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This study addresses the optimal power flow (OPF) problem incorporating renewable energy sources (RES) and flexible alternating current transmission systems (FACTS) using the Chaos Game Optimization (CGO) algorithm. Five objective functions are considered, which include minimizing generation costs, emissions, active power loss, voltage deviation, and enhancing voltage profiles. The OPF formulation considers the anticipated electricity production from wind turbines (WT) and photovoltaic (PV) units as dependent variables, while the voltage magnitude at WT and PV buses is treated as a control variable. Probabilistic models based on wind speed and solar irradiance are used to forecast the electrical output of WT and PV units. The proposed OPF methodology and solution method are validated on the IEEE 30-bus test network. This paper introduces and applies four optimization techniques inspired by biological and natural phenomena, namely CGO, Osprey Optimization Algorithm (OOA), RIME Algorithm, and Slime Mould Algorithm (SMA), to address both single-OPF and multi-OPF objective problems in electric power networks. The suggested optimization approaches are tested under different operational scenarios, considering various combinations of FACTS, renewable energy sources (solar PV and wind), and their locations in the network. To predict wind and solar PV power generation, Weibull and lognormal probability density functions are utilized, respectively. The objective function accounts for reserve cost due to overestimation and penalty cost due to underestimation of intermittent solar and wind power. The results demonstrate that the CGO technique is more efficient than other methods in solving OPF instances.

The utilization of renewable energy in power generation has been increasing in recent years, with the use of wind power sources being the most promising solution for sustainable power generation. The doubly-fed induction generator (DFIG) is one of the most commonly used generators in wind power generation applications, as it offers some technical of advantages. However, the increasing penetration of wind power generation poses tremendous technical challenges in power system operation as this can potentially affect system stability, requiring better control and monitoring schemes. Dynamic state estimation (DSE) offers the ability to achieve this purpose. With respect to this, the present paper proposes a DSE framework on a high-order model of DFIG consisting of 18 states. The method uses the unscented Kalman filter (UKF) which provides an accurate estimate of DFIG states under a strong system non linearity present in the wind turbine system. Furthermore, this paper demonstrates the robustness of the proposed method under different faults and noisy conditions. Finally, the paper also extends the use of UKF to estimate the unknown inputs of a DFIG system, such as control references in the rotor-side converter (RSC) and grid-side converter(GSC).

This paper introduces a 6-phase switched flux permanent magnet (SFPM) generator with high permanent magnet (PM) utilization, designed for a 2 kW wind power generation application. Two different SFPM generator variants were designed and analyzed by applying E-core and C-core techniques to a state-of-the-art (SOTA) benchmark SFPM machine structure. Key design variables, namely, the rotor pole number, split ratio, rotor pole width, and the number of turns per coil were optimized. The electromagnetic performance, specifically as a wind generator considering objectives of achieving high PM utilization, minimizing voltage waveform distortion, and reducing cogging torque, was evaluated using finite element analysis. The results obtained from the analysis demonstrated that both the E-core and C-core SFPM generators exhibited significantly higher flux-linkage per unit PM volume compared to the SOTA benchmark generator. In particular, the optimized C-core SFPM generator demonstrated nearly double the magnet utilization, half the cogging torque, and higher efficiency when compared to the benchmark SFPM generator. Notably, the C-core SFPM generator achieved a power per PM volume of 24,979 kW/m3, positioning it as the second-highest performing PM generator among all SOTA generators. Lastly, a prototype of the 6-phase 12-stator/25-rotor C-core SFPM generator was manufactured and tested to validate the analytical design.

This study proposes an available transfer capability (ATC) assessment approach in an intraday market that would enable wind energy participants to raise their level of integration without exposing them to high risk. The presented methodology allows the transmission system operator (TSO) to assess ATC near to the real state while considering voltage stability concerns as well as the uncertainties of the forecasted load and wind power. The proposed formulation is designed so that the existing VAR sources are appropriately exploited during the ATC assessment to maximize its anticipated value and boost the transaction between various zones. To alleviate the complexity of analyzing N-1 contingencies in the ATC estimation, a linear sensitivity technique is applied for ranking the most severe contingencies to be examined carefully. A two-level hybrid algorithm using the primal-dual interior point method (PDIPM) and an improved grey wolf optimizer (IGWO) is suggested for solving the problem. The performance of the proposed approach has been evaluated by its application on IEEE 30- reliability test system (RTS). The outcomes confirm the viability of the proposed approach for optimizing ATC value for various transactions through the best use of installed VAR devices, considering different locations of wind farms. The obtained findings also demonstrate the effectiveness of the suggested method for determining the most severe contingency associated with each scenario under consideration.

Ever increasing integration of renewable resources into contemporary power systems has imposed a unique set of challenges for system protection. Effectiveness of techniques based on conventional system protection theory has long been questionable in protecting power systems with higher penetration of inverter-based resources (IBRs). Fault direction identification is one of the commonly affected protection functions in the presence of IBRs. This paper presents a novel algorithm to correctly identify fault direction using negative sequence admittance measurements. The proposed technique is robust and reliable and yields better results in comparison to conversional techniques in presence of IBRs. In addition, its operation is simple and can be conveniently incorporated into a modern digital relay.

