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Showing papers in "IEEE Transactions on Power Systems in 2014"


Journal ArticleDOI
TL;DR: In this paper, an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation is proposed to account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance.
Abstract: Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems.

586 citations


Journal ArticleDOI
TL;DR: A fully distributed and robust algorithm for OPF is proposed which does not require any form of central coordination and is based upon the alternating direction multiplier method (ADMM).
Abstract: Distributed optimal power flow (OPF) is a challenging non-linear, non-convex problem of central importance to the future power grid. Although many approaches are currently available in the literature, these require some form of central coordination to properly work. In this paper a fully distributed and robust algorithm for OPF is proposed which does not require any form of central coordination. The algorithm is based upon the alternating direction multiplier method (ADMM) in a form recently proposed by the author, which, in turn, builds upon the work of Schizas The approach is customized as a region-based optimization procedure, and it is tested in meaningful scenarios.

489 citations


Journal ArticleDOI
TL;DR: In this article, a framework for optimal design of battery charging/swap stations in distribution systems based on life cycle cost (LCC) is presented, where the battery swapping station is more suitable for public transportation than rapid charging stations.
Abstract: Electric vehicle (EV) is a promising technology for reducing environmental impacts of road transport. In this paper, a framework for optimal design of battery charging/swap stations in distribution systems based on life cycle cost (LCC) is presented. The battery charging/swap station models are developed to compare the impacts of rapid-charging stations and battery swap stations. Meanwhile, in order to meet the requirements of increased power provided during the charging period, the distribution network should be reinforced. In order to control this reinforcement cost, stations should be placed at appropriate places and be scaled correctly. For optimal cost-benefit analysis and safety operation, the LCC criterion is used to assess the project and a modified differential evolution algorithm is adopted to solve the problem. The proposed method has been verified on the modified IEEE 15-bus and 43-bus radial distribution systems. The results show that battery swap station is more suitable for public transportation in distribution systems.

414 citations


Journal ArticleDOI
TL;DR: A distributed algorithm is presented to solve the economic power dispatch with transmission line losses and generator constraints based on two consensus algorithms running in parallel using a consensus strategy called consensus on the most up-to-date information.
Abstract: A distributed algorithm is presented to solve the economic power dispatch with transmission line losses and generator constraints. The proposed approach is based on two consensus algorithms running in parallel. The first algorithm is a first-order consensus protocol modified by a correction term which uses a local estimation of the system power mismatch to ensure the generation-demand equality. The second algorithm performs the estimation of the power mismatch in the system using a consensus strategy called consensus on the most up-to-date information. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. Simulations performed on standard test cases demonstrate the effectiveness of the proposed approach for both small and large systems.

384 citations


Journal ArticleDOI
TL;DR: In this paper, an integrated distribution locational marginal pricing (DLMP) method designed to alleviate congestion induced by electric vehicle (EV) loads in future power systems is presented, which considers EV aggregators as price takers in local DSO market and demand price elasticity.
Abstract: This paper presents an integrated distribution locational marginal pricing (DLMP) method designed to alleviate congestion induced by electric vehicle (EV) loads in future power systems. In the proposed approach, the distribution system operator (DSO) determines distribution locational marginal prices (DLMPs) by solving the social welfare optimization of the electric distribution system which considers EV aggregators as price takers in the local DSO market and demand price elasticity. Nonlinear optimization has been used to solve the social welfare optimization problem in order to obtain the DLMPs. The efficacy of the proposed approach was demonstrated by using the bus 4 distribution system of the Roy Billinton Test System (RBTS) and Danish driving data. The case study results show that the integrated DLMP methodology can successfully alleviate the congestion caused by EV loads. It is also shown that the socially optimal charging schedule can be implemented through a decentralized mechanism where loads respond autonomously to the posted DLMPs by maximizing their individual net surplus.

