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• A novel aggregated system frequency response (ASFR) model is proposed. • It fully considers the frequency characteristics of VSC HVDC with renewables. • The aggregation transforms the multi-machine SFR to a single-machine SFR. • Simulation studies validate the performance of the proposed ASFR. This letter proposes a novel aggregated system frequency response (ASFR) model, which can fully capture the frequency characteristics of the voltage source converter-based high voltage direct current (VSC HVDC) interconnected power system with high penetration of wind power. First, a multi-machine SFR model is presented, considering the inertia and frequency control processes of VSC HVDC and wind turbines (WTs). Then, a novel aggregation approach is proposed to transform the multi-machine SFR model into a single-machine SFR model on the basis of different control modes of the VSC HVDCs and WTs. Finally, simulation studies are conducted to validate the performance of the proposed ASFR model and its application in frequency response analysis. [ABSTRACT FROM AUTHOR]

This article proposes a comprehensive coordinated suppression strategy for DC overvoltage to achieve the receiving-end fault ride-through of DC grid connected large-scale wind power. For the monopolar DC overvoltage, by reasonably switching the control mode of bipolar modular multilevel converter (MMC), the power transfer capability of the healthy pole MMC can be improved while maintaining DC voltage stability, and the precise load-shedding control of wind farm and its coordination scheme with dissipative resistors are designed, which can effectively reduce the input capacity of dissipative resistors and the risk of wind turbines being cut off. For the bipolar DC overvoltage, the load-shedding control strategy and its parameter selection principles are proposed, which can make wind farm and capacitors in each MMC absorb excess power together, to reduce the change rate and amplitude of DC voltage. Then its cooperation scheme with the dissipative resistors is designed to achieve fault ride-through in the full power range of wind farm. Finally, based on the simulation model of four- terminal DC grid, the effectiveness of the proposed coordinated control methods is verified. [ABSTRACT FROM AUTHOR]

Modular multilevel converter based high voltage direct current (MMC-HVDC) transmission is the prospective technology for large-scale wind farms integration. In this paper, a comprehensive coordinated control strategy for DC fault ride-through (FRT) of wind farms connected to the grid through bipolar MMC-HVDC transmission is proposed. To reduce the requirement of breaking speed and capacity of DC breakers, an active current-limiting control method is proposed to limit the rise rate of DC fault current, and the principle of parameter selection is designed. After DC breakers isolate fault, the control mode switching methods of bipolar MMC are designed to make the fault pole MMC operate as a STATCOM to support the AC grid and improve the active power transmission capability of the healthy pole MMC, which can reduce the impact range of unbalanced power between wind farms and converter station. In addition, a coordinated control strategy for wind farms load-shedding considering speed constraints is designed to realize the optimal distribution of load-shedding power among wind turbines, which can regulate unbalanced power together with the healthy pole MMC to realize DC FRT. Finally, the effectiveness and feasibility of the proposed control methods are verified by simulation results. [ABSTRACT FROM AUTHOR]

Carbon capture and storage (CCS) is an effective means to achieve the goals of carbon peaking and carbon neutrality. To improve the operating economics and low-carbon emission of an integrated energy system, the strong exothermic property of power-to-gas is utilized for heat recovery and injection into the heat network. This expands the adjustable range of electric output of combined heat and power (CHP) units which will improve wind power accommodation. The CO2 produced by the coal-fired unit is captured using post-combustion carbon capture technology, and then stored and used to manufacture methane, in order to realize the electric–gas–heat integrated energy system coupled with power-to-gas. Based on the ladder-type carbon trading mechanism, a low-carbon economic dispatch model of integrated energy system is proposed, which considers the incorporation of power-to-gas heat recovery and carbon capture and storage. The objective function is to minimize the total operation cost of the system. The model is simulated in the revised IEEE 39-bus power network, Belgium 20-node gas network and 6-node heat network by CPLEX solver and simulation results verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In wind-integrated power systems, the stochastic and random wind power brings more uncertainties to power system operation. To address multiple uncertain factors including wind power forecast error, generator failure, and load forecast error, a conditional value-at-risk (CVaR)-based optimal spinning reserve determination method for wind-integrated power system is proposed in this paper with consideration of both reliability and economy of system operation under a certain level of risk. The CVaR model of spinning reserve considering different uncertain factors is built and then incorporated into the coordinated scheduling model of power generation and spinning reserve in wind integrated power system. The optimization model is solved by mix integer linear programming, and the CVaR-based spinning reserve allocation and the power output of each unit are optimally determined at a certain confidence level. The proposed method can provide a trade-off between the economy and reliability of system operation with respect to the confidence level of risk. Simulation results on 10-unit system and 100-unit system demonstrate the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• A decentralized economic dispatch model under low-carbon economy is proposed. • Carbon capture power plants and carbon emission trading scheme are considered. • A time-varying carbon emission quota allocation method is proposed. • An over-relaxed ADMM algorithm is adopted to solve the proposed model. This paper proposes a decentralized power system economic dispatch (DPSED) model considering carbon capture power plants (CCPPs) and carbon emission trading scheme (CETS) under a low-carbon economy. First, CETS is introduced and analyzed, and a time-varying carbon emission quota allocation method is proposed. The centralized power system economic dispatch model (CPSED) under a low-carbon economy is established with the objective function to minimize the total operation cost in which CCPPs are introduced that can capture CO 2 to reduce carbon emission trading cost. Then, the centralized economic dispatch problem is reformulated into a decentralized model. It is composed of a set of sub-problems in individual areas that can preserve privacy and be solved independently. The alternating direction method of multipliers (ADMM) algorithm is employed to solve the decentralized optimization model, reducing the amount of exchanged information and preserving the privacy of information shared among participating areas. Moreover, the over-relaxed ADMM algorithm is further adopted to accelerate the convergence. Finally, case studies are performed on a 6-bus system and an IEEE 118-bus system to validate the effectiveness of the proposed model and algorithm. Numerical results demonstrate that CCPPs could reduce carbon emissions, help wind power utilization, and achieve economic operation. [ABSTRACT FROM AUTHOR]

• An adjustable robust power dispatch model under low-carbon economy is proposed. • Combined wind-storage system and carbon capture power plants are considered. • Carbon emissions are considered as controllable variables to be minimized. • The robust dispatch model is transformed into a single level LP problem. This paper proposes an adjustable robust power dispatch (ARPD) model under low-carbon economy to accommodate the uncertainty of wind power with combined wind-storage system (CWSS) and carbon capture power plants (CCPPs). First, battery energy storage (BES), which is considered as adjustable under worst-case scenarios, is introduced to wind power plants to form a combined wind storage system structure to improve operation flexibility. Then, a dispatching model of CCPPs is presented and analysed. The operating characteristics of CCPPs make it possible to control net power outputs and carbon emissions independently and flexibly. The integration of BES and carbon capture and storage (CCS) improves the operation flexibility of both wind power plants and conventional power plants. The objective function of the proposed model under interval-uncertain wind power is to minimize the total cost under the worst wind power output case and the context of low-carbon economy. The proposed model is implemented on the modified PJM 5-bus system with one wind farm and the IEEE-118 bus system with five wind farms. Numerical results demonstrate that the proposed model is effective at reducing carbon emissions and wind power curtailment, and verify that as the number of CCPPs increases, the overall costs come down. [ABSTRACT FROM AUTHOR]

Global warming caused by excessive CO 2 emissions has make it urgent to develop low-carbon economy. The carbon capture system is effective to reduce the carbon footprint of coal-fired power plants. Meanwhile, using more renewable energies such as wind power will require less generations from traditional power plants and hence limit the overall carbon emission. However, the wind power is intermittent by nature and hence may fluctuate and present a stochastic feature. In order to effectively reduce the overall carbon emission, a stochastic day-ahead scheduling optimization model with wind power integration incorporating carbon capture power plant (CCPP) and coupon-based demand response (CDR) is proposed in this work. Firstly, the formulation of CDR and the operating mechanism of CCPPs are clarified. Then the flexible operation mechanism aiming at reducing wind power curtailment and CO 2 emissions is analyzed. The random forecasting errors of the day-ahead hourly wind power output and loads are considered. Monte Carlo method is applied to simulate stochastic scenarios and a scenario reduction method is applied to ease the computational burden. Simulation results with on the PJM 5-bus system and the IEEE 118-bus system demonstrate the effectiveness of the proposed method in carbon emission reduction and wind curtailments decrease. [ABSTRACT FROM AUTHOR]

