Showing papers in "Journal of Modern Power Systems and Clean Energy in 2019"
TL;DR: An overview of the primary and secondary control methods under the hierarchical control architecture for DC MGs is provided, specifically, inner loop and droop control approaches in primary control are reviewed.
Abstract: With the rapid development of power electronics technology, microgrid (MG) concept has been widely accepted in the field of electrical engineering. Due to the advantages of direct current (DC) distribution systems such as reduced losses and easy integration with energy storage resources, DC MGs have drawn increasing attentions nowadays. With the increase of distributed generation, a DC MG consisting of multiple sources is a hot research topic. The challenge in such a multi-source DC MG is to provide voltage support and good power sharing performance. As the control strategy plays an important role in ensuring MG’s power quality and efficiency, a comprehensive review of the state-of-art control approaches in DC MGs is necessary. This paper provides an overview of the primary and secondary control methods under the hierarchical control architecture for DC MGs. Specifically, inner loop and droop control approaches in primary control are reviewed. Centralized, distributed, and decentralized approach based secondary control is discussed in details. Key findings and future trends are also presented at last.
142 citations
TL;DR: These new methods in controlling power system frequency following a disturbance are effective in recovering the fallen frequency response and present a great potential in controlling the frequency in future power systems.
Abstract: Integration of more renewable energy resources introduces a challenge in frequency control of future power systems This paper reviews and evaluates the possible challenges and the new control methods of frequency in future power systems Different types of loads and distributed energy resources (DERs) are reviewed A model representation of a population of the water heater devices for the demand side frequency response is considered A model representation of a population of battery energy storage system (BESS)-based DERs such as smart electric vehicles (EVs) charging, large-scale BESSs, and residential and non-residential BESSs, are highlighted The simplified Great Britain power system and the 14-machine South-East Australian power system were used to demonstrate the effectiveness of the new methods in controlling power system frequency following a disturbance These new methods are effective in recovering the fallen frequency response and present a great potential in controlling the frequency in future power systems
117 citations
TL;DR: As an influential flexibility solution in current power systems integrated with renewable resources, market design improvement is widely reviewed in this paper, and required modifications in market design mechanisms are investigated pertaining to various time horizons.
Abstract: Power systems are evolving to the networks with proliferated penetration of renewable energy resources to leverage their environmental and economic advantages. However, due to the stochastic nature of renewables, the management of the rapidly increasing uncertainty and variability in power system planning and operation is of crucial significance. This paper represents a comprehensive overview of power system flexibility as an effective way to maintain the power balance at every moment. Definitions of power system flexibility from various aspects are explained to reach the reliable and economic planning and operation of the power system. The effects of the high penetration of variable energy resources on power systems and the evolution of flexibility in response to renewables are studied. A variety of resources during the flexibility evolutionary transition are introduced and discussed. As an influential flexibility solution in current power systems integrated with renewable resources, market design improvement is widely reviewed in this paper, and required modifications in market design mechanisms are investigated pertaining to various time horizons.
109 citations
TL;DR: This paper presents a comprehensive summary of synchrophasor technology, its architecture, optimal placement techniques and its applications in electric power transmission and distribution systems.
Abstract: Synchrophasors are time-synchronized electrical measurements that represent both the magnitude and phase angle of the electrical sinusoids. Synchrophasors are measured by fast time-stamped devices called phasor measurement units (PMUs) to constitute the basis of real-time monitoring and control actions in the electric grid. Due to its enhanced situational awareness capabilities, many applications of PMUs are presented in the literature in the past decades. This paper presents a comprehensive summary of synchrophasor technology, its architecture, optimal placement techniques and its applications in electric power transmission and distribution systems. These applications include wide-area situational awareness and monitoring, state estimation, fault location and protective relaying, islanding detection etc. This review also covers some of the existing challenges in its implementation and its potential applications.
87 citations
TL;DR: A comprehensive review of quality and cybersecurity challenges for synchrophasors and identifies the interdependencies between them, and summarizes different methods used to evaluate the dependency and surveys how quality checking methods can be used to detect potential cyberattacks.
Abstract: Synchrophasor devices guarantee situation awareness for real-time monitoring and operational visibility of smart grid. With their widespread implementation, significant challenges have emerged, especially in communication, data quality and cybersecurity. The existing literature treats these challenges as separate problems, when in reality, they have a complex interplay. This paper conducts a comprehensive review of quality and cybersecurity challenges for synchrophasors, and identifies the interdependencies between them. It also summarizes different methods used to evaluate the dependency and surveys how quality checking methods can be used to detect potential cyberattacks. This paper serves as a starting point for researchers entering the fields of synchrophasor data analytics and security.
