Search

This article proposes a framework as a remedial action scheme (RAS) for close to real-time system operation when system operator becomes aware of possibility of decentralized outages due to simultaneous attacks on several market retailers. The upper level of the framework is an offline RAS where system operator immediately implements a customized reconfiguration preventing the decentralized outages. Due to immediate implementation of upper-level reaction, it is possible to face decentralized congestions in the new topology having minimum reliance on the attacked market retailers. Since severity of congestions is less than that of outages, the lower level of the framework is implemented as an online RAS. In this level, eligible nonattacked market retailers are invited into a secondary market to compete for generation/consumption redispatch and contribute into alleviation of congestions with the purpose of increasing their income. The effectiveness of the proposed framework is validated on a well-known 136-bus distribution system.

The technology of supercapacitor is vastly examined by researchers to cope with a lower power density of battery. It is validated that a hybrid battery-supercapacitor storage (HBSS) framework can improve the overall efficiency due to taking advantage of battery's higher energy density and supercapacitor's higher power density. Despite the extensive investigations on this hybrid storage scheme in the electric vehicle (EV) industry, its experimental implementation in the power grid to cope with non-dispatchable nature of wind and solar energy is still in its initial stages. As a result, this paper concentrates on analysis of its experimental implementation on a lab-scale hybrid microgrid located at Southern Illinois University, Carbondale, IL, USA containing two wind turbines and two photovoltaic (PV) modules. The objective of this work is to promote the utilization of the experimentally validated lab-scale HBSS in power industry.

Modern distribution power systems face more complex challenges compared to the conventional systems. A powerful technique to react to such challenges is network reconfiguration, which needs to be adapted with “recent” concerns of modern power systems. Hence, it is essential to determine optimal configurations “rapidly” for better time management of hour-ahead system's decision making. This paper proposes an optimization algorithm, named parallel frog migrating algorithm (PFMA), to rapidly identify the optimal system topology for hour-ahead systems operation. The significance of the proposed technique is the development of a fuzzy-based decision making unit to realistically evaluate the necessity of implementing the identified topologies. This will noticeably decrease the computational burden of system operator in analyzing the identified optimal configurations. The effectiveness of the proposed PFMA is validated on an unbalanced 136-bus distribution network containing wind turbine and photovoltaic (PV) distributed generation units (DGs). It is demonstrated that the presented PFMA is able to identify close to optimal solutions faster than the nonlinear solver of General Algebraic Modeling System (GAMS) software. More importantly, it is verified that the developed decision making mechanism effectively takes advantage of renewable DGs and reduces the necessity of network reconfiguration.

Chitosan/Mal-A nanocomposite membranes were synthesized and used for adsorption of Reactive Yellow 84 (RY 84) from aqueous solution. The membranes were prepared by solution-casting method with 2 wt% Chitosan (Cs) and various ratios of Mal-A (0.3–0.9 wt%). FTIR and SEM analyses were used to characterize the Cs-based membrane. The results showed that a dense-thin-face layer associated with an open-structure layer was formed in the composite membrane containing 0.5 wt% Mal-A. Membrane performance was investigated as dye removal and permeate flux. The significant effects of the operating variables including pH, initial concentration of dye, and Cs/Mal-A dose, were evaluated on RY removal efficiency using response surface methodology (RSM). The results obtained from the experimental data were presented with the quadratic regression model. The adsorption kinetics and equilibrium isotherm were well described by the pseudo-second order and the Langmuir model, respectively. Adding Mal-A nanoparticles to the Cs matrix (0.5wt%) improved liquid permeability by 21% compared with the Cs membrane, while dye removal efficiency for both membranes remained approximately 100% at acidic pH and under a trans-membrane pressure of 3 bar. The protonation of the amine groups in CS/Mal-A results in a positive charge on the membrane surface which causes a complete removal of the anionic dye. The permeability improvement of ∼57.2% for the composite membrane was achieved at a pressure difference of 5 bar, which corresponded to an efficiency reduction of 10%.

Maleate-alumoxane nanoparticles (Mal-A) were synthesized from boehmite and applied for adsorption of an azo dye (Reactive Yellow 84) from aqueous solution. Its adsorption capacity was compared with three types of carboxylate alumoxane nanoparticles synthesized from boehmite including salicylate alumoxane (Sal-A), para-aminobenzoate alumoxane (Pab-A) and fumarate alumoxane (Fum-A). The characterizations of prepared materials were analyzed using FTIR, SEM, X-ray diffraction and BET measurements. Among utilized alumoxanes at natural pH, the adsorption capacity of Mal-A was 45, 67, 116 and 215% higher than that of Fum-A, Boehmite, Pab-A, Sal-A nanoparticles, respectively. Response surface methodology (RSM) using Box-Behnken design of experiment was employed to investigate the influence of pH, initial concentration of dye and adsorbent dosage on dye removal efficiency of Mal-A. Box-cox transformation was chosen to improve model adequately and a good prediction (R2: 0.998) was achieved. Under optimum condition, i.e., pH: 4.3, dye concentration: 151.5 mg/L and adsorbent dosage: 1.2 g/L, the adsorption capacity and dye removal efficiency were obtained 130.6 mg/g and 99.2%, respectively. The kinetics and equilibrium data were perfectly represented with linear pseudo-second-order and linear Langmuir isotherm models, respectively.

This paper proposes a day-ahead market framework for congestion management in smart distribution networks. The presented scheme provides a platform for collaboration between distribution-level market operator (DMO) and data traffic operator (DTO) to alleviate congested feeders such that data transmission traffic between market participants is effectively managed in a smart grid. In addition, a decentralized mechanism is developed for collaboration of electric vehicle (EV) aggregators with common clients to take advantage of EVs not only as flexible loads but also as mobile distributed storage (MDS) for congestion management. Moreover, the proposed framework outlines an administrative action for distribution system operator (DSO) to support the market when the decentralized competitions among distributed generation (DG) aggregators and EV aggregators do not fully relieve a serious congestion. The proposed day-ahead congestion management scheme is validated on an unbalanced 136-bus distribution system massively integrated with wind turbine DGs (WTDGs), photovoltaic DGs (PVDGs), diesel-engine DGs (DEDGs), and EVs.

This paper presents a framework to perform experimental validation of a remedial action for a lab-scale microgrid targeted by false data injection (FDI) cyberattack. The microgrid, which is located at Southern Illinois University (SIU), Carbondale, IL, USA, is managed to operate based on an electricity market mechanism. The corresponding renewable-based distributed generation (DG) owners submit their packages (i.e., power/bid) to be evaluated by market operator aiming at minimizing the total cost. Targeting the system by an FDI cyberattack, an attacker manipulates the submitted package associated with one of the DG owners at a specific time. After experiencing the attack, the operator reacts to the suspicious DG via excluding it from the market. Then, an invitation signal will be submitted to the non-attacked DG owners such that they can rapidly re-dispatch their generation in a secondary market to cope with the shortage of supply associated with the attacked DG.

Voltage source converter (VSC) based high-voltage direct current (HVDC) system is an emerging technology in high-power applications. Previous research has shown that VSCs, with commonly used vector current control (VCC) technique, cannot transmit more than 0.4 p.u. of active power when connected to a very weak ac grid. This paper presents a virtual synchronous generator (VSG) control strategy integrated with VCC to allow stable power transfer for very weak ac grid integration. Impedance analysis is used to analyze the designed controller in various scenarios. In addition, sensitivity analysis is performed to study the effect of virtual inertia gains on system stability. Time-domain simulation results using MATLAB/Simscape Power System toolbox show the effectiveness of the developed controller in very weak ac grid integration of VSC-HVDC systems.

