueezing transform coefficients quantify the undamped frequency components of the original voltage/current signal at each time instant. For an unknown number of undamped frequency components, unsupervised clustering is applied to the non-zero synchrosqueezing transform coefficients in the frequency domain, so as to determine how many modes comprise the signal, as well as which mode each non-zero synchrosqueezing transform coefficient belongs to. Then, for each detected mode, the corresponding non-zero synchrosqueezing transform coefficients are utilized to reconstruct a component of the original voltage/current signal. Finally, the magnitude, damping factor and phase angle of each mode are estimated by applying a least square estimation algorithm to the reconstructed component signal. The effectiveness of the proposed approach is revealed through several case studies using IEEE benchmark models. Further, practical issues involving missing data, measurement noise and transform basis functions are also systematically addressed in this study.

Conference Paper/Presentation