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This paper presents a robust optimization problem of the flexible bidirectional power management of a smart distribution network and harmonic compensation of nonlinear loads using electric vehicles (EVs) equipped with bidirectional chargers. The base deterministic model of the proposed problem is as mixed-integer nonlinear programming (MINLP), having the objective function to minimize the economic and technical indices subject to harmonic load flow equations, EVs constraints, system operation and harmonic indices limits. This model is converted to a mixed-integer linear programming (MILP) model in the next step. In the proposed MILP model, the active, reactive and apparent loads, electrical energy, reactive power and harmonic current prices, as well as EVs characteristics, are considered uncertain parameters. Accordingly, two alternative robust optimization approaches have been implemented for the conditions of having both the probability distribution function or the bounded uncertainty in the proposed MILP problem model. The proposed model is tested on distribution networks to demonstrate its efficiency and performance. The results show that the MINLP model can be substituted by the proposed high-speed MILP model. In addition, the capacity of the injecting power of EVs is reduced in the worst-case scenario with respect to the scenario that is used in the deterministic model, while the consumed power of loads and EVs and energy price increases in this scenario. Finally, the payment of EV owners is reduced by considering EVs power and harmonic control. [ABSTRACT FROM AUTHOR]

Flexible voltage controllers (FVCs), such as voltage regulation transformers (VRTs) and shunt capacitor banks (SCBs), can play a significant role in enhancing the resilience of active distribution systemS (ADS) equipped with large-scale renewable distributed generation. In this paper, a comprehensive resilience response framework is presented, which provides preventive and emergency actions both before and after hurricane events. In this study, a preventive action identifies the on/off state of dispatchable distributed generation (DDG) units and discrete settings of FVCs for both before and after a hurricane event, whereas an emergency action includes the redispatch of DDG unit, load curtailment, and voltage regulation after a hurricane event. The core of the proposed framework is a trilevel max–min robust optimization (MMRO) model, which enhances the resilience of the ADS operation problem against multistage extreme N − k contingencies and renewable power generation uncertainty. The proposed MMRO model is formulated as a mixed-integer convex programming model by relaxing nonconvex AC power flow equations into mixed-integer second-order cone relaxation (MISOCR) equations. Finally, to test the effectiveness of the proposed trilevel MMRO model, we conduct experiments on a modified IEEE-33/69 bus distribution system. The numerical results show that the proposed trilevel MMRO model is an effective model for enhancing the resilience of ADSs. [Display omitted] • A comprehensive resilience response framework is presented for electrical distribution networks. • A min-max robust optimization is presented to handle extreme N − k contingencies and wind uncertainty. • Nonconvex AC power flow equations are converted into mixed-integer second-order cone relaxation. • A hybrid column-and-constraints optimization solution is implemented. [ABSTRACT FROM AUTHOR]

During the recent years, the power system has entered a new technological era. The trends associated with increased commitment to wind farms (WFs) and energy storage systems (ESSs) as well demand side flexibility require disruptive changes in the existing power system structures and procedures. Being at the heart of a paradigm shift from passive users of the grid to active prosumers, storage owners and demand responsive actors, this paper expresses a flexible coordinated power system expansion planning (CPSEP) while considering local WFs, ESSs and incentive-based demand response programs (DRPs). This model minimizes the summation of the expansion planning, operation and reliability costs while taking the network model based on AC optimal power flow constraints, and the reliability and flexibility considerations into account. The proposed framework is firstly formulated by mixed integer non-linear programming (MINLP), then to have a well-handed optimization model it is converted to mixed-integer linear programming (MILP). Additionally, the uncertainties of load, energy price, maximum WF generation and availability/unavailability of the network equipment are included in the proposed model where the first three parameters are modeled based on the bounded uncertainty-based robust optimization (BURO), and the scenario-based stochastic programming (SBSP) is used to model the last uncertain parameter. Finally, the proposed method is examined on several test networks to assess the performance of the proposed framework for flexi-reliable transmission network operation and planning. • This paper expresses a reliable, flexible coordinated power system expansion planning. • Local wind farms, energy storage system and demand response programs are considered. • The model minimizes the summation of the expansion planning, operation and reliability costs. • The performance of the strategy efficiency is assessed for enhancing network operational indices. [ABSTRACT FROM AUTHOR]

The ever-increasing penetration of wind energy generation (WEG) and electric vehicle (EV) batteries in power systems could bring two significant challenges to day-ahead energy balancing markets. First challenge, the uncertainty of WEG and random behavior of the EV batteries can raise energy imbalance in energy markets. Second challenge, the intermittent nature of WEG and uncontrolled EV batteries charging, bring high congestion costs and new congestion patterns in transmission system. In this condition, there is no guarantee that the WEG is deliverable throughout the power system. The hourly coordination of WEG units with EV batteries can play important roles in addressing the first challenge. Similarly, to addressing the second challenge, a transmission impedance adjustment (TIA) device is taken as a promising way to reduce transmission congestion and promote the integration of large-scale WEG and EV batteries through controlling the power flows. However, utilization of TIA device is limited nowadays due to the complexity of this device in the optimization problem of day-ahead energy market clearing with the DC approximation of the power flow network. Consequently, a computationally efficient methodology, which is compatible with existing customary solvers, is proposed to adjust the TIA device set point with minimal modification efforts. An adaptive robust optimization formulation is adapted in the proposed problem to handle the WEG uncertainty. Finally, the simulation results on six and IEEE 118 bus test systems suggest that: 1) substantial economic savings can be achieved through utilization of WEG and storage capability of EV batteries, beyond the independent capabilities of TIA technology; 2) the TIA device plays a critical role in removing transmission congestion; and 3) the storage capability of EV batteries could relieve the uncertainty of WEG and increase its dispatchability. • Effects of transmission impedance adjustment and electric vehicle batteries are considered. • Daily scheduling problem and flexibility is studied in the presence of uncertainty. • Wind uncertainty in a coordinated framework is also considered. [ABSTRACT FROM AUTHOR]