345 citations


Journal ArticleDOI
TL;DR: In this paper, a model for microgrid optimal scheduling considering multi-period islanding constraints is presented, where the objective of the problem is to minimize the microgrid total operation cost which comprises the generation cost of local resources and cost of energy purchase from the main grid.
Abstract: This paper presents a model for microgrid optimal scheduling considering multi-period islanding constraints. The objective of the problem is to minimize the microgrid total operation cost which comprises the generation cost of local resources and cost of energy purchase from the main grid. The microgrid optimal scheduling problem is decomposed into a grid-connected operation master problem and an islanded operation subproblem. The microgrid capability in operating in the islanded mode for multiple hours is scrutinized by a T-τ islanding criterion. The integer scheduling decisions determined in the master problem will be examined against the microgrid islanding feasibility in the subproblem. The scheduling decisions will be revised using proper islanding cuts if sufficient generation is not available to guarantee a feasible islanding. Islanding cuts will revise generating units, energy storage systems, and adjustable loads schedules. Any change in the schedule of adjustable loads outside the operating time interval specified by consumers is penalized by an inconvenience factor in the objective. Numerical simulations demonstrate the effectiveness of the proposed microgrid optimal scheduling model and explore its economic and reliability merits.

345 citations


Journal ArticleDOI
TL;DR: In this paper, the optimal allocation of Dispersed Storage Systems (DSSs) in active distribution networks (ADNs) is studied by defining a multi-objective optimization problem aiming at finding the optimal trade-off between technical and economical goals.
Abstract: Dispersed storage systems (DSSs) can represent an important near-term solution for supporting the operation and control of active distribution networks (ADNs). Indeed, they have the capability to support ADNs by providing ancillary services in addition to energy balance capabilities. Within this context, this paper focuses on the optimal allocation of DSSs in ADNs by defining a multi-objective optimization problem aiming at finding the optimal trade-off between technical and economical goals. In particular, the proposed procedure accounts for: 1) network voltage deviations; 2) feeders/lines congestions; 3) network losses; 4) cost of supplying loads (from external grid or local producers) together with the cost of DSS investment/maintenance; 5) load curtailment; and 6) stochasticity of loads and renewables produc- tions. The DSSs are suitably modeled to consider their ability to support the network by both active and reactive powers. A convex formulation of ac optimal power flow problem is used to define a mixed integer second-order cone programming problem to opti- mally site and size the DSSs in the network. A test case referring to IEEE 34 bus distribution test feeder is used to demonstrate and discuss the effectiveness of the proposed methodology.

344 citations


Journal ArticleDOI
TL;DR: In this paper, a combination of Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind power forecasting up to one day ahead, and the proposed model provided around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data.
Abstract: Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data.

343 citations


Journal ArticleDOI
TL;DR: In this article, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems, where the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs).
Abstract: An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method.

314 citations


Journal ArticleDOI
TL;DR: In this article, a framework for modeling energy technologies with inter-temporal characteristics in an active network management (ANM) context is presented, which includes the optimization of non-firm connected generation, principles of access for nonfirm generators, energy storage, and flexible demand.
Abstract: Active Network Management is a philosophy for the operation of distribution networks with high penetrations of renewable distributed generation. Technologies such as energy storage and flexible demand are now beginning to be included in Active Network Management (ANM) schemes. Optimizing the operation of these schemes requires consideration of inter-temporal linkages as well as network power flow effects. Network effects are included in optimal power flow (OPF) solutions but this only optimizes for a single point in time. Dynamic optimal power flow (DOPF) is an extension of OPF to cover multiple time periods. This paper reviews the generic formulation of DOPF before developing a framework for modeling energy technologies with inter-temporal characteristics in an ANM context. The framework includes the optimization of nonfirm connected generation, principles of access for nonfirm generators, energy storage, and flexible demand. Two objectives based on maximizing export and revenue are developed and a case study is used to illustrate the technique. Results show that DOPF is able to successfully schedule these energy technologies. DOPF schedules energy storage and flexible demand to reduce generator curtailment significantly in the case study. Finally, the role of DOPF in analyzing ANM schemes is discussed with reference to extending the optimization framework to include other technologies and objectives.

