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This study proposes new metrics for evaluating the resiliency index specific to the generation sector by considering the factors that affect the generation sector, such as location, capacity, number, and density of units and critical loads. These indexes can be useful for pre-crisis action (PCA). By calculating and considering these indexes before any installation or expansion of the generation sector, the destructive effects of extreme events can be greatly reduced. In fact, the main purpose of this study is not to increase the level of the resiliency of the generation sector, but to only propose new indexes for calculating the resiliency. For this study, firstly, the factors that impact the resiliency of generation will be discussed, secondly, for each index, the calculation formula will be presented. Then, resiliency is calculated in the generation sector as a composite resiliency index, which is a combination of all indexes. For validation of these indexes, a criterion such as loss of critical load factor is introduced and case studies are performed based on the IEEE six-bus and IEEE RTS-24-bus systems for different scenarios. The results show that the proposed indexes can effectively evaluate the resiliency of the generation sector.

The many well-established advantages of distributed generation (DG) make their usage in active distribution networks prevalent. However, uncontrolled operation of DG units can negatively interfere with the performance of other equipment, such as tap-changers, in addition to resulting in sub-optimal usage of their potential. Thus, adequate scheduling/control of DG units is critical for operators of the distribution system to avoid those adverse effects. A linearised model of a multi-objective method for coordinating the operation of photovoltaics, battery storage systems, and tap-changers is proposed. Three objective functions are defined for simultaneously enhancing voltage profile, minimising power losses, and reducing peak load power. The formulated multi-objective problem is solved by means of the epsilon-constraint technique. A novel decision-making methodology is offered to find the Pareto optimality and select the preferred solution. To assess to proposed model's performance, it is tested using 33-bus IEEE test system. Consequently, tap-changers suffer lessened stress, the batteries state-of-charge is kept within adequate limits, and the DG units operation is at higher efficiency. The obtained results verify the effectiveness of this approach.

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.

© 2010-2012 IEEE. This paper proposes a coordinated strategy of a hybrid power plant (HPP), which includes a wind power aggregator and a commercial compressed air energy storage (CAES) aggregator to participate in three electricity markets (day-ahead, intraday, and balancing markets). The CAES aggregator has an extra ability which is called a simple-cycle mode operation that makes it works like a gas turbine when needed, which helps the HPP to economically handle the miscalculations of the wind power and electricity price predictions. The coordinated strategy of the HPP is formulated as a three-stage stochastic optimization problem. To control the financial risks, the conditional value-at-risk model is added to the optimization problem. Moreover, the proposed offering method is capable of submitting both bidding quantity and curves to the day-ahead market. A mixed integer linear programming formulation is written for the problem that can be easily solved by commercially available software such as GAMS. The results that were tested on a realistic-based case study located in Spain show the applicability of the suggested method to increase the joint operation profit and decrease the financial risks.

Multi-microgrid systems can improve the resiliency and reliability of the power system network. Secure communication for multi-microgrid operation is a crucial issue that needs to be investigated. This paper proposes a multi-controller software defined networking (SDN) architecture based on fog servers in multi-microgrids to improve the electricity grid security, monitoring and controlling. The proposed architecture defines the support vector machine (SVM) to detect the distributed denial of service (DDoS) attack in the storage of microgrids. The information of local SDN controllers on fog servers is managed and supervised by the master controller placed in the application plane properly. Based on the results of attack detection, the power scheduling problem is solved and send a command to change the status of tie and sectionalize switches. The optimization application on the cloud server implements the modified imperialist competitive algorithm (MICA) to solve this stochastic mixed-integer nonlinear problem. The effective performance of the proposed approach using an SDN-based architecture is evaluated through applying it on a multi-microgrid based on IEEE 33-bus radial distribution system with three microgrids in simulation results.

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.

In security-constrained unit commitment (SCUC) problems, one approach to decrease operation costs is using a transmission switching (TS) tool. In SCUC problems with TS, one of the main challenges is that there is no limitation for the number of switching of circuit breakers (CB) in the system. In this article, the reliability of CB is merged into the SCUC problem with the TS and is considered as a limiting factor for switching. With a more reliable CB, the overall reliability of the system will be increased. So, it can be concluded that the reliability of a CB affects the amount of load shedding. Reliability of a CB is a nonlinear equation based on the number of switching in a period. An approach is presented to linearize the switch reliability equation. In this article, the power flow model uses an improved linear ac optimal power flow and a dynamic thermal line rating (DTLR) model, which considers the weather conditions. Other than CB reliability, DTLR in SCUC problems affects the number of switching and, as a result, operation costs will be significantly decreased. The proposed model is empowered by Bender's decomposition and is tested on 6-bus and 118-bus IEEE test systems.

Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance-current-power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods. Post-print / Final draft

This paper proposes a two-objective linearized resilient architecture (LRA) model for distribution networks to achieve a strictly resilient network during natural disasters like earthquakes and floods. To obtain this goal, the proposed LRA framework is based on the planning of the energy storage system (ESS), hardening and tie lines, and backup distributed generation (DG). Therefore, the proposed model minimizes the sum of planning and expected operation costs in the first objective function, and the total load shedding and repair costs originates from earthquakes and floods in the second objective function. Also, it constraints to the network planning model, linearized equations of the system operation, and system reconfiguration formulation. Moreover, stochastic programming models the uncertain availability of the network equipment during the natural disaster condition, the load and electricity price. In the next step, the e-constraint-based Pareto optimization is used to achieve an equivalent single-objective LRA model and obtain the best compromise solution. Finally, the proposed strategy is applied to a standard test distribution network. Numerical simulation confirms the capability of the proposed method in obtaining a resilient distribution network during natural disasters.

Due to the fact that distributed generation (DG) has many advantages in the power systems, DG implementation is indispensable. However, it could introduce some problems in the system such as changing the voltage profile. In this paper, an optimum voltage control model based on photovoltaic (PV) inverters is proposed. In the daytime, the PVs inject current to the distribution network, and therefore, in that time there will be a potentially high voltage profile. In contrast, in the evening, the customers consume more power and PV has nothing to compensate, and a low voltage profile is seen. This work seeks to provide a power control scheme for the active and reactive power of the inverter and integrates it with a night mode control of PVs, by a modified hysteresis controller. To gain a suitable voltage profile, all voltages of the buses should get close to the reference voltage. For preventing the interference between different inverters in the network, this control scheme is applied to the whole network coordinately. The 33-bus IEEE system is used to test the performance of the control model, and the results show the effectiveness of the proposed model.

In smart grid application, residential energy consumption as well as production and customer participation in demand response programs, can hugely affect the overall load demand of the utility grid (UG). Additionally, in several countries, the number of houses connected to Plug-in Electric Vehicles (EVs) are growing. With the appropriate infrastructure, These houses are able to participate in energy transactions in UGs throughout a day and alleviate several existing issues, such as high peak demands, while being advantageous to their owners in terms of transportation and electricity bill reduction. In this study, the operation of a Home Energy Management System (HEMS) is evaluated to minimize a customer expenses including purchasing energy from the power grid. The proposed demand response-based model comprises a residence, a small-scale Renewable Energy Source (RES) and an EV enabled with grid-to-vehicle (G2V) and vehicle-to-grid (V2G) energy exchange capability while considering battery condition and aging. A robust optimization model is taken into account in order to consider the uncertainty associated with the residential load demand. The problem is formulated as a Mixed-Integer-Linear Programming (MILP) model. Finally, The validity of the proposed system is evaluated using numerical studies on various optimistic and pessimistic scenarios.

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 study relies on a dynamic reliability-based model for distributed energy resources (DER) planning in electric energy distribution networks (EEDN) with the aim of maximizing the profit of EEDN companies by increasing income and reducing costs. Load uncertainty is considered in the proposed planning model and the robust optimization (RO) approach is employed to cope with the uncertainty. The developed methodology is illustrated using real-world voltage-dependent load models, including residential, commercial and industrial types. These load models are used in evaluating the reliability cost and energy selling for customers. The reliability cost is calculated based on the total unsupplied load after an outage. Furthermore, a new modified harmony search algorithm is proposed to solve the formulated robust dynamic DER planning problem. The solution of the proposed optimization model provides the size, location, and power factor of DER. Furthermore, the need for transformers or lines upgrades and the best year for DER installation are other decision variables determined by the model. The effectiveness and capability of the developed model have been demonstrated with the aid of a case study based on a typical EEDN. The obtained results indicate that installing DER in EEDNs can relieve congestion on feeders; therefore, it can mitigate or defer upgrade investment. Moreover, if carefully planned, other benefits of DER integration such as reliability improvement and energy loss reduction can be achieved.

