To achieve flexible operation of modern distribution networks with increasing renewable energy sources (RESs), this study proposes a stochastic operation method for a novel energy management platform that combines energy storage with a power flow controller, which is presented as a multi-terminal soft open point (MT-SOP). A Wasserstein metric-based distributionally robust chance-constrained optimization for the operation of the MT-SOP is proposed under the assumption that there is no prior knowledge about the distribution of forecast errors for both the load and RES outputs. The proposed method stochastically relaxes the reliability constraints on the bus voltage and branch power flow to maximize the cost-benefit of the distribution system operator (DSO). The proposed problem was reformulated into a tractable convex program using the concept of conditional value-at-risk. Based on case studies using modified IEEE-33 systems, the trade-offs between operational risks and cost–benefits were analyzed using parameters on the risk level and radius of the ambiguity set. Through numerical comparisons of the simulation results, we show that the DSO can achieve economic efficiency for imported electricity by relaxing conservativeness in distribution network reliability.

Low-cost optical particle sensors have the potential to supplement existing particulate matter (PM) monitoring systems and to provide high spatial and temporal resolutions. However, low-cost PM sensors have often shown questionable performance under various ambient conditions. Temperature, relative humidity (RH), and particle composition have been identified as factors that directly affect the performance of low-cost PM sensors. This study investigated whether NO₂, which creates PM_2.5 by means of chemical reactions in the atmosphere, can be used to improve the calibration performance of low-cost PM_2.5 sensors. To this end, we evaluated the PurpleAir PA-II, called PA-II, a popular air monitoring system that utilizes two low-cost PM sensors and that is frequently deployed near air quality monitoring sites of the Environmental Protection Agency (EPA). We selected a single location where 14 PA-II units have operated for more than 2 years, since July 2017. Based on the operating periods of the PA-II units, we then chose the period of January 2018 to December 2019 for study. Among the 14 units, a single unit containing more than 23 months of measurement data with a high correlation between the unit's two PMS sensors was selected for analysis. Daily and hourly PM_2.5 measurement data from the PA-II unit and a BAM 1020 instrument, respectively, were compared using the federal reference method (FRM), and a per-month analysis was conducted against the BAM-1020 using hourly PM_2.5 data. In the per-month analysis, three key features – namely temperature, relative humidity (RH), and NO₂ – were considered. The NO₂, called collocated NO₂, was collected from the reliable instrument collocated with the PA-II unit. The per-month analysis showed that the PA-II unit had a good correlation (coefficient of determination R²>0.819) with the BAM-1020 during the months of November, December, and January in both 2018 and 2019, but their correlation intensity was moderate during other months, such as in July and September 2018 and August, September, and October 2019. NO₂ was shown to be a key factor in increasing the value of R² in the months when moderate correlation based on only PM_2.5 was achieved. This study calibrated a PA-II unit using multiple linear regression (MLR) and random forest (RF) methods based on the same three features used in the analysis studies, as well as their multiplicative terms. The addition of NO₂ had a much larger effect than that of RH when both PM_2.5 and temperature were considered for calibration in both models. When NO₂, temperature, and relative humidity were considered, the MLR method achieved similar calibration performance to the RF method. In addressing the feasibility of utilizing distant NO₂ measurements for calibration in lieu of collocated data, the study highlights the effectiveness of distant NO₂ when correlated strongly with collocated measurements. This finding offers a practical solution for situations where obtaining collocated NO₂ data proves to be challenging or costly. We assessed the performance of different PA-II units to determine their efficacy. Our investigation reveals a significant enhancement in calibration performance across different PA-II units upon integrating NO₂. Importantly, this improvement remains consistent even when employing models trained with different PA-II units within the same location. Overall, this investigation emphasizes the significance of NO₂ in improving calibration for low-cost PM_2.5 sensors and presents insights into leveraging distant NO₂ measurements as a viable alternative for calibration in the absence of collocated data.

Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numerical approaches, as well as data-driven forecasting approaches, have been studied for ozone forecasting. Data-driven forecasting models, in particular, have gained momentum with the introduction of machine learning advancements. We consider planetary boundary layer (PBL) height as a new input feature for data-driven ozone forecasting models. PBL has been shown to impact ozone concentrations, making it an important factor in ozone forecasts. In this paper, we investigate the effectiveness of utilization of PBL height on the performance of surface ozone forecasts. We present both surface ozone forecasting models, based on multilayer perceptron (MLP) and bidirectional long short-term memory (LSTM) models. These two models forecast hourly ozone concentrations for an upcoming 24-h period using two types of input data, such as measurement data and PBL height. We consider the predicted values of PBL height obtained from the weather research and forecasting (WRF) model, since it is difficult to gather actual PBL measurements. We evaluate two ozone forecasting models in terms of index of agreement (IOA), mean absolute error (MAE), and root mean square error (RMSE). Results showed that the MLP-based and bidirectional LSTM-based models yielded lower MAE and RMSE when considering forecasted PBL height, but there was no significant changes in IOA when compared with models in which no forecasted PBL data were used. This result suggests that utilizing forecasted PBL height can improve the forecasting performance of data-driven prediction models for surface ozone concentrations.

Tracking the source of air pollution plumes and monitoring the air quality during emergency events in real-time is crucial to support decision-makers in making an appropriate evacuation plan. Internet of Things (IoT) based air quality tracking and monitoring platforms have used stationary sensors around the environment. However, fixed IoT sensors may not be enough to monitor the air quality in a vast area during emergency situations. Therefore, many applications consider utilizing Unmanned Aerial Vehicles (UAVs) to monitor the air pollution plumes environment. However, finding an unhealthy location in a vast area requires a long navigation time. For time efficiency, we employ deep reinforcement learning (Deep RL) to assist UAVs to find air pollution plumes in an equal-sized grid space. The proposed Deep Q-network (DQN) based UAV Pollution Tracking (DUPT) is utilized to guide the multi-navigation direction of the UAV to find the pollution plumes’ location in a vast area within a short duration of time. Indeed, we deployed a long short-term memory (LSTM) combined with Q-network to suggest a particular navigation pattern producing minimal time consumption. The proposed DUPT is evaluated and validated using an air pollution environment generated by a well-known Gaussian distribution and kriging interpolation. The evaluation and comparison results are carefully presented and analyzed. The experiment results show that our proposed DUPT solution can rapidly identify the unhealthy polluted area and spends around 28% of the total time of the existing solution.

Power system operators have recently introduced some AI-based techniques in load prediction, fault diagnosis, scheduling, and maintenance. Operators require a grid analysis that includes wind turbines to mitigate impacts by environmental factors. Among them, a method for modeling wind turbines that reflects the dynamic characteristics and their output characteristics is receiving attention. Recently, a data-based power curve modeling method has been adopted for a simplified model that characteristics as much as possible. However, the simplified EMT simulation is difficult to reflect the output characteristics according to nonlinear wind conditions accurately. This paper proposes a wind turbine model based on artificial neural network techniques using real supervisory control and data acquisition (SCADA) data from a wind farm. The proposed strategy derive the similar to real output value through the trained wind turbine model in various wind scenarios. For the verification of the proposed strategy, the case study was conducted using a real-time digital simulator (RTDS).

Attention has been paid to low-cost, light-scattering-based particulate matter (PM) sensors, which provide PM measurements in order to supplement a small number of expensive air quality monitoring stations. However, low-cost PM sensors produce measurement data of questionable quality. In this paper, we evaluate the performance of low-cost PM sensors, specifically PurpleAir PA-II units. To evaluate the PurpleAir PA-II units, we use accurate air quality data measured from monitoring stations within close proximity to the PA-II units as reference. By means of linear regression, we compare PurpleAir PA-II units with air quality monitors. From the result, PurpleAir PA-II units have high correlations (R ² ≥ 0.84) with nephelometers, which are based on the principle of light-scattering, and thus the PurpleAir PA-II is a sufficient substitute for nephelometers. PurpleAir PA-II units have a good agreement (R ² = 0.72 and 0.89) with MetOne BAM 1020 monitors based on beta ray attenuation but show a non-linear behavior. Furthermore, there is an essential observation that the PurpleAir PA-II unit needs to have significantly high precision in order to have a high correlation with an expensive reference monitor and thus be used supplementally. The considered PurpleAir PA-II units overestimate PM 2.5 concentrations compared with air quality reference monitors. Therefore, a calibration algorithm for PurpleAir PA-II units should be considered to give a correct air quality index at sites considered in this paper. However, our results show the PurpleAir PA-II to be a promising low-cost air quality sensor for supplementing a conventional air quality sensing network with expensive monitors.