The high proliferation of electric vehicles has intensified the interdependency between traffic networks (TNs) and power distribution networks (PDNs). Accordingly, this article proposes a decentralized optimization framework for the multiarea optimal traffic-power flow (OTPF) problem, in which the models of PDNs and TNs are established separately in a distributed manner. A multistage distributionally robust optimization (MDRO) model is formulated for PDNs to address wind power uncertainty, with the introduction of a traffic assignment problem to describe the distribution of traffic flows in TNs. Furthermore, an improved alternative direction method of multipliers (I-ADMM) algorithm is developed to solve the multiarea OTPF problem. Numerical results from a three-area traffic-power coupled system demonstrate that the proposed MDRO model bears a cost $89.50 lower than that of the multistage robust optimization model, while the I-ADMM algorithm yields a solving time only one-third that of the traditional ADMM. [ABSTRACT FROM AUTHOR]

This article investigates the problem of delay-dependent stability for the one-area load frequency control (LFC) system with electric vehicles (EVs). Two closed-loop models of the LFC system with EVs are proposed, including the model based on the model reconstructed technique and the model with uncertain parameters that considers state of charge. By employing the Lyapunov–Krasovskii functional method, two delay-dependent stability criteria are presented for the systems under study such that a more accurate admissible delay upper bound (ADUB) can be obtained. Case studies are finally carried out to disclose the interrelationship between the ADUB, PI controller gains, and other parameters of the EVs. [ABSTRACT FROM AUTHOR]

As theincrease of wind power penetration, the fluctuations of wind power results in serious power system frequency turbulences. This article proposes a low-pass virtual filter (VF) for wind energy conversion systems (WECSs) to smoothen output power. First, a linearized model is established for a WECS. Then, a low-pass VF is derived for the active power control loop in WECS to modulate the kinetic energy stored in rotating mass for output smoothing. The principle is to modify the traditional wind power controller to emulate a physical energy storage system controlled as a low-pass filter to smooth the wind power output. The proposed low-pass VF does not need the physical deployment of energy storage system but still can filter the high-frequency fluctuations in the output wind power. Furthermore, in order to ensure operation stability of the wind turbine (WT), a stability-constrained coefficient is determined according to the proposed stability criteria to ensure the stability of WTs. Simulation and experiment results verify the effectiveness of the proposed VF in WECS for output wind power fluctuation reduction and the stability-constrained coefficient for stability ensuring. [ABSTRACT FROM AUTHOR]

The actively commutated current source converter (CSC) does not need large energy storage capacitors and can realize the black start of wind farms, which provides a practicable scheme for the lightweight offshore wind power DC transmission system. However, resulted from utilizing pulse width modulation, the existing CSC has the disadvantages of high DC harmonics and high switching loss. To solve this problem, an offshore wind power transmission system based on the current source converter with fundamental frequency modulation employed (FFM-CSC) is investigated. Firstly, the topology and mathematical model are analyzed. Then, a steady-state control strategy coordinated by the offshore CSC and the onshore CSC is put forward, and the power operating range is researched. A black start control strategy for wind farms is also proposed. Finally, the offshore wind power transmission system is tested by simulation in PSCAD/EMTDC. The results demonstrate that the FFM-CSC-based offshore wind power transmission system can smoothly realize the black start, stably control the frequency and amplitude of AC voltage, adapt to output changes of wind turbines, and realize the fault ride-through. [ABSTRACT FROM AUTHOR]

This article analyzes the power transfer limit (Pmax) of wind power plants (WPPs) under the weak grid condition. It is pointed out that the impact of different grid parameters and grid configurations on Pmax cannot be fully reflected by the short-circuit ratio (SCR), but can be readily captured by voltage sensitivity (∂Q/∂V). Hence, ∂Q/∂V turns out to be a more appropriate metric for the assessment of Pmax compared with the SCR. Based on this insight, the voltage control of the WPP is suggested to be implemented at the bus with the highest ∂Q/∂V to maximize the power transfer capability. Moreover, a dynamic voltage control is further added at the turbine level to improve the disturbance rejection capability of the WPP as well as its dynamic robustness against grid strength variations. Finally, simulation and field test results are given to demonstrate the theoretical analysis and the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

The wind–thermal-bundled transmission system is a feasible way to transmit wind power generation; however, the stability of the system should be paid more attention under high wind power penetration. In this article, the impact of wind energy penetration on torsional vibration and electrical characteristics is investigated for wind–thermal-bundled transmitted by high-voltage direct current system. The torsional vibration responses of generator shaft with different wind–thermal-bundled ratios are analyzed by time and frequency methods. The electrical characteristics of wind–thermal-bundled transmission system are disclosed under the influence of the wind farm penetration. The analysis illustrates that the penetration of doubly fed induction generation makes the torsional vibration of turbo generator and wind turbine shaft present different mode characteristics, and the risks of different modes are different. The current and voltage of wind–thermal-bundled transmission system show regular evolution with the change of wind–thermal-bundled proportion. Analytical results provide the value for the design and control of the wind–thermal-bundled transmitted system, which contributes to the improvement of power system stability. [ABSTRACT FROM AUTHOR]

In this brief, a novel event-triggered intermittent-based impulsive control (ETIIC) strategy is designed to stabilize a class of nonlinear systems, which possesses the strengths of event-triggered control, intermittent control and impulsive control. Particularly, ETIIC allows that impulsive instants appear only at the control intervals. Moreover, the control intervals can be activated by mean of the well-designed event-triggered mechanism that depends explicitly on threshold-value and check-period. A sufficient criterion is provided to ensure exponential stabilization of nonlinear systems via ETIIC, meanwhile, the non-Zeno behavior is confirmed in our results. At last, an example with numerical simulation is presented to illustrate the feasibility of ETIIC and the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic event data-based stability risk assessment method for power grids with high penetrations of inverter-based resources (IBRs). This method relies on obtaining the IBRs’ DQ admittance through dynamic event data and computing the system’s eigenvalues based on the admittance models. Two critical technologies are employed in this research, including time-domain and frequency-domain data fitting and $dq$ -frame voltage and current signal derivation. The first technology is key to obtaining the $s$ -domain expressions from the transient response data, and the $s$ -domain DQ admittance model from the frequency-domain measurements. The second technology is key to obtaining the $dq$ -frame voltage and current signals from either the three-phase instantaneous measurements or the phasor measurement unit (PMU) data. The method is illustrated using data generated from a Type-4 wind power plant modeled in PSCAD. This paper demonstrates the technical feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