Due to the massive number of rooftop photovoltaic (PV) arrays installed by residential customers, the demand side of modern power systems can also act as the producer. In this platform, the reverse direction of power can lead to power quality-related challenges including overvoltage in distribution systems. Focusing on this challenge, this paper proposes a robust framework to collaboratively manage the injected power to the grid by the PVs to minimize the probability of overvoltage across the network. The entity called energy scheduling coordinator (ESC) is employed to improve the end customers' contribution to voltage regulation. The ESCs solve two problems: 1) primarily optimize their clients' bill by minimizing their daily energy cost, 2) minimize the penalty cost associated with voltage deviations caused by PVs through a collaborative process. The effectiveness of the introduced scheme is validated on the IEEE 33-bus distribution system. The results verify that the overvoltage problem caused by decentralized PVs can be addressed via sufficient collaborations among ESCs. Therefore, the system power quality will be ensured by the presented “market-based” mechanism. As a result, the necessity for the intervention of system operator in addressing overvoltages will be prevented by the developed framework, which enhances the convenience of market participants.

With the ever-increasing energy demand and enormous development of generation capacity, modern bulk power systems are mostly pushed to operate with narrower security boundaries. Therefore, timely and reliable assessment of power system security is an inevitable necessity to prevent widespread blackouts and cascading outages. In this paper, a new white-box decision tree-based preventive strategy is presented to evaluate and enhance the power system dynamic security versus the credible N-K contingencies originating from transient instabilities. As well, a competent operating measure is expertly defined to detect and identify the islanding and non-islanding conditions with the aid of a wide-area phasor measurement system. The newly developed strategy is outlined by a three-level simulation with the aim of guaranteeing the power system dynamic security. In the first-level, six hundred islanding and non-islanding scenarios are generated using an enhanced version of the ID3 algorithm, referred to as the C4.5 algorithms. In the second-level, optimal C4.5 decision trees are offline trained based on operating parameters achieved by the reduction error pruning method. In the third level, however, all trained decision trees are rigorously investigated offline and online; and then, the most accurate and reliable decision tree is selected. The newly developed strategy is examined on the IEEE New England 39-bus test system, and its effectiveness is assured by simulation studies.

In this paper, the authors present an efficient direct torque control (DTC) for doubly fed induction generator (DFIG) in wind energy conversation systems (WECSs). The DFIGs are most employed in the WECSs because of their advantages, such as appropriate performance in variable wind speeds, control flexibility, and low cost. Many kinds of control techniques are proposed for the DFIG. One of the most simple and efficient control strategies with the fast-dynamic response is the DTC. The proposed control strategy is developed through a proportional-integral (PI) controller. This control strategy does not require any wind speed measurement and sensors. Moreover, in this strategy, torque ripple is a small amount; and then, the DTC can be used for rotor side converter (RSC). The new control strategy was analyzed and simulated in MATLAB/SIMULINK. The test system consists of a wind turbine model that drives a DFIG connected to the power grid through DC-link. It was also applied to a 2-pole, 0.2-KW DFIG. The obtained simulation results are illustrated pleasant and attractive advantages of the proposed control strategy, including simple control system structure, fast response, and easy integration of DFIG turbines with a large-scale power grid.

One of the severe challenges that the distribution system operators (DSOs) are faced in deregulated active distribution networks is the presence of massive functional entities such as distributed energy resources (DERs) and electric vehicles (EVs). The unpredictable actions from these entities increase the risk of congestion in the system. With an adaptive framework, it is possible to take advantage of EVs as mobile energy storage systems and suppress the congestion risks. New intermediary entities, such as aggregators (EGGs) and microgrid operators (MGOs), can make the interaction more fluent. The advantage of EVs can be evaluated under such a framework.

International audience; The use of distributed generation resources, in addition to considerable benefits, causes some problems in the power system. One of the most critical problems in the case of disruption is increasing short-circuit current level in grids, which leads to change the protection devices settings in the downstream and upstream grid. By using fault current limiters (FCL), short-circuit currents in grids with distributed generation can be reduced to acceptable levels, so there is no needed to change the protection relays settings of the downstream grid (including distributed generations). However, by locating the FCL in the tie-feeder, the downstream grid is not more effective than the upstream grid and thus its reliability indices also will be changed. Therefore, this paper shows that by locating the unidirectional fault current limiter (UFCL) in the tie-feeder, the necessity of changing in the relay protection settings of upstream grids is prevented. In this paper, the proposed method is implemented, and its efficiency is reported in six scenarios.