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.

The deployment of batteries in the distribution networks can provide an array of flexibility services to integrate renewable energy sources (RES) and improve grid operation in general. Hence, this paper presents the problem of optimal placement and sizing of distributed battery energy storage systems (DBESSs) from the viewpoint of distribution system operator to increase the network flexibility. The problem is formulated as an optimization framework wherein the objective function is to minimize the annualized sum of investment costs and operational costs of DBESSs while it is constrained to power flow, DBESS and RES constraints as well as distribution network operation limits. In addition, while the problem model is as nonlinear programming, this paper suggests an equivalent linear programming model for all constraints and quadratic formulation for the objective function to reach the global optimal solution with low error calculation. In the next step, the Benders decomposition approach is deployed to acquire better calculation speed. Finally, the proposed problem is applied to 19-bus LV CIGRE benchmark grid by GAMS software to investigate the capability of the model.

The hybrid AC/DC microgrids have become considerably popular as they are reliable, accessible and robust. They are utilized for solving environmental, economic, operational and power-related political issues. Having this increased necessity taken into consideration, this paper performs a comprehensive review of the fundamentals of hybrid AC/DC microgrids and describes their components. Mathematical models and valid comparisons among different renewable energy sources’ generations are discussed. Subsequently, various operational zones, control and optimization methods, power flow calculations in the presence of uncertainties related to renewable energy sources are reviewed.

This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30-bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.

This paper presents a systematic procedure for partitioning smart distribution systems into supply-sufficient microgrids. Firstly, renewable distributed generations (DGs) are optimally allocated in the distribution system. A multiobjective performance index including voltage profile and energy losses indices is utilized in this problem as the objective function. Two alternative control approaches of future smart grids including on load tap changer (OLTC) control and adaptive power factor control (PFc) are assessed to maximize potential benefits and increase the penetration level of DGs. Then, optimal allocation of protection devices and energy storage systems (ESSs) for constructing supply-sufficient microgrids is presented for a feeder equipped with capacity-constrained DGs. To this end, two different optimization problems are formulated and proper indices are developed for minimizing power exchange between microgrids and minimizing generation-load imbalance within microgrids. Finally, test results of the proposed models on 33-bus IEEE radial distribution system are presented and discussed. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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 proposes a time-based security (TBS) evaluation mechanism for radial microgrids (MGs) short-term operation. In the proposed framework, system security is modelled using time-based sensitivity analysis in the load points. Occurring branch contingencies in a radial MG creates some islands with oscillating frequencies. The settling time of frequency oscillation, as the successful islanding time, and the island's capability to continue the load supply are considered in TBS assessment. A system frequency response model and the stored energy of storages are used to calculate the two mentioned factors. Depending on the loads' sensitivities to the successful islanding time and the service continuity duration, they are exposed to extreme damage. In the case of branches' sudden outages, it may be impossible to operate the MG without any extreme damage of loads, due to the capacity shortage in the island, dynamic behaviours of the MG and inadequate stored energy of storages. Under these conditions, the load shedding unit participates considering the customer damage function. To evaluate the performance of the proposed security assessment mechanism, the 123-bus distribution test system is precisely analysed.

A novel linear approach for Phasor Measurement Units (PMUs) placement is proposed in this paper to ensure the topological observability taking into consideration the impacts caused by the adjacent zero-injection buses (ZIBs). The observability analysis in the paper is performed utilizing merely the main topological observability rules. Furthermore, the model is developed to include all possible single contingencies both on the lines and PMUs even where adjacent ZIBs are highly interconnected. To this end, the common approach for handling contingencies is enhanced. Moreover, the presented framework incorporates a subsidiary objective, i.e. maximizing the measurement redundancy among the set of solutions with the minimum number of PMUs beside the main objective, i.e. minimizing the number of the required PMUs. In this regard, a unified Integer Linear Programming (ILP) is used to solve the proposed optimisation problem. It is worth noting that the suggested approach is applied to different test systems like IEEE 14-bus, 30-bus, 39-bus, 57-bus as well as 118-bus system to validate the effectiveness and efficiency of the proposed framework and the obtained results are compared to those reported by the prior researches.

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.

