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This paper explores the procurement of flexibility services in the power system by the collaboration between transmission system operators (TSO) and distribution system operators (DSO) in the day‐ahead market (DAM). With the coordination and interaction among the operators, the existing flexibility sources in transmission and distribution levels are exploited efficiently to procure flexibility services. For motivation of the distributed generation (DGs) sources and microgrids (MGs) to provide flexibility services, DSO aggregates the flexible sources of its grid and offers them to the upstream market. Doing so addresses the TSO uncertainties and improves the flexibility of the entire grid. The simulation results suggest the increased market efficiency and reduced operating costs for transmission and distribution levels using flexible sources. Furthermore, the results demonstrate that utilization of existing flexible sources in the distribution grid enhances the flexibility of the entire system and, consequently, increases the absorption from renewable energy sources (RES). Also, the flexibility procurement for DSO is associated with an increase in revenue and motivation of MGs. An examination of the impact of MG's ownership on the market clearing mechanism is also investigated in this regard. Finally, the reserve affects the system's flexibility. This effect is helpful for flexible systems while reducing the flexibility metrics for inflexible systems. [ABSTRACT FROM AUTHOR]

The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. Smart meters and sensors monitoring the system provide a huge amount of data in various part of smart grid. To logically manage this trouble, a new lossy data compression approach for big data compression is proposed. The optimal singular value decomposition (SVD) is applied to a matrix that achieves the optimal number of singular values to the sending process, and the other ones will be neglected. This goal is done due to the quality of retrieved data and the compression ratio. In the presented scheme, to implement the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using a wide range of data types, from electricity market data to image processing benchmarks. The comparisons show that the compression level obtained by the proposed method can dominate the points given by the existing SVD rank reduction methods. Also, as the other finding of this article, the performance of the rank reduction methods depends on the application and data types. It means that a rank reduction method can reveal a good performance in one application and performs unacceptably for another purpose. So, the optimized rank reduction can pave the way toward a robust and reliable performance. [ABSTRACT FROM AUTHOR]

In this paper, a stable fuzzy controller in the form of linear matrix inequalities (LMIs) is designed for an electric vehicle (EV). The proposed controller evaluates the stabilization of the EV speed with robust disturbance rejection approach throughout both transient and steady states and in the presence of external disturbances and parametric uncertainties. In order to achieve an efficient control, EV's battery voltage, as control input, and EV speed, as system output, are constrained. The proposed control strategy is based on Takagi–Sugeno (T-S) fuzzy model and the parallel distributed compensation (PDC) fuzzy controller. Proposed controller can be practically implemented due to low computational load of control input that is owing to the offline process of obtaining the feedback gains, very small amplitude of control input and low power consumption. To evaluate the performance of the designed controller, simulations in five steps are conducted on an EV equipped with a brushed direct current (BDC) motor as a case study in MATLAB simulation environment. Ultimately, the real-time digital simulation results using real-time digital power system simulator (RTDS) confirm the efficiency of the proposed input-output constrained robust disturbance rejection stable fuzzy controller (IOCRDRSFC) in stabilizing the EV speed. The simulation results obtained from MATLAB and the RTDS confirm the energy-efficient and robust performance of the proposed controller in quick stabilization of the EV speed in the presence of all structured and unstructured uncertainties. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study presents a new solution to cope with the intermittent nature of renewable generations (RGs) and facilitate the integration of RGs in the smart active distribution network. Electric spring (ES) as one of the most influential solutions in demand-side management is proposed as a flexible resource for flexible operation of grid-connected microgrid against other sources of uncertainty such as forecasted load demand and energy price as well as the RGs output. This innovative approach has been considered in the context of a stochastic problem by presenting the static model of ES for the first time. The modelling of uncertainties is done by the roulette wheel mechanism as a scenario generation process and backward method for reducing the number of scenarios. In the proposed optimisation problem, the objective function is minimising the operating cost and voltage deviation as well as maximising system flexibility, subject to the AC power flow, ES and RGs constraints and technical system limitations. Finally, the proposed solution is tested on 33-bus IEEE test system by the general algebraic modelling system software. The case studies demonstrate the efficiency of the proposed ES model in different simulation and experimental cases in providing flexi-renewable microgrid. [ABSTRACT FROM AUTHOR]

