Wind power is the fastest growing and environmentally sustainable source of energy among all available renewable energy resources. The primary objective of this paper is to analyze the wind characteristics and power potential at Hyderabad, Southeastern province in Pakistan. Two years of wind speed data measured at 10 m above ground level (AGL) have been considered in this study. The annually, monthly, and seasonal variations in wind speed were analyzed, and minimum, maximum, and average values of recorded wind speeds are presented in this paper. Weibull and Rayleigh distribution functions have been applied to analyze the wind characteristics and evaluate the wind power potential of the proposed site. The Weibull shape k and scale c parameters have been estimated using the Weibull function. The higher values of k showed that the wind speed is steady at the site. The average wind speed was found above 6 m/s throughout the year. The most probable wind speed (V_mp) and maximum carrying energy (V_maxE) wind speed were also calculated using Weibull parameters. Root mean square error (RMSE), the coefficient of determination (R²), and mean bias error (MBE) were computed to ensure the good fit of Weibull distribution function. The annual average wind power and energy densities were estimated at more than 255 W/m² and 2245 kWh/m², respectively. The power density calculated by Weibull and Rayleigh functions was compared with that calculated by using measured wind data. The energy output and capacity factor of different commercially available wind turbines (WTs), i.e., power ratings from 0.33 to 2.75 MW, have been calculated. The cost of energy was estimated and ranged from $19.27 to $32.80 per MWh. Wind power potential and economic analysis of the collected data reveals that the site is suitable for developing wind power generation projects to power the local communities.

Inverter-interfaced distributed generators (IIDGs) have been widely applied due to their control flexibility. The stability problems of IIDGs under large signal disturbances, such as large load variations and feeder faults, will cause serious impacts on the system. The virtual synchronous generator (VSG) control is an effective scheme for IIDGs to increase transient stability. However, the existing linearized stability models of IIDGs are limited to small disturbances. Hence, this paper proposes a Lyapunov approach based on non-linearized models to assess the large signal stability of VSG-IIDG. The electrostatic machine model is introduced to establish the equivalent nonlinear model. On the basis of Popov’s theory, a Lyapunov function is derived to calculate the transient stability domain. The stability mechanism is revealed by depicting the stability domain using the locus of the angle and the angular frequency. Large signal stability of the VSG-IIDG is quantified according to the boundary of the stability domain. Effects and sensitivity analysis of the key parameters including the cable impedance, the load power, and the virtual inertia on the stability of the VSG-IIDG are also presented. The simulations are performed in PSCAD/EMTDC and the results demonstrate the proposed large signal stability assessment method.

The southwestern part of Pakistan is still not connected to the national grid, despite its abundance in renewable energy resources. However, this area becomes more important for energy projects due to the development of the deep-sea Gwadar port and the China Pakistan Economic Corridor (CPEC). In this paper, a voltage source converter (VSC) based high voltage DC (HVDC) transmission model is proposed to link this area to the national gird. A two-terminal VSC-HVDC model is used as a case study, in which a two-level converter with standard double-loop control is employed. The proposed model has a capacity of transferring bulk power of 3500 MW at 350 kV from Gwadar to Matiari. Furthermore, the discounted cash flow analysis of VSC-HVDC against the HVAC system shows that the proposed system is economically sustainable. The outcomes of this study reveal that the implementation of this project can bring economic stability and energy security in the southwestern region.

The integration of wind power as an alternative energy source has gotten much attention globally. In this paper, the Weibull distribution model based on different artificial intelligent algorithms and numerical methods is used to evaluate the wind profile. The application of Weibull distribution in wind data assessment can be extensively found, but the methods applied for estimating the parameters still need improvement. Three artificial intelligent algorithms are presented as an alternative method for estimation of Weibull parameters, and an objective function is proposed through the concept of maximum distance metric. Its convergence was proven mathematically through its boundedness for all wind data types. The optimization methods based on the proposed objective function are compared with the conventional numerical approaches for Weibull parameter estimation. Two-year wind data from the site in the southern area of Pakistan has been used to conduct this analysis. Furthermore, this work provides an eloquent way for the selection of a suitable area, evaluation of parameters, and appropriate wind turbine models through real-time data for power production.

Distribution network reconfiguration involves altering the topology structure of distribution networks by adjusting the switch states, which plays an important role in the smart grid since it can effectively isolate faults, reduce the power loss, and improve the system stability. However, the fault reconfiguration of the distribution network is often regarded as a single-objective or multi-objective optimization problem, and its multimodality is often ignored in existing studies. Therefore, the obtained solutions may be unsuitable or infeasible when the environment changes. To improve the availability and robustness of the solutions, an improved discrete multimodal multi-objective particle swarm optimization (IDMMPSO) algorithm is proposed to solve the fault reconfiguration problem of the distribution network. To demonstrate the performance of the proposed IDMMPSO algorithm, the IEEE33-bus distribution system is used in the experiment. Moreover, the proposed algorithm is compared with other competitors. Experimental results show that the proposed algorithm can provide different equivalent solutions for decision-makers in solving the fault reconfiguration problem of the distribution network.

Increasing renewable energy penetration exacerbates voltage violation in highway transportation power supply networks (HTPSNs). The technique of flexible connectivity based on soft open points (SOPs) could improve the controllability and voltage stability of the network. However, the generally used voltage control of SOPs may be too sluggish to accommodate the frequent voltage fluctuations and requires further investigation. This paper proposes a real-time model predictive control (MPC)-based voltage optimization method for SOPs in HTPSNs. Combining the sensitivity analysis and the operational constraints of the network with SOPs, a localized real-time optimization model is established. With the rolling of the control horizon, the SOPs regulate the voltage and optimize the power flow at a time scale of seconds. Using simply the voltage at the connected node of the SOPs, the proposed method can significantly enhance the voltage stability of the HTPSNs and decrease power losses under the rapid fluctuations of the network voltage. To further improve the performance of voltage control, the optimization model is modified through feedback correction. The case studies demonstrate the effectiveness of the proposed method under different scenarios.