One of the short-coming challenges of power systems operation and planning is the difficulty to quantify the variability of power systems Kinetic Energy (KE) to unveil online additional information for the system operators’ decisions support. KE monitoring requires innovative methods to analyse the continuous fluctuations in the KE power’s systems. In this paper, we propose the use of information theory, specifically the concept of Information Length (IL), as a way to provide useful insights into the power system KE variability and to demonstrate its utility as a starting point in decision making for power systems management. The proposed IL metric is applied to monthly collected data from the Nordic Power System during three consecutive years in order to investigate the KE evolution. Our results reveal that the proposed method provides an effective description of the seasonal statistical variability enabling the identification of the particular month and day that have the least and the most KE variability. Additionally, by applying a Long Short-Term Memory (LSTM) neural network model to estimate the value of the IL on-line, we also show the possibility of using the metric as data-driven support. [ABSTRACT FROM AUTHOR]

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Cooperative control among the turbines has the potential to improve the power output. However, existing centralized power optimization methods are computationally expensive and does not scale well for large wind farms, limiting their practical use in real-time control for time-varying wind conditions and turbine configuration (with adding or maintaining of turbines). To address this problem, this paper proposes a fully distributed power optimization method for wind farms using alternating direction method of multipliers (ADMM). The proposed method allows the wind farm power output to be optimized in fully distributed manner with turbine-to-turbine message passing over a mesh network, guarantees the implemented control actions satisfy the control constraints of all turbines, and provably converges to a stationary point of the wind farm power optimization problem. Simulation results demonstrate that the proposed method can significantly reduce the computation time with hardly sacrificing the power gain compared with centralized method and thus is computationally efficient for real-time power optimization of large wind farms. [ABSTRACT FROM AUTHOR]

The loss of small signal stability under parameter variation (e.g., fluctuation of loads and wind powers) can be ascribed to local bifurcations, i.e., saddle-node, Hopf, singularity-induced, and limit-induced bifurcations. Classic bifurcation calculation methods like the direct method and continuation method can only provide a bifurcation point or two-parameter bifurcation curve. Based on Galerkin method and the implicit function theorem, this paper proposes accurate polynomial approximations of bifurcation hypersurfaces in the multi-dimensional parameter space of interest. The proposed method can ensure high accuracy in this parameter space, and thus is preferable to the existing Taylor expansion-based local approximation method, given the intrinsic large-variation characteristic of wind powers. Acquired bifurcation hypersurfaces are immediately used to construct the small signal stability region of the continuous system, and then compute the stability margin for the operating point subjected to uncertainty of wind generation. Besides, the validity scope of the proposed method is analyzed, and the handling of limit-triggered equation switching as well as its computational difficulty is discussed. Computational results on the two-parameter 11-bus two-area and six-parameter IEEE 145-bus test systems validate the high accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stochastic robust real-time power dispatch model considering wind uncertainty. In this dispatch model that incorporates automatic power generation control, system constraints are constructed with affinely adjustable robust optimization to enhance the system security, while the objective concerning the expected value of generation cost is optimized to improve the cost efficiency. The optimized objective is consistent with stochastic optimization, while the formulation for the objective is different. A Nataf conversion-based three-point estimate (NC-TPE) is adopted, which uses an approximating function to formulate the expected generation cost with deterministic estimate points. Compared to traditional stochastic sampling methods, the deterministic assessment method NC-TPE achieves an accurate formulation with more stable and efficient computation performance. The dispatch model is derived as a problem with nonconvex objective and bilinear constraints using the dual theory. To address the highly nonconvex problem, the objective and bilinear constraints are first converted to difference-of-convexity functions, and then a tailored difference-of-convexity optimization is employed to convexify the nonconvex functions, such that the original problem can be solved with an iterative quadratically constrained program. Numerical simulations demonstrate the economic superiority and robustness of the proposed model, as well as the effectiveness of the convexified solution. [ABSTRACT FROM AUTHOR]

As the wind energy is progressively pursued to supplement conventional power generation, the stochastic wind behavior considerably deteriorates power quality (PQ) and stability of the connected grid. This article is attributed to the comprehensive control design of the grid-integrated doubly fed induction generator (DFIG) wind energy system that aims to address encountered wind variability and PQ intricacies. Battery energy storage with bidirectional dc–dc converter control is assimilated at the dc-link of the DFIG that regulates the dc-link voltage to mitigate the wind power fluctuations in specified grid power modes. A higher order adaptive control for grid-interfaced converter accurately computes desired weights of various components to exercise accurate power balance and PQ control. Moreover, a novel Jaya particle swarm optimization algorithm is adopted for the optimal tuning of wind maximum power point tracking and the dc-link voltage PI controller parameters that significantly improves steady-state and dynamic responses of both the controllers. The rotor speed converges faster with precise reference tracking that leads to almost zero steady-state error using optimized PI controller gains. Along with the attenuated initial overshoot and fast settling time during sudden large step wind speed change, the dc-link voltage also characterizes superior response with optimal tuning during load unbalance and abnormal grid voltage conditions. The presented control strategy analyzes the capability of DFIG system under variable wind-speed changes, specified grid power modes, load changes, and other grid disturbances. The satisfactory system response is endorsed through experimental results conducted on a laboratory prototype. [ABSTRACT FROM AUTHOR]

Probabilistic power flow (PPF) is pivotal to quantifying the state uncertainties of distribution power systems. However, it is very challenging due to underlying complex correlations among renewable outputs. To address this problem, a graph attention enabled convolutional network (GAECN) is proposed to approximate PPF in this article. Specifically, the graph convolutional layer of GAECN is used to aggregate the correlations among the nodal power injections during the training process. Within this layer, a full self-adaptive graph convolutional operation is proposed to automatically capture and learn the implicit correlation for achieving significantly enhanced accuracy of PPF. This layer is then followed by the convolutional neural network to capture the uncertain generation of renewable energy to achieve the robust computation of system state variable distributions. The simulation results demonstrate the accuracy and efficiency of the proposed method in IEEE 33, PG&E 69-node, 118-node, and practical 76-node distribution systems. [ABSTRACT FROM AUTHOR]

In this article, a sliding mode control (SMC) is applied to regulate the delivered power of a symmetrical six-phase induction generator. For this, the SMC strategy associated to the field-oriented control strategy is used in order to adjust the inner current loops of a squirrel cage six-phase induction generator (SC6PIG). In this way, the control algorithm switches between positive and negative feedback according to the position of the operating point in the phase plane. This technique guarantees the robustness of the SC6PIG in case of the loss of one to three stator phases or machine converter legs. The switching line slope and the initial operating point have been set so that the system starts with a negative feedback, then it continuously switches back and forth to positive feedback in order to maintain the desired trajectory of the system. The simulation and experimental results demonstrate the effectiveness and the robustness of this technique since the delivered power of the SC6PIG presents the same dynamics in healthy or faulty modes with one to two phase losses. [ABSTRACT FROM AUTHOR]

Smart Grid 2.0 is the energy Internet based on advanced metering infrastructure and distributed systems that require an instantaneous two-way flow of energy information. Edge computing benefits from its proximity to the servers and edge nodes of the smart grid distributed systems, which can provide efficient and low latency information transmission to the smart grid. With the massive number of Internet of Things being used, the amount of real-time power usage information generated by that represents a huge challenge for edge computing. To improve the efficiency of information transmission and processing in power systems, this article combines different deep learning algorithms with edge computing to analyze and process distributed renewable energy generation and consumer power data in smart microgrid. Experiments on two real-world datasets from China and Belgium show that the proposed framework can obtain satisfactory prediction accuracy compared to existing approaches. [ABSTRACT FROM AUTHOR]

This paper first points out and carefully analyzes the complementary regulation characteristics (CRC) of wind power generators (WGs) and demand response resources (DRRs), which indicate the fact that their regulation capacities and costs of upward and downward regulation are significantly different and complementary. Based on the CRC of WGs and DRRs, this paper proposes a coordinated control to achieve a more robust and cost-efficient primary frequency regulation (PFR). In the proposed coordinated control, the droop gains of WGs and DRRs for upward and downward regulation are designed in a coordinated way so that the resources with larger capacities and lower costs could provide more frequency support. Meanwhile, the stability and robustness conditions of their coordinated droop gains are constructed by small-signal analysis method and the potential instability risks arising from unreasonable coordinated droop gains and time delays are addressed. An optimal $H_{\infty }$ control method and an updated mechanism are supplemented to obtain the optimal droop gains under the proposed coordinated structure for ensuring optimal system robustness under different CRCs. The robust performances and costs of the proposed coordinated control are evaluated on the IEEE 39-bus test system and its advantages are verified. [ABSTRACT FROM AUTHOR]

As wind power generation transits from centralized development mode to decentralized on-site consumption mode, microgrid (MG) can provide an efficient solution for wind power integration into the distribution network. However, the high-penetration wind power MG is the typical weak power grid system. The traditional wind turbine generator (WTG) participates in system frequency regulation through grid-following current source, which relies on the phase-locked loop for voltage phase synchronization and is unable to provide strong frequency support in weak power grid conditions. To fill this gap, this paper presents a decentralized grid-forming control strategy of high-penetration wind power MG. A wind power adaptive dynamic droop mechanism considering wind energy characteristics and rotor speed dynamic is proposed, cooperating with the implementation of wind maximum power point tracking (MPPT) for economical operation. A detailed small-signal model for voltage source wind power-based system considering electromechanical transients and adaptive droop mechanism is developed. The dominant modes are figured out and the critical control parameters are established and optimized. The proposed wind power adaptive droop mechanism can effectively provide frequency regulation and robust control performance. Theoretical analysis, time-domain simulation results and hardware-in-the-loop experiments under various scenarios verify the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

With the rapid growth of intermittent renewable energy sources, it is critical to ensure that renewable power generators have the capability to perform primary frequency response (PFR). This paper proposes a framework for using a shared battery energy storage system (BESS) to undertake the PFR obligations for multiple wind and photovoltaic (PV) power plants and provide commercial automatic generation control (AGC) service in the ancillary service market at the same time. Furthermore, an optimal bidding strategy in the hour-ahead energy and ancillary service markets is proposed to maximize the BESS' benefits, considering frequency regulation performance, battery degradation cost, and the state of charge (SoC) limits. The optimization model is first built as a chance-constrained optimization problem and then transformed into mixed-integer quadratically constrained programming using Wasserstein metric-based distributionally robust optimization technique. Simulation results show that the proposed optimal bidding strategy and solution method can effectively take advantage of a BESS to perform PFR and provide AGC services simultaneously and improve its economic benefits. [ABSTRACT FROM AUTHOR]

We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its non-affine characteristics. Over the training phase, the model sequentially maps input examples onto samples of base distribution, given the conditional contexts, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually maps samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art. [ABSTRACT FROM AUTHOR]