74 citations
TL;DR: A new strategy to solve the shortcomings of traditional SVM is proposed, which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.
Abstract: Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment (TSA) has always been a tough problem in power system analysis. Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine (SVM) method. However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear. This paper proposes a new strategy to solve the shortcomings of traditional SVM, which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms. In this strategy, two improved SVMs, which are called aggressive support vector machine (ASVM) and conservative support vector machine (CSVM), are proposed to improve the accuracy of the classification. And two improved SVMs can ensure the stability or instability of the power system in most cases. For the small amount of cases with undetermined stability, a new concept of grey region (GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system. Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.
73 citations
TL;DR: A comprehensive survey on the interdependence between transportation system and power distribution system is conducted, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community.
Abstract: The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.
71 citations
TL;DR: A review of the state-of-the-art researches related to the wind farm layout optimization as well as electrical system design including cable connection scheme optimization is presented in this paper, where the most significant factors that should be considered in the offshore wind farm optimization work is highlighted after reviewing the latest works, and the future needs have been specified.
Abstract: There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. Since a huge amount of money is required for constructing an offshore wind farm, many types of research have been done on the optimization of the offshore wind farm with the purpose of either minimizing the cost of energy or maximizing the total energy production. There are several factors that have an impact on the performance of the wind farm, mainly energy production of wind farm which is highly decided by the wind condition of construction area and micro-siting of wind turbines (WTs), as well as initial investment which is influenced by both the placement of WTs and the electrical system design, especially the scheme of cable connection layout. In this paper, a review of the state-of-art researches related to the wind farm layout optimization as well as electrical system design including cable connection scheme optimization is presented. The most significant factors that should be considered in the offshore wind farm optimization work is highlighted after reviewing the latest works, and the future needs have been specified.
66 citations
TL;DR: A multi-objective optimization problem to obtain the simultaneous placement and sizing of F CSs and distributed generations (DGs) with the constraints such as the number of EVs in all zones and possible number of FCSs based on the road and electrical network in the proposed system is presented.
Abstract: The large-scale construction of fast charging stations (FCSs) for electrical vehicles (EVs) is helpful in promoting the EV. It creates a significant challenge for the distribution system operator to determine the optimal planning, especially the siting and sizing of FCSs in the electrical distribution system. Inappropriate planning of fast EV charging stations (EVCSs) cause a negative impact on the distribution system. This paper presented a multi-objective optimization problem to obtain the simultaneous placement and sizing of FCSs and distributed generations (DGs) with the constraints such as the number of EVs in all zones and possible number of FCSs based on the road and electrical network in the proposed system. The problem is formulated as a mixed integer non-linear problem (MINLP) to optimize the loss of EV user, network power loss (NPL), FCS development cost and improve the voltage profile of the electrical distribution system. Non-dominated sorting genetic algorithm II (NSGA-II) is used for solving the MINLP. The performance of the proposed technique is evaluated by the 118-bus electrical distribution system.
62 citations
TL;DR: A multi-frequency combination prediction model based on variational mode decomposition (VMD) is proposed that shows that the prediction accuracy of the proposed VMD model is higher than the single prediction models built to tackle this problem.
Abstract: Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition (VMD). We use a back propagation neural network (BPNN), autoregressive moving average (ARMA) model, and least squares support vector machine (LS-SVM) to predict high, intermediate, and low frequency components, respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value. Finally, the prediction performance of the single prediction models (ARMA, BPNN, LS-SVM) and the decomposition prediction models (EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error, and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.
58 citations
TL;DR: An effort is given to explain SMES device and its controllability to mitigate the stability of power grid integrated with wind power generation systems.
Abstract: Due to interconnection of various renewable energies and adaptive technologies, voltage quality and frequency stability of modern power systems are becoming erratic. Superconducting magnetic energy storage (SMES), for its dynamic characteristic, is very efficient for rapid exchange of electrical power with grid during small and large disturbances to address those instabilities. In addition, SMES plays an important role in integrating renewable sources such as wind generators to power grid by controlling output power of wind plant and improving the stability of power system. Efficient application of SMES in various power system operations depends on the proper location in the power system, exact energy and power ratings and appropriate controllers. In this paper, an effort is given to explain SMES device and its controllability to mitigate the stability of power grid integrated with wind power generation systems.