308 citations


Journal ArticleDOI
TL;DR: In this article, a graph-theoretic DSR strategy incorporating microgrids that maximizes the restored load and minimizes the number of switching operations is presented, where a spanning tree search algorithm is applied to find the candidate restoration strategies by modeling micro-grids as virtual feeders.
Abstract: Distribution system restoration (DSR) is aimed at restoring loads after a fault by altering the topological structure of the distribution network while meeting electrical and operational constraints. The emerging microgrids embedded in distribution systems enhance the self-healing capability and allow distribution systems to recover faster in the event of an outage. This paper presents a graph-theoretic DSR strategy incorporating microgrids that maximizes the restored load and minimizes the number of switching operations. Spanning tree search algorithms are applied to find the candidate restoration strategies by modeling microgrids as virtual feeders and representing the distribution system as a spanning tree. Unbalanced three-phase power flow is performed to ensure that the proposed system topology satisfies all operational constraints. Simulation results based on a modified IEEE 37-node system and a 1069-node distribution system demonstrate the effectiveness of the proposed approach.

Journal ArticleDOI
TL;DR: In this paper, a battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas.
Abstract: Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude This will make delivering reliable power, on demand, a major challenge In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCO's profit from energy transactions, system planning and operation cost savings The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a delay margin as an additional performance index for the synthesis of wide-area damping controller (WADC) for flexible ac transmission systems (FACTS) devices to damp inter-area oscillations.
Abstract: The usage of remote signals obtained from a wide-area measurement system (WAMS) introduces time delays to a wide-area damping controller (WADC), which would degrade system damping and even cause system instability. The time-delay margin is defined as the maximum time delay under which a closed-loop system can remain stable. In this paper, the delay margin is introduced as an additional performance index for the synthesis of classical WADCs for flexible ac transmission systems (FACTS) devices to damp inter-area oscillations. The proposed approach includes three parts: a geometric measure approach for selecting feedback remote signals, a residue method for designing phase-compensation parameters, and a Lyapunov stability criterion and linear matrix inequalities (LMI) for calculating the delay margin and determining the gain of the WADC based on a tradeoff between damping performance and delay margin. Three case studies are undertaken based on a four-machine two-area power system for demonstrating the design principle of the proposed approach, a New England ten-machine 39-bus power system and a 16-machine 68-bus power system for verifying the feasibility on larger and more complex power systems. The simulation results verify the effectiveness of the proposed approach on providing a balance between the delay margin and the damping performance.

Journal ArticleDOI
TL;DR: In this article, the authors proposed a hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand.
Abstract: Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with associated confidence levels are generated through direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility and has a high potential of practical applications in power systems.

Journal ArticleDOI
TL;DR: In this paper, an integrated methodology that considers renewable distributed generation (RDG) and demand responses (DR) as options for planning distribution systems in a transition towards low-carbon sustainability is presented.
Abstract: This study presents an integrated methodology that considers renewable distributed generation (RDG) and demand responses (DR) as options for planning distribution systems in a transition towards low-carbon sustainability. It is assumed that demand responsiveness is enabled by real-time pricing (RTP), and the problem has been formulated as a dynamic two-stage model. It co-optimizes the allocation of renewables [including wind and solar photovoltaic (PV)], non-renewable DG units (gas turbines) and smart metering (SM) simultaneously with network reinforcement for minimizing the total economic and carbon-emission costs over planning horizons. The behavior compliance to RTP is described through a nodal-based DR model, in which the fading effect attended during the load recovery is highlighted. Besides, uncertainties associated with renewable energy generation and price-responsiveness of customers are also taken into account and represented by multiple probabilistic scenarios. The proposed methodology is implemented by employing an efficient hybrid algorithm and applied to a typical distribution test system. The results demonstrate the effectiveness in improving the efficiency of RDG operations and mitigating CO2 footprint of distribution systems, when compared with the conventional planning paradigms.

