Lidong Song

• Doctor of Engineering
• North Carolina State University

Co-simulation offers an integrated approach for modeling the large-scale integration of inverter-based resources (IBRs) into transmission and distribution grids. This paper presents a scalable communication interface design and implementation to enable reliable and stable real-time co-simulation of power systems with high IBR penetration. The commu...

This paper introduces "invisible manipulation", an innovative cyber-attack mechanism achieved through strategically timed stealthy false data injection attacks (SFDIAs). By stealthily manipulating measurements of a critical asset prior to the target time period, the attacker can subtly guide the engineering system toward a predetermined operational...

This paper introduces under-frequency load shedding (UFLS) schemes specially designed to fulfill the power reserve requirements in islanded microgrids (MGs), where only one grid-forming resource is available for frequency regulation. When the power consumption of the MG exceeds a pre-defined threshold, the MG frequency will be lowered to various se...

This paper introduces under-frequency load shedding (UFLS) schemes specially designed to fulfill the power reserve requirements in islanded microgrids, where only one grid-forming resource is available for frequency regulation. When the power consumption of the microgrid exceeds a pre-defined threshold, the microgrid frequency will be lowered to va...

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven fluctuation forecasting (FF) model. Three TCNs are integrated in the framewor...

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-time...

This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options. Microgrid-UC schedules the operation of switches, generators, battery energy storage systems, and demand response resources to supply 3-pha...

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). We propose...

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This en...

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). We propose...

This paper presents a novel iterative, bidirectional, gradient boosting (bidirectional-GB) algorithm for estimating the baseline of the Conservation Voltage Reduction (CVR) program. We define the CVR baseline as the load profile during the CVR period if the substation voltage is not lowered. The proposed algorithm consists of two key steps: selecti...

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity f...

This paper presents a novel two-stage load profile super-resolution (LPSR) framework, ProfileSR-GAN, to upsample the low-resolution load profiles (LRLPs) to high-resolution load profiles (HRLPs). The LPSR problem is formulated as a Maximum-a-Posteriori problem. In the first-stage, a GAN-based model is adopted to restore high-frequency components fr...

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMG is challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-timesc...

This paper proposes a two-stage PV forecasting framework for MW-level PV farms based on Temporal Convolutional Network (TCN). In the day-ahead stage, inverter-level physics-based model is built to convert Numerical Weather Prediction (NWP) to hourly power forecasts. TCN works as the NWP blender to merge different NWP sources to improve the forecast...

It is a common practice for utilities to down-sample smart meter measurements from high resolution (e.g. 1-min or 1-sec) to low resolution (e.g. 15-, 30- or 60-min) to lower the data transmission and storage cost. However, down-sampling can remove high-frequency components from time-series load profiles, making them unsuitable for in-depth studies...

This paper presents an algorithm for power curtailment of photovoltaic (PV) systems under fast solar irradiance intermittency. Based on the Perturb and Observe (P&O) technique , the method contains an adaptive gain that is compensated in real-time to account for moments of lower power availability. In addition, an accumulator is added to the calcul...

This paper presents an algorithm for power curtail-ment of photovoltaic (PV) systems under fast solar irradiance intermittency. Based on the Perturb and Observe (P&O) technique, the method contains an adaptive gain that is compensated in real-time to account for moments of lower power availability. In addition, an accumulator is added to the calcul...