Here, an optimisation framework is proposed for integrated resource expansion planning (IREP) including conventional generation units, wind generation units, and transmission lines while taking into account the role of demand response program (DRP) aggregators. This problem is a bi-level optimisation problem. In the upper-level problem, the objective function is to maximise the profit for each resource, that is, the generation company (GENCO), wind generation company (WINDCO), and transmission company (TRANSCO) as well as the DRP aggregator. Also, the lower-level problem considers a market model with the participation of private GENCOs, WINDCOs, TRANSCOs, and DRP aggregators. The lower-level problem minimises energy cost subject to AC power flow (PF) equations, power network limitations, pollution constraint, GENCO, WINDCO, TRANSCO constraints, and technical limitations of DRPs. Here, the lower-level problem is non-linear and non-convex. Accordingly, to facilitate the solution of the proposed bi-level optimisation problem, a linear model is proposed. Then, the proposed bilevel optimisation problem is converted into an integrated single-level one using the Karush-Kuhn-Tucker (KKT) conditions. Eventually, a mixed-integer linear programming (MILP) model is proposed. The proposed method is applied to the IEEE 6-bus and the IEEE 30-bus test systems, and finally, the capabilities of the proposed scheme are evaluated.

The incorporation of the reconfiguration into the expansion planning of smart distribution networks is addressed in this paper, in which the potential of distributed energy resources and demand response (DR) are modeled. The system of systems (SoS) architecture is employed to model the strategy of a distribution company (DISCO), a private investor (PI), and a DR provider (DRP). The SoS is an efficient modeling architecture to model the behavior of independent and autonomous systems with distinct objective functions who are able to share some data and work together. The aim of the DISCO is to upgrade the system with the optimal cost and reliability, whereas the PI and DRP want to maximize their profit. The DISCO should try to persuade the PI to install DGs (Distributed generations) by offering the guaranteed purchasing prices. Furthermore, the DRP is a market player who can negotiate with the DISCO to sign a contract to sell the purchased DR capacities from the customers. The uncertainties of the DISCO problem is handled by using the chance-constraint method, but the PI and DRP use the conditional value at risk method to model their uncertainties. Finally, to solve the proposed model, the multiobjective optimization algorithm is employed.

This paper introduces various models for optimal and maximal utility-based distributed generation penetration in the radial distribution systems. Several problems with different probabilistic indices as objective functions constrained by power flow equations, distributed generation penetration, voltage, and thermal limits are proposed to obtain the optimal penetration of distributed generations on rural distribution networks. There are tradeoffs between interests and risks that the distribution network operators or distribution companies may be willing to take on. Thus, to have an effective method for maximal allocation of distributed generations, new indices are proposed, and the problems are formulated as a risk-constrained optimization model. The obtained problems have mixed-integer nonlinear programming and nonconvex forms because of nonlinearity and nonconvexity of the optimal power flow (OPF) equations and indices, leading to computationally nondeterministic polynomial-time-hard problems. Accordingly, in this paper, convex relaxations of OPF are introduced instead of the conventional nonlinear equations. Efficient linear equivalents of the objective function and constraints are introduced to reduce the computational burden. Test results of the proposed models on a radial distribution system are presented and discussed. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Recently-developed smart grids have a diverse range of load types that makes their energy scheduling different from conventional power systems. In this paper, an energy scheduling is proposed for smart grids to minimize operational cost and energy losses taking into account flexible (adjustable and shiftable) loads, which are used here as network resources. Special attention is paid to Electric Vehicles (EVs) as a sort of flexible loads. It is shown that the Vehicle-to-Grid (V2G) service may intensify power losses by charging and discharging EVs for multiple times and consequently, it is necessary to observe and optimize energy losses while using V2G. Range anxiety, which is one of main barriers to promote EVs, is formulated and optimized in order to address the concern of EV owners in depletion of battery energy and to encourage them to more actively participate in V2G service. Under the proposed scheme, EVs act as distributed energy storage systems to more employ renewables and also to make it possible to take the advantage of lower tariff electric energy from upstream networks. Results from examining the proposed method on a standard test system are presented and discussed.