Evaluation of available transfer capability (ATC) is a complicated process involving the determination of the total transfer capability (TTC) and the existing transfer commitments (ETC). Considering the uncertain renewable generation such as wind power will bring more challenge to this process. Previously the uncertainty of wind power is considered either in the ED model or the following TTC calculation, but not included in two process simultaneously. Therefore, the ATC output may not be accurate since the uncertainty impact two problem simultaneously. To consider the uncertainty and correlation of wind power in both ISO's ED and ATC evaluation, this paper proposes a bi‐level optimization framework in which the ATC evaluation is formulated as the upper level problem and the ED is the lower level. The bi‐level model is first converted to a mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush‐Kuhn‐Tucker (KKT) optimality conditions, and then transformed to a mixed‐integer linear programming (MILP) problem which can be solved by existing optimization tools. Case studies on the PJM 5‐bus and IEEE 118‐bus systems are presented to verify the proposed methodology and the impacts of correlation of wind power on ATC are analysed. [ABSTRACT FROM AUTHOR]

Wind power has received extensive attention due to its superiorities of clean and pollution-free. However, because of the randomness and volatility of wind power, accurate power prediction is needed to help its consumption. Wind speed is the key to wind power prediction, but traditional prediction method cannot accurately grasp the wind speed variation trend and the traditional wind process partition method has some defects, an ultra-short-term wind speed hybrid prediction method based on wind process pattern forecasting is proposed in this paper. Firstly, a wind process (WP) division method considering the influence of wind speed on the operation state, mode switch and output power of wind turbine is proposed. Secondly, according to the operating characteristics of the wind turbine, all the WPs is classified into different wind process patterns (WPP), and the effectiveness of the classification is verified. Then, the Adaboost algorithm is used to forecast the WPP of the next 4 h. Finally, the wind speed hybrid prediction model of each pattern is established, the corresponding model is automatically selected based on WPP to predict the wind speed. Simulation results show that the proposed model can reliably forecast future WPP and the prediction accuracy is better than conventional models. • Wind fluctuation process is divided considering the key values of wind speed. • Wind process show varies fluctuation patterns according to operating characteristic. • Adaboost classification algorithm is applied to predict future wind process pattern. • Targeted modeling for specific wind process pattern can improve prediction accuracy. [ABSTRACT FROM AUTHOR]

• A novel stochastic bi-level strategic bidding model for P2G facility is proposed. • Carbon emission trading scheme (CETS) and its impact on LMP are considered. • The bi-level model is converted into mixed-integer linear programming (MILP). • The proposed bidding model can increase the profit of P2G facility. • CETS can increase the profit of P2G facilities and reduce carbon emission. As an emerging energy storage technology for renewable power utilization, power-to-gas (P2G) requires efficient bidding strategies to increase profit. This paper proposes a bi-level strategic bidding model for P2G with the consideration of a carbon emission trading scheme (CETS), an embedded locational marginal price (LMP) and wind power uncertainty, where the upper level model aims to maximize the profit of P2G facilities and the lower level model carries out the market clearing process. Under a CETS, a P2G facility purchases electricity power according to the LMP and is assumed to sell synthetic natural gas (SNG) and carbon emission permits for revenue. The electricity market clearing process is represented by a stochastic low carbon economic dispatch (ED) model considering the CETS, P2G and wind power uncertainty. On this basis, the formulation of a CETS embedded LMP (CETS-LMP) is presented and analyzed here. The bi-level model is reformulated and converted into mixed-integer linear programming (MILP) using strong duality theory. Case studies performed on a modified PJM 5-bus system and an IEEE 118-bus system verify the effectiveness of the proposed model and demonstrate that considering the CETS can increase the profit of P2G facilities and reduce carbon emission. [ABSTRACT FROM AUTHOR]

