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Modern power systems are likely to suffer from cascading contingencies, which pose multiple challenges to operators. In the event of such contingencies, power system operators must implement corrective actions, which could either be load curtailment or fast-ramping of generation assets, such as natural gas-fired units. Nevertheless, increased utilization of these units makes the power system vulnerable to gas pressure loss and interruption in the natural gas supply. Thus, it is essential to deliberate on the security constraints of natural gas networks in the operation of power systems. Nevertheless, the state-of-the-art energy flow models, applicable to both power and gas energy systems, are incapable of handling the complex relations between cascading contingencies and pipeline gas pressure loss. This study investigates an alternative approach that incorporates a novel AC power flow model and a dynamic gas flow model to treat these energy system interactions in an interoperable and simultaneous manner. Furthermore, electricity and natural gas system operators are independent of one another, which allows information privacy to be maintained. Using a hierarchical iterative algorithm covering both energy systems, the decentralized decision-making outlined in this paper ensures that only a very limited amount of information can be shared between the power system and the natural gas system operators, thus ensuring privacy. Finally, the numerical simulation is carried out on modified IEEE 30-bus electricity and 10-node gas systems and also on a larger test system of the IEEE 118-bus electricity and 10-node gas systems to demonstrate the efficiency of the proposed framework and the adopted decentralized approach.

This paper investigates the optimal grid reconfiguration (OGR) strategy in the smart grids with the random behavior of the many electric vehicle mobility (EVM) fleets and wind energy uncertainty. The increased utilization of EVMs in the power system with stochastic volatile behavior along with the high penetration of wind energy sources (WESs) can create new challenges in the system that will affect the system flexibility greatly. Here, the electric vehicle is mobile and there are distributed devices with deferrable options to supply energy at various times and locations. Accordingly, in this paper, the OGR is used as the remedial action to enhance the system flexibility to handle uncertainties. The main aim of this paper is to increase system flexibility and reduce expected operating costs by the coordination between EVM fleets, OGR and WESs’ uncertainty. Beside, this paper provides a novel method to evaluate the system flexibility while explicitly considering thermal unit generation and the EVM fleets. Numerical tests demonstrate the effectiveness of the proposed approach for analyzing the impact of EVM fleets on the system flexibility and hourly wind energy dispatch.

In co-scheduling of the integrated electrical energy and natural gas systems with high penetration of renewable energy sources, it is vital to have flexible resources to handle uncertainties, viz., continuous wind uncertainty and discrete equipment failures. Accordingly, this paper evaluates the effectiveness of electrical grid flexibility, i.e., optimal grid reconfiguration (OGR), on the scheduling plan of both energy systems. Also, to characterize the continuous/discrete uncertainty sources, this paper aims to develop a new computationally-efficient hybrid stochastic/IGDT (HS-IGDT) optimization framework for the co-scheduling of electrical energy and natural gas systems. Finally, the IEEE Reliability Test System with ten-node gas transmission system have been analyzed to show the efficiency of the proposed framework.

In this paper, the large-scale integration of plug-in electric vehicles (PEVs) with Vehicle-to-Grid (V2G) technology is taken as a promising way to promote the integration of renewable energy sources (RESs). But, it is a challenging task for the independent system operator (ISO) to manage the power flows in its system, especially in the large-scale integration of PEVs and RESs into the transmission system. The coordination between PEV, as distributed storage devices, and variable RESs create variable power flows and loop flows and quests for utilization of controllable devices to manage these flows. The On-load tap changing (OLTC) transformers and phase shifting transformers (PSTs) are two flexible transmission technologies that can be solved this challenge through controlling the power flows. In this paper, a novel model is developed for optimal operation of flexible transmission technologies for coordinated integration of PEVs and RESs in power transmission networks. The proposed model has been implemented on the six-bus and the IEEE 118-bus test systems. The simulation results imply that: (i) The V2G technology of PEV battery can ease the impact of variability of RES on power system operations and reduce operation cost and wind power spillage; (ii) substantial economic savings can be achieved through utilization of both OLTC transformer and PSTs beyond the independent capabilities of each technology.

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.

Since renewable energy resources such as wind and solar energy have intermittent nature, they cause significant fluctuations in distribution system voltages. This paper proposes a hierarchical and decentralized voltage management method for renewable energy resources in smart distribution system of electrical energy with the participation of reactive power of these resources. In this regard, the partitioning strategy of renewable energy resources into different independent areas, the modeling of optimal coordinated control level and a novel local real-time control level for reactive power control of renewable resources have been considered. The solving solution is based on the network sensitivity matrices. Finally, the obtained results from simulating the 34-bus test system as well as PG&E 69-bus distribution system with different levels of generation and load show the efficacy of the proposed method.

