Thermostatically controlled loads (TCLs) are good candidates for direct load control (DLC). They can be aggregated to join the electricity market through a centralized management performed by virtual power plant (VPP). However, there are two main concerns that arise when TCLs participate in DLC. The first is related to the dispatchability of VPP against normal energy demand of individual TCLs in uncertain time-variant environments. The second refers to the tremendous increase of communication and computational requirements needed to perform DLC on a large population of TCLs. This paper introduces aggregators between the DLC controller and TCLs with a novel robust control mechanism to reconcile these concerns. The control mechanism is implemented with two layers: the upper layer suppresses the control payback effect with a quadratic optimization model, and the lower layer addresses the power trajectory tracking with a novel payback tracking error model (PTEM). The control method needs minimum sensing infrastructure since it requires power data only at the aggregation level. Our simulations resulted in a robust reference power tracking by the aggregator with a percentage root mean squared error between 3.33% -5.69% under uncertain time-variant environments. The continuous responsiveness indicates that the aggregators manage to convert the aggregated TCLs into “manageable resources” that ensure the dispatchability of VPP.

This article presents the employment of battery storage systems (BSSs) to improve power system survivability. This application of BSSs is based on setting BSS charging/discharging as responses to integrating distributed generation units and/or adjusting load demands (load-side activities). The improvement of power system survivability is quantified by the survivability index $\Gamma$ , which can exceed its boundary values due to load-side activities. The improvement of power system survivability is achieved by maintaining $\Gamma$ within its boundary values post a load-side activity. The proposed approach to improve power system survivability is implemented and tested for the Barbados power system. Tests are conducted for integrating solar power generation units, and implementing demand response. Test results show that accurate BSS charging/discharging can maintain the index $\Gamma$ within its boundary values post-load-side activities.

This research explores the potential of Machine Learning (ML) to enhance wireless communication networks, specifically in the context of Wireless Smart Grid Networks (WSGNs). We integrated ML into the well-established Routing Protocol for Low-Power and Lossy Networks (RPL), resulting in an advanced version called ML-RPL. This novel protocol utilizes CatBoost, a Gradient Boosted Decision Trees (GBDT) algorithm, to optimize routing decisions. The ML model, trained on a dataset of routing metrics, predicts the probability of successfully reaching a destination node. Each node in the network uses the model to choose the route with the highest probability of effectively delivering packets. Our performance evaluation, carried out in a realistic scenario and under various traffic loads, reveals that ML-RPL significantly improves the packet delivery ratio and minimizes end-to-end delay, making it a promising solution for more efficient and responsive WSGNs.

Domestic Electric Water Heaters (DEWHs) can provide operational flexibility for load control due to their energy storage capacity. Load forecasting for aggregated DEWHs is important for providing information of baseline load and controlling electricity demand profile without negative impact to the normal end use. Advanced Metering Infrastructures (AMI) nowadays provide more possibilities to further enhance forecasting with bottom-up method. This paper proposes a bottom-up forecasting with Markov-based error reduction method to predict power consumption of aggregated DEWHs for multiple forecast horizons. DEWHs are randomly divided into small aggregations, whose power consumption is forecasted by independent forecast engines. In this paper, the engines are K-means and wavelet decomposition-based neural networks (KWNN). After summing all forecasting of small aggregations up, a new Markov-based error reduction method is proposed to extract features in residuals and mitigate forecasting error accumulation introduced by the summation, providing opportunities to further improve forecasting accuracy for the total DEWH load. Differing from traditional Markov-based error reduction, two new compensation parameters (Compensation Coefficient, and Compensation Threshold) are proposed. They are determined by using Particle Swarm Optimization (PSO) algorithm. Experiments on real and simulated DEWH loads verified the effectiveness of the proposed forecasting method. The proposed method improved the forecast accuracy over selected benchmark algorithms by about 20% to 80%, according to four performance metrics: MAE, MAPE, RMSE, NRMSE. The aggregation effects on performance were also analyzed in theory and tested with simulated DEWHs, providing a good indication of the forecast dependence on the aggregation size.

This article presents a performance comparison of the solid, low impedance, high impedance, frequency-selective, and isolated grounding systems. A grounding system (for any power system component) is designed as an impedance ( $\bar{Z}_{G}$ ) that connects the neutral point to the ground. The impedance $\bar{Z}_{G}$ is constructed using a combination of $R$ , $L$ , and $C$ elements. The values and combination type (series or parallel) of $R$ , $L$ , and $C$ determine the possible influence of $\bar{Z}_{G}$ on ground currents and potentials. Each grounding system is related to a system voltages level, a specific combination of $R$ , $L$ , and $C$ , and a range of $R$ , $L$ , and $C$ values. The solid, low impedance, high impedance, frequency-selective, and isolated grounding systems are designed for a $3\phi$ transformer and a $3\phi$ synchronous generator in order to compare their influences on ground currents and potentials during ground faults. The transformer and generator are tested for line-to-ground and double line-to-ground faults with all designed grounding systems. Test results show that some grounding systems can reduce ground currents only, while others can reduce ground potentials only. Such capabilities can be used to fulfill certain system and operation requirements (e.g., service continuity, ground capacity, etc.).

In this article, experimental performance assessment and comparison are presented for solid, low impedance, high impedance, frequency-selective, and isolated grounding (IG) systems. The typical design of a grounding system (for low and medium voltage generation, transmission, and distribution systems) is based on selecting an impedance ( $\bar{Z}_{G}$ ), which is used to connect the neutral point and the ground. The impedance $\bar{Z}_{G}$ is typically composed from a combination of $R$ , $L$ , and $C$ elements. The combination type (series or parallel), along with the values of $R$ , $L$ , $C$ elements, allow estimating the possible effects of $\bar{Z}_{G}$ on ground currents and potentials during ground faults. In this article, solid, low impedance, high impedance, frequency-selective, and IG systems are designed for a 35-kVA $3\phi$ transformer and a 5-kVA $3\phi$ synchronous generator, for purposes of assessing and comparing their effects on ground currents and potentials during ground faults. The laboratory transformer and generator are tested for line-to-ground and double line-to-ground faults with all designed grounding systems under different loading levels. Experimental results show that some grounding systems can effectively reduce ground currents, and other grounding systems can effectively reduce ground potentials. These capabilities and features can be used to achieve certain system and operation mandates, including ground capacity, maximum allowed overvoltage, and service continuity.