Search

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.

Wind power curtailment (WPC) occurs because of the non-correlation between wind power generation (WPG) and load, and also due to the fast sub-hourly variations of WPG. Recently, advances in energy storage technologies facilitate the use of bulk energy storage units (ESUs) to provide the ramping required to respond to fast sub-hourly variations of WPGs. To minimize the sub-hourly WPC probability, this paper addresses a generic continuous-time risk-based model for sub-hourly scheduling of energy generating units and bulk ESUs in the day-ahead unit commitment (UC) problem. Accordingly, the Bernstein polynomials are hosted to model the continuous-time risk-based UC problem with ESU constraints. Also, the proposed continuous-time risk-based model ensures that the generating units and ESUs track the sub-hourly variations of WPG, while the load and generation are balanced in each sub-hourly intervals. Finally, the performance of the proposed model is demonstrated by simulating the IEEE 24-bus Reliability and Modified IEEE 118-bus test systems.

The major challenge in coordinating between fast-acting energy storage systems (FA-ESSs) and renewable energy sources (RESs) in the existing transmission grid is to determine the location and capacity of the FA-ESS in the power systems. The optimal allocation of FA-ESS with conventional hourly discrete time method (DTM) can result in the increased operation cost, non-optimal placements and larger storage capacity and therefore, having an opposite effect on the operation. Accordingly, in this paper, a continuous-time method (CTM) is proposed to coordinate FA-ESS and RESs to cover fast fluctuations of renewable generations (RGs). Besides, based on the CTM, an adaptive interval-based robust optimization framework, to deal with uncertainty of the RGs, has been proposed. The proposed optimal allocation of FA-ESS with CTM provides the best sitting and sizing for the installation of the FA-ESSs and the best possible continuous-time scheduling plan for FA-ESSs. Also, in other to have better implementations of their ramping capability to track the continuous-time changes and deviations of the RGs rather than hourly DTM. The proposed model has been implemented and evaluated on the IEEE Reliability Test System (IEEE-RTS).

This paper studies the role of electricity demand response program (EDRP) in the co-operation of the electric power systems and the natural gas transmission system to facilitate integration of wind power generation. It is known that time-based uncertainty modeling has a critical role in co-operation of electricity and gas systems. Also, the major limitation of the hourly discrete time model (HDTM) is its inability to handle the fast sub-hourly variations of generation sources. Accordingly, in this paper, this limitation has been solved by the operation of both energy systems with a continuous time model (CTM). Also, a new fuzzy information gap decision theory (IGDT) approach has been proposed to model the uncertainties of the wind energy. Numerical results on the IEEE Reliability Test System (RTS) demonstrate the benefits of applying the continuous-time EDRP to improve the co-scheduling of both natural gas and electricity systems under wind power generation uncertainty.

Today, coordinated expansion planning is one of the key challenges for electricity systems including active distribution networks (ADNs) and transmission networks (TNs) hosting distributed renewable generation as well as large‐scale wind energy generation. Accordingly, this study presents a decentralised hybrid robust and stochastic (HR&S) expansion planning optimisation method to determine a robust generation and transmission planning for a TN and stochastic expansion planning for ADNs. The proposed HR&S planning model is formulated with the objective of achieving an effective expansion of both TN&ADN while minimises the investment and operation costs of TN&ADN planning considering wind uncertainty in TNs and load uncertainty in ADNs. Finally, the IEEE 30‐bus test system has been analysed to show the effectiveness of the proposed TN&ADN expansion planning framework and decentralised solution strategy. © 2020 The Authors. Free Access.

