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This paper proposes an effect orientated approach to enhance the short-term voltage stability for wind-penetrated power systems. The low system stability with high uncertain renewable energy is addressed by coordination of network and non-network solutions, i.e., STATCOM placement and UVLS respectively in this paper. The proposed model considers the uncertainties of power systems, including operating points load dynamics and multiple contingencies. Unlike other methods that try to minimize the cost, the proposed method aims to maximize the voltage stability enhancement with a fixed cost. The high computing burden is alleviated by a net flow-based scenario reduction algorithm. Compared with the conventional approaches, this paper has three major contributions: 1) Device placement is coordinated with demand response. 2) Operating points and type 4 wind turbines are considered. 3) An effect maximizing DP algorithm with a robust scenario reduction method is applied. The effectiveness and efficiency of the proposed method is demonstrated on the New England 39-bus test system.

High penetration of wind energy in the modern power system exposes the need of smoothing the fluctuating output power in an effective and conducive way. In this context, this paper proposes two novel control strategies that utilize the self-capability of permanent magnet synchronous generator-based wind turbine to realize power smoothing. The first strategy pursues to offer power smoothing support via simultaneous utilization of dc-link voltage control, rotor speed control, and pitch angle control. The second control strategy seeks to coordinate the three concerned individual control schemes in a hierarchical manner, where the power smoothing tasks are allocated to individual control modules or their combinations dynamically in line with WT's operation status. Both two strategies are able to provide power smoothing support by fully exploiting wind turbine's self-capability, whereas the second strategy has the merits on 1) reducing the activation frequency of pitch angle control, and 2) enhancing wind energy harvesting. Case studies of the proposed control strategies are carried out to compare and verify their effectiveness in achieving power smoothing.

In this paper, an operating cost optimization problem of electric vehicles (EVs) is studied in a large-scale logistics and transportation network. An extended EV operational model is proposed for a multiple depots and charge stations environment where practical constraints are included. In the proposed model, new practical mathematical schemes are proposed to describe the constraints. Then, a new two-step clustering heuristic optimization (TCHO) method is developed to minimize the total operating cost of the EV routes while satisfying all the constraints. In the first step, a novel heuristic edge sharing assigning algorithm is designed to split the large scale logistic network into different clusters. In the second step, a new shortest path heuristic method is developed to minimize the total expense of the EV routes for each cluster. Furthermore, based on the TCHO, a novel discrete differential evolution-TCHO is proposed to improve the performance on solving the problem. The effectiveness of the proposed models and methods is verified by comprehensive numerical simulations where the well-known vehicle routing problem benchmarks are applied.

This paper proposes a day-ahead dispatch framework of thermostatically controlled loads (TCLs) for system peak load reduction. The proposed day-ahead scheduling framework estimates the user's indoor thermal comfort degree through the building thermal inertia modelling. Based on the thermal comfort estimation, a day-ahead TCL scheduling model is formulated, which consists of 3 stages: ① TCL aggregator estimate their maximal controllable TCL capacities at each scheduling time interval by solving a optimization model; ② the system operator performs the day-ahead system dispatch to determine the load shedding instruction for each aggregator; and ③ the TCL aggregators schedules the ON/OFF control actions of the TCL groups based on the instruction from the system operator. A heuristic based optimization method, history driven differential evolution (HDDE) algorithm, is employed to solve the day-ahead dispatch model of the TCL aggregator side. Simulations are conducted to validate the proposed model.

This paper proposes a novel high-efficiency generation technique for photovoltaic (PV) system, named maximum power point capturing (MPPC) technique. This is an aperiodic perturbation MPPC technique compared to the conventional periodic perturbation maximum power point tracking technique. Firstly, under a closed-loop circuit and an open-loop circuit, the complete I–V curves and P–V curves are defined. Secondly, the proposed MPPC technique is based on the complete I–V curves and a practical model of solar PV systems. The proposed method realizes that maximum power point (MPP) is captured online, and its control strategy is designed to set a steady operating area around MPP. The duty cycle keeps constant when the operating point is within the steady operating area, i.e., aperiodic perturbation, and when the operating point is outside the steady operating area, MPPC is triggered to capture a new MPP with an updated steady operating area. Simulation results demonstrate that no oscillations exist in steady-state; dynamic performances are improved; and only two perturbations are required to capture the new MPP. Using the proposed MPPC method, low voltage ride through and high voltage ride through can be prevented.