In the power system with high proportion of wind power, a day-ahead and intraday coordinated optimal scheduling model of wind power and thermal power units (TPUs) is established considering variable virtual inertia. Firstly, the frequency regulation characteristics of wind turbines (WTs) are analyzed, the feasibility of reserve of WTs is elaborated, and the dynamic deloading strategies of WTs in different wind speed zones are proposed. Then, the feasibility of variable virtual inertia realized by rotor overspeed control (ROC) of WTs is analyzed, which is combined with synchronous inertia of TPUs to deduce the deloading level constraints of WTs and minimum inertia constraints of power system considering inertia demand. Referring to the characteristics of reserve resources in different time scales on the generation side, the reserve capacities of TPUs are divided into minute and second levels according to response time, and the scheduling strategy of reserve capacity in different time scales is proposed, so as to improve the flexibility of TPUs and realize the refined management of reserves. Simulations indicate that the proposed model and method can effectively improve the inertial support capability of the system and reduce the operation cost. [ABSTRACT FROM AUTHOR]

Compared with commonly-used point forecasting, probabilistic forecasting provides quantitative information on the uncertainty associated with wind power output. However, most studies focus on modelling only a fixed set of quantiles or intervals, which cannot provide the entire information of wind power distribution. In this paper, we propose a non-parametric and flexible method for probabilistic wind power forecasting. First, the distribution of wind power output is specified by spline quantile function, which avoids assuming a parametric form and also provides flexible shape for wind power density. Then, autoregressive recurrent neural network is used to build the non-linear mapping from input features to the parameters of quadratic spline quantile function. A novel loss function based on continuous ranked probability score (CRPS) is designed to train the forecasting model. We also derive the closed-form solution of the integral required in computing the CRPS-based loss function in order to improve the computational efficiency in training. The effectiveness of our proposed method has been validated with public real-world data from Global Energy Forecasting Competition 2014. Compared with some advanced benchmarks, our proposed approach outperforms them at least six percent in terms of CRPS, indicating that it can provide probabilistic wind power forecasting with high quality. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic continuous-time optimization model for coordinating the operation of flexibility reserve in a hybrid hydro-thermal-wind power system. The proposed continuous-time model captures the sub-hourly uncertainty and variations of wind power and load, and accurately models and schedules the ramping flexibility of the hydrothermal system to match the uncertainty and variations of wind power and load. A simplified Northern European system is studied over a 30-hour period to examine the potential of using hydropower as a comprehensive flexibility provider. Norwegian hydropower is shown to be a significant source of flexibility used to mitigate wind power variations, especially during ramping constrained periods. The hydropower provides 73.5% of the balancing energy in the base case, which includes smoothing out longer wind power deviations as well as rapid ramping relief. The short-term implications of increasing the offshore wind power in the North Sea by 50% compared to 2020 was also studied in the Northern European test system. The increased wind power causes steeper ramping in the net load, which drives the hydropower to its full balancing potential to allow thermal units to operate within their ramping limits. [ABSTRACT FROM AUTHOR]

The power flow calculation of the multiterminal high voltage direct current (MTDC) system is essential for planning sustainable energy sources and power flow analysis for the MTDC system. However, the traditional unified methods require a large system of non-linear equations leading to low calculational efficiency. Also, for a large DC grid, there is a concern about the convergence of sequential methods. This paper proposes a general power flow calculation method for voltage source converter (VSC) based MTDC systems. Based on an extended topology of an AC grid, a generalized calculation model of the MTDC power flow is proposed. Then, a novel sensitivity analysis-based power flow (SAPF) method is proposed, in which the state variables of the extended AC grid are calculated via sensitivity analysis. With a smaller system of equations, the proposed SAPF method has less computational burden than the traditional unified methods and causes no convergence problem compared to sequential methods. To further improve the calculational efficiency, the sensitivity-based variable updating is adopted to accelerate the iterative process. By comparing with the existing methods in calculating power flow for different MTDC systems, including large systems with multiple AC/DC grids, the effectiveness and scalability of the proposed methods are verified. [ABSTRACT FROM AUTHOR]

The article presents the concept of a new design of a multi-phase doubly-fed induction generator (DFIG). The innovative design approach uses a five-phase power supply from the rotor side of the generator with a three-phase classic stator power supply. Modern three-phase doubly-fed induction generators are the dominant choice for Wind Energy Conversion Systems (WECS). Solutions of this type are sensitive to the loss of at least one phase of the power supply from the rotor side due to the failure of the rotor side inverter. The proposed design solution in the form of a multi-phase power supply in the rotor circuit aims to extend the range of possible failure-free operation of the generator system, thus reducing system downtime due to the failure of power electronic systems. The correctness of the adopted conceptual assumptions was confirmed by the results of laboratory tests. The main contribution is to prove that using five-phase rotor winding improves the overall reliability of the proposed electrical energy generation system significantly.

Wind energy resource assessments at two islands in the Cook Islands are carried out in the present work. The wind data were collected for one year from sites on Mauke and Rarotonga Islands in the Cook Islands and the daily, monthly and seasonal average wind speeds, the diurnal variations of the wind shear coefficient, average temperature and turbulence intensity were estimated. Eleven frequentist methods and a Bayesian technique were used to determine the Weibull parameters and the wind power density (WPD) for each site. The best method was determined using the goodness of fit test and error measures. The average wind speeds were 4.65 m/s and 3.86 m/s at 34 m above ground level for the sites on Mauke and Rarotonga respectively. Based on the goodness of fit tests and error measures, the Least Squares Method performed best for estimating the Weibull parameters at the Mauke site, while for the Rarotonga site, the median and quartiles method performed the best. For both the sites, the Bayesian method, which is being used for the first time for wind resource assessments, ranked second of the twelve methods, indicating good potential for this method. The annual energy production (AEP) was also determined which was calculated to be 2192.34 MWh from a total of ten Vergnet 275 kW turbines at the two sites. Finally, an economic analysis, carried out for the two sites, indicated a payback period of 7.72 years.

