Minimization of the total transmission loss of an interconnected AC-DC grid plays an important role for the economic operation of the AC-DC grid. Different from the conventional AC grid where the ...transmission loss is usually minimized by reactive power regulation, the transmission loss of a meshed AC-DC grid can be optimized by adjusting the active power exchange between the AC and DC grids. Additionally, smaller DC voltage deviation after grid disturbances is very desirable since it can bring less impact to the operations of AC-DC grid. This paper firstly presents two improved sequential power flow algorithms for modular multilevel converters (MMCs) based AC-DC grid under DC power-voltage droop control. An optimization algorithm is then proposed to minimize the total loss of the AC-DC grid and the overall DC voltage deviation after the change of operating conditions. Adaptive droop control is used in the proposed optimization algorithm in which the power references are control variables solved from the optimal AC-DC power flow. The proposed algorithm is verified in an AC-DC grid consisting of a six-terminal DC grid connected to the IEEE 39-bus AC grid and a classical five-terminal AC-MTDC system.
Conventional droop control methods for power-sharing in a multi-terminal high voltage DC (MTDC) grid lead to voltage deviation from the nominal value. Moreover, the power-sharing is inaccurate in the ...droop-controlled MTDC system. This paper proposes a secondary controller with a distributed architecture to compensate the voltage deviation and to achieve equal power sharing automatically among the converter stations. A distributed observer is used to estimate the average voltage of the converter stations. The proposed distributed controller and observer use local measurements of output powers and voltages, and only communicate with neighboring stations. Therefore, the requirement of the global information in the centralized secondary control schemes is eliminated, which reduces the communication requirement and improves the reliability of the MTDC grid. The case studies illustrate that the proposed distributed controller regulates the stations' average voltage and shares the power mismatch accurately. Impact of the communication time-delays and the control gain on stability of the MTDC grid are also investigated using the Lyapunov-KrasovskiiLMI condition. It is shown that reducing the control gain can stabilize the MTDC grid in the case of large communication time-delays.
This paper presents a combined control scheme that consists of two complementary control strategies, namely hierarchical control and autonomous control to handle contingencies in a multi-terminal ...high voltage direct current (MTDC) system. The proposed hierarchical control considers three optimal targets, i.e., equal or proportional power sharing, minimizations of transmission loss and total DC voltage variation. The primary control layer implements the droop control for voltage source converters (VSCs). While in the secondary control layer, voltage reference setpoints of the VSCs in droop control scheme are calculated by the proposed optimal power flow method with the equal or proportional power sharing as equality constraints. As the hierarchical control relies on communication system, the autonomous control for power sharing can be activated during communication system malfunctioning or rapid power variation caused by renewable energy sources. The combined control strategy can greatly increase the reliability of the MTDC system. The simulation results of a five-terminal MTDC network conducted in the MATLAB/Simulink software verify the performance of the proposed combined control strategy.
Information about power consumption patterns of devices, helps the residential consumers manage their energy usage. Nonintrusive load monitoring is an effective tool to extract the consumption ...patterns from the measured aggregated data at the meter. In this paper, an optimization-based method is proposed to disaggregate the total load, using low frequency data. The proposed algorithm is enhanced by enforcing the power profiles of appliances to be piecewise constant over specific time durations. Moreover, the state transitions of the appliances are determined and then employed as the optimization constraints to improve the estimation results. The proposed method is evaluated using almanac of minutely power data set (AMPds) and reference energy disaggregation data set (REDD) datasets by several performance metrics. Results indicate that the designed algorithm is able to recognize the frequently varying appliances in spite of piecewise constancy presumption. Furthermore, breaking down the optimization problem to the smaller parts enhances the ability of the algorithm to be operated in real time.
Home energy management requires accurate information about the appliances' consumption pattern. This information can help consumers save energy, control their usage by shifting their usage to ...off-peak hours and reduce their electricity costs. Non-intrusive load monitoring (NILM) in which the power consumption profile of appliances are extracted from the aggregated signal of a household, provides this information. For the NILM problem, machine learning approaches as the training-based solutions require large training datasets for an accurate disaggregation and the optimization-based approaches employs prior information about the characteristics of appliances. This paper proposes a novel event-based optimization algorithm. In its first stage, the prior information about appliances is extracted from the events of the consumption profiles of appliances by means of clustering. Then, a new event-based down-sampling method and transition filtering are designed for decreasing the computation time of optimization. At the last stage of the proposed algorithm, post-processing considering ON duration of appliances and varying states are proposed to increase the accuracy of the power profile reconstruction. The proposed approach was successfully tested for the low-frequency dataset of a house from the REDD. Numerical results show the advantages of the proposed algorithm, marked improvement over classification-based NILM considering small training dataset and its applicability in disaggregating the power consumption measured by the smart meter.