High voltage direct current (HVDC) systems are one of the major solutions to transmit electric power in minimum loss and optimum cost. HVDC systems have many types of which voltage source converter based HVDC systems (VSC-HVDC) are one of the many types of HVDC systems. These systems are extremely useful in order to integrate renewable energy resources into a common grid. Although HVDCs have several advantages, they are vulnerable to DC short circuit faults. AC circuit breakers are able to cut off the faulted branch at t=t0 when the magnitude of voltage is zeros, on the other hand, DC voltage has no zero points to be cut off by a circuit breaker. Despite several prototypes of DC circuit breakers (DCCB) had been deployed, there is any version of these circuit breakers which is commercialized and as it has been explained above, it is because of their inability to mitigate fault currents. To overcome the aforementioned problem, utilization of fault current limiters (FCL) is vital to limit the fault current level. These devices apply massive impedance to the faulted region to suppress the fault current passing through the network. Superconducting fault current limiters (SFCL) are the new type of FCLs, which put superconductors in use to limit the fault current passing through grids. In this regard, SFCLs are purposed as one of the solutions of HVDC systems protection and their ability in mitigation of fault currents are tested in an HVDC system. This system is a multi-terminal HVDC system with 2 kA of rated current. For the purpose of finding the best performance of DC SFCL, the NSGA II algorithm is applied in simulations, which are accomplished in SIMULINK/MATLAB.

This paper introduces an innovative method for implementing demand response (DR) to enable household appliance scheduling to minimize CO2 emissions and improve the voltage security in transmission networks. A new demand response (DR) based on the time-varying emission curve is proposed in this paper to reduce CO2 emissions. In addition to emission-based DRs, non-responsive loads are considered. On the other hand, load modeling is believed to be one of the significant parts of the power system studies so that inaccurate load models can lead to dramatically incorrect simulation outputs leading to an unfortunate event such as the 1983 Swedish blackout. DR is therefore applicable to a number of loads, including induction type motors as well as exponential loads. In addition, both active and reactive DRs are considered in this model. This paper introduces a new model called the Security Constraint Two-Stage Framework arising from the complexity of the problem. This model includes a large scale (LS) stage and a small scale (SS) stage set in which the SS stage uses the LS stage results as inputs. The proposed design is being implemented on the IEEE 300 bus power network to investigate the desired objectives.

Demand Side Management (DSM) can bring numerous benefits to power systems, such as decreasing peak load demand, reshaping demand and enhancing system security. In order to achieve these goals, this paper proposes a two-stage model for DSM considering polynomial and induction motor loads. Induction motor includes refrigerator (RFER), dishwasher (DWSH), clothes washing machine (CWSH) and dryer style motor (DRYR) loads. The polynomial and RFER loads follow a continuous DSM while the DWSH, CWSH and DRYR loads follow a selective DSM. Considering both the active and the reactive demand response, this paper provides a new DR based on the power factor. Electrical appliances are further classified as responsive/controllable and non-responsive/uncontrollable devices on the basis of their distinct power consumption constraints. Here, all appliances' operation patterns are modeled through their functions in real systems; as a result, the polynomial load model and induction motor type loads are adopted to represent consumers' behavior. A stochastic Multi-Objective Optimal Network Operation (SMONO) framework is further proposed to solve the aforementioned DSM problem. The simulations conducted in a generic distribution network reveals that the proposed method can successfully reach an optimal trade-off between four objectives, namely, voltage security, power losses, customer costs, and peak demand.

The role of distribution system operators (DSOs) has been changed due to deregulation in the distribution sector of the power systems. In this paper, a bi-level optimization problem is proposed to manage the day-ahead scheduling in an unbalanced distribution system with several micro-grids (MGs) in a decentralized manner. In the proposed framework, at the first level, the micro-grid operators (MGOs) solve their optimization problem to minimize their cost for the next 24 hours independently and send their aggregated demand to the DSO. Then, in the next level of optimization, DSO adjusts the main transformer and capacitor banks' tap position through an optimal power flow (OPF) problem to minimize the power loss with the minimum tap changing. To increase accuracy, the effect of earth is considered in unbalanced optimal power flow equations. To check the effectiveness of the proposed method, a modified unbalanced 13-bus IEEE test system is used as a case study. The results show that with the proposed framework, the MGOs and DSO can cooperate mutually. Also, by adjusting the main transformer and capacitor banks' tap position, the daily power loss is reduced by 30%.