Wind has been admitted as one of the most promising renewable energy resources in multinational regionalization policies. However, due to the randomness and volatility of wind energy and the rigid transmission of traditional wind turbines, it is difficult to control the active power of wind turbines with high precision. Thus the hydraulic wind turbines (HWT) is taken as the research object, and the stable operation of wind turbines at a demand power point under certain wind speed conditions is realized. The mathematical model of HWTs is established, and the HWT operation characteristics are analyzed. The small signal linearization method is adopted to solve the multiplying nonlinear problems in the system, and the working area of the power transmission system to adjust the transmission power stably is analyzed. According to the demand of active power control for wind turbine, the HWT active power control strategy is proposed. However, the power response of the HWT is inconsistent with the time-varying operating point and the proposed controller fixed parameters. The controller parameter online adjustment method of system pressure and power gain is proposed. The effectiveness of the optimized control method is verified based on the semi-physical simulation experiment platform of HWT, and the consistency of active power response is achieved. At the same time, the power response characteristics are analyzed, and the HWT active power response time can satisfy the demand of electrical grid under the optimal control strategy.

Sufficient fault ride-through (FRT) of large wind power plants (WPPs) is essential for the operation security of transmission system. The majority of studies on FRT do not include all disturbances originating in the transmission system or the disturbances irrelevant to the operation security. Based on the knowledge of power quality, this paper provides a guide to stakeholders in different aspects of FRT for wind turbines (WTs) and WPPs. This paper details the characteristics of the most common disturbances originated in the transmission system, how they propagate to the WT terminals, and how they impact the dynamic behavior of a large WPP. This paper shows that the details of the voltage disturbances, not only in the transmission system, but also at the WT terminals, should be taken into consideration. Moreover, a detailed representation or characterization of voltage dips is important for FRT studies, despite that the simplified models used in the literature are in-sufficient. This paper strongly recommends that distinct events and additional characteristics such as the phase-angle jump and oscillations in the transition segments should be considered in FRT analysis.

With the large-scale wind power integration into the power system, the inertia and damping characteristics of the system have been weakened, which is not conducive to fulfill the grid-friendly operation of wind power. Aiming at this, an adaptive control scheme combining virtual inertia control (VIC) and supplementary damping controller (SDC) is proposed in this paper. Firstly, the mathematical model of wind grid-connected power system with VIC is established. Secondly, the polytopic linear parameter varying (LPV) system is built to describe the uncertainty of system operating points, and the gap metric-based nonlinear evaluation method is used for polytope vertex configuration. Next, Youla-Kucera parametrization are extended to polytopic LPV system to design an adaptive damping control, in which VIC and SDC are coordinated to provide damping and virtual inertia simultaneously. Finally, the proposed adaptive control scheme is compared with several existing control techniques to validate its effectiveness and advantages in suppressing system oscillation in the modified IEEE 39-bus system integrated with 2 wind turbines. [ABSTRACT FROM AUTHOR]

In this paper, an approximate aggregated impedance model (AAIM) of a grid-connected wind farm with multiple wind turbine generators (WTGs) for the small-signal stability study is derived. The derivation is conducted under the condition that the WTGs inside the wind farm are of the same type and from the same manufacturer. The AAIM derived can be seen as the impedance model (IM) of a grid-connected aggregated WTG. The IM of the aggregated WTG is the weighted average of the IMs of the individual WTGs in the wind farm and hence is simple to be built. The reactance of the line connecting the aggregated WTG to the external AC grid is the maximum eigenvalue of the network reactance matrix of the grid-connected wind farm. In addition, the derivation justifies the representation of a wind farm by an aggregated WTG in the IM-based small-signal stability analysis. Finally, two example grid-connected wind farms are used to evaluate and demonstrate the effectiveness of the AAIM proposed in the small-signal stability analysis. It is also shown that the computational burden of the stability analysis of a large-scale wind farm could be effectively reduced when the AAIM is used. [ABSTRACT FROM AUTHOR]

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

This paper presents a wind turbine (WT) control strategy to deliver frequency response (FR) that is similar to conventional synchronous generators, i.e., to deliver accurate and efficient FR. This is achieved by the coordination of WT’s rotor speed control (RSC) and pitch angle control (PAC), wherein the RSC is the summation of the proposed deloading power point tracking curves and FR signal; the PAC is kept as adjusting its control law according to the WT rotor speed. Through the above coordination, the strategy pre-reserves a portion of wind power and releasing it during underfrequency events. Compared with existing methods, the strategy has two significant advantages: 1) it does not need the switching of WT controls according to the real-time wind speed (WS), nor the identification of the boundaries among high, medium and low WSs according to the required reserved power, ultimately reducing the complexity of applying the strategy to practice; 2) its active power response during FR period is independent of the deviated rotor speed and is equal to the FR signal, ultimately enabling the WT to provide accurate and efficient FR. The simulations focusing on the grid frequency side demonstrated the advantages of the strategy over existing methods. [ABSTRACT FROM AUTHOR]

