Large-scale Wind Power Integration Research Articles

Composition and Control of a New Type of Hybrid Voltage-Source Converter Based on DRUs and FB-MMC for Large-Scale Offshore Wind Power Integration and Transmission

Composition and Control of a New Type of Hybrid Voltage-Source Converter Based on DRUs and FB-MMC for Large-Scale Offshore Wind Power Integration and Transmission

Flexible industrial power-to-X production enabling large-scale wind power integration: A case study of future hydrogen direct reduction iron production in Finland

Hydrogen Direct Reduction of Iron (HDRI) combined with renewable electricity is an attractive option for low-carbon steel production. In this paper, we present a novel and computationally efficient techno-economic power-to-x-plant optimization model which is then applied to an HDRI plant located in Finland. Plant dimensioning was carried out in several current and future power market and regulatory scenarios. We predict a production cost of 373 €/t for hot briquetted iron, and 351 €/t for a future scenario of 2025–2030. When the recently introduced EU rules for renewable fuels of non-biological origin are applied, the production costs increased by 30–46 €/t. The rules also have a significant increasing effect on the required hydrogen storage. The flexibility of the direct reduction shaft emerged as an important parameter affecting the required hydrogen storage as well as total production cost. The results of this paper hold significance for the optimal design of future low-carbon steel plants.

Power system planning considering demand side response

The large-scale integration of wind power into the grid requires the redesign and expansion of the transmission network. The volatility of wind power and the limited application of energy storage have prompted the inclusion of demand-side response in transmission planning to improve the resilience of the grid. In this paper, a power system planning model that incorporates demand-side response is developed and the model is solved by using a greedy stochastic adaptive search process. Based on the results of the Garver 6-node test, the old and new models are compared to validate the effectiveness of the power system planning model considering demand side response.

Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model

The integration of large-scale offshore wind power into the power grid presents significant challenges for grid operation and dispatch due to the variability and intermittency of offshore wind energy. However, non-stationarities and noise in wind speed time series pose challenges. This study proposes a VMD-PE-FCGRU methodology to address this challenge of predicting offshore wind speeds. Firstly, the Variational Mode Decomposition (VMD) algorithm decomposes the original wind speed signal, considering the strong fluctuations and high noise inherent to offshore wind energy. A corresponding performance evaluation index is established to assess the effectiveness of this deconstruction process. To further enhance the model's (GRU) ability to capture time series information, a Position Encoding layer (PE) has been added, and the network is deepened through a Fully Connected Neural Network (FCNN). This allows multi-dimensional time series features to be input into the Gated Recurrent Unit (GRU) network for forecast. The accuracy of the proposed method is then verified using measured historical data from the wind tower of an offshore wind farm in Guangdong. Experimental results demonstrate that the proposed method can accurately forecast short-term wind speeds for offshore wind farms, with a Mean Absolute Error (MAE) of 0.199 and a Mean Absolute Percentage Error (MAPE) of only 2.45%. Moreover, the qualified rate (r) reaches 100%, providing an accurate reference for real-time power grid dispatching and can inform decision-making for the integration of offshore wind power into the grid.

ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution

The integration of large-scale wind power into the power grid threatens the stable operation of the power system. Traditional wind power prediction is based on time series without considering the variability between wind turbines in different locations. This paper proposes a wind power probability density prediction method based on a time-variant deep feed-forward neural network (ForecastNet) considering a spatio-temporal distribution. First, the outliers in the wind turbine data are detected based on the isolated forest algorithm and repaired through Lagrange interpolation. Then, based on the graph attention mechanism, the features of the proximity node information of the individual wind turbines in the wind farm are extracted and the input feature matrix is constructed. Finally, the wind power probability density prediction results are obtained using the ForecastNet model based on three different hidden layer variants. The experimental results show that the ForecastNet model with a hidden layer as a dense network based on the attention mechanism (ADFN) predicts better. The average width of the prediction intervals at achieved confidence levels for all interval coverage is reduced by 34.19%, 35.41%, and 35.17%, respectively, when compared to the model with the hidden layer as a multilayer perceptron. For different categories of wind turbines, ADFN also achieves relatively narrow interval average widths of 368.37 kW, 315.87 kW, and 299.13 kW, respectively.

Assessment of High Wind Power Permeability Power System Frequency Regulation

Global wind power has expanded quickly in recent years to help with the energy issue and environmental degradation. The large-scale integration of wind power has become a hot topic for academic research in recent years due to the potential to improve the stability of the power system through the application of wind power participation in power grid frequency control. This study reviews the research on power system frequency regulation approach with high wind power permeability and classes the frequency control difficulties of wind power integration. A review and analysis are conducted on the energy storage system that is involved in the frequency management of the power system with high wind permeability.

Research on the optimal configuration method of energy storage based on wind power randomness

Large-scale wind power integration into the power grid will bring some negative effects or even large faults to the power system, such as voltage instability, the change in the transient stability of the system, etc. This could lead to the low accuracy of the wind power prediction results and the unreasonable capacity configuration of the energy storage. In this paper, considering the uncertainty of wind power, the optimization model of minimizing the operating cost, voltage deviation, and pollutant emission is constructed. Additionally, the mixed probability optimization algorithm (PLF-NSGAII-MOPSO) is calculated and analyzed. The results show that the method proposed in this paper is beneficial to finding the optimal configuration of energy storage under the condition of wind power access.

Large-scale offshore wind energy integration by wind-thermal bundled power system: A case study of Yangxi, China

A large-scale offshore wind power integration has a reverse peak regulation effect on the electric load and requires newly built transmission corridors. This work adopts a wind-thermal (WT) bundled system for offshore wind power integration and transmission line capacity factor improvement. The Qingzhou I-VII offshore wind farms (5000 MW) and the Yangxi coal-fired power plant units #5, #6 (2 × 1240 MW) are taken as a case study. The offshore wind farms (OWF) act as the must-take generator to increase the renewable fraction while the thermal power plants (TPP) provide peak regulation service to trace the changing net load. The OWF and TPP share the transmission line and point of interconnection. The OWF integration level is determined by the 8760-h production simulation and the wind-thermal (W-T) capacity ratio is 1.4516:1 (3600 MW OWF) in the studied site. The curtailment rate, electricity shortage rate, transmission line capacity factor (CF), load following precision, renewable fraction, CO2 emission of WT-W3600 condition are 9.02%, 5.41%, 61.36%, 91.43%, 50.46%, 6.97 million ton/year. There is a trade-off relationship between renewable fraction and curtailment rate in the WT system which restricts higher OWF integration. The demand-side response technologies such as power-to-X and energy storage are required to break the trade-off relation and reduce the electricity shortage rate. The WT system is economically viable and the net present value (NPV), levelized cost of electricity (LCOE), internal rate of return (IRR) of WT-W3600 are 3.55 billion¥, 0.4859¥/kWh, 4.82%. Co-locating the OWF and the floating solar photovoltaic (PV) is the future direction that benefits the intensive exploitation of sea area and offshore renewable energy development.

Identification of dominant propagation paths based on sub-synchