Sensing-assisted predictive beamforming shows significant promise for enhancing various future unmanned aerial vehicle (UAV) applications in integrated sensing and communication (ISAC) systems. However, the impact of such beamforming technique on the communication reliability was largely unexplored and challenging to characterize. To fill this research gap and tackle this issue, this paper proposes a cellular-connected UAV tracking scheme leveraging extended Kalman filtering (EKF), where the predicted UAV trajectory, sensing duration ratio, and target constant received signal-to-noise ratio (SNR) are jointly optimized to maximize the outage capacity at each time slot. To address the implicit nature of the objective function, analytical outage probability (OP) approximations are proposed based on second-order Taylor expansions, providing an efficient and full characterization of outage capacity. Subsequently, an efficient algorithm is proposed based on a combination of bisection search and successive convex approximation (SCA) to address the non-convex optimization problem with guaranteed convergence. To further reduce computational complexity, a second efficient algorithm is developed based on alternating optimization (AO). Simulation results validate the accuracy of the derived OP approximations, the effectiveness of the proposed algorithms, and the significant outage capacity enhancement over various benchmarks. Furthermore, we show that the optimized predicted UAV trajectory tends to be parallel to the base station’s uniform linear array antennas with a nonzero minimum distance, indicating a trade-off between decreasing path loss and enjoying wide beam coverage for outage capacity maximization.

The distribution system's voltage has more fluctuations due to the increasing intermittent and uncertain generation of renewable energy sources. To deal with massive and abrupt voltage issues, more operating reserves should be established. Compared with traditional regulation resources from generators, demand response by regulating the power consumption of demand-side resources is getting greater attention. On the demand-side, inverter air conditioners (IACs) account for a high power consumption percentage and have huge regulation potential. However, it remains a significant challenge to control large-scale IACs. Traditional control methods only consider active power and do not consider the compressor's complex characteristics combining active and reactive power. To address this issue, this article proposes a two-stage method considering system voltage quality. The first stage is using the photovoltaics' operating reserve for maintaining the system voltage in the safe range. The second stage is under more serious voltage deviations to regulate IACs' active power and reactive power based on model predictive control. Finally, hardware-in-the-loop experiments with realistic IAC are conducted to verify the proposed method. The proposed method improves the voltage fluctuation by 23.45% compared to the traditional method. The experimental results demonstrate that the proposed method can effectively maintain the distribution system's voltage within the allowable range.

The increasing penetration of distributed photovoltaics(PV) brings volatility and uncertain power outputs to micro-grids. Larger local regulation capacity is needed for maintaining the system balance between power supply-side and demand-side. It is promising to utilize widely distributed demand-side resources to provide regulation services, such as battery energy storage system (BESS), heating, ventilation, and air conditioning (HVAC), et al. However, most heterogeneous demand-side resources are regulated without coordination, resulting in the insufficient utilization of the regulation potential. To address this issue, this paper establishes a multi-time scale optimization model for micro-grids considering large-scale heterogeneous BESS and HVAC. Firstly, elements inside the urban micro-grids are modelled, where the HVAC systems and buildings are modelled as building based energy storage systems(BBESS), providing short-term energy storage. Then, a day-ahead optimization is carried out with the participation of day-ahead electricity market and ancillary market. Next, an intra-day rolling optimization is carried out in the real-time market. Finally, the case study shows that lower operation cost of the urban micro-grid and higher self-consumption rate of PV can be achieved by applying the proposed method, and BBESS can replace the demand for energy storage construction to a large extent.

The wide application of energy conversion facilities on the demand side, such as combined heat and power units, has accelerated the integration of multiple energy carriers in the form of energy hub (EH). EH can flexibly schedule its electricity and gas consumption patterns to provide demand response (DR) services to the electricity system. However, DR can introduce significant uncertainties in gas demands, posing challenges to the real-time balance of the integrated electricity and gas systems (IEGSs). The gas stored in the pipeline (i.e., linepack) is a promising flexible resource to accommodate the gas demand uncertainties during the DR. However, using linepack is challenging due to the complex physical characteristics of gas flow dynamics. This article proposes a coordinated optimal control framework for both EH and IEGS, focusing on leveraging the linepack flexibility to enhance DR capabilities. First, a multilevel self-scheduling framework for the EH is developed to comprehensively explore the DR potential. The gas flow dynamic constraints are then formulated to ensure that the fluctuating gas demand can be accommodated by the linepack in the IEGS. The second-order cone (SOC) relaxation is adopted to convexify the nonlinearity in the motion equation of gas flow dynamics. To tackle the overall mixed-integer SOC programming problem, an enhanced Benders decomposition strategy that incorporates the lift-and-project cutting plane method is developed, along with a novel solution procedure. The proposed method is validated using the IEEE 24-bus Reliability Test System and the Belgium natural gas transmission system to demonstrate its effectiveness.

The interdependency among the electricity, gas, heat, and cooling energy systems is ever-increasing. The flexible energy utilization patterns on the demand side and gas flow dynamics in the transmission system bring both opportunities and challenges to the reliable operation of integrated energy systems (IES). For example, if the electricity supply is interrupted, the gas system can ramp up the gas supply to the gas-fired units using linepacks. By this means, the reliability of the electricity system at this moment can be improved, while the gas system's capability of withstanding future risks may be undermined. Therefore, the operational reliability between different energy systems and time periods should be carefully balanced. This article proposes an operational reliability evaluation framework for the IES considering flexibilities from both the demand side and transmission system. First, the flexibilities of end-users and linepacks are explored based on the Energy Hub and gas flow dynamics models. Then, the reliability models of IES components are developed using the discretized-time Markov process to characterize the temporal state evolution in the operational horizon. A look-ahead contingency management scheme of the IES is then proposed to minimize the electricity and gas load curtailments. Taking account of all the possible system states, the operational reliabilities of the IES are evaluated using the time-sequential Monte Carlo simulation. Finally, the proposed method is validated by using the IEEE Reliability Test System and the practical Belgium gas transmission system.

Distributed event-triggered secondary control in microgrids has been widely investigated to improve system efficiency. But most of them are based on consecutive triggering condition monitors, which would in turn increase the computation burden of the system. To this end, this article presents distributed self-triggered algorithmic solutions to the frequency restoration control and active power sharing control of islanded microgrids. Different from event-triggered control schemes, in our self-triggered solutions, each distributed generator is equipped with a local algorithm that enables it to pre-compute the next triggering time instant according to the states at the previous one. Our starting point is to design a triggering condition with a novel estimate error. Then, the next triggering time instant is determined by solving a quadratic equation established based on the triggering condition, rather than monitoring the triggering condition consecutively. Theoretical analysis and simulation results show that the proposed distributed self-triggered secondary controllers can highly reduce the communication and computation costs simultaneously.

Rapidly increasing renewable energies bring more fluctuations to the power system and put forward higher requirement on operating reserve for maintaining the system balance. Traditional generating units are phasing out and may be insufficient to satisfy this reserve requirement in the near future. Therefore, much attention is paid to demand-side regulation resources. Inverter air conditioners (IACs) account for about 30% of the total power consumption in cities and have huge regulation potential. However, the control of large-scale dispersed IACs is intractable. Most existing studies adopt the centralized control scheme to regulate IACs, while it requires a high-cost communication infrastructure and induces privacy concerns. To address this issue, this article investigates the distributed control scheme of large-scale dispersed IACs for providing operating reserve. First, a normalization approach is proposed to uniformly quantify the regulation capacities of heterogeneous IACs. Then, a distributed consensus algorithm with a nonlinear protocol is developed for IACs to achieve the regulation objective under guaranteeing customers’ comfort requirements. Based on the Lyapunov stability theorem, the convergence of the proposed algorithm on large-scale dispersed IACs is proved to be always guaranteed. Finally, numerical studies verify the feasibility and performance of the proposed method, which features better data privacy protection and less communication and computation burden than the centralized control methods.

Rapidly increasing distributed energy resources (DERs) bring more fluctuating output power to the distribution network and put forward a higher requirement on local regulation resources for maintaining the network's balance. Heating, ventilation, and air conditioning (HVAC) loads account for more than 40% of power consumption in modern cities and have huge regulation potential as flexible loads. However, HVACs equipped with inverter devices have rarely been studied for providing regulation services in the local electricity market (LEM), even though they have exceeded regular fixed-speed HVACs. To address this issue, this article proposes a real-time LEM and a distribution network's optimization framework to exploit the regulation potential of inverter-based HVACs considering multiple DERs. This LEM can avoid iterations in real time and significantly decrease the difficulty related to the participation of small end-users in urban distribution networks. Moreover, in this article, we propose a transactive capacity evaluation method to assist end-users in deciding their inverter-based HVACs regulation capacities in the real-time LEM, which considers buildings’ thermal features, users’ multiple comfort requirements, and dynamic ambient temperature. On this basis, a multilevel bidding strategy is developed for inverter-based HVACs to decrease energy cost, increase fluctuating DERs local utilization rate, and alleviate the distribution network's congestion. Finally, a realistic distribution network is utilized to verify the effectiveness of the proposed methods.