The integration of large-scale renewables into the multienergy system has brought a series of problems and challenges to the optimal dispatch of system, especially how to effectively realize the dispatch and accommodation of wind power. In this article, a dynamic economic dispatch (DED) model of wind/combined heat and power (CHP) hybrid energy system considering uncertainty of multiple wind farms and different dispatch time scales is proposed, which aims to minimize expected total energy generation cost based on the typical scenarios of upper bound of wind farm output. The spatial dependence of hourly output of multiwind farm is modeled by copula-based model and improved scenario reduction technique, and the different adjustment time scales of heating system and power system are modeled as well to achieve more reasonable and accurate system dispatching. In addition, an improved adaptive cuckoo search algorithm (ACSA) is proposed for the solution of the DED model of wind/CHP hybrid energy system with highly nonlinear constraints, and a shared parallel computing platform is employed to realize the acceleration of the ACSA. Finally, the corresponding numerical simulation and comparative analysis are performed on two test systems to verify the validity of the proposed model and method. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid development of hydrogen powered vehicles, the demand for hydrogen refueling stations (HRSs) in the transportation field is growing. The renewable energy, e.g., wind energy, is used to produce and store hydrogen on site for HRSs, which may be a relatively cheap and clean solution. Considering the wind farm (WF) and HRSs belong to different entities, a new cooperative operation model of the WF-HRSs combined system is proposed. Specifically, the robust optimization methods are implemented to characterize the uncertainties of wind power and market electricity price, respectively, to alleviate risk. To ensure the fairness of profit allocation, the bargaining power is measured by various contribution levels of each stakeholder based on the Nash–Harsanyi bargaining game theory. Furthermore, the model is transformed into two sequential subproblems: energy trading problem (SP1) and the payment bargaining problem (SP2). Accordingly, a solving technique adopting column and constraint generation algorithm is provided to solve the SP1. In addition, for the sake of privacy protection, a distributed approach based on data-centric mode is developed to solve SP2. Finally, the numerical results can validate the effectiveness and scalability for the proposed scheme and algorithms. [ABSTRACT FROM AUTHOR]

One of the major challenges in evacuating power from the offshore renewable energy source is reducing the number of equipment involved in the energy conversion process. In the strategy presented in this article, the output of a permanent magnet synchronous generator coupled with a wind turbine is converted to a low voltage DC by an AC–DC converter and then directly connected to a Medium Voltage Direct Current (MVDC) corridor using a new fault tolerant soft switched high gain DC–DC converter. During the fault in the MVDC corridor, the DC–DC converter is blocked forcing the fault current to zero, the faulty MVDC corridor is bypassed using the simple DC disconnectors, and then the DC–DC converter is connected to the healthy MVDC corridor. In this article, the efficacy of the proposed scheme is discussed in detail and substantiated through extensive simulation studies done in PSCAD/EMTDC. The theoretical and simulation analysis is also verified by experimental results. [ABSTRACT FROM AUTHOR]

This article presents an investigation and evaluation of an integrated flux-modulated machine for wind power generation. The integrated flux-modulated machine has two rotors which function as two contrarotating rotors connected to two sets of turbine blades. Hence, compared to conventional wind generators, more wind energy could be captured by this wind power generation system. Moreover, the integrated machine consists of two sets of stator windings. By regulating the currents in these windings, dual maximum power point tracking control strategy could be achieved. As a result, the wind power conversion efficiency is further improved. Moreover, this wind power generation system exhibits the advantage of high torque/power density due to the enhanced magnetic-gearing effect involved in the integrated flux-modulated machine. Hence, this machine is more suitable for direct-drive wind power generation, where the reliability is improved without the maintenance issues related to mechanical gearboxes. The topology and operating principle of the investigated machine are demonstrated in detail. Decoupled design of the two sets of windings is investigated, and a general rule to achieve decoupled windings by appropriate slot-pole combination selection is illustrated. The advantages of the investigated machine are confirmed in comparison to a benchmark machine. Finally, the investigated flux-modulated machine is prototyped. The validity of the operating principle and the simulation results are verified by experimental results. [ABSTRACT FROM AUTHOR]

The low-frequency AC (LFAC) transmission is a competitive scheme for offshore wind power integration, in which the modular multilevel matrix converter (M3C) is the key equipment. In this brief, the selection methods of the M3C main circuit parameters are proposed. First, the maximum arm voltage is derived and the selection method of the sub-module (SM) number is established. Then, according to the switching function approach, the analytical expressions of the average SM capacitor current and the average SM capacitor voltage are derived. On this basis, the selection method of the SM capacitance is proposed. Additionally, the design principle of the arm inductance is proposed. Finally, a case is elaborated to illustrate the proposed M3C main circuit parameter selection methods and provide a reference for practical projects. [ABSTRACT FROM AUTHOR]

Wind speeds in geographically adjacent areas are highly correlated, which correspondingly leads to wind power correlation. It is essential to consider the wind power correlation for steady-state related calculations in the modern power system with high wind power penetration. This short article presents a novel continuous permutation method for wind power correlation analysis. With the novel eigen-permutation, the proposed method is applicable to tackle the correlation analysis even the correlation matrix of random variables is not positive definite. A continuous permutation is then proposed to reduce the permutation deviation and, hence, to reduce the error of steady-state related calculations. Simulation results show the effectiveness of the proposed method in dealing with the nonpositive definite correlation matrix of the wind speeds. Also, compared to the traditional methods, the proposed method achieves more than an 80% reduction of the general error in the correlation analysis, which also contributes to higher accuracy in the power flow calculation. [ABSTRACT FROM AUTHOR]

This article proposes a hybrid method to find the optimal place of dc power flow controllers (DC-PFC)s in HVdc grids. This hybrid method consists of max flow-min cut theory with Ford–Fulkerson augmenting paths and combining sensitivity analysis to find the most important HVdc lines and the best placement. In this article, series DC-PFC is modeled and get placed. A significant advantage of this method is, reducing the computational burden of calculations by finding the most important and susceptible HVdc lines to get overloaded without focusing on other unnecessary HVdc lines. The static security of the power system strongly depends on the placement of DC-PFCs, which has become an important issue because of rapid-developing of HVdc grids. Static security studies have been done on an eight-terminal HVdc grid incorporating double scenarios. [ABSTRACT FROM AUTHOR]

