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The concept of smart cities has emerged as an ongoing research in recent years. In this case, there is a proven association between the smart cities and the smart devices, which have caused the power systems to become more flexible, controllable and detectable. Along with these promising results, many disputes have been generated over the cyber-attacks as unpredictable destructive threats, if not properly repelled, which could seriously endanger the power system. With this in mind, this paper explores a novel stochastic virtual assignment (SVA) method based on a directed acyclic graph (DAG) approach, where the essential data of the system sections are broadcasted decentralized through the data blocks, as a worthwhile step to deal with the cyber attacks' risk. To do so, an additional security layer is added to the data blocks aiming to enhance the security of the data against the long lasting data sampling by virtually assigning the hash addresses (HAs) to the data blocks, which are randomly changed based on a stochastic process. The basic network architecture is based on a Provchain structure as a new framework to constantly monitor data operation. Two pivotal strategies also represented to deal with the energy and time needed for the HAs generation process, which have improved the proposed method. In this paper, the proposed security framework is implemented in a smart city environment to provide a secure energy transaction platform. Results show the authenticity of this model and demonstrate the effectiveness of the SVA method in decreasing the successful probability of cyber threat, increasing the time needed for the cyber attacker to decrypt and manipulate the data block.

This paper endeavors a probabilistic framework to ascertain optimal operation of a microgrid with a special focus on challenges that storage systems bring about. The objective function optimizes the operation of renewable energy resources and startup/shutdown costs of nonrenewable energy resources along with the main grid and storage units’ costs as a mixed-integer nonlinear programming (MINLP) problem. The optimization problem is solved based on a modified bird mating optimization (MBMO) algorithm and a novel cumulative mutation process. In order to capture the high uncertainties associated with the market price, Photovoltaic, Wind Turbine output powers, and load demands, a reduced unscented transformation (RUT) method has been exploited. The RUT method can effectively model the correlation of variables using (m+2) sampling points. The framework presented in this paper has considered five case studies with multiple seasonal and property features. Implementing the proposed framework on a typical test microgrid and a real large-scale microgrid proves its effectiveness and accuracy through various operational conditions, changing the storage units’ structure and characteristics, RESs’ correlation modeling, and avoiding convergence to local minimums by adopting a newly mutated population. This extensive analysis provides options for MG operation by studying compound cases and providing solutions for every scenario. Promising results regarding the execution time, cost function and its SD values as well as head-to-head points for battery investment costs have been found.

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

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.

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

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

A new probabilistic approach for microgrids (MGs) optimal energy management considering ac network constraints is proposed in this paper. The economic model of an energy storage system (ESS) is considered in the problem. The reduced unscented transformation (RUT) is applied in order to deal with the uncertainties related to the forecasted values of load demand, market price, and available outputs of renewable energy sources (RESs). Moreover, the correlation between market price and load demand is taken into account. Besides, the impact of the correlated wind turbines (WT) on MGs’ energy management is studied. An enhanced JAYA (EJAYA) algorithm is suggested to achieve the best solution of the considered problem. The effective performance of the presented approach is verified by applying the suggested strategy on a modified IEEE 33-bus system. It can be observed that for dealing with probabilistic problems, the suggested RUT-EJAYA shows accurate results such as those of Monte Carlo (MC) while the computational burden (time and complexity) is lower.

It is important to forecast the wind speed range for managing the operation of wind turbines (WTs). Since the electrical power generated by WTs is highly dependent on the uncertain inherent of atmosphere meteorology, improving the accuracy of wind speed range forecasting models leads to the improvement of wind generation prediction. For the sake of uncertainties, it is very challenging to develop an effective and practical model to achieve accurate wind speed range forecasting in large forecasting horizons. This paper presents a novel hybrid classifier based on extended-classifier system with real input (XCSR) and an adaptive neuro-fuzzy inference system (ANFIS), for classification of the wind speed range. It should be mentioned that by employing a cooperative fuzzy classifier system (XCSR–ANFIS), the accuracy and number of the rules that XCSR system must be learned during the training process in the proposed model will be higher and fewer than the XCSR model, respectively. Finally, the comparison of obtained results by implementing the proposed model with other methods for long and short horizons confirms the desirable performance of the proposed model.