In smart distribution networks, an essential responsibility of system operators is to effectively respond to the detected cyberattacks in order to prevent any possible blackout. This paper proposes a reaction framework for the distribution system operator (DSO) to respond to decentralized cyberattacks implemented on several distributed generation (DG) retailers, electric vehicle (EV) retailers, and demand response (DR) retailers. Due to the false data injections on the clients of DG / EV / DR retailers, wrong aggregated data will be submitted to the DSO. If a proper response is not provided, system has to rely on the targeted market participants which can result in congestion / blackout. As a result, we propose a real-time smart distribution network reconfiguration (RSDNR) framework for DSO to result in a system topology such that 1) minimum reliance on attacked retailers is required for system operation and 2) convenience of market participants is minimally affected by the administrative reaction of DSO. The effectiveness of the proposed scheme is validated on a well-known 136-bus distribution system modified with DGs and flexible loads which can provide DR.

This paper reports a new method for network transmission pricing technique which is based on load following and correlation factors in restructured environments. Considering new environments, transmission pricing analysis has become a challenging task because transmission network has a high constant cost and virtually no variable costs. As a result, literature pertaining to this subject proposes different methods that aim at considering equitable pricing in a competitive environment. This paper discusses their advantages and disadvantages. Therefore, a load following-based method which uses correlation factors for estimating transmission costs of each participant before entering the market is introduced. In previously proposed methods, only linear regression was used to estimate transmission costs. In this paper, however, the importance of using a combination of the linear regression and correlation factors is highlighted. A factor of partnership can be obtained that better describes the shares in transmission costs. Finally, the factor of partnership is calculated for a network with 39-IEEE bus system and a real-world system of northeastern part of Iranian national 400-kV transmission grid. Simulation results show the proposed method performs better than other methods.

This paper proposes a cooperative framework among microgrids (MGs) in a distribution power network to systematically manage pollution and substation transformer congestion (PSTC). In the first layer, each MG conducts an optimization individually for the day-ahead unit commitment with the objective of cost minimization. In the second layer, the MGs cooperate with each other to result in an effective PSTC management. The proposed operator in this paper, named microgrids aggregator (MGA), supervises the cooperative mechanism of MGs such that 1) purchased power from the distribution system operator (DSO) is minimized to result in congestion management of substation transformer (ST), 2) reliance on fossil fuels is controlled to prevent excessive CO 2 emission, 3) sensitive data of each MG is only shared with MGA and other MG operators will have no access to such information. Finally, in the third layer, DSO receives the modified schedules from MGAs to ensure no technical constraint in the entire system is violated. If such a violation exists, a mandatory load shedding will be implemented by DSO. The effectiveness of the introduced framework is validated on the 33-bus IEEE distribution network modified with diesel-engine distributed generation (DEDG) units, wind turbine DGs (WTDGs), photovoltaic DGs (PVDGs), and electric vehicles (EV) stations.

Modernization of distribution power systems is accompanied with 1) higher integration of wind turbine distributed generation (WTDG) and photovoltaic DG (PVDG) units as rooftop DGs or small power plants operated by private owners in de-regulated markets, 2) massive utilization of electric vehicles (EVs) in smart cities, and 3) extensive demand response (DR) services offered by smart homes. This paper proposes a systematic framework for modern distribution networks operation where DG aggregators (DGAGs), EV aggregators (EVAGs), and demand response aggregators (DRAGs) are motivated to contribute to real-time congestion prevention via greater local generation, flexible platform for charging schedule, and non-firm load curtailment, respectively. Moreover, a realistic mechanism is developed for microgrid operators (MGOs) to 1) relieve the system operator (SO) from the pressure of centralized congestion management and 2) optimally utilize the assets of each microgrid to prevent congestions in a decentralized manner. The performance of the proposed scheme is validated on an unbalanced distribution system containing 4 microgrids, 250 EVs, 31 WTDGs, 54 PVDGs, and 140 smart homes with DR contract.