Journal ArticleDOI
TL;DR: In this paper, a modified system frequency response model is derived and used to find analytical representation of system minimum frequency in thermal-dominant multi-machine systems, and an effective piecewise linearization (PWL) technique is employed to linearize the nonlinear function representing the minimum system frequency, facilitating its integration in the SCUC problem.
Abstract: Rapidly increasing the penetration level of renewable energies has imposed new challenges to the operation of power systems. Inability or inadequacy of these resources in providing inertial and primary frequency responses is one of the important challenges. In this paper, this issue is addressed within the framework of security-constrained unit commitment (SCUC) by adding new constraints representing the system frequency response. A modified system frequency response model is first derived and used to find analytical representation of system minimum frequency in thermal-dominant multi-machine systems. Then, an effective piecewise linearization (PWL) technique is employed to linearize the nonlinear function representing the minimum system frequency, facilitating its integration in the SCUC problem. The problem is formulated as a mixed-integer linear programming (MILP) problem which is solved efficiently by available commercial solvers. The results indicate that the proposed method can be utilized to integrate renewable resources into power systems without violating system frequency limits.

Journal ArticleDOI
TL;DR: In this article, a stochastic modeling and simulation technique for analyzing impacts of electric vehicles charging demands on distribution network is proposed, where the feeder daily load models, electric vehicle start charging time, and battery state of charge used in the impact study are derived from actual measurements and survey data.
Abstract: A stochastic modeling and simulation technique for analyzing impacts of electric vehicles charging demands on distribution network is proposed in this paper. Different from the previous deterministic approaches, the feeder daily load models, electric vehicle start charging time, and battery state of charge used in the impact study are derived from actual measurements and survey data. Distribution operation security risk information, such as over-current and under-voltage, is obtained from three-phase distribution load flow studies that use stochastic parameters drawn from Roulette wheel selection. Voltage and congestion impact indicators are defined and a comparison of the deterministic and stochastic analytical approaches in providing information required in distribution network reinforcement planning is presented. Numerical results illustrate the capability of the proposed stochastic models in reflecting system losses and security impacts due to electric vehicle integrations. The effectiveness of a controlled charging algorithm aimed at relieving the system operation problem is also presented.

Journal ArticleDOI
TL;DR: An early event detection algorithm based on the change of core subspaces of the PMU data at the occurrence of an event is proposed and theoretical justification for the algorithm is provided using linear dynamical system theory.
Abstract: This paper studies the fundamental dimensionality of synchrophasor data, and proposes an online application for early event detection using the reduced dimensionality. First, the dimensionality of the phasor measurement unit (PMU) data under both normal and abnormal conditions is analyzed. This suggests an extremely low underlying dimensionality despite the large number of the raw measurements. An early event detection algorithm based on the change of core subspaces of the PMU data at the occurrence of an event is proposed. Theoretical justification for the algorithm is provided using linear dynamical system theory. Numerical simulations using both synthetic and realistic PMU data are conducted to validate the proposed algorithm.

Journal ArticleDOI
TL;DR: In this paper, the frequency stability challenges at high and ultra-high wind penetrations were examined in the All-Island system (AIS) and the impact of both largest infeed loss and network fault induced wind turbine active power dips was examined.
Abstract: Synchronous island power systems, such as the combined Ireland and Northern Ireland power system, are facing increasing penetrations of renewable generation. As part of a wider suite of studies, performed in conjunction with the transmission system operators (TSOs) of the All-Island system (AIS), the frequency stability challenges at high and ultra-high wind penetrations were examined. The impact of both largest infeed loss and network fault induced wind turbine active power dips was examined: the latter contingency potentially representing a fundamental change in frequency stability risk. A system non-synchronous penetration (SNSP) ratio was defined to help identify system operational limits. A wide range of system conditions were studied, with results showing that measures such as altering ROCOF protection and enabling emulated inertia measures were most effective in reducing the frequency stability risk of a future Ireland system.