Microgrid is an effective means to integrate distributed generation (DG) resource. However, uncertain renewable DG such as wind turbine and photovoltaic outputs and load demands can introduce tremendous difficulties for energy management in microgrids. To mitigate such difficulties, price-based demand response (PBDR) can adjust the loads to adapt to the renewables. On the other hand, dispatchable DG such as microturbines can coordinate with the PBDR to further manage the power balance and achieve economic benefits. In this paper, a two-stage robust microgrid coordination strategy is proposed: a PBDR is scheduled a day ahead and microturbine outputs are modified hourly. A two-stage robust optimization model is proposed to address the coordination problem with guaranteed robustness against the uncertainties of renewable DG and load demands. Simulation results show PBDR and multiple DG units can coordinate effectively to accommodate the renewable and demand uncertainties while maximizing the microgrid benefits.

Under maximum power point tracking control, wind power is not dispatchable and can be highly disturbing to the supply–demand balance control in the context of microgrid particularly. To effectively dispatch wind power according to, e.g., operator command or market schedule, a variable utilization level (UL) scheme is proposed for a wind power plant (WPP) to fulfill the dispatch order while reducing the loss of total energy production. Considering different wind conditions, the proposed scheme directs the power output for each wind turbine (WT) according to a specific UL, which is adaptively adjusted according to WT rotor speed so that less reduction of energy production can be ensured. Meanwhile, more rotational kinetic energy stored can be stored in WPP, which can be later released for system support when needed. The proposed variable UL scheme is fully investigated in a doubly fed induction generator-based WPP and the results clearly indicate the proposed scheme can harvest more energy than the conventional same UL one while fulfilling the dispatch demand.

In order to mitigate over-frequency events in the power systems, this paper proposes a new variable gain based control strategy to maximize the frequency support while minimizing impacts to the overall energy production for a wind power plant (WPP). Because of different wind conditions, the energy production by each WT is not equally compromised by adaptively adjusting the droop gains according to the different rotor speeds so that minimal reduction of energy production can be ensured. The control performance was fully investigated in a doubly fed induction generator (DFIG) based WPP and the results clearly indicate the proposed variable droop gain scheme can harness more wind energy than the conventional same droop gain scheme while providing similar frequency support.

Increasing penetration of plug-in electric vehicles (PEVs) has a substantial impact on the operation of power distribution networks. Given the fast-growing load demands from PEVs and unmatched infrastructure investment in transformer and feeder capacity, the PEV charging is subjected to both spatially and temporally security constraints beyond which the network failure may occur. This paper proposes a game-theory-based distributed charging control method to coordinate large-scale PEVs without compromising the security of the distribution network. Under a noncooperative game framework, a price-driven charging model is designed to minimize the cost of each individual PEV customer while satisfying the network loading constraints. Then, a Newton-type method is developed to find a better Nash equilibrium of the game model at a superlinear convergence rate. Furthermore, an accelerated gradient method is proposed to tackle the subproblem for each user's best response. The update of the user's best response is implemented in a distributed way in order to protect user's privacy. The convergence rate of the proposed algorithms is rigorously proved. The effectiveness and efficiency of the proposed methods are tested on the IEEE 13-bus system.

A novel system frequency support strategy is proposed for the two-stage three phase photovoltaic generation system, which involves simultaneously utilising a direct current (DC)-link capacitor and deloading generation output. When frequency variation is detected, the energy stored in an inverter DC-link capacitor is utilised to enable virtual inertia characteristic for system frequency support. At the same time, a deloading control strategy is also activated to provide system frequency support based on reserved generation beforehand. To address impacts due to the changing working conditions with respect to irradiance and ambient temperature, a variable droop scheme is designed for dynamically adjusting the frequency regulation ability based on deloading. Case studies using a microgrid system embedded with a solar and diesel generator are carried out to verify the effectiveness of the proposed control strategy.

In this paper, a hierarchical security-constrained optimal power flow (SCOPF) model is proposed for a meshed ac/multiterminal HVDC (MTDC) system with high wind penetration. The two interacting levels in the proposed model are as follows: 1) the high level is a traditional SCOPF problem in an ac system that aims to minimize total generation and security control costs; and 2) the low level is a dynamic power dispatching problem, which regulates power flow in an MTDC grid according to reference signals from the high level. Thus, the proposed method utilizes an MTDC system to provide support for the ac system by redistributing power flow across the entire grid and reducing control costs. Two modified IEEE meshed ac/dc systems are used to demonstrate the performance of the proposed method.

Dynamic insecurity risk of a power system has been increasingly concerned due to the integration of stochastic renewable power sources (such as wind and solar power) and complicated demand response. In this paper, an intelligent early-warning system to achieve reliable online detection of risky operating conditions is proposed. The proposed intelligent system (IS) consists of an ensemble learning model based on extreme learning machine (ELM) and a decision-making process under a multiobjective programming framework. Taking an ensemble form, the randomness existing in individual ELM training is generalized and reliable classification results can be obtained. The decision making is designed for ELM ensemble whose parameters are optimized to search for the optimal tradeoff between the warning accuracy and the warning earliness of the proposed IS. The compromise solution turns out to significantly speed up the overall computation with an acceptable sacrifice in the accuracy (e.g., from 100% to 99.9%). More importantly, the proposed IS can provide multiple and switchable performances to the operators in order to satisfy different local dynamic security assessment requirements.

With power networks undergoing an unprecedented transition from traditional power systems to modern electric grids integrated with renewable energy sources, maintaining frequency stability of generators in modern power systems has become one of the major concerns. Targeting this issue, in this paper, we propose a novel frequency restoration strategy in photovoltaics (PV)-connected power systems using decentralized dynamic state estimation technique and PV power plant as a contingency power source. When a sudden increase in load demand occurs, the output power of PV panels is increased in order to compensate for the shortage of real power capacity of the generator, in order to restore the frequency of a certain generator bus bar. An unscented Kalman filter-based decentralized dynamic estimation is utilized in this study to estimate the frequency of a selected generator bus bar with local noisy voltage and current measurement data acquired by using phasor measurement units. Solar luminous intensity may vary over a period of time in different seasons, weather conditions, etc., which causes the variations in the output power of PV power plants. This irradiance uncertainty is also considered in this study. The proposed control strategy not only incorporates the frequency deviations of a generator bus-bar, but also takes into account the tie-line power deviations under disturbances. Simulation results demonstrate the capacity of proposed control schemes in restoring the frequency of generator bus-bars and also maintaining the tie-line power flowing between adjoining areas at it scheduled value.

In order to mitigate energy crisis and to meet carbon-emission reduction targets, the use of electrical energy produced by solar photovoltaic (PV) is inevitable. To meet the global increasing energy demand, PV power capacity will be expanded ranging from large-scale (from ten to several hundred MWs) PV farms at high and medium voltage level to kilowatt residential PV systems at low voltage level. It is expected that the PV penetration will increase in power systems with the retirement of traditional carbon-emission emitting power plants. Solar energy is diurnal in nature and in practice, it is highly uncertain due to various perturbation effects. With the recent technological advancements and rapid cost reductions in electrical energy storage (EES), EES could be deployed to enhance the system's performance and stability. This paper presents a comprehensive review on the emerging high penetration of PV with an overview of EES for PV systems. The crucial element, the building block of solar panel and the solar cell are reviewed. The emerging cell technologies are presented. A study of solar power forecasting techniques, an important tool for the successful operation and planning of PV and EES is included. A selection of EES is presented and studied for PV system purposes. Future research and areas for improvements in recent works and related areas are identified.