Recently, demand-response (DR) programmes are one of the appropriate tools for energy systems to encourage flexible customers to participate in the operation of energy systems. One of the complex tasks in multi-energy environments is optimal energy flow (OEF) problem of these systems. In this regard, this study investigates the OEF of an integrated electrical, heat, and gas system considering flexible heat and electrical demands. The conventional DR programme has been combined with the demand-side energy supplying management activity by introducing switching concept among input energy carriers. The way of the supplying energy of flexible customer can be changed by switching among input energy carriers. Here, the integrated system operator minimises the system operation costs subject to supply flexible consumers’ energy. To solve the complex OEF problem, this study presents a new optimisation algorithm named modified biogeography-based optimisation (BBO) algorithm. In this study, the proposed modification for the original BBO increases the robustness and the capability of the proposed optimisation method. The numerical results show that the flexible DR programme creates smoother energy demand curves in heat and electrical networks and reduce the operating costs of the integrated system.

In this study, a continuous-time hybrid stochastic/robust optimisation is proposed for the integrated investment in transmission lines (TLs) and energy storage systems (ESSs) with high penetration of uncertain wind power generation (WPG) sources from a central planner viewpoint. The main objective of the problem is to achieve a simultaneous expansion of transmission assets, TLs and ESSs, whereas minimising the investment cost while taking the operational aspects of a power system into account to accommodate higher shares of uncertain and intermittent WPGs. However, the integrated expansion planning of joint TL and ESS to integrate WPGs via conventional hourly discrete time model can increase the operation cost and result in a non-optimal sizing and siting of TLs and ESSs, hence, can impose an opposite effect on the favourite. Accordingly, a continuous-time model is proposed to coordinate the expansion planning of both TL and ESS to deal with sub-hourly uncertainty of WPGs. Also, the WPG uncertainty in expansion planning problem is characterised using a hybrid stochastic/robust optimisation framework. Numerical tests are implemented on a modified IEEE RTS 24-bus system and the achieved results confirm the efficiency of the proposed model.

Enhanced utilization of the existing transmission grid is a cheaper and paramount way to have a high penetration of large-scale wind energy resources. Smart wire devices (SWDs) are a new technology to enhance the transfer capability through power flow control. The SWDs are distributed, cheap, self-governing smart assets of FACTS technologies, which can adjust the power flow in an interconnected transmission network. Accordingly, this paper presents a comprehensive three-stage robust SWD placement model, which minimizes the generation and investment costs while guaranteeing that the adaptive and secure robust solution is accustomed to cover the wind uncertainty interval. Since the proposed robust model is not solvable via an off-the-shelf optimization package and to reduce the computation burden of the solution process, an effective solution strategy is proposed to solve it. Detailed simulation results on the IEEE 24-bus system verify the effectiveness of the proposed robust SWD placement model.

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.

Today's power systems are subject to various challenges arising from the large-scale integration of renewable energy sources (RES), especially wind energy production. System flexibility, or the capability of a system to address deviations in variable RES production, is becoming more and more relevant. This paper aims to provide a systematic approach to evaluate the level of flexibility of a power system by unequivocally considering fast-ramping units (FRU), hourly demand response (DR) and energy storage (ES). In addition, to research the flexibility role in power system operation, an “online” index is considered to evaluate the technical aptitude of the FRU, hourly DR and ES system to deliver the required flexibility. The mathematical representation of day-ahead scheduling, with the added modeling of an online flexibility index, is a mixed-integer nonlinear program (MINLP). This paper presents a method to convert this MINLP into a mixed-integer linear program without loss of accuracy. The adapted 6-bus and IEEE 118-bus systems are employed to assess the suggested models and flexibility metric, demonstrating the proficiency of the online flexibility index.

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.

This study presents an interval based robust chance constrained (IBRCC) optimisation model for allocating demand response program to effective buses of the power systems considering wind uncertainty and equipment failures. In the proposed formulation, an interval based robust approach is applied to evaluate the highest uncertainty spectrum of the wind power generation that the power system can tolerate. Accordingly, to cope with the uncertainty sources, chance-based constraints are implemented. In the proposed IBRCC optimisation framework, the level of the optimal solution robustness is probabilistically maximised subject to a set of operational constraints. Besides, to facilitate the massive integration of uncertain wind generation and to mitigate congestion in the transmission grid, an efficient allocation, and scheduling scheme of demand response programs is proposed. The proposed model is evaluated on the IEEE 24 bus system.