Rapid urbanization, population growth, agricultural practices, and industrial activities have led to widespread groundwater contamination. This study evaluated heavy metal contamination in ...residential drinking water in Shiraz, Iran (2021). The analysis involved 80 groundwater samples collected across wet and dry seasons. Water quality was comprehensively assessed using several indices, including the heavy metals evaluation index (HEI), heavy metal pollution index (HPI), contamination degree (CD), and metal index (MI). Carcinogenic and non-carcinogenic risk assessments were conducted using deterministic and probabilistic approaches for exposed populations. In the non-carcinogenic risk assessment, the chronic daily intake (CDI), hazard quotient (HQ), and hazard index (HI) are employed. The precision of risk assessment was bolstered through the utilization of Monte Carlo simulation, executed using the R software platform. Based on the results, in both wet and dry seasons, Zinc (Zn) consistently demonstrates the highest mean concentration, followed by Manganese (Mn) and Chromium (Cr). During the wet and dry seasons, 25% and 40% of the regions exhibited high CD, respectively. According to non-carcinogenic risk assessment, Cr presents the highest CDI and HQ in children and adults, followed by Mn, As and HI values, indicating elevated risk for children. The highest carcinogenic risk was for Cr in adults, while the lowest was for Cd in children. The sensitivity analysis found that heavy metal concentration and ingestion rate significantly impact both carcinogenic and non-carcinogenic risks. These findings provide critical insights for shaping policy and allocating resources towards effectively managing heavy metal contamination in residential drinking water.
The transportation sector is one of the main contributors to the emission of greenhouse gases globally, and the electrification of this sector can significantly reduce the emission of pollutants. The ...widespread connection of electric vehicles (EVs) to the power grid may bring challenges, such as increasing the peak load of the network. Therefore, the optimal use of EVs is necessary to improve the network’s economic, security, and stability indicators. This review article examines the deterministic control model and centralized control model, the types of EV models, and their tabular comparison. Additionally, expressing the communication standards to deal with compatibility challenges in charging stations, the effects of EV integration with the power grid, and various methods such as smart charging, dumb charging, and flexible charging are the main goals of this review article. In addition, since batteries play a crucial role in the electric vehicle industry, this research investigates the oldest type of rechargeable battery to the latest battery technology and the energy management system of these batteries. Finally, the authors have conducted studies on government incentives, the adverse effects of these incentives, and the combination of EVs with renewable energy sources.
Smart grids have introduced several key concepts, including demand response, prosumers—active consumers capable of producing, consuming, and storing both electrical and thermal energies—retail ...market, and local energy markets. Preserving data privacy in this emerging environment has raised concerns and challenges. The use of novel methods such as online learning is recommended to address these challenges through prediction of the behavior of market stakeholders. In particular, the challenge of predicting prosumers’ behavior in an interaction with retailers requires creating a dynamic environment for retailers to set their optimal pricing. An innovative model of retailer–prosumer interactions in a day-ahead market is presented in this paper. By forecasting the behavior of prosumers by using an online learning method, the retailer implements an optimal pricing scheme to maximize profits. Prosumers, however, seek to reduce energy costs to the greatest extent possible. It is possible for prosumers to participate in a price-based demand response program voluntarily and without the retailer’s interference, ensuring their privacy. A heuristic distributed approach is applied to solve the proposed problem in a fully distributed framework with minimum information exchange between retailers and prosumers. The case studies demonstrate that the proposed model effectively fulfills its objectives for both retailer and prosumer sides by adopting the distributed approach.
Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a ...significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households’ attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t-test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior.
This paper investigates the robust multiloop control for power flow controller in power transmission grids. A hybrid power flow controller (HPFC) is proposed, which comprises two shunt ...modular-multilevel-converter (MMC) based voltage source converters and one series capacitor. A nonlinear multiloop controller is designed via control Lyapunov function to achieve fast tracking performance, and robustness against system uncertainties and disturbances. Stability of the closed-loop nonlinear HPFC system is proved using Lyapunov stability theorem. The proposed finite-time controller (FTC) is decentralized using adaptive observer to estimate the nonlocal system parameters, in case of communication failure. Simulation and experimental studies are used to validate the proposed FTC with detailed model of MMCs, and the interarea oscillation damping with Phasor model of HPFC. Comparisons of the proposed FTC and the conventional PI controller show that the FTC has fast control response, small transient overshoot, and improved robustness.