This paper addresses the stochastic security constrained unit commitment (SSCUC) problem with flexibility resources for managing the uncertainty of wind power generation (WPG). Departing from the traditional flexibility resources such as the thermal units with fast up/down spinning reserves and transmission switching (TS), this paper explores also the use of demand response (DR) and energy storage (ES) systems in an innovative integrated scheme. The proposed scheme utilizes a stochastic optimization framework to coordinate the flexibility resources dealing with the uncertainty of WPGs and equipment failures. The stochastic optimization model is formulated as a mixed-integer linear programming (MIP), and this problem is large and computationally complex even for medium sized systems. Accordingly, we present a novel accelerating decomposition technique aimed at solving this problem and reducing the number of iterations and CPU time. Numerical simulation results on the modified 6-bus system and on large-scale power systems, i.e. IEEE 118 and 300-bus systems, clearly demonstrate the benefits of applying flexibility resources for uncertainty management and the efficacy of the proposed solution strategy for large-scale systems.

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.

In this paper, a robust solution algorithm has been presented for SCUC problem including the uncertainties of wind power generation (WPG). Different flexibility resources have been considered in the proposed model, namely, transmission switching (TS) action and energy storage (ES) systems for managing the uncertainty risk of significant penetration of WPGs to sustain the load-generation balance. Indeed, the flexibility resources have been allocated using a robust optimization model based on the information gap decision theory (IGDT). That is, the IGDT reveals the robust optimal decision variables against the wind power uncertainties without requiring the probability density function of WPGs that leads to low computational burden. The optimization SCUC model is formulated as a mixed-integer linear programming (MIP), being solved by proposed effective solution strategy based on Benders decomposition (BD) technique. Numerical simulation results on the modified 6-bus system and IEEE 118-bus system, clearly demonstrate the benefits of applying flexibility resources for managing the high penetration of WPG uncertainty and validate the applicability of the proposed IGDT-based SCUC model.

This paper proposes a stochastic model for the scheduling of short-term AC security-constrained unit commitment (AC-SCUC) considering reliability and the value of lost load (VOLL). The uncertainty of load and wind power generation, active and reactive power losses, voltage profile of the network’ buses and congestion management for different VOLLs are investigated in this paper. Furthermore, the random outages of generating unit and transmission lines are modeled based on the scenario trees in the Monte Carlo simulation and the reserve requirements of the power system are implicitly scheduled based on the VOLL and by considering corrective actions of the generation units. A computationally efficient two-stage algorithm based on bender's decomposition is proposed to solve the proposed problem. The first stage deals with the base case where all the network components, the units' outputs and on/off status can be determined based on the forecasting load and wind farms' output. The second stage investigates the stochastic part of the problem and runs the possible scenarios in parallel for all the network elements and the available units of the base case. In the case of any violation for a scenario, a bender's cut is added to the first stage which modifies the commitment state and the power units' outputs in order to tackle the violation for that scenario. The method is applied to the IEEE 118/300-bus test system to assess its applicability and capability.

This paper proposes a stochastic framework to augment the integration of variable renewable energy sources (VRESs) in power system scheduling. In this way, the fast-response capability of gas-fired generator units (GFGUs) and vehicle-to-grid (V2G) capability of electric vehicles (EVs) can play important roles in large-scale integration of VRESs. However, the growth of GFGUs utilization can increase the grade of interdependency between power and natural gas systems. In this condition, the power system tends to demand more reliability and flexibility from the natural gas system, which creates new challenges in power system scheduling. The likely significant growth of EVs can solve this challenge and reduce the correlation between power and natural gas systems, bringing new opportunities for power system scheduling. However, a considerable literature in the field of operation of GFGUs and EVs has only focused on using the hourly discrete time model (HDTM). Undoubtedly, the major limitation of HDTM is its inability to handle the fast sub-hourly dispatch of GFGUs and energy storage capability of EVs. Accordingly, in this paper, this limitation has been solved by the operation of both energy systems with a continuous time model (CTM). The reliability test system with a ten-node gas transmission system has been analysed to show the effectiveness of the proposed problem.

This paper presents a robust planning of distributed battery energy storage systems (DBESSs) from the viewpoint of distribution system operator (DSO) to increase the network flexibility. Initially, the deterministic model of the proposed problem is expressed by minimizing the difference between the DBESS planning, degradation and operation (charging) costs and the revenue of DBESS from selling its stored energy subject to the constraints of AC power flow equations in the presence of RESs and DBESSs, and technical limits of the network indexes, variable renewable energy sources (vRESs) and DBESSs. This problem is modeled as a non-linear programming (NLP), then, an equivalent linear programming (LP) model is proposed using the first-order expansion of Taylor's series for linearization of power flow equations and a polygon for linearization of circular inequalities. Also, to model the uncertain parameters in the proposed problem including forecasted active and reactive loads, energy and charging/discharging prices and the output power of vRES, the bounded uncertainty-based robust optimization (BURO) framework is proposed in the next step. Finally, the proposed scheme is applied to 19-bus MV CIGRE benchmark grid by GAMS software to investigate the capability and efficiency of the model.