TL;DR: In this paper, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers by triggering an incentive price to influence customer behaviors to change their demand consumptions.
Abstract: The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers. To address this issue, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers. The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions. The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price. Then, the sensitivity of incentive price on retailers’ benefits is analyzed and the optimal incentive price is obtained according to the DR model. The case study verifies the effectiveness of the proposed model.
TL;DR: In this paper, the authors investigated the impact of demand response (DR) programs and smart charging/discharging of plug-in electric vehicles (PEVs) on radial distribution systems adopting particle swarm optimization (PSO) algorithm.
Abstract: Nowadays, utilities aim to find methods for improving the reliability of distribution systems and satisfying the customers by providing the continuity of power supply. Different methodologies exist for utilities to improve the reliability of network. In this paper, demand response (DR) programs and smart charging/discharging of plug-in electric vehicles (PEVs) are investigated for improving the reliability of radial distribution systems adopting particle swarm optimization (PSO) algorithm. Such analysis is accomplished due to the positive effects of both DR and PEVs for dealing with emerging challenges of the world such as fossil fuel reserves reduction, urban air pollution and greenhouse gas emissions. Additionally, the prioritization of DR and PEVs is presented for improving the reliability and analyzing the characteristics of distribution networks. The reliability analysis is performed in terms of loss of load expectation (LOLE) and expected energy not served (EENS) indexes, where the characteristics contain load profile, load peak, voltage profile and energy loss. Numerical simulations are accomplished to assess the effectiveness and practicality of the proposed scheme.
TL;DR: An energy sharing scheme that allows users to share DERs with neighbors, and a novel incentive mechanism for benefit allocation without users’ bidding on electricity prices is developed.
Abstract: To improve the controllability and utilization of distributed energy resources (DERs), distribution-level electricity markets based on consumers’ bids and offers have been proposed. However, the transaction costs will dramatically increase with the rapid development of DERs. Therefore, in this paper, we develop an energy sharing scheme that allows users to share DERs with neighbors, and design a novel incentive mechanism for benefit allocation without users’ bidding on electricity prices. In the energy sharing scheme, an aggregator organizes a number of electricity users, and trades with the connected power grid. The aggregator is aimed at minimizing the total costs by matching the surplus energy from DERs and electrical loads. A novel index, termed as sharing contribution rate (SCR), is presented to evaluate different users’ contributions to the energy sharing. Then, based on users’ SCRs, an efficient benefit allocation mechanism is implemented to determine the aggregator’s payments to users that incentivize their participation in energy sharing. To avoid users’ bidding, we propose a decentralized framework for the energy sharing and incentive mechanism. Case studies based on real-world datasets demonstrate that the aggregator and users can benefit from the energy sharing scheme, and the incentive mechanism allocates the benefits according to users’ contributions.
TL;DR: In this article, the authors provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable penetration and present an integrated analytical framework for interlinked technical and environmental systems.
Abstract: Investment for renewables has been growing rapidly since the beginning of the new century, and the momentum is expected to sustain in order to mitigate the impact of anthropogenic climate change. Transition towards higher renewable penetration in the power industry will not only confront technical challenges, but also face socio-economic obstacles. The connected between environment and energy systems are also tightened under elevated penetration of renewables. This paper will provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable penetration. An integrated analytical framework for interlinked technical, environmental and socio-economic systems will be presented at the end.
TL;DR: This study proposes segregation of the power disturbance from regular values using one-class support vector machine (OCSVM), a semi-supervised machine learning algorithm which is able to automatically detect any types of disturbances in real time, even unknown types which are not available in the training time.
Abstract: Power quality assessment is an important performance measurement in smart grids. Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters. Addressing this issue, in this study, we propose segregation of the power disturbance from regular values using one-class support vector machine (OCSVM). To precisely detect the power disturbances of a voltage wave, some practical wavelet filters are applied. Considering the unlimited types of waveform abnormalities, OCSVM is picked as a semi-supervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data. This model is able to automatically detect the existence of any types of disturbances in real time, even unknown types which are not available in the training time. In the case of existence, the disturbances are further classified into different types such as sag, swell, transients and unbalanced. Being light weighted and fast, the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring. The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.