Wind farm industrial control systems (ICSs) play an important role in the operation of wind turbines (WTs). To increase revenues, a corrupt wind power producer tends to compromise the ICSs of the opponents’ wind farms by launching false data injection (FDI) attacks. In this article, we investigate a novel profit-oriented FDI (POFDI) attack against wind farms and study corresponding attack detection schemes. Specifically, we proposed a potential FDI attack model against ICSs of wind farms considering the power distribution of wind farms. Based on the characteristics of the proposed attack model, a profit-oriented attack strategy is discussed as a composition of attack detection. Considering the characteristics of the compromised data and those of normal data, we develop countermeasures to detect the POFDI attack, through the relationship between the characteristics of WTs in wind farms. A relation-based detection network is used to evaluate the similarity between the data to be tested and the historical data. Simulation results show the impact of the proposed attack strategy and the effectiveness of the attack detection scheme. [ABSTRACT FROM AUTHOR]

Under-frequency load shedding (UFLS) is an important measure for tackling low-frequency events caused by load-generation imbalance. However, the uncertainty of wind power amplifies power imbalances and can potentially impair frequency stability. Electric vehicles (EVs) present a more effective means for addressing this issue compared to load shedding. However, EVs have several limitations such as commute randomness. To ensure frequency stability and simultaneously reduce load shedding, a bi-level confidence-interval-based optimal strategy is proposed to enable the participation of EVs in UFLS, where the uncertainties of wind power and the commute randomness of EVs are estimated using a non-parametric kernel density estimation (KDE) method. In bi-level optimization, the upper level reduces the dependency on commute randomness and the wind power uncertainty during load-shedding events. Further, the upper-level solutions are sent to EV charging stations for emergency dispatch. By contrast, at the lower level, an approximation-function-based priority is proposed to optimize the task allocation. Simulation results show the advantages of the proposed approach in maintaining a stable frequency compared with traditional and adaptive UFLS schemes. [ABSTRACT FROM AUTHOR]

The green hydrogen provides a pathway to the 100% sustainable energy future, rendering hydrogen energy service providers (HESPs) an important role in energy supply. Different from the conventional electricity service providers, HESP is not only responsible for the production, purchase and retailing but also the transportation of hydrogen, which makes its operation a challenging problem. To this end, this paper proposes a bi-level strategic operation model for HESPs. In the operation level, the bidding, hydrogen production and hydrogen transportation are coordinated to minimize the overall cost of HESPs. In the market level, the market clearing is simulated to estimate the influence of HESP behavior on electricity prices in the market. Then, a model reformulation technique is developed to connect the discrete-time based hydrogen production and continuous-time based hydrogen transportation. The bi-level optimization model is transformed into a single-level mixed-integer linear programming (MILP) problem with Karush-Kuhn-Tucker (KKT) conditions. The case studies with the modified IEEE-RTS-79 system were given to validate the proposed model and confirm the necessity to coordinate the hydrogen production and transportation in the strategic operation of HESPs. Sensitivity analysis is also conducted to study the potential influencing factors. [ABSTRACT FROM AUTHOR]

This article is concerned with the stability analysis of time-varying hybrid stochastic delayed systems (HSDSs), also known as stochastic delayed systems with Markovian switching. Several easy-to-check and less conservative Lyapunov-based sufficient criteria are derived for ensuring the stability of studied systems, where the upper bound estimation for the diffusion operator of the Lyapunov function is time-varying, piecewise continuous, and indefinite. It should be stressed that our results can be directly used to analyze the stabilization of HSDSs via aperiodically intermittent control (AIC). Compared with the existing results about AIC, the restrictions on the bound of each control/rest width and the maximum proportion of rest width in each control period are removed. Thus, the conservativeness is reduced. Finally, two examples, together with their numerical simulations, are provided to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

DC collection and transmission is one of the main developing directions of the large-scale offshore wind power system. This article proposes a multifunctional dc collector (MDC) to construct a low-cost, high reliability, and high flexibility all-dc offshore wind power system. By introducing the MDC to achieve energy collection and cascade boost, not only the system cost, size, and intermediate links of power conversion are reduced but also the coupling among wind turbines (WTs) is greatly weakened. Accordingly, the maximum power point tracking operation can be achieved independently for each WT unit. Furthermore, a constant voltage ratio control with first harmonic elimination iterative algorithm is proposed for dc collector to link the HVdc bus voltage and discretized dc voltage for triggering the energy consumption operation and suppressing the inductor current ripple, and the detailed control parameter design is provided. Meanwhile, the various operating modes are considered for MDC, including practical engineering configuration, fault isolation, dynamic cut-in/out, and energy consumption operation, which provides a practical solution for the construction of a long-distance and large-capacity offshore system. Finally, the comprehensive experimental results are given to illustrate the effectiveness of MDC. [ABSTRACT FROM AUTHOR]

A power decoupling control for wind power converter based on series-connected modular multilevel converter (SCMMC) and open-winding permanent magnetic synchronous generator (OW-PMSG) is proposed in this article. The power converter based on SCMMC and OW-PMSG achieves medium voltage direct conversion and transmission and has higher dc-bus voltage compared with conventional MMC, which makes it a promising solution for copper consumption reduction of cable, low power transmission loss, and the elimination of the step-up converter. The proposed power decoupling control realizes independent power control of each phase in the wind power converter by decoupling the negative-sequence and zero-sequence current and guarantees the phase dc-link voltages balance of the SCMMC and the robustness to restrain the imbalance power disturbance. Instantaneous three phase dc-link voltages balance of the SCMMC is achieved especially when the imbalance of phase dc-link voltages occurs. The feasibility of the wind power converter based on SCMMC and OW-PMSG and the proposed power decoupling control is verified through simulations and a scaled-down laboratory prototype. [ABSTRACT FROM AUTHOR]