The development of monitoring technologies in smart grid is undergoing an essential transition from periodical magnitude-based measurement by supervisory control and data acquisition (SCADA) to real-time phasor measurement unit (PMU)-based wide area monitoring and control. The implementation of PMUs can substantially facilitate grid-wide state estimation and operation control, which is dependent on an effective PMU placement strategy. In this study, the optimal PMU placement (OPP) problem is investigated with respect to zonal voltage controllability and voltage magnitude estimation in cooperation with SCADA. In particular, the OPP is formulated as a multi-objective minimal Ncut partitioning problem, in which various operating scenarios are represented by probabilistic load flows to achieve a general solution for planning purpose. Spectral clustering is employed to efficiently find the optimal clustering boundaries, which indicate the resulting placement layout. The proposed placement scheme is tested on New England 39-bus and IEEE 118-bus systems. Simulation results demonstrate the effectiveness of the proposed approach.

Being more actively involved in the electricity market and power systems, wind farms are urgently expected to have similar controllable behavior to conventional generations so that demand assigned by the system operator can be met. However, determining the method of dispatching the reference among the widely spread and low-rating wind turbines is difficult. This paper provides a cooperation-driven distributed control scheme for wind farm active power regulation. Instead of competing with neighboring controllers completely, the control strategy evaluates system-wide impacts of local control actions, and aims to achieve coordinated control effect. In addition, the kinetic energy storage potential in a wind turbine is tapped to provide a buffer for power dispatch. Case studies demonstrate that a large wind farm can be effectively controlled to accurately track the demand power through the proposed control scheme.

In the smart grid environment, demand side management makes it possible to encourage consumers participating more actively via proper pricing schemes. In this paper, we analyze previous real-time pricing models and then provide a comprehensive model. A new criterion for designing real-time pricing models is proposed, which can guarantee that the optimal solution obtained from centralized algorithms is the same as that from the distributed algorithms. Furthermore, a fast distributed dual gradient algorithm is proposed to achieve the optimal solution. Compared with the widely used distributed dual sub-gradient algorithm, theoretically, the proposed one does not only accelerate the convergence rate, but also overcome the possible non-convergence during iteration process, which is a demerit in the traditional method. This new solver is of great importance in the application of dynamic pricing mechanism due to real-time requirement. More specifically, this new algorithm can largely reduce the information exchange between the energy provider and its customers, making the proposed method more practical. The simulations also validate its effectiveness and efficiency in solving real-time pricing problems.

In this paper, for the first time, electric vehicles are used for both the primary and secondary frequency controls to support power plants to rapidly suppress fluctuations in the system frequency due to load disturbances. Via networked control and wide-area communication infrastructures, multiple interval time-varying delays exist in the communication channels between the control center, power plant, and an aggregation of electric vehicles. By coordinating batteries’ state of charge control, the behaviors of the vehicle owners and the uncertainties imposed by the changes of the batteries’ state of charge are taken into consideration. A power system model incorporating multiple time-varying delays and uncertainties is first proposed. Then, a robust static output feedback frequency controller is designed to guarantee the resulting closed-loop system stable with an $H_\infty$ attenuation level. By utilizing a novel integral inequality, namely refined-Jensen inequality, and an improved reciprocally convex combination, the design conditions are formulated in terms of tractable linear matrix inequalities which can be efficiently solved by various computational tools. The effectiveness of the proposed control scheme is verified by extensive simulations.

This paper proposes a new robust security-constrained optimal power flow (SCOPF) method to balance the economy. and security requirements under uncertainties associated with renewable generation and load demand. Given the significant growth in microgrid (MG) deployments over the world, this paper explores the potential of using multiple MGs in supporting main grid's security control. Corrective control is employed to relieve postcontingency overflows by effectively coordinating system generators and multiple MGs. An incentive-based mechanism is designed to encourage the MGs to actively cooperate with the main grid for postcontingency recovery, which makes the proposed method to distinguish from the previous models using a traditional centralized control method, such as direct load control. A scenario-decomposition-based approach is then developed to solve the proposed robust SCOPF problem. Numerical simulations on IEEE 14- and IEEE 118-bus systems demonstrate the effectiveness and efficiency of the proposed method.