TL;DR: Three convex OPF models are proposed to improve the performance of the second-order cone alternating current OPF (SOC-ACOPF) model by dropping assumptions by convex relaxation and approximation methods and a heuristic algorithm to recover feasible ACOPF solution from the relaxed solution of the proposed SOC-ACopF models is developed.
Abstract: Optimal power flow (OPF) is the fundamental mathematical model to optimize power system operations. Based on conic relaxation, Taylor series expansion and McCormick envelope, we propose three convex OPF models to improve the performance of the second-order cone alternating current OPF (SOC-ACOPF) model. The underlying idea of the proposed SOC-ACOPF models is to drop assumptions of the original SOC-ACOPF model by convex relaxation and approximation methods. A heuristic algorithm to recover feasible ACOPF solution from the relaxed solution of the proposed SOC-ACOPF models is developed. The proposed SOC-ACOPF models are examined through IEEE case studies under various load scenarios and power network congestions. The quality of solutions from the proposed SOC-ACOPF models is evaluated using MATPOWER (local optimality) and LINDOGLOBAL (global optimality). We also compare numerically the proposed SOC-ACOPF models with other two convex ACOPF models in the literature. The numerical results show robust performance of the proposed SOC-ACOPF models and the feasible solution recovery algorithm.
TL;DR: The results indicate that the SSI-MSVM method is effective in fault diagnosis for a wind turbine bearing and can successfully identify fault types of bearing and achieve higher diagnostic accuracy than that of K-means clustering, fuzzy means clustering and traditional SVM.
Abstract: In order to accurately identify a bearing fault on a wind turbine, a novel fault diagnosis method based on stochastic subspace identification (SSI) and multi-kernel support vector machine (MSVM) is proposed. First, the collected vibration signal of the wind turbine bearing is processed by the SSI method to extract fault feature vectors. Then, the MSVM is constructed based on Gauss kernel support vector machine (SVM) and polynomial kernel SVM. Finally, fault feature vectors which indicate the condition of the wind turbine bearing are inputted to the MSVM for fault pattern recognition. The results indicate that the SSI-MSVM method is effective in fault diagnosis for a wind turbine bearing and can successfully identify fault types of bearing and achieve higher diagnostic accuracy than that of K-means clustering, fuzzy means clustering and traditional SVM.
TL;DR: The efficiency and robustness of the proposed MILP formulation are successfully verified using a large-scale test MG and the model is developed based on AC power flow constraints so as to respect reactive power and voltage security constraints.
Abstract: With the increasing interdependence of various energy carriers, the operation of power systems is found to correlate closely with the limitations on the other energy infrastructures. This paper presents a mixed-integer linear programming (MILP) model for the microgrid (MG) optimal scheduling considering technical and economic ties between electricity and natural gas (NG) systems. In the proposed methodology, different energy converters and storages, including combined heat and power (CHP) units, electricity/heat storage units, and distributed energy resources (DERs) are considered. The proposed model allows the MG operator to minimize the operation cost of the MG while different operational limitations on the energy hub are satisfied. The model is developed based on AC power flow constraints so as to respect reactive power and voltage security constraints. The efficiency and robustness of the proposed MILP formulation are successfully verified using a large-scale test MG.
TL;DR: A variable and adaptive perturb and observe (P&O) method with current predictive control in three-phase three-level neutral-point clamped (NPC) photovoltaic (PV) generation systems to improve maximum power point tracking performance is proposed.
Abstract: In order to improve maximum power point tracking (MPPT) performance, a variable and adaptive perturb and observe (P&O) method with current predictive control is proposed. This is applied in three-phase three-level neutral-point clamped (NPC) photovoltaic (PV) generation systems. To control the active power and the reactive power independently, the decoupled power control combined with a space vector modulation block is adopted for three-phase NPC inverters in PV generation systems. To balance the neutral-point voltage of the three-phase NPC grid-connected inverter, a proportional and integral control by adjusting the dwell time of small voltage vectors is used. A three-phase NPC inverter rated at 12 kVA was established. The performance of the proposed method was tested and compared with the fixed perturbation MPPT algorithm under different conditions. Experimental results confirm the feasibility and advantages of the proposed method.
TL;DR: A simplified sizing method, integrating an energy management strategy, is proposed that allows the selection of the adequate storage technologies and determines the required least-cost storage capacity by considering their technological limits associated with different power dynamics.
Abstract: The high penetration of renewable energy systems with fluctuating power generation into the electric grids affects considerably the electric power quality and supply reliability Therefore, energy storage resources are used to deal with the challenges imposed by power variability and demand-supply balance The main focus of this paper is to investigate the appropriate storage technologies and the capacity needed for a successful tidal power integration Therefore, a simplified sizing method, integrating an energy management strategy, is proposed This method allows the selection of the adequate storage technologies and determines the required least-cost storage capacity by considering their technological limits associated with different power dynamics The optimal solutions given by the multi-objective evolutionary algorithm are presented and analyzed
TL;DR: A personalized–real time pricing (P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it and achieves significant reduction of theEnergy cost without sacrificing at all the welfare of electricity consumers.
Abstract: This paper contributes to the well-known challenge of active user participation in demand side management (DSM). In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency. Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness. In this paper, a personalized–real time pricing (P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it. Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare (user satisfaction) of electricity consumers. The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.
TL;DR: The results of comparative experiments show that the forecasting framework proposed by this paper outperforms the classical support vector machine method and a method based on knowledge transfer in terms of mean absolute percent error and mean absolute scaled error.
Abstract: Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays. First, we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrAdaBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities from Guangdong province to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the forecasting framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.
TL;DR: A wavelet-based power management system with a combination of the battery and ultracapacitor (UC) hybrid energy storage system (HESS) and a frequency-based filter that moderates the usage of battery current, consequently improving its lifetime.
Abstract: A wavelet-based power management system is proposed in this paper with a combination of the battery and ultracapacitor (UC) hybrid energy storage system (HESS). The wavelet filter serves as a frequency-based filter for distributing the power between the battery and UC. In order to determine the optimal level of wavelet decomposition as well as the optimal activation power of the wavelet controller, an optimization procedure is established. The proposed frequency-based power management system moderates the usage of battery current, consequently improving its lifetime. Compared with the conventional threshold-based power management systems, the proposed system has the advantage of enhanced battery and UC power management. A LiFePO4 battery is considered and its life loss is modeled. As a case study, an electric motorcycle is evaluated in the federal test procedure (FTP) driving cycle. Compared with a conventional energy storage system (ESS) and a state of available power (SoP) management systems, the results show an improvement for the battery lifetime by 115% and 3%, respectively. The number of battery replacements is increased, and the energy recovery is improved. The 10-year overall costs of the proposed HESS strategy using wavelet are 1500 dollars lower, compared with the ESS.
TL;DR: This paper presents a two-level method for intentional islanding of a reconfigurable distribution system, considering high impact events, and the results show the validity and effectiveness of the proposed algorithm.
Abstract: Participation of distributed energy resources in the load restoration procedure, known as intentional islanding, can significantly improve the distribution system reliability Distribution system reconfiguration can effectively alter islanding procedure and thus provide an opportunity to supply more demanded energy and reduce distribution system losses In addition, high-impact events such as hurricanes and earthquake may complicate the procedure of load restoration, due to disconnection of the distribution system from the upstream grid or concurrent component outages This paper presents a two-level method for intentional islanding of a reconfigurable distribution system, considering high impact events In the first level, optimal islands are selected according to the graph model of the distribution system In the second level, an optimal power flow (OPF) problem is solved to meet the operation constraints of the islands by reactive power control and demand side management The proposed problem in the first level is solved by a combination of depth first search and particle swarm optimization methods The OPF problem in the second level is solved in DIgSILENT software The proposed method is implemented in the IEEE 69-bus test system, and the results show the validity and effectiveness of the proposed algorithm
TL;DR: An advanced model predictive control (MPC) based scheme to control the PE-interfaced DER units, minimize the impact of transients and disruptions, speed up the response and recovery of particular metrics and parameters, and maintain an acceptable operation condition is introduced.
Abstract: Modern power delivery systems are rapidly evolving with high proliferation of power-electronic (PE)-interfaced distributed energy resources (DERs). Compared to the conventional sources of generation, the PE-interfaced DERs, e.g., solar and wind resources, are attributed substantially different characteristics such as lower overload capability and limited frequency response patterns. This paper focuses on effective management and control mechanisms for PE-interfaced DERs in power distribution systems with high penetration of renewables, particularly under fault, voltage-sag, load variations, and other prevailing conditions in the grid. Aiming at the solutions to enhance the system performance resilience, we introduce an advanced model predictive control (MPC) based scheme to control the DER units, minimize the impact of transients and disruptions, speed up the response and recovery of particular metrics and parameters, and maintain an acceptable operation condition. The performance of the suggested control scheme is tested on a modified IEEE 34-bus test feeder, where the proposed solution demonstrates its effectiveness to minimize the system transient during faults, with an enhanced grid-edge and system-wide resilience characteristics in voltage profiles.