This paper proposes an optimization framework to deal with the uncertainty in a day-ahead scheduling of smart active distribution networks (ADNs). The optimal scheduling for a power grid is obtained such that the operation costs of distributed generations (DGs) and the main grid are minimized. Unpredictable demand and photovoltaics (PVs) impose some challenges such as uncertainty. So, the uncertainty of demand and PVs forecasting errors are modeled using a hybrid stochastic/robust (HSR) optimization method. The proposed model is used for the optimal day-ahead scheduling of ADNs in a way to benefit from the advantages of both methods. Also, in this paper, the ac load flow constraints are linearized to moderate the complexity of the formulation. Accordingly, a mixed-integer linear programming (MILP) formulation is presented to solve the proposed day-ahead scheduling problem of ADNs. To evaluate the performance of the proposed linearized HSR (LHSR) method, the IEEE 33-bus distribution test system is used as a case study.

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 presents sub-transmission and distribution network expansion planning (S&DEP) including distributed generation (DG) and distribution automation (DA) considering reliability indexes. The objective function is to minimize investment, operation, maintenance and reliability costs subjected to AC power flow, system operation and generating units and DG limits, reliability, and distribution automation constraints (including the constraints of protection devices and volt/VAr control mechanism). The proposed model is a mixed integer non-linear programming (MINLP) model which is hard to solve. For this reason, a MINLP problem is transformed to mixed integer linear programming (MILP) model. The validity of the proposed method is investigated in the two synthetic test networks.

Traditionally, electric system operators have dispatched generation to minimise total production costs ignoring the flexibility of the AC transmission system (ACTS). One available option to enhance power system security is to harness the flexibility of the ACTS, where a variety of flexible AC transmission system (FACTS) devices can be incorporated in the ACTS. However, utilisation of FACTS devices is limited today due to the complexities that these devices introduce to the AC optimal power flow (ACOPF) problem. The mathematical representation of the full ACOPF problem, with the added modelling of FACTS devices, is a non-linear programming (NLP) optimisation problem, which is computationally burdensome for large-scale systems. This study presents a method to convert this NLP problem into a mixed-integer linear program (MILP) where a certain level of solution accuracy can be achieved for a time budget. In this regard, this study first proposes a linear AC OPF model, using which the OPF solution with the operation of FACTS devices is obtained. In addition, the loadability of the power systems is utilised to quantify the impacts of FACTS devices on improving the security of system. The OPF problem including FACTS devices based on a linearised model is tested on a 6-bus and the IEEE 118-bus test systems.

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.

This study recommends a stochastic optimization model for the security constrained unit commitment (SCUC), which incorporates the optimal transmission switching (OTS) for managing the uncertainty of wind power generation and equipment failures, i.e. unit/line outages. Also, this study presents a technique in stochastic SCUC model with the OTS action using the AC optimal power flow (AC OPF). The AC OPF provides a more accurate picture of power flow in the power system compared to the DC optimal power flow that is usually considered in the literature for the stochastic SCUC models and the OTS action. While the stochastic SCUC model with the OTS action based on AC OPF is a mixed-integer non-linear programming model, this study transforms it into a mixed-integer linear programming (MILP) model. The MILP approach uses a piecewise linear model of AC OPF, which allows the reactive power and voltage to be considered directly in power flow model. The proposed stochastic SCUC problem is evaluated on the 6 bus, IEEE 118-bus and 662-bus test systems in pre- and post-OTS action. Obtained results demonstrate the effectiveness of the proposed model.

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.