In addition to increasing penetration of distributed generation (DG), the distribution system power flow may be significantly impacted by direction and magnitude. This paper proposes a method for optimal placement of wind DG considering the unbalanced operation of distribution systems. The objective function includes static voltage stability index, three-phase unbalance index, system reliability index, and DG investment cost. The untransposed distribution lines and unbalanced load are modelled, and corresponding static voltage stability index and system reliability considering DG penetrations are derived. The expected and stochastic daily distributed generation and demand profiles in four seasons are calculated to improve the accuracy. To solve this multi-objective optimization model, a fuzzy membership function is used to integrate the four individual objectives, and a sensitivity-based method is proposed to solve the model efficiently. Case study on IEEE 13-bus distribution 3-phase networks and 123-node test feeder successfully verifies the performance of the proposed approach.

This paper investigates two novel control strategies that enable system inertia supports by permanent magnet synchronous generator (PMSG) wind turbines during transient events. The first strategy seeks to provide inertia support to the system through simultaneous utilization of dc-link capacitor energy, and wind turbine (WT) rotor kinetic energy (KE). The second strategy supports system inertia through orderly exerting dc-link capacitor energy of WT and then WT rotor KE via a cascading control scheme. Both strategies can effectively provide system inertia support by fully utilizing WT's own potentials, while the second strategy distinguishes itself by minimizing its impacts on wind energy harvesting. Case studies of one synchronous generator connected with a PMSG-based WT considering sudden load variations have been studied to validate and compare the two proposed strategies on providing rapid inertia response for the system.

This paper proposes a novel control strategy for converter stations of multi-terminal DC (MTDC) grid system. It optimizes the offshore wind farms power generation dispatched to the separate onshore AC systems while minimizing their impacts on AC grids. Based on the MTDC power flow analysis, the minimal control dimensions of MTDC converters are firstly proposed. The impact of DC resistances on power dispatching among MTDC grid is then fully discussed by analyzing the structure of inverse Jacobian matrix of MTDC grid power flow equation and the power dispatch law for each onshore AC grid is deduced subsequently. Finally, an optimization strategy that seeks to re-dispatch the power to each onshore converter according to the system inertia of connected AC grid is proposed. Case study using a typical five-terminal MTDC system shows that the proposed strategy can effectively mitigate wind power fluctuations by approximately equalizing the induced disturbances to the system frequency of each AC grid.

High penetration of solar energy can result in voltage rise in midday, while growth in residential air conditioning is the main contributor of overloading and voltage drop issues during peak demand time. This paper provides a hierarchical control scheme to coordinate multiple groups of aggregated thermostatically controlled loads to regulate network loading and voltage in a distribution network. Considering the limited number of messages that can be exchanged in a realistic communication environment, an event-triggered distributed control strategy is proposed in this paper. Through intermittent on and off toggling of air conditioners, the required active power adjustment is shared among participating aggregators to solve the issue. A case study is conducted and simulation results are presented to demonstrate the performance of the proposed control scheme.

This paper proposes a second-order sliding mode-based damping controller of DFIG for interarea oscillations. The proposed damping control strategy aims to utilize the reactive power modulation ability of DFIG to stabilize the power system in the event of oscillations caused by disturbances. First, the proposed controller is derived based on a two-area power system, and then it is extended to multiarea power systems. Compared with the conventional damping controller, the proposed one is insensitive to modeling uncertainties and parameter variations. Given the wide variation of operation points, the proposed sliding mode-based damping controller demonstrates a better robustness than the conventional damping controller. Simulation results on a two-area power system and a 10-machine 39-bus power system with DFIG-based wind farm integration show the improvement on system performance in interarea oscillation damping and demonstrate the robustness of the proposed control scheme in a wide operation region.

With rapid growth of installed wind power capacity worldwide and significant development of wind turbine generator (WTG) technologies, WTGs gradually have the capability to participate in system frequency regulation. To this regard, an overview of grid-integration codes on WTG's frequency regulation in several typical countries is first carried out. Then, typical WTG frequency control strategies, such as inertial emulation, de-loading control, overproduction, and droop control, are investigated in detail to reflect the development of related WTG technologies. In particular, existing research works about frequency coordination control at three levels, WTG level, wind-farm level, and system level, are discussed in depth and summarized. Future trends are discussed in the end.