Optimal energy management of microgrids with different objectives such as operation cost, air pollution and wider use of renewable energy sources is investigated in many research works. In this paper, the effect of different operation modes of microgrids to solve environmental/economic dispatch problem is evaluated. Different modes of operation of microgrid include islanding operation, grid-connected and unilateral or bilateral exchange of power with main grid. The imperialist competitive algorithm (ICA) is one of the evolutionary algorithms that can model the competing imperialists to absorb colonies and uses it to find optimum solution. In this paper, a new method to improve the ICA algorithm is proposed in which the movement radius of the colonies toward the imperialist is adaptively adjusted in accordance with their position by means of adding direction and velocity parameters to the colonies. Thus, the colonies are attracted and repelled more intelligently by imperialists. The proposed method can create a trade-off between exploitation (i.e. local search) and exploration (i.e. global search) and prevents premature convergence while minimizing trapping in the local minima. Simulation results and comparison with other algorithms confirm high performance of the proposed algorithm.

Recently, the provision of the reserve from energy storage systems (ESSs) is introduced as a source for ancillary services to address the uncertainties of renewable power generations. The performance of ESSs is analysed while they are applied as a provider of regulation reserves. It has been revealed that previous stochastic models neglect the impact of corrective dispatches, related to the provision of regulation reserves, on the energy level stored in the ESSs, which can lead to large deviations. This study coordinates the stored energy of ESSs to be feasible regarding the dispatches in the base schedule and rescheduling within scenarios. Also, the wind speed fluctuations are considered as the source of uncertainty, and scenarios of wind energy are generated using the Weibull distribution function. The IEEE 24‐Bus standard test system is applied for the examination of the proposed model. The results show that the proposed model can manage the performance of ESSs in rescheduling within scenarios, while the coordinated reserve provision of ESSs can remove the concerns about insufficient stored energy of ESSs.

Ensuring the security and quality of supply in a power system after a contingency event is one of the most challenging tasks for an electricity system operator. This work is initiated by this challenge and proposes a solution based on the use of provided reserves by fast generators, storage devices, and wind farms. A coordinated model is proposed in a joint energy and reserves market considering their corresponding cost to ensure the adequacy in the simultaneous deployment of reserves for the different sources of uncertainties. The Benders decomposition approach is used in the modeling of the stochastic security-constrained unit commitment, and considering the large-scale and complex nature of the model, acceleration techniques are suggested to reduce the execution time. The proposed model is tested on the 6-bus and the IEEE 118-bus test systems. Numerical results show that the optimal values of reserves successfully address contingencies in both of the critical and normal periods after the contingencies and the optimal solution is calculated in a reasonable computing time.

This paper presents a decision model of planning the optimal configuration of hybrid wind/solar off-grid power systems. The proposed decision model is utilized to evaluate the effectiveness of reducing the hourly power supply variability through integrating the wind and solar power generation. In addition, the tradeoff between the cost minimization and power supply variability reduction is analyzed. The decision variables are composed of the number of installed wind turbines and solar panels. A multi-objective particle swarm optimization algorithm is applied to solve the proposed model. The computational results provide the optimal configurations of the hybrid wind/solar power systems due to various preferences of cost and power supply variability to meet the load requirements.

Over-voltage in low voltage feeders due to the increasing penetration level of photovoltaic (PV) is a crucial issue to be addressed. There is a need to use more advanced voltage control methods to satisfy the response time of the control system. In this paper, a two-level voltage control strategy is presented. In the first level, based on day-ahead PV production scenario, Both the on-load tap changer (OLTC) and the battery energy storage systems (ESSs) are applied to deal with over-voltage in the peak of PV generation and as well the voltage drop in the peak of demand. In this level, the batteries and the tap position of the feeder transformer are optimally set in order to improve the voltage profile for the entire planning horizon (next day) taking into account the uncertainties in the PV production. In the second level, based on the partitioning of the distribution network, reactive power compensation capability of PV inverters is employed to fine-tune the voltage profile for the next operating hour. To model the uncertainty pertaining to the output power of PV units, the parameters of beta distribution function are estimated for each hour time interval, and then Monte Carlo simulation method is used to generate daily scenarios. In order to reduce the complexities and computational burden, the linearized model of power flow equations and PV inverters have been implemented. A real and practical, 10 kV, 37-bus system is used to test the performance of the proposed method.