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.

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.

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 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 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.

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.

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.

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.

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.

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

Demand response (DR) has become a key player in energy efficiency programs of current electric power systems. This paper presents an effective decision-making model for energy service providers with a focus on day-ahead electricity market participation and demand allocation in the distribution network. A two-step iterative framework is proposed to consider the mutual effects of the DR and market prices, in which unit commitment and economic dispatch problems are solved in the first step to determine the locational marginal prices, and successively DR program is applied in the second step to minimize the total cost of providing energy for distribution network customers. This total cost includes the cost of power purchase from the market and distributed generation units, incentive cost paid to customers, and compensation cost of power interruptions. To consider the unexpected behaviors of other market participants, prices are modeled as uncertainty parameters using the robust optimization technique. Simulation results demonstrate the significant benefits of the proposed framework for the strategic performance of energy service providers.

In the optimal and economic operation of the power system, generation scheduling is an essential task. Conventional short-term generation scheduling does not regard the huge important operational issues related to the generators, such as initial enterprise costs, maintenance costs, fuel availability, monthly load, etc. Hence, due to the time horizon scheduling of the daily short-term generation scheduling, it is not optimal in the long-term operation while considering the mentioned effects. In this context, this paper proposes a stochastic higher level of scheduling named Stochastic Mid-Term Generation Scheduling of Wind-Thermal systems by considering fixed and variable maintenance costs of generators units. In the proposed model, the 2m + 1 Point Estimate Method is applied to accurately evaluate the uncertainty related to the operation cost wind power and the load uncertainties for the proposed problem. To effectively solve it, a heuristic algorithm named Adaptive Modified Cuckoo Search Algorithm is employed with a novel self-adaptive Wavelet mutation tactic. To assess the performance of the proposed algorithm on solving the problem, the results are compared with the latest algorithms presented in the literature. Numerical results confirm the efficiency and superiority of the 2m + 1 point estimate method model and stability of the novel adaptive modified cuckoo search algorithm on solving the stochastic mid-term generation scheduling of wind-thermal systems problem.

In this paper, a new type of single-level power-conditioning system for photovoltaic arrays connected to the single-phase grid is presented in which transformer-based impedance source inverter (Trans-ZSI) is employed to reduce cost and volume, and meanwhile to improve the efficiency. As a buck–boost inverter, the Trans-Z-source inverter includes just an inverter bridge and a passive network, which itself consists of a pulse transformer and a capacitor. This inverter can be an appropriate alternative for the common structures consisting of inverters and DC/DC converters. In this article, presenting an efficient controlling method, Trans-Z-source inverter is used to connect the photovoltaic system to a single-phase grid. In the controlling structure, the improved INC method is proposed to track the maximum power point, while the proportional–resonance and proportional–integral controllers are employed to inject power to the grid and stabilize the Trans-ZSI capacitor voltage, respectively. Simulations show the functionality of the proposed system.

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.

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.

Integration of wind energy and other renewable energy resources in electrical systems create some challenges due to their uncertain and variable characteristics. In the quest for more flexibility of the electric systems, combination of these endogenous and renewable resources in accordance with strategies of Demand Response (DR) allows an increment/improvement of the demand potential, as well as a more secure, robust, sustainable and economically advantageous operation. This paper proposes a new model for integration of wind power and DR, thus optimizing supply and demand side operations through a price rule Time of Use (TOU), or incentive with Emergency DR Program (EDRP), as well as combining TOU and EDRP together. The problem is modelled using a stochastic Heuristic Multi-Objective Multi-Criteria Decision Making (HMM) method which aims to minimize operation costs and environmental emissions simultaneously, ensuring the security constraints through two-stage stochastic programming, considering various techno-economic indices such as load factor, electricity market prices, Energy Not Supplied (ENS) and Share Weighted Average Lerner Index (SWALI). Comprehensive numerical results indicate that the proposed model is entirely efficient in DR planning and power system operation.

High penetration of renewable energy sources, especially weather-dependent sources, has increased the power systems uncertainties. For any analysis in power systems such as planning and operation, it is essential to confront the stochastic nature of these sources in order to get much more precise results. Since operators need proper strategies and methods to decline negative effects of the stochastic behaviour of renewable power generators, such as total operation cost growth, this paper provides a review of different state-of-the-art approaches from the operator's viewpoint for handling the stochastic behaviour of renewable sources. Hence, in this paper, three different strategies are categorized for stochastic analysis of these sources. The first strategy is mathematical modelling including stochastic dependency and independency, multi-dimensional dependence, forecast and scenarios. Afterwards, demand side management, which is one of the other approaches for dealing with these uncertainties, is investigated and different demand response programs and some methods to model them are presented. Finally, the effect of different electricity market schemes and relevant optimization methods to mitigate the variations of renewable energy sources are discussed. The study demonstrates that an operator should choose one or a combination of these three approaches based on its requirements.

Local Electricity Markets (LEM) and peer-to-peer trading are new mechanisms to encourage the uptake of solar PV and to support the emergence of consumer-centric electricity markets. However, the coordination to trade between consumers and prosumers has different definitions depending on the context and features of the energy system. This paper introduces a new vision: creating virtual LEMs by cooperatively mixing (optimal matching) different load and renewable profiles that complement each other. Since consumer and prosumer profiles change every day (weather conditions or demand behaviors), the dynamic formation of virtual LEMs changes daily. To reward flexibility, Electric Vehicles (EV) are also pooled into forming a virtual LEM. That is, we investigate: What is the value of creating virtual local markets (via clustering)?, and what is the impact of EV flexibility on the creation of virtual LEMs? Through implementing a LEM optimization model with a clustering approach, we analyze the formation of LEMs for a set of end-users in London. Results indicate that a single large LEM (no clustering) is comparatively similar to multiple LEMs (clustering). EV flexibility obtains more revenue in this new marketplace. Findings are encouraging as dynamic virtual LEMs can enable, accelerate and bring scalability for a ubiquitous deployment of LEMs.

The present research aims to formulate competition in a retail energy market in the presence of an Integrated Demand Response (IDR) program to reduce prosumer costs and increase retailer profits. This gives prosumers more degrees of freedom to reduce their energy costs. The retail energy market includes retailers and prosumers equipped with an energy hub containing a boiler for producing heat and combined heat and power (CHP). Retailers aim to maximize profit, whereas prosumers seek to minimize their costs. Hence, a multi-leader-follower game with a bi-level program emerges in which the upper level deals with the profit maximization of each retailer while the lower level considers the cost minimization of each prosumer. The strategic behaviour of each retailer is modelled as a Mathematical Program with Equilibrium Constraints (MPEC) problem. Simultaneously solving all MPECs, which leads to an Equilibrium Problem with Equilibrium Constraints (EPEC), determines the market equilibrium point. The equilibrium point is achieved using mathematical, analytical methods and linearization of nonlinear constraints by accurate techniques. Two different case studies are developed to investigate how the number of retailers influences the market equilibrium point. The first case includes two retailers, while the second case considers an increase in the number of retailers. The results demonstrate that with an increase in retailers' number, their competition increases, causing the prosumers costs to reduce. Furthermore, our results suggest the IDR impact on reduced prosumers cost and increased retailers profit.

In this paper, a novel deterministic approach for the planning of active distribution networks within a distribution market environment considering multi-configuration of wind turbines (WTs) and photovoltaic (PV) cells is proposed. Multi-configuration multi-period market-based optimal power flow is utilized for maximizing social welfare taking into account uncertainties associated with wind speed, solar irradiance and load demand as well as different operational status of WTs and PVs. Multi-period scenarios method is exploited to model the aforementioned uncertainties. The proposed approach assesses the effect of multiple-configuration of PVs and WTs on the amount of wind and solar power that can be produced, the distribution locational marginal prices all over the network and on the social welfare. The application of the proposed approach is examined on a 30-bus radial distribution network.

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.

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