The following article published in IET Generation, Tranmission and Distribution , Nikoobakht, A., Aghaei, J., Mardenah, M., ‘Optimal transmission switching in the stochastic linearised SCUC for uncertainty management of the wind power generation and equipment failures’, Gener. Transm. Distrib. , 2017, 11 , (10), pp. 2664 – 2676, doi:10.1049/iet-gtd.2016.1956, has been withdrawn by agreement between the authors, the Editors-in-Chief, Innocent Kamwa and Christian Rehtanz, and the Institution of Engineering and Technology. This is because the incorrect text was uploaded due to a technical error. The correct version has now been published as: Nikoobakht, A.,Aghaei, J., Mardenah, M. et al, 'Moving beyond the optimal transmission switching: stochastic linearised SCUC for the integration of wind power generation and equipment failures uncertainties', Gener. Transm. Distrib. , 2018, 12 , (15), pp. 3780 – 3792, 10.1049/iet-gtd.2017.0617.

This study investigates the application of a robust method to solve the problem of security constrained unit commitment (SCUC) with flexible resources for managing the uncertainty of significant wind power generation (WPG) to sustain the load-generation balance. The flexible resources include up/down ramping capability of thermal units, hourly demand response, energy storage system and transmission switching action through an integrated scheme. The application of mixed-integer linear programming to deal with the SCUC problem with flexibility resources has been discussed in this study using information-gap decision theory (IGDT) to realise a robust strategy for power system decision maker. Besides, this study proposes an effective solution strategy based on Benders' decomposition to solve the proposed problem. Numerical simulation results on the modified six-bus system and IEEE 118-bus system clearly demonstrate the benefits of applying flexibility resources for managing the WPG uncertainty and validate the applicability of the proposed IGDT-based SCUC model.

Stochastic security constrained unit commitment (SSCUC) based on a DC model could be problematic in AC networks because the DC model is potentially inaccurate. However, solving SSCUC problems using an AC model is still very challenging. Accordingly, an improved linearised AC optimal power flow (ILACOPF) model is provided in order to optimise the SSCUC problem. The proposed SSCUC model includes a linear representation of network losses and reactive power and bus voltage magnitudes. Moreover, in this study, transmission switching (TS), a powerful tool for grid side flexibility, is introduced and utilised to facilitate the mitigation of the uncertainty of wind power generation. Nevertheless, solving the SSCUC problem with TS in a full AC network is still one of the challenges in practical implementation, which is facilitated by the proposed ILACOPF model. Additionally, the aim of this study is to develop a more accurate power flow model to obtain a more realistic SSCUC solution using TS. The proposed ILAC-SSCUC model using TS is formulated as a mixed-integer linear programme, being solved by the proposed effective solution approach based on Benders’ decomposition. Numerical simulations on a 6-bus and IEEE 118-bus systems have been performed to evaluate the effectiveness of the method.

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.

In order to decrease operation costs in a security constrained unit commitment (SCUC) problem, strategies such as transmission switching (TS) can be utilized. However, having no limit for the switching of circuit breakers (CBs) in the system is troublesome, since a high number of switching increases the failure probability and decreases the reliability of a CB, in addition to resulting in shorter CB lifespans and higher maintenance and operating costs. In this paper, the reliability of CBs is integrated into the SCUC problem with TS to limit its switching. Since the higher reliability of CBs will increase the reliability of the system, it can be inferred that the reliability of CBs will affect the amount of load shedding. A linearization method is presented to linearize the CB reliability equation. Also, an improved linear AC optimal power flow (ILACOPF) with dynamic thermal line rating (DTLR) which considers weather conditions is used in the model to further reduce the number of switching. In order to evaluate the suggested approach, numerical testing is performed for 6-bus and large-scale 118-bus IEEE test systems with different scenarios.