This paper intends to give an effective hybrid planning of distributed generation and distribution automation in distribution networks aiming to improve the reliability and operation indices. The distribution automation platform consists of automatic voltage and VAR control and automatic fault management systems. The objective function minimizes the sum of the expected daily investment, operation, energy loss and reliability costs. The scheme is constrained by linearized AC optimal power flow equations and planning model of sources and distribution automation. A stochastic programming approach is also implemented in this paper based on a hybrid method of Monte Carlo simulation and simultaneous backward method to model uncertainty parameters of the understudy model including load, energy price and availability of network equipment. Finally, the proposed strategy is implemented on an IEEE 69-bus radial distribution network and different case studies are presented to demonstrate the economic and technical benefits of the investigated model. By allocating the optimal places for sources and distribution automation across the distribution network and extracting the optimal performance, the proposed scheme can simultaneously enhance economic, operation, and reliability indices in the distribution system compared to power flow studies.

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. With the rapid development of smart grids, smart meters and sensors are used to monitor the system and provide a wide reporting which produce a huge amount of data in various part of the grid. To logical manage this trouble, the presented paper proposes a new lossy data compression approach for big data compression. In the proposed method, at the first step, the optimal singular value decomposition (OSVD) 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 rate of compression ratio. In the presented scheme, to implementation of 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 the experimental dataset of several residential microgrid consumers and market dataset. Simulation results show the high performance and efficiency of the proposed model in smart grids with big data.

This paper addresses the optimal energy management and scheduling of renewable based microgrids (MGs) in the highly correlated uncertain environments. The proposed MG incorporates different types of dispatchable and non-dispatchable units such as micro turbine (MT), fuel cell (FC), wind turbine (WT), photovoltaic panel (PV) and a battery bank (BAT). Due to the nonlinear and non-convex nature of the proposed problem, a heuristic approach based on the bird mating optimization (BMO) algorithm is employed to solve the problem. The uncertainties associated with the market price (MP), PV output power, WT output power and hourly load uncertainties are modeled using the unscented transformation (UT) method. In addition, two sampling procedures are adopted, namely as the (2m+1) and the (2m) point methods, to provide a comparative analysis showing the highly capabilities of the proposed framework. Moreover, the correlation among uncertain variables is taken into consideration and their effects on the MG's optimal operation are analyzed. The simulation results reveal the high accuracy, effectiveness, relatively timesaving and tending to avoid local minimums by the proposed model.

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.

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.

Electric spring (ES) as a breakthrough in power electronics has led to a revolution in demand-side management. This paper presents flexible power management (FPM) of a networked microgrid (MG) in the presence of renewable energy sources (RESs) and flexibility sources (FSs). The FSs include the novel topology of the integrated unit of ES with electric vehicles (EVs) parking (IUEE) and incentive-based demand response program (DRP). The proposed FPM model is formulated as an optimization problem that minimizes the difference between the expected energy cost and the expected profit of FSs' flexibility subject to the AC optimal power flow (AC-OPF), RESs, FSs, and MG flexibility constraints. In the proposed model, flexibility pricing is performed for both upward and downward flexibility services in which the expected flexibility profit is proportional to the product of the FSs' flexibility and flexibility incentive price (FIP). Further, this paper uses a stochastic programming to model uncertain parameters associated with load demand, energy prices, maximum RES generation capacity, and EVs’ parameters. Finally, the potential of the proposed FPM model in improving the economic, operational and flexibility conditions of the MG is investigated through implementation of the proposed model on the 32-bus radial MG.

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.

In this paper, a new decomposing strategy is proposed to solve the power flow problem in the large-scale multienergy carrier (MEC) systems, including gas, electrical, and heating subnetworks. This strategy has been equipped with a novel noniterative method named holomorphic embedding (HE) to solve the energy flow of the electrical subnetwork. Moreover, it benefits from the less-computational graph method for solving the energy flows of the heating subnetwork. The HE method unlike initial-guess iterative methods guarantees to find the power flow solution, if there is a solution. In addition, it finds only the operational power flow solution without concern about the convergence of the solution. In the proposed strategy, the decomposing method decouples various energy flows of subnetworks without losing the major benefits of the simultaneous analysis of the subnetworks and losing accuracy. Moreover, the proposed decomposing strategy has more reliability and faster computation time than the Newton-Raphson technique. In order to demonstrate the efficiency and superiority of the proposed decomposing strategy on solving large-scale MEC systems, the strategy is tested on three large-scale case studies. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. First, based on duality theory, the max min form of the model is converted to a max form. Second, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network.