The ongoing noticeable growth of energy demand in the world dramatically increases the thermally overloaded distribution lines, which can seriously threaten the security of distribution power systems. This paper proposes an electricity market framework in which renewable energy-based market participants are motivated to contribute to the “power system security” via congestion management. Moreover, the communications between market participants are supervised to manage the “data transmission traffic”. In addition, electric vehicles are customized to contribute to system security not only as flexible loads but also as virtual distributed generation (DG) units. Furthermore, administrative support of the distribution system operator is modeled in the introduced holistic scheme to take into account the scenarios in which market participants are unable to fully resolve congested distribution feeders in the system. The effectiveness of the proposed market mechanism is verified on a 136-bus “unbalanced” distribution network equipped with wind turbines and photovoltaic DGs.

The problem of finding optimal configuration of automated/smart power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when the time varying nature of loads is taken into account. In this paper, a systematic approach is proposed to determine an optimal long-term reconfiguration schedule. To solve the optimization problem, a novel adaptive fuzzy-based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into the proposed method enhances the efficiency of the parallel GA by adaptively modifying the migration rates among different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed, which automatically generates radial topologies and prevents the construction of infeasible radial networks in the optimization process. In order to consider the dynamic behavior of the load and reduce the load condition scenarios over the year under study, fuzzy C-mean clustering method is utilized. Finally, the performance of the proposed method is demonstrated on a 119-bus distribution network, and is compared with that of conventional single GA and conventional parallel GA.

Wind farms (WFs) have already been subjected to a considerable challenge to participate in competitive markets due to their power output uncertainty. Gas turbines (GTs) and energy storages could be an effective support for wind energy integration because of their fast and reliable response through providing the required flexibility in generating and storing electrical energy (EE). In this paper, optimal size of GTs, EE storage systems (ESS), and natural gas storage (GS) are addressed in adjacent a large WF in a generation company (GenCo) in order to maximize the GenCo’s profit in EE markets and to hedge its risks in either natural gas (NG) and EE markets. The proposed framework maintains a self-scheduling strategy for coordinated operation of GT, ESS, NGS, and the WF in different weather regimes as a stochastic mixed integer linear programing (MILP). Technical constraints of power system transmission and natural gas pipeline are considered, as well as the uncertainties of wind power generation, NG and electricity market prices, and also NG pipeline deliverability. The efficiency of the proposed method is benchmarked in different case studies.

In a deregulated electricity market, power system operator should systematically identify the optimal schedule of renewable distributed generation (DG) units to not only optimize the market profits but also improve the network conditions. This paper proposes a parallel computation-based methodology using fuzzy logic designed in the structure of a genetic algorithm (GA). Due to the efficient communication among the processors during the optimization, the proposed fuzzy-based parallel computation GA (FPCGA) addresses the shortcoming of the classic GA in convergence speed and quality of results. The proposed optimization algorithm is utilized in this paper to identify the optimal daily schedule for the system operator including the energy purchased from 1) the power grid, 2) each wind turbine DG, and 3) each photovoltaic DG. The efficiency of the proposed method is verified by its implementation on a 136-bus distribution system and its effectiveness is compared with similar methods.

Nanocomposite membranes were fabricated by dip-coating technique. Para-aminobenzoate alumoxane, boehmite–epoxide and chitosan were consecutively coated on the Kevlar fabric surface. The membranes were utilized for removing n-hexane from water using a gravity-driven dead-end filtration setup. UV–vis, FTIR and SEM analyses were performed for characterization. Water affinity analyses revealed highly hydrophilic nature of the modified membranes. Effect of pH was examined with the pH 2 providing the best condition for oil–water separation. Maximum flux and rejection were obtained as 1128 L m−2 h−1) and 94%, respectively. Deposited oil layer was effectively removed from the membrane surface using hot citric acid solution.