This study presents a new solution to cope with the intermittent nature of renewable generations (RGs) and facilitate the integration of RGs in the smart active distribution network. Electric spring (ES) as one of the most influential solutions in demand‐side management is proposed as a flexible resource for flexible operation of grid‐connected microgrid against other sources of uncertainty such as forecasted load demand and energy price as well as the RGs output. This innovative approach has been considered in the context of a stochastic problem by presenting the static model of ES for the first time. The modelling of uncertainties is done by the roulette wheel mechanism as a scenario generation process and backward method for reducing the number of scenarios. In the proposed optimisation problem, the objective function is minimising the operating cost and voltage deviation as well as maximising system flexibility, subject to the AC power flow, ES and RGs constraints and technical system limitations. Finally, the proposed solution is tested on 33‐bus IEEE test system by the general algebraic modelling system software. The case studies demonstrate the efficiency of the proposed ES model in different simulation and experimental cases in providing flexi‐renewable microgrid. [ABSTRACT FROM AUTHOR]

The electric spring (ES) is a reactive compensator which is series with a noncritical load. This paper utilizes the ES in the distribution networks and examines the underlying performance operationally and economically. A virtual generator model for the electric spring is proposed to ease the study of power systems. An algorithm based on the backward/forward sweep method is proposed for solving the optimal power flow. Then, with a 24‐hour real price and load data of ISO‐NE market, the performance of ES is evaluated operationally and economically in two distribution systems. The ESs may exchange reactive power between each other and this raises active and reactive power losses. To cope this problem, the feeders' voltages of the middle load demand are set as the reference voltages of ESs in each feeder so, each ES has a different reference voltage to cease the ES power exchanges. In this work, indexes such as voltage profile, active power loss, reactive power loss, power factor, electric bill payment, net present value, internal rate of return and the payback year are measured. The results of case study confirm the ES improves power loss, voltage profile, power factor, and stability while it reduces the operation cost. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • The co-operation of integrated electric and natural gas systems is modeled. • Continuous–time model is developed to capture sub-hourly variations of wind energy. • Responsive demands are used to increase penetration of wind energy sources. • Fuzzy-based IGDT method is proposed to model the uncertainty of wind energy. [ABSTRACT FROM AUTHOR]

Highlights • Optimal grid reconfiguration mechanism is used to handle wind energy uncertainty. • Electric vehicle mobility is modeled as a flexibility tool to transfer energy. • The paper deals with day-ahead electricity energy markets. • Stochastic programming formulation is proposed to model wind energy uncertainty. Abstract 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. [ABSTRACT FROM AUTHOR]

The future of energy flexibility in microgrids (MGs) is steering towards a highly granular control of the end-user customers. This calls for more highly accurate uncertainty forecasting and optimal management of risk and flexibility options. This paper presents a novel data-driven model to optimize the operation of MGs based on a risk-averse flexi-intelligent energy management system (RFEMS), considering the rising challenge of global climate change. It considers the presence of renewables, a diesel generator, and flexibility resources (FRs) containing a demand response program (DRP), distributed electric vehicles (EVs), and electric springs (ESs). In the first phase, the proposed model, by means of a novel hybrid deep-learning (DL) model, forecasts uncertain parameters associated with wind and solar generations, load demand, and day-ahead energy market price. The architecture of the proposed hybrid forecasting model is composed of several stacked restricted Boltzmann machines and a cascade neural network. In the second phase, the MG operator (MGO), based on the obtained uncertainty forecasting results, in the context of a hybrid risk-controlling model, optimizes the MG operation using the provided demand-side flexibility. The proposed optimization problem is linearized stochastic programming with robust concepts, subject to AC optimal power flow constraints, MG flexibility restrictions, and operating limits of local resources. Finally, the efficiency of the proposed RFEMS by using real German datasets on a 33-bus test MG is analyzed. Numerical results demonstrate the superior performance of the proposed forecasting model over several hybrid DL models. In particular, the root mean square error (RMSE) index for wind, solar, load, and price forecasting is improved by 53.35%, 73.24%, 80.24%, and 58.1%, respectively. Further analysis of the proposed RFEMS reveals that operating indices in two 33-bus and 69-bus test networks are significantly improved. It paves pathways to risk-averse, flexible, and economic operation of smart active distribution networks. [Display omitted] • A data-driven model is proposed to optimize microgrids' operation. • A hybrid deep-learning model is proposed to forecast uncertain parameters. • Uncertain parameters include wind and solar generations, load, and energy price. • The forecasting is composed of Boltzmann machines and cascade neural network • Hybrid stochastic/robust optimization is proposed to handle uncertainty. [ABSTRACT FROM AUTHOR]

This paper evaluates the voltage security of the distribution networks in the presence of electric vehicles in the optimization framework. Accordingly, the main objective functions of this optimization problem include maximization of voltage security margin and minimization of operational cost. Also, it is supposed that the electric vehicles are equipped with bidirectional chargers to control active and reactive power in smart distribution networks, simultaneously. The objective functions are subject to the constraints of power flow equations, system operating limits and electric vehicle constraints. The proposed model is implemented on the 33-bus distribution network to evaluate the performance of the proposed optimization scheme for the management of the smart distribution networks in the presence of electric vehicles. The results show that the operational cost and network voltage security margin are reduced in the case of the higher electric vehicle penetration rate when electric vehicles are used for charging active power and reactive power control capability, with respect to the case that does not include electric vehicles. [ABSTRACT FROM AUTHOR]

