Participation of electric vehicles (EVs) aggregators in frequency regulation service may cause time-varying delays in load frequency control (LFC) systems. These time-varying delays affect the performance and even cause instability of power systems. In this paper, we investigate a single and multiple time-varying delays-dependent stability of an LFC system with participation of EV aggregators. Based on the Lyapunov theory and linear matrix inequality approach, we propose delay-dependent stability criteria for a single and multiple time-varying delays using the Wirtinger-based improved integral inequality. With the stability criteria, we obtain delay margins for LFC with a single and two EV aggregators and investigate the interaction among the delay margins, PI controller gains, and participation ratios of EV aggregators. In addition, we investigate the significance of time-varying delays in fast-response resources by comparing the delay margin obtained in EV aggregator model with that obtained in delayed conventional generators. We discuss an extension to multiarea LFC systems and guidelines on how to improve performance of LFC systems under time-varying delays. We expect that the proposed criteria can help to guide the determination of delay requirements for EV aggregators participating in frequency regulation service.

We mathematically derive an upper bound of bit- error rate (BER) of $uplink$ non-orthogonal multiple access sys- tems with quadrature phase shift keying (QPSK) modulation in fading channels. In particular, we exploit the $joint\, maximum-likelihood$ (ML) detector at a base station (BS) with multiple antennas since it results in the $optimal$ BER performance of a super-imposed QPSK modulated symbols from multiple mobile stations (MSs). In particular, we obtain a closed-form integral of Q-function with Erlang distributed random variable to derive the BER. We also obtain diversity order of the uplink NOMA systems. Through extensive computer simulations, we validate that our analytical results match well with the simulation results especially in high signal-to-noise ratio (SNR) regime.

Provision of frequency regulation service with a large number of electric vehicles (EVs) through EV aggregators requires to exchange regulation related messages through communication infrastructures in EV aggregators’ domain. Communication delays in the aggregators’ domain may significantly affect regulation performance. We investigate the effect of communication delays in an EV aggregator’s domain on the regulation performance. We present an underlying communication network for the EV aggregator controlling a group of EVs at a parking lot. The underlying communication network consists of two separate subnetworks: a PLC network between a pair of EV and EVSE and a LTE network between one EV aggregator and a group of EVSEs. In order to evaluate the delay effect on the regulation performance, we propose a co-simulation platform which is composed of an LTE network model and a load frequency control model. For the LTE network, we analyze the potential sources of delays and identify the repeated service request and release procedures as a critical delay source. We implement the identified repeated procedure by using network simulator-3 and evaluate the delay performance of frequency regulation service. We evaluate the effect of the communication delay in the EV aggregator’s domain on regulation performance.

Electric vehicle (EV) aggregators participating in frequency regulation services with a large number of EVs may have a delayed response problem due to their underlying communication infrastructure and scheduling. In this paper, we investigate the effect of delays in EV aggregators' domain on their frequency regulation payments, especially mileage payment. Since the delays affect frequency regulation performance, in order to consider the delay effect on regulation performance, we enhance the conventional load frequency control model by including multiple EV aggregators with varying delays. Under this model, we evaluate the effect of delays in EV aggregators' domain on their profits under two ISOs' mileage payments such as PJM and NYISO. Through simulations, it is shown that the profit of EV aggregators increases as the delays increases. The conventional mileage payments did not motivate EV aggregators to reduce the delay in order to improve regulation performance. To solve the problem, we propose a new mileage payment to reduce the delay. We finally show that the proposed payment can provide an incentive to reduce the delay.

In this letter, we propose a distributed interference alignment (DIA) technique in a two-cell multiple-input multiple-output (MIMO) network with dynamic time division duplex (TDD) mode under local channel state information assumption. The dynamic TDD operation introduces new types of interference, such as downlink-to-uplink [base station to base station (BS-BS)] and uplink-to-downlink [mobile station to mobile station (MS-MS)] interferences. Under the assumption of rank-deficient BS-BS MIMO interference channel, we mathematically characterize the achievable degrees-of-freedom of the proposed DIA technique for a given number of antennas at BS/MS. Through extensive computer simulations, it is shown that the proposed DIA technique outperforms the conventional techniques in terms of sum-rate at the cost of signaling overheads for the BS-BS and MS-MS channel overhearing.

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.