Search

The purpose of this research is to determine the optimum location to construct gas power plants (GPPs) in semi-arid regions. A combination of ordered weight averaging (OWA) and analytic hierarchy process (AHP) method named OWA-AHP is utilized to prepare land suitability maps with different risk scenarios when fuzzy method is to homogenize inputs. In the proposed method, AHP is to weigh each different parameter while OWA considers risk levels. In order to validate the accuracy of the proposed method, the receiver operating characteristics (ROC) curve is investigated. Besides, the self-organizing map (SOM) algorithm and Pearson correlation are used to determine the most important parameters for constructing GPP. According to obtained results, the areas located in the north and parts of the east and south of the selected case study (about 15%) at all risk levels are the optimum areas to be hosted for GPPs. Furthermore, ROC curve shows that the Area Under the Curve (AUC) values are high for both AHP and OWA-AHP methods (AUC Fuzzy-AHP = 94.0%, AUC OWA = 89.0%). The results of the SOM algorithm and Pearson correlation with high accuracy (R Model1 : 0.853 and R Model2 : 0.940) also depict that distance to the pipeline and road are the most important parameters to identify suitable locations for GPP.

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.

This paper proposes a load control module based on the consumers' vulnerability indices to improve the ability of radial microgrid (MG) to deal with line contingencies. This consumer switching module (CSM) increases the operational flexibility and is suitable for the radial MGs, where, a sudden line outage restricts access to the energy resources and deviates the system frequency in the created island. To cope with this condition, CSM can instantly disconnect some of the loads and reconnects them in an appropriate time sequence, considering loads' vulnerabilities. To include the CSM in the short-term operation of MG, with the lowest complexity, a simple four-stage structure is used. At first, the capability of CSM is determined in different islands related to potential line events. Then, the approximate cost function of CSM is applied to the short-term MG operation which is hour-ahead scheduling (HAS) including both the normal and the contingency state operation. Eventually, the determined optimal CSM participation level in each island is allocated to the consumers based on their exact offered price to calculate the load switching sequence. To evaluate the performance of the proposed scheduling manner, the 13-bus and 123-bus IEEE test systems are precisely analyzed considering different simulation scenarios.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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

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

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

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

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.