In growing economic energy systems, the interdependency of various energy infrastructures has led to a change in countries' policies in their expansion planning of energy networks. In this study, a mixed‐integer non‐linear programming (MINLP) model is proposed for expansion planning of the multi‐carrier systems including electricity and gas distribution networks. The optimal planning determines the best location, time, and alternative for network assets in order to minimise investment costs and reduce losses. Also, as another distinctive feature of this study, given the integration of electricity and gas distribution networks and the complexity of the problem, a new MILP model using linearisation methods is presented. In this planning model, several types of alternative plans and a set of candidates for the new placements or increase of the capacity of transformers, feeders, distributed generation, gas pipelines, and city gate stations are considered. The proposed MILP model provides the convergence of the problem to a global optimum response using the powerful commercial software. In addition, a solution based on Benders decomposition algorithm is finally proposed to reduce the solution time of the problem. Finally, the efficiency of the proposed model is evaluated by means of some simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching-learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method. [ABSTRACT FROM AUTHOR]

A potentially beneficial new opportunity is emerging around the exchange of energy between electric vehicles and the electrical energy grid, particularly as more low-carbon energy sources are connecting to the grid. Accordingly, this paper presents an optimization framework to activate the potential capabilities of electric vehicles equipped with bidirectional chargers for energy conditioning (including energy management and power quality improvement) of the future distribution networks. The proposed nonlinear optimization seeks to concurrently enhance the operation performance (using the network voltage deviation index) as well as power quality of the grid (using total harmonic distortion index). The proposed model is tested on a 33-bus distribution network to demonstrate its efficiency and performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for the contingency constrained phasor measurement units (PMUs) placement. The proposed approach is based on n − k redundancy criterion using robust optimisation. This security criterion ensures observability of the network under any contingency state, containing the loss of up to k PMUs. In the proposed method, the effects of zero injection and power flow measurements as well as possible contingency states (such as branch outage and single or multiple PMU loss) are considered. The non‐linear modelling of observability function for measurements is reformulated by linearisation process. The resulting bi‐level programming model is solved by its evolution to an equivalent single‐level mixed‐integer programming problem. The objective function of the optimal PMU placement is aimed at minimising the number of PMUs in a way that guarantees economic goals and the observability of all network buses. The advantage of this model is to significantly reduce the computational burden compared with other methods. The proposed method is tested on modified 7‐bus test network, 118‐ and 2383‐bus IEEE test networks. The results of the case studies clearly demonstrate the simplicity and efficiency of the proposed robust optimisation method in different cases. [ABSTRACT FROM AUTHOR]

This study proposes combined framework for proactive operation, i.e. bidirectional active and reactive power management, of the smart distribution network as well as harmonic compensation of non‐linear loads using electric vehicles (EVs) equipped with bidirectional chargers. The problem is in the form of non‐linear programming (NLP) where the objective function is to minimise the voltage deviation at the fundamental frequency and the total harmonic distortion. The harmonic load flow equations, EVs constraints, system operation and harmonic indexes limits are formulated as problem constraints. The proposed NLP problem is converted to an equivalent mixed integer linear programming (MILP) model using Taylor series and linearisation techniques for AC power flow formulation. Also, the Benders decomposition (BD) algorithm is used to solve the proposed MILP problem that is tested on different distribution test networks to demonstrate its efficiency and performance. The results show that the NLP model can be substituted with the high‐speed linear programming model. Moreover, the computation speed is improved by using the BD method. Finally, the network and harmonic indexes improved and charging cost reduced using the proposed idea. [ABSTRACT FROM AUTHOR]

This paper develops a robust bundled active and reactive power management of EV integrated smart distribution networks. To model the problem, at first, the deterministic formulation of the problem is expressed as a non-linear programing (NLP), which minimizes the difference between the energy cost and the revenue of EVs’ (parking lot’s) reactive power exchange with the network as the objective function, subject to the AC power flow equations, system operation limits and EVs’ characteristics as the problem constraints. Then, while the NLP optimization reveals local optima, the NLP model is converted into a linear programming (LP) model using linearized AC power flow equations. The system uncertainties including active and reactive loads, electrical energy and reactive power prices as well as EVs’ charging/discharging schedules are modeled in the proposed linear model. Accordingly, the robust model is implemented and it considers one scenario, namely the most-conservative scenario of the objective function in the main problem. To decrease the calculation time, Benders decomposition (BD) approach is used to speed up the total processing time. The proposed robust linear architecture is tested on three distribution test networks to demonstrate its efficiency and performance. The results show that the NLP model can be substituted with the high-speed LP model. Moreover, the computation speed is improved by using the BD method. In addition, the capacity of the injected power of EVs is reduced in the most-conservative scenario in comparison with the deterministic model’s scenario, while the consumed power of loads and EVs have been increased in this scenario. The proposed robust architecture against uncertainties is shown to yield a more robust solutions at the expense of higher operation cost. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for the contingency constrained phasor measurement units (PMUs) placement. The proposed approach is based on n - k redundancy criterion using robust optimisation. This security criterion ensures observability of the network under any contingency state, containing the loss of up to k PMUs. In the proposed method, the effects of zero injection and power flow measurements as well as possible contingency states (such as branch outage and single or multiple PMU loss) are considered. The non-linear modelling of observability function for measurements is reformulated by linearisation process. The resulting bi-level programming model is solved by its evolution to an equivalent single-level mixed-integer programming problem. The objective function of the optimal PMU placement is aimed at minimising the number of PMUs in a way that guarantees economic goals and the observability of all network buses. The advantage of this model is to significantly reduce the computational burden compared with other methods. The proposed method is tested on modified 7-bus test network, 118- and 2383-bus IEEE test networks. The results of the case studies clearly demonstrate the simplicity and efficiency of the proposed robust optimisation method in different cases. [ABSTRACT FROM AUTHOR]

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]

The main objective of this paper is to determine which voltage regulation strategies best allow grid-tied Photovoltaic (PV) Systems to be integrated into distribution networks. More particularly, this involves performing a literature review on the negative impacts of grid-tied PV systems on distribution networks, and also the current and proposed voltage regulation methods available. The next step is to assess, through simulation, which strategies allow the greatest amount of PV to be integrated with minimal effect on power quality. This will be done by simulating various Medium Voltage (MV) and Low Voltage (LV) radial feeders under varying load conditions and changing solar irradiance levels. Real network data will be incorporated with an accurate model for PV output power to accurately model high penetrations of PV. Our research highlights the necessity of reducing peak loadings through strategies such as energy storage, off-peak loading methods, and reactive power control of inverters to decrease the voltage deviations. It has been shown that voltage rise and reverse power-flows can and should be avoided on distribution feeders. Voltage unbalances can also be easily avoided by following the simple steps outlined in this paper. Now, a proactive approach should be taken by Governments, Distribution Networks System Providers, and customers with PV to ensure that as penetrations increase, Power Quality is not diminished. [ABSTRACT FROM AUTHOR]

In this research article, we uphold the principles of the No Free Lunch theorem and employ it as a driving force to introduce an innovative game-based metaheuristic technique named Golf Optimization Algorithm (GOA). The GOA is meticulously structured with two distinctive phases, namely, exploration and exploitation, drawing inspiration from the strategic dynamics and player conduct observed in the sport of golf. Through comprehensive assessments encompassing fifty-two objective functions and four real-world engineering applications, the efficacy of the GOA is rigorously examined. The results of the optimization process reveal GOA's exceptional proficiency in both exploration and exploitation strategies, effectively striking a harmonious equilibrium between the two. Comparative analyses against ten competing algorithms demonstrate a clear and statistically significant superiority of the GOA across a spectrum of performance metrics. Furthermore, the successful application of the GOA to the intricate energy commitment problem, considering network resilience, underscores its prowess in addressing complex engineering challenges. For the convenience of the research community, we provide the MATLAB implementation codes for the proposed GOA methodology, ensuring accessibility and facilitating further exploration. [ABSTRACT FROM AUTHOR]

This article has been withdrawn as the incorrect text was uploaded due to a technical error. [ABSTRACT FROM AUTHOR]

This paper introduces a method for reducing damages arising from voltage fluctuations, voltage flicker, imbalance in the three-phase and power factor reduction caused by electric arc furnaces. A novel equation is defined to calculate the susceptance values of the static var compensator control system, the active power functions of phase load as well as the reactive power ones that have been used to suppress the voltage fluctuation. By compensating the impulsive part of active power component in the impulsive loads, not only can we reduce voltage fluctuations, flicker effects, balance the system and increase the power factor, but also voltage stabilization can be directly controlled. By studying this method and simulating on Electro-Magnetic Transients Program (EMTP) software, we can see that, according to the allowable flicker limit, a compensation method can balance the system, increase power factor and resolve the problem of voltage fluctuations and flicker. [ABSTRACT FROM AUTHOR]

The increasing penetration of Wind Energy Sources (WES) in an ac-network requires more Flexibility Resources (FRs), such as thermal units and transmission topology control by transmission switching (TS) action. The FRs, especially the TS action, can help to accommodate the intermittent and volatiles from WESs. Nevertheless, obstacles still remain and must be dealt before the TS action can be implemented by the real power systems. Indeed, the challenges comprise AC feasibility, the ability to handle large-scale real power systems and computational complexity. This paper investigates these challenges by developing the TS based on a linearized AC network (LAC-based TS) model that includes a linear representation of reactive power and bus voltage magnitudes. The proposed LAC-based TS model can handle high penetration of WES uncertainty in the stochastic security constrained unit commitment (SCUC). Also, the WES is the only source of uncertainty considered in this paper, which is modeled through an appropriate set of scenarios. Accordingly, the proposed model is formulated as a two-stage stochastic programming problem, wherein, the first-stage relates to the day-ahead scheduling, and the second-stage refers to the real-time operating conditions. An iterative algorithm based on the Benders decomposition method is used to solve the problem. The performance of the proposed model is investigated in details using a modified 6-bus and IEEE 118/662-bus test systems. [ABSTRACT FROM AUTHOR]

In this study, capacity allocation of demand‐response (DR) and the real‐time savings earned from implementing DR programmes (DRPs) are investigated based on a mixed integer non‐linear multi‐objective programming (MINMOP) framework according to two conflicting concepts, titled; maximum achievable potential (MAP) and realistic achievable potential (RAP). MAP indicates that placing a value on DR resources must be strictly weighed against the value of avoiding the acquisition of short‐term resources to meet critical peak period, whereas RAP takes into accounts the amount of savings that might be achieved through DRPs. The proposed MINMOP includes both technical and economic aspects of integrating DRPs into the power systems by considering optimal generation scheduling cost, voltage drop, voltage stability margin, network loss, and incentive payment as objective functions. In addition, augmented ɛ ‐constraint method is applied to solve the proposed MINMOP by means of off‐the‐shelf conventional optimisation software. The IEEE 24‐bus reliability test system (RTS 24‐bus) is used to demonstrate the applicability of the proposed method. Eventually, DR allocation is analysed through four cases based on different number of candidate buses which demonstrate the interest and effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• An operational framework presented to improve distribution system resilience using Mobile Energy Hubs. • This paper provides an effective management to enhance the distribution system resilience. • MEHs installed on trucks are expected to deliver the required power for supplying the islanded critical loads. • The optimization algorithm determines the shortest possible path for transporting the MEHs to supply critical loads in the least time. In this paper, an operational framework is presented to improve electrical distribution network resilience based on the Mobile Energy Hubs (MEHs) concept. In fact, critical loads should be immediately islanded in a post-flood state and then recovered. Accordingly, this paper focuses on providing an effective management solution to enhance the functioning of electricity distribution systems with the objective of maximizing restoration of critical loads and minimizing their restoration time span based on MEH. To this end, MEHs are installed on trucks to deliver the required power for supplying the islanded critical loads in zones affected by a flood. Besides, in order to demonstrate a practical resilient structure, possible damage inflicted on other critical infrastructures is considered. Moreover, obstacles resulting from the destruction of the transportation infrastructure caused by a flood are overcome by using the shortest path algorithm (SPA). In this case, the optimization algorithm determines the shortest possible path for transporting the MEHs to supply critical loads in the least time aiming to improve the network resilience indicators. Finally, the proposed framework is studied in a standard test electricity distribution network. Simulations are carried out to evaluate the network resilience indicators of the proposed framework in obtaining a resilient distribution network during natural disasters. [Display omitted] [ABSTRACT FROM AUTHOR]

• Developing a framework for probabilistic electricity price forecasting. • A deep Gabor filter-oriented convolutional network is designed as the strong deep structure. • A deep mixture network is developed to predict a set of probability density functions in the next hours. This paper establishes a framework for probabilistic electricity price forecasting in the electrical markets using an end-to-end and direct approach. In the proposed method, firstly, a deep Gabor filter-oriented convolutional network is designed as the strong deep structure. Then, the designed deep Gabor network is developed as a deep mixture network to predict a set of probability density functions (PDFs) in the next hours. To do so, a probabilistic loss function is formulated. The results are validated by the actual electricity market data in California independent system operator (CAISO) and show high level of accuracy and reliability in comparison with other state-of-the-arts methods [ABSTRACT FROM AUTHOR]

• A model to enhance the resilience of the integrated electrical distribution and gas networks. • A min-max robust optimization model considering worst-case scenarios. N-k and wind power generation • A multi-stage and multi-zone model to capture the spatial-temporal dynamic of extreme hurricane. • A hybrid column-and-constraints optimization solution. The flexibility of the power-to-gas (P2G) technologies and natural gas units (NGUs) can enhance the resilience of the electric distribution system (EDS) considering the high penetration of renewable energy resources (RERs). The decentralized collaborative operation (co-operation) of electric distribution systems and natural gas systems (EDSs&NGSs) considering information privacy preserving can enhance the resilience during an extreme hurricane. To address this issue, this paper proposes a three-level hierarchal solution to decompose the centralized co-operation of the EDS&NGS framework in which an independent decision-making strategy and information privacy preserving for both network operators are simultaneously addressed. Furthermore, in this paper, a min–max robust resilience-constrained co-optimization model is presented to enhance the resilience of the integrated EDS&NGS against worst-case N − k contingencies and wind power generation uncertainty under extreme hurricane events. To attain the spatial dynamics of extreme hurricanes, a the multi-zone and multi-time extreme hurricanes model is considered. Then, a column-and-constraints generation (C&CG) algorithm is used to solve the proposed robust resilience-constrained co-optimization model. To verify the effectiveness of the model, we conducted experiments on a modified IEEE-33-bus-10-node/123-bus-20-node EDS&NGS. The numerical results show that the proposed robust resilience-constrained co-operation of EDS&NGS problem is an effective model for enhancing EDS resilience. [Display omitted] [ABSTRACT FROM AUTHOR]

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. • The security constraint of natural gas networks is deliberated in power system operation. • The cascading contingencies are considered both power and gas energy systems. • The decentralized decision-making method is based on a hierarchical iterative algorithm. • Limited set of information between the power system and natural gas system is shared. [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]

This paper proposes a new, efficient and powerful heuristic-hybrid algorithm using hybrid DE (differential evolution) and PSO (particle swarm optimization) techniques DEPSO (differential evolution particle swarm optimization) designed to solve eight optimization problems with benchmark functions and the MAED (multi-area economic dispatch), RCMAED (reserve constrained MAED) and RCMAEED (reserve constrained multi area environmental/economic dispatch) problems with reserve sharing in power system operations. The proposed hybridizing sum-local search optimizer, entitled HSLSO, is a relatively simple but powerful technique. The HSLSO algorithm is used in this study for solving different MAED problems with non-smooth cost function. The effectiveness and efficiency of the HSLSO algorithm is first tested on a number of benchmark test functions. Experimental results showe the HSLSO has a better quality solution with the ability to converge for most of the tested functions. [ABSTRACT FROM AUTHOR]

Electric vehicles, including both PEVs and PHEVs have been recently interested to a large extent in global markets due to their capabilities. These plug-in vehicles are able to absorb/inject power from/to the electric grid that turns them into an interesting solution for the power systems. However, large numbers of such plug-in vehicles can be a threaten to power systems. In this regard, it seems necessary to investigate the problems caused by the uncertain driving nature of such electric vehicles. On the other hand, the opportunities provided by the presence of a large fleet of plug-in vehicles as mobile storage/load should be discussed. For this end, this paper reviews the challanges and the problems caused by charging/discharging of PHEV/PEVs in large numbers and investigates their capabilities as a solution to integrate the RESs and demand response programs in power systems. [ABSTRACT FROM AUTHOR]

Nowadays, demand response has become one of the essential components of recent deregulated power systems as it can offer many distinguished features, such as availability, quickness, and applicability. DRPs (Demand response programs), announced by the federal energy regulatory commission, are among the most accepted and practical features of demand side management. DRPs not only can contribute in mitigating the intermittent effects of renewable energy resources but also can be used either to lower high energy prices, occurred in wholesale electricity markets, or when the security of power systems is at risk. In this paper, the influence of emergency demand response programs in improving reliability in case of failure of generation units is investigated. In the proposed reliability based optimization approach, the generation failure is modeled based on its forced outage rate. The proposed method can help independent system operators to schedule day-ahead generating units in a more reliable manner and can facilitate the participation of consumers to increase the total social welfare in the case of an emergency. Moreover, the mixed integer programming formulation allows implementing the proposed method by using available tractable linear solvers. Eventually, the applicability of the proposed model is tested on the IEEE 24-bus reliability test system and its effects on the value of lost load and the expected load not served are discussed. [ABSTRACT FROM AUTHOR]

In this paper, a small remote area which is located in Nigeria has been considered as a model to be tested by a managing scheme for providing both electricity and water. In this strategy, the groundwater is pumped into a water tank which can be later used for supplying required irrigation and drinking water. A PAT (Pump as Turbine) is used as a hybrid system for supplying electricity and water as well as storing water in the water tank. Also, a PV (photovoltaic plant), a package of batteries (BAT) in addition to a diesel ICE (internal combustion engine) are used and optimized along with the best size of devices and managing system, with the purpose of obtaining the maximum economical operating strategy. In this paper, two cases are considered to assess the effectiveness of the system under study. Firstly, all of the mentioned devices are used to show how internal combustion engine system dominates all the other components due to the low cost of fuel. In the second case, all renewable resources have been exploited and optimized in order to make a 100% renewable system with least possible cost. Having about 53 variables makes this problem very complicated which requires to be solved by an algorithm with more accuracy. Therefore, a MGSA (Modified Gravitational Search Algorithm) with an adapted mutation tactic is used to find the best cost and management strategy. In the first case, although the cost of diesel oil is very low, by using the PAT, about 5% of diesel oil consumption is reduced. In the second case, in order to make a 100% renewable system, the size of PV is enlarged approximately 16 times in comparison with the first case. The hybrid PV-PAT storing structure is capable to deliver the water for irrigation and domestic requirements as well as 9% of the electricity needed for the rural community. [ABSTRACT FROM AUTHOR]

In this study, the calculations of flexible AC transmission system (FACTS) devices energy function and corrected transient energy function (CTEF) in presence of FACTS are formulated. The most versatile FACTS devices are modelled for transient‐stability assessment using CTEF. Furthermore, the effects of FACTS devices on the corrected transient margin are evaluated by considering the corrected transient energy margin (CTEM), corrected transient kinetic energy (CTKE), and fault critical clearing time (CCT). The proven formulations are applied and compared on three different networks, 3‐bus, 39‐bus, and 145‐bus test systems. All evaluations are compared with each other in the presence and absence of the FACTS devices, and the results of simulation and direct method are investigated. It is proven that by using the energy functions of static VAr compensator, thyristor‐controlled series capacitor, and unified power flow controller, the results of the direct method and the simulation method are almost equal. Also, the efficient criteria in identifying first swing stability behavior are developed for the hybrid method to evaluate the CTEM. Test results do not show any irregular non‐linearity on the variations of CTEM, CTKE, and CCT for both plant and inter‐area mode disturbances. [ABSTRACT FROM AUTHOR]

A multi-objective optimal phasor measurement unit placement model using integer linear programming is presented in this article. The proposed model simultaneously optimizes two objectives,i.e., minimization of phasor measurement unit numbers and maximization of measurement redundancy. To calculate the redundancy criteria, the single-line outage and the phasor measurement unit loss are considered simultaneously. A linear formulation is presented for both objective functions. Also herein, to address conflicting attributes and identify Pareto optimal solutions of the multi-objective optimal phasor measurement unit placement problem, a new multi-objective mathematical programming method is proposed. Finally, a new index,i.e., minimum distance to utopia point, is implemented to select the most preferred solution among the available Pareto front based options on the goal to achieve judicious decision makers. Two test systems,i.e., a modified 9-bus and an IEEE 118-bus test systems, are used to illustrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Nowadays, risk-averse management is a principal concern for transmission system (TS) operator that involve different types of uncertainty including continuous uncertainties (e.g., wind energy uncertainty) and discrete uncertainties (e.g., generator/line outages). In this condition, risk-averse decision making for managing these uncertainties are extremely complex, and the complexity is more amplified by the worst-case uncertainties. Accordingly, in this study a novel contingency-constrained information gap decision theory (CC-IGDT) approach has been proposed to cope with worst-case continuous and discrete uncertainties. Also, active distribution systems (ADSs) with distributed energy resources are important components in a TS, and can play an important role in addressing the issue of risk-averse management for TS operator. Therefore, in this study a coupled operation model for the TS & ADSs with the CC-IGDT approach has been proposed. But, solve proposed coupled operation model is problematic, thus, to solve this problem a new four-level hierarchical optimization technique has been proposed. Finally, the IEEE 30-bus transmission and IEEE 33-bus distribution systems have been analyzed to show the effectiveness of the proposed CC-IGDT approach and the co-operation of TS & ADSs. • Developing a new uncertainty approach to handle worse-case continuous and discrete uncertainties. • Proposed a new risk-averse energy management. • Co-operation of transmission system and active distribution systems. [ABSTRACT FROM AUTHOR]

Recent improvements in computer processing power have enabled the development of intelligent electronic devices (IEDs) to be capable of interpreting a substantial quantity of data at high speeds. The distribution industry is one of the largest users of real-time data during the operation of their energy distribution network and as such, has an inherent need for data processing technology. The development of intelligent devices and communication protocols has given rise to the concept of distribution system automation (DSA). In this paper the major features of DSA development are discussed. A method of communication along with the integration of wired and wireless communication solutions with IED and Mote technology is described. Protection scheme automation is investigated, showing that automated response to network fault conditions is possible. Network reconfiguration is another key aspect of DSA research and two distinct calculation techniques are discussed. The current index (CI) and the load balance index (LBI) calculation methods are presented and the benefits of LBI are highlighted. Distributed generation (DG) is addressed in the context of automation with a demand side management (DSM) program proposed to manage load during peak and off-peak periods. The paper discusses switching DG sources, load deferral strategies and direct load control to effect a reduction in load during peak periods. The paper furthers this load management by load filling during off-peak periods. Finally, conclusions on the effectiveness of the proposed method are drawn and recommendations are made for further research. [ABSTRACT FROM AUTHOR]

Today, many Independent System Operators (ISOs) establish programs to manipulate the reserve provided by Demand Response (DR) in ancillary service markets. In this study, Ancillary Service DR (ASDR) programs, recently introduced by Federal Energy Regulatory Commission, is integrated to an n–k Contingency Constrained Unit Commitment problem to investigate the capability of the DR Providers (DRPs), newly added ancillary market participants, in mitigating the impacts of simultaneous multiple contingencies in a power system. In the proposed model, an n–k security criterion by which power balance constraint is satisfied under any contingency state comprising simultaneous outages in generation units is investigated in the presence of the ASDR programs. Demand side reserve is supplied by DRPs, which have the responsibility of aggregating and managing customer responses to offer a bid‐quantity to the ISO. The proposed formulation is a Mixed Integer Programming problem based on primal‐dual optimisation, reported recently in literature. In addition, a detailed discussion about versatile effects of ASDR programs on n–k security criterion is presented to demonstrate the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Optimal allocation of a Wind-Storage Unit (WSU) is developed. • Congestion in energy transmission is considered. • The paper deals with price-maker WSU in electricity markets. • Unscented Transform is adapted to handle wind power uncertainty. This paper proposes an optimal allocation of a Wind-Storage Unit (WSU). Since transmission lines congestion varies according to the size, the location, and the operation of a generation unit in power systems, we assess the optimal location of a unit as a function of its variable operating condition. An independently operated wind-storage unit is assumed as a price-maker that seeks to maximize its market payoff without any prior information on optimally locating the wind and storage units. The main problem is provided as a tri-level optimization problem in which the first level is the WSU profit maximization, the second level is the power system operation cost minimization from the perspective of the independent system operator (ISO), and the third level is the maximization of the robustness of the system by using an appropriate transmission switching interval robust based chance constrained (TSIRC) method in order to minimize the operation cost of the system and transmission lines congestion problem. The tri-level model is converted to a bi-level optimization model by using Karush-Kuhn-Tucker (KKT) conditions provided as a Mathematical Programming with Equilibrium Constraint (MPEC). An effective binary particle swarm optimization algorithm (BPSO) is used in order to find the optimal location of the wind and storage units. Unscented Transform (UT) as a key element is suggested to model the uncertainties associated with the output power of the wind turbines. The proposed method is tested on an IEEE 24-bus test system and the results reveal the validity of this work. [ABSTRACT FROM AUTHOR]

This paper describes a new probabilistic model for generation and transmission expansion planning (G&TEP) problem considering reliability criteria. Probabilistic reliability criteria accounts for random generator or line outages with known historical forced outage rates (FOR). The resultant model considers the installation and operation costs as well as the cost of expected energy not supplied (EENS) to optimally determine the number and location of new generating units and circuits in the network, power generation capacity for those units and the voltage phase angle at each node. Also, efficient linear formulations are introduced in this paper to deal with the nonlinear nature of the problem including objective functions and constraints. Modified 6-bus test system, IEEE 24-bus RTS and IEEE 118-bus test system are utilized to illustrate the effectiveness of the proposed framework. [ABSTRACT FROM PUBLISHER]

Abstract: Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution Expansion Planning) in the presence of DGs using nonlinear formulations. The objective functions of the MDEP consist of minimization of costs, END (Energy-Not-Distributed), active power losses and voltage stability index based on SCC (Short Circuit Capacity). A MPSO (modified Particle Swarm Optimization) algorithm is developed and used for this multiobjective MDEP optimization. In the proposed MPSO algorithm, a new mutation method is implemented to improve the global searching ability and restrain the premature convergence to local minima. The effectiveness of the proposed method is tested on a typical 33-bus test system and results are presented. [Copyright &y& Elsevier]

Highlights: [•] A multi-period multiobjective generation expansion planning model is presented. [•] The model simultaneously optimizes three objectives: costs, emission, and reliability. [•] A probabilistic approach is implemented to evaluate the system reliability. [•] A MNBI method is proposed to solve the multiobjective problem. [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]