The work reported in this paper jointly addresses two major challenges in modern power systems: 1) systematically maximizing wind power generation (WPG) utilization under worst-case uncertainty and 2) employing mixed integer-nonlinear programming (MINLP) in the co-optimization of variable reactance devices (VRD) and transmission switching (TS) in an AC optimal power flow problem (ACOPF). The first challenge is solved by proposing an interval based robust approach to identify the worst-case WPG uncertainty. Similarly, to overcome the second challenge, a tri-level decomposition algorithm is used to decompose the MINLP representation into one consisting of one mixed-integer linear programming (MILP) and two nonlinear programming (NLPs) problems. Finally, the effectiveness and efficiency of the proposed model is shown by analysing results from testing on the modified RTS-96 system.

This paper presents an AC optimal power flow (AC-OPF) model including flexible resources (FRs) to handle uncertain wind power generation (WPG). The FRs considered are thermal units with up/down re-dispatching capability, corrective topology control (CTC), and thyristor-controlled series capacitors (TCSC). WPG uncertainty has been modeled through a proposed interval-based robust approach, the goal of which is to maximize the variation range of WPG uncertainty in power systems while maintaining an adequate reliability level at a reasonable cost with the aid of FRs. However, utilization of FRs (especially CTC and TCSC devices) is limited due to the difficulty of their incorporation in the AC-OPF. The optimization framework of the full FR-augmented AC-OPF problem is a mixed-integer nonlinear programming (MINLP) in which the solution for large-scale systems is very hard to obtain. To solve this issue, this paper uses a two-stage decomposition algorithm to decompose the MINLP representation into a mixed-integer linear program (MILP) and a nonlinear program (NLP). Finally, the robust AC-OPF model with FRs is implemented and tested on a 6-bus and the IEEE 118-bus test systems to evaluate its efficiency and performance.

As renewable energy sources (RESs) penetration increases in the power system, the transmission system operators face new challenges to ensure system reliability and flexibility while ensuring high utilisation of uncertain RES generation. Controllable transformers with on-load tap changers and phase shifting capability are the promising flexibility tools to keep the system acceptable security and flexibility levels by controlling the voltage levels and energy flow. The AC optimal power flow (AC OPF) with detailed modelling considerations such as the bus voltage magnitude by including these devices is challenging. This study develops the AC OPF model to propose a robust flexibility optimisation framework for daily scheduling problem with uncertain wind energy sources. Nevertheless, the proposed formulation representation is an intractable mixed integer nonlinear programming (MINLP) while it includes AC grid constraints and the augmented modelling of the mentioned transformers. Accordingly, the proposed MINLP problem has been converted into a mixed-integer linear program where a certain level of solution accuracy can be achieved for the available time budget. The effectiveness of the proposed method is demonstrated using a modified 6-bus and IEEE 118-bus test systems. © 2019. This is the authors' manuscript to the article. The final authenticated version is available online at: http://dx.doi.org/10.1049/iet-rpg.2018.5136

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.

This study jointly addresses two major challenges in power system operations: (i) sustained growth of intermittent offshore wind farms (OWFs) connected to AC grid via multi-terminal voltage source converter (VSC)-based high-voltage DC (HVDC) grid that brings new challenges to the power system operation, and (ii) dealing with non-linearity of the AC power flow equations with the multi-terminal VSC-based HVDC grid model. To overcome these challenges, firstly, to deal with the uncertainties caused by the high penetration of the intermittent OWFs, this study introduces a robust optimisation approach. The proposed framework is computationally efficient and does not require the probability density function of the wind speed. The proposed decision-making framework finds the optimal decision variables in a way that they remain robust against the set of uncertainties. Secondly, the mathematical representation of the full AC optimal power flow (OPF) problem, with the added modelling of multi-terminal VSC-based HVDC grid in a day-ahead scheduling problem, is a mixed-integer non-linear programming (MINLP) optimisation problem, which is computationally burdensome for large-scale systems. Accordingly, this paper proposes a computationally efficient method for adjustment of solutions set points, which is also compatible with existing customary solvers with minimal modification efforts. This paper is a postprint of a paper submitted to and accepted for publication in [IET Renewable Power Generation] and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library