Journal ArticleDOI
TL;DR: In this paper, the problem posed by complex, nonlinear controllers for power system load flows employing multi-terminal voltage source converter (VSC) HVDC systems is addressed.
Abstract: This paper addresses the problem posed by complex, nonlinear controllers for power system load flows employing multi-terminal voltage source converter (VSC) HVDC systems. More realistic dc grid control strategies can thus be carefully considered in power flow analysis of ac/dc grids. Power flow methods for multi-terminal VSC-HVDC (MTDC) systems are analyzed for different types of dc voltage control techniques and the weaknesses of present methods are addressed. As distributed voltage control is likely to be adopted by practical dc grids, a new generalized algorithm is proposed to solve the power flow problems with various nonlinear voltage droops, and the method to incorporate this algorithm with ac power flow models is also developed. With five sets of voltage characteristics implemented, the proposed scheme is applied to a five-terminal test system and shows satisfactory performance. For a range of wind power variations and converter outages, post-contingency behaviors of the system under the five control scenarios are examined. The impact of these controls on the power flow solutions is assessed.

Journal ArticleDOI
TL;DR: In this article, a new load frequency control (LFC) for multi-area power systems is developed based on the direct-indirect adaptive fuzzy control technique, which guarantees stability of the overall closed-loop system.
Abstract: In this paper, a new load frequency control (LFC) for multi-area power systems is developed based on the direct–indirect adaptive fuzzy control technique. LFCs for each area are designed based on availability of frequency deviation of each area and tie-line power deviation between areas. The fuzzy logic system approximation capabilities are exploited to develop suitable adaptive control law and parameter update algorithms for unknown interconnected LFC areas. An ${H}_{\infty}$ tracking performance criterion is introduced to minimize the approximation errors and the external disturbance effects. The proposed controller guarantees stability of the overall closed-loop system. Simulation results for a real three-area power system prove the effectiveness of the proposed LFC and show its superiority over a classical PID controller and a type-2 fuzzy controller.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a distributed direct load control scheme for large-scale residential demand response (DR) built on a two-layer communication-based control architecture, which utilizes the average consensus algorithm to distribute portions of the desired aggregated demand to each EMC in a decentralized fashion.
Abstract: This paper proposes a distributed direct load control scheme for large-scale residential demand response (DR) built on a two-layer communication-based control architecture. The lower-layer network is within each building, where the energy management controller (EMC) uses wireless links to schedule operation of appliances upon request according to a local power consumption target. The upper-layer network links a number of EMCs in a region whose aggregated demand is served by a load aggregator. The load aggregator wants the actual aggregated demand over this region to match a desired aggregated demand profile. Our approach utilizes the average consensus algorithm to distribute portions of the desired aggregated demand to each EMC in a decentralized fashion. The allocated portion corresponds to each building's aforementioned local power consumption target which its EMC then uses to schedule the in-building appliances. The result will be an aggregated demand over this region that more closely reaches the desired demand. Numerical results show that our scheme can alleviate the mismatch between the actual aggregated demand and the desired aggregated demand profile.