• A power system unit commitment model with flexibility of CCGT is proposed. • Natural gas transmission constraints and reserve scheduling are considered. • The model is transformed into an iterative MISOCP problem. • Startup-shutdown cost and wind power curtailment can be reduced. With the integration of large-scale renewable energy including wind power into the power grid, efficient flexible resources are required for wind power accommodation. The combined-cycle gas turbines (CCGTs) are widely used in the power system with flexible operation characteristics, which leads to enhanced interdependencies between the power grid and the natural gas network. In this paper, a unit commitment (UC) model with natural gas transmission constraints and reserve scheduling considering the flexibility of CCGT with quick startup-shutdown and flexible operation characteristics is proposed, to reduce wind power curtailment and high startup and shutdown cost of conventional generation units in the power system. Considering the complex operating conditions of CCGT and the non-convexity of Weymouth equation in natural gas network, this paper adopts an improved mode-based model (MBM) for modeling of CCGT and converts the Weymouth equation into a second-order cone relaxation (SOCR) formulation with penalty term through a concave-convex process (CCP) to make the relaxation tight, thus transforming the UC model into an iterative mixed-integer second-order cone programming (MISOCP) problem to be efficiently solved. The effectiveness of the proposed model on reducing wind power curtailment and startup-shutdown cost is demonstrated through case studies. [ABSTRACT FROM AUTHOR]

• A five-terminal HVDC model suitable for wind farm clusters integration is built. • An improved droop control method is proposed with an adjustable coefficient. • The application of lag compensator technique improves dynamic response capability. • The effectiveness and superiority of proposed method are verified via 4 test cases. With the continuous expansion of wind farm clusters, the multi-terminal modular multilevel converter based high voltage direct current (MMC-HVDC) transmission has been employed for wind power delivery. To improve the control flexibility, dynamic voltage adjustment speed and rationality of power allocation, various coordination control methods have been recently investigated in multi-terminal MMC-HVDC transmission system. An attempt is made in this paper to contribute to the optimization of coordination control in the multi-terminal MMC-HVDC system connected to wind farms. Based on the analysis of DC voltage, power deviation and droop coefficient, an improved droop control strategy with adjustable coefficients is first proposed and thereby applied to multi-terminal MMC-HVDC transmission system with wind farm clusters. Arguably, the droop coefficient correction should be an effective method for optimizing power allocation and reducing DC voltage deviation between the receiving converters. In addition, a lag compensator is introduced to improve the response speed of the inner loop controller when the improved droop control is adopted. Furthermore, the influence of droop coefficients on the stability of the control system is discussed, and the dynamic response of the improved control system is also analyzed under the different droop and time coefficients, which can provide guidelines for the control optimization of multi-terminal MMC-HVDC system. Finally, a model of a five-terminal MMC-HVDC transmission system is built based on the RT-LAB simulator, and the simulation results show that the proposed method can not only optimize the voltage control and power allocation, but also improve the system response speed. [ABSTRACT FROM AUTHOR]

• Thermal inertia of DHS and aggregated buildings is applied to IEDHS scheduling. • A novel scheduling model of IEDHS is proposed to reduce operation costs. • Flexibility assessment is carried out to assess flexibility enhancement of IEDHS. • The scheduling model can reduce operating cost, and hence reduce wind power curtailment. • Thermal inertia of DHS and aggregated buildings can improve the flexibility. In the traditional scheduling of integrated electricity and district heating systems (IEDHS) with high penetrations of wind power, large amounts of wind power curtailments can still occur. Exploring the underlying flexibilities within the existing infrastructure can be beneficial to the further integration of wind power. The thermal inertia of district heating network and aggregated buildings can provide such flexibility. This paper proposes a collaborative scheduling model of integrated electricity and district heating systems considering the thermal inertia of district heating network and aggregated buildings and a flexibility assessment method of the integrated electricity and district heating systems. With the detailed thermal model of the aggregated buildings and the transmission time delay characteristics of heating network pipelines, a thermal inertia model of the district heating systems is proposed. Then, the scheduling model considering thermal inertia of district heating network and the aggregated buildings is formulated as a quadratic programming problem, the objective function of which is to minimize the operating cost of integrated electricity and district heating systems. Four scheduling cases based on whether to consider the transmission time delay characteristics of heating network pipelines or the adjustable indoor temperature of aggregated buildings are established, and a flexibility assessment method of different cases for the electricity system and coupling component is proposed. The validity of the model is verified by cases studies. Numerical results show that the proposed optimization scheduling model can make use of the heat storage capacity of district heating network and aggregated buildings, reduce operating costs, increase the flexibility of electricity system and effectively promote wind power integration. [ABSTRACT FROM AUTHOR]