TL;DR: An adaptive segmentation method for dividing large number of market players into multiple segments to enhance scalability of the P2P trading by reducing data exchange and communication overheads is proposed.
Abstract: This paper proposes an adaptive segmentation method as a market clearing mechanism for peer-to-peer (P2P) energy trading scheme with large number of market players. In the proposed method, market players participate in the market by announcing their bids. In the first step, players are assigned to different segments based on their features, where the balanced k-means clustering method is implemented to form segments. These segments are formed based on the similarity between players, where the amount of energy for trade and its corresponding price are considered as features of players. In the next step, a distributed method is employed to clear the market in each segment without any need to private information of players. The novelty of this paper relies on developing an adaptive algorithm for dividing large number of market players into multiple segments to enhance scalability of the P2P trading by reducing data exchange and communication overheads. The proposed approach can be used along with any distributed method for market clearing. In this paper, two different structures including community-based market and decentralized bilateral trading market are used to demonstrate the efficacy of the proposed method. Simulation results show the beneficial properties of the proposed segmentation method.
TL;DR: Simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system.
Abstract: The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage (ES) to mitigate wind power fluctuation. We studied the strategy of smoothing wind power fluctuation and the strategy of hybrid ES power distribution. Firstly, an effective control strategy can be extracted by comparing constant-time low-pass filtering (CLF), variable-time low-pass filtering (VLF), wavelet packet decomposition (WPD), empirical mode decomposition (EMD) and model predictive control algorithms with fluctuation rate constraints of the identical grid-connected wind power. Moreover, the mean frequency of ES as the cut-off frequency can be acquired by the Hilbert Huang transform (HHT), and the time constant of filtering algorithm can be obtained. Then, an improved low-pass filtering algorithm (ILFA) is proposed to achieve the power allocation between lithium battery (LB) and supercapacitor (SC), which can overcome the over-charge and over-discharge of ES in the traditional low-pass filtering algorithm (TLFA). In addition, the optimized LB and SC power are further obtained based on the SC priority control strategy combined with the fuzzy control (FC) method. Finally, simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system.
TL;DR: In this article, the impact of wind energy on the small-signal stability of the power system is investigated and different combinations of AVR and PSS types are considered to mitigate the undesirable alterations.
Abstract: The increasing penetration of wind farms in the energy sector directly affects the dynamic behavior of the power system. The increasing use of wind energy in the power system worsens its stability and inherently influences the firmness of a small signal. To investigate these effects, one of the synchronous generators (SGs) of the grid is considered defective and is replaced by a doubly fed induction generator (DFIG)-based wind farm of the same rating. The small-signal stability of a power system is usually evaluated via eigenvalue analysis where local-area and inter-area oscillatory modes for the New England test system are identified. SG controls, such as automatic voltage regulator (AVR) and power system stabilizer (PSS), are added to attenuate the generated disturbances. In this study, the impact of wind energy on the small-signal stability of the power system is investigated. Different combinations of AVR and PSS types are considered to mitigate the undesirable alterations. A comparative study is performed based on numerical simulations to choose the best combination of AVR and PSS types. The obtained results prove that the proposed combination yields good results in terms of stability enhancement both under normal operating conditions and in DFIG-based wind farms.
TL;DR: In this article, the improvement effect on the delay margin if fractional-order proportional integral (PI) controller is used in the control of a single-area delayed load frequency control (LFC) system is determined.
Abstract: This study aims to determine the improvement effect on the delay margin if fractional-order proportional integral (PI) controller is used in the control of a single-area delayed load frequency control (LFC) system. The delay margin of the system with fractional-order PI control has been obtained for various fractional integral orders and the effect of them has been shown on the delay margin as a third controller parameter. Furthermore, the stability of the system that is either under or over the delay margin is examined by generalized modified Mikhailov criterion. The stability results obtained have been confirmed numerically in time domain. It is demonstrated that the proposed controller for delayed LFC system provides more flexibility on delay margin according to integer-order PI controller.