Journal ArticleDOI
TL;DR: In this article, an optimal reactive power coordination strategy based on the load and irradiance forecast is proposed to minimize the number of tap operations so as not to reduce the operating life of the tap control mechanism and avoid runaway.
Abstract: The uptake of variable megawatts from photovoltaics (PV) challenges distribution system operation. The primary problem is significant voltage rise in the feeder that forces existing voltage control devices such as on-load tap-changers and line voltage regulators to operate continuously. The consequence is the deterioration of the operating life of the voltage control mechanism. Also, conventional non-coordinated reactive power control can result in the operation of the line regulator at its control limit (runaway condition). This paper proposes an optimal reactive power coordination strategy based on the load and irradiance forecast. The objective is to minimize the number of tap operations so as not to reduce the operating life of the tap control mechanism and avoid runaway. The proposed objective is achieved by coordinating various reactive power control options in the distribution network while satisfying constraints such as maximum power point tracking of PV and voltage limits of the feeder. The option of voltage support from PV plant is also considered. The problem is formulated as constrained optimization and solved through the interior point technique. The effectiveness of the approach is demonstrated in a realistic distribution network model.

Journal ArticleDOI
TL;DR: In this article, the instability of grid side converters of wind turbines defined as loss of synchronism (LOS), where the wind turbines lose synchronism with the grid fundamental frequency (e.g., 50 Hz) during very deep voltage sags, is explored with its theory, analyzed and a novel stability solution based on PLL frequency is proposed; and both are verified with power system simulations and by experiments on a grid connected converter setup.
Abstract: In recent grid codes for wind power integration, wind turbines are required to stay connected during grid faults even when the grid voltage drops down to zero; and also to inject reactive current in proportion to the voltage drop. However, a physical fact, instability of grid-connected converters during current injection to very low (close to zero) voltage faults, has been omitted, i.e., failed to be noticed in the previous wind power studies and grid code revisions. In this paper, the instability of grid side converters of wind turbines defined as loss of synchronism (LOS), where the wind turbines lose synchronism with the grid fundamental frequency (e.g., 50 Hz) during very deep voltage sags, is explored with its theory, analyzed and a novel stability solution based on PLL frequency is proposed; and both are verified with power system simulations and by experiments on a grid-connected converter setup.

Journal ArticleDOI
TL;DR: In this paper, a general optimization and modeling framework for coupled power flow studies on different energy infrastructures is proposed, which decomposes the multi-carrier optimal power flow problem into its traditional separate OPF problem in such a way that the major advantages of simultaneous analysis of MCE systems would not be sacrificed.
Abstract: Presence of energy hubs in the future vision of energy networks creates a great opportunity for system planners and operators to move towards more efficient systems. The role of energy hubs as the intermediate in multi-carrier energy (MCE) systems calls for a generic framework to study the new upcoming technical as well as economical effects on the system performance. In response, this paper attempts to develop a general optimization and modeling framework for coupled power flow studies on different energy infrastructures. This, as a large-scale nonlinear problem, is approached through a robust optimization technique, i.e., multi-agent genetic algorithm (MAGA). The proposed procedure decomposes the multi-carrier optimal power flow (MCOPF) problem into its traditional separate OPF problem in such a way that the major advantages of simultaneous analysis of MCE systems would not be sacrificed. The presented scheme is then applied to an 11-hubs test system and introduces its expected applicability and robustness in the MCE systems analysis.

Journal ArticleDOI
TL;DR: In this paper, a new analytical expression is proposed to size a PV unit, which can supply active and reactive powers, based on the derivation of a multiobjective index (IMO) that is formulated as a combination of three indices, namely active power loss, reactive power loss and voltage deviation.
Abstract: A constant or voltage-dependent load model is usually assumed in most distributed generation (DG) planning studies. However, this paper proposes several different types of time-varying voltage-dependent load models to determine the penetration level of photovoltaic (PV) units in a distribution network. Here, a new analytical expression is first proposed to size a PV unit, which can supply active and reactive powers. This expression is based on the derivation of a multiobjective index (IMO) that is formulated as a combination of three indices, namely active power loss, reactive power loss and voltage deviation. The expression is then adapted to allocate PV units while considering the time-varying load models and probabilistic PV generation. The effectiveness of the proposed approach was validated on 69- and 33-bus test distribution systems. The results showed that PV allocation with different types of time-varying load models can produce dissimilar penetration levels.

Journal ArticleDOI
TL;DR: In this paper, the authors discuss the extension of electromechanical stability models of voltage source converter high voltage direct current (VSC HVDC) to multi-terminal (MTDC) systems.
Abstract: This paper discusses the extension of electromechanical stability models of voltage source converter high voltage direct current (VSC HVDC) to multi-terminal (MTDC) systems. The paper introduces a control model with a cascaded DC voltage control at every converter that allows a two-terminal VSC HVDC system to cope with converter outages. When extended to an MTDC system, the model naturally evolves into a master-slave set-up with converters taking over the DC voltage control in case the DC voltage controlling converter fails. It is shown that the model can be used to include a voltage droop control to share the power imbalance after a contingency in the DC system amongst the converters in the system. Finally, the paper discusses two possible model reductions, in line with the assumptions made in transient stability modeling. The control algorithms and VSC HVDC systems have been implemented using both MatDyn, an open source MATLAB transient stability program, as well as the commercial power system simulation package EUROSTAG.

Journal ArticleDOI
TL;DR: In this article, a mixed-integer linear programing (MILP) security-constrained optimal power and gas flow model is proposed for the IEEE 24-bus system and a modified Belgian high-calorific gas network.
Abstract: Continuous liberalization and interconnection of energy markets worldwide has raised concerns about the inherent interdependency between primary energy supply and electric systems. With the growing interaction among energy carriers, limitations on the fuel delivery are becoming increasingly relevant to the operation of power systems. This paper contributes with a novel formulation of a mixed-integer linear programing (MILP) security-constrained optimal power and gas flow. To this end, an iterative methodology, based on development of linear sensitivity factors, determines the stabilized post-contingency condition of the integrated network. The proposed model allows system operators not only to perform security analysis but also to adjust in advance state variables of the integrated system optimally and fast, so that $n-1$ contingencies do not result in violations. Case studies integrate the IEEE 24-bus system and a modified Belgian high-calorific gas network for analyzing the performance of the formulation and solution methodology.

Journal ArticleDOI
TL;DR: In this article, a chance-constrained stochastic programming formulation with economic and reliability metrics is presented for the day-ahead scheduling, where reserve requirements and line flow limits are formulated as chance constraints in which power system reliability requirements are to be satisfied with a presumed level of high probability.
Abstract: This paper proposes a day-ahead stochastic scheduling model in electricity markets. The model considers hourly forecast errors of system loads and variable renewable sources as well as random outages of power system components. A chance-constrained stochastic programming formulation with economic and reliability metrics is presented for the day-ahead scheduling. Reserve requirements and line flow limits are formulated as chance constraints in which power system reliability requirements are to be satisfied with a presumed level of high probability. The chance-constrained stochastic programming formulation is converted into a linear deterministic problem and a decomposition-based method is utilized to solve the day-ahead scheduling problem. Numerical tests are performed and the results are analyzed for a modified 31-bus system and an IEEE 118-bus system. The results show the viability of the proposed formulation for the day-ahead stochastic scheduling. Comparative evaluations of the proposed chance-constrained method and the Monte Carlo simulation (MCS) method are presented in the paper.

Journal ArticleDOI
TL;DR: In this article, the authors present an in-depth review on implementing and assessing conservation voltage reduction (CVR) for peak demand reduction and energy savings through reducing the voltage level of the electrical distribution system.
Abstract: Conservation voltage reduction (CVR) is widely adopted by utilities for peak demand reduction and energy savings through reducing the voltage level of the electrical distribution system. This paper presents an in-depth review on implementing and assessing CVR. The methodologies to quantify CVR effects are categorized into comparison-based, regression-based, synthesis-based and simulation-based methods. The implementation strategies for voltage reduction are classified into open-loop and closed-loop methods. The impacts of emerging smart-grid technologies on CVR are also discussed. The paper can provide researchers and utility engineers with further insights into the state of the art, technical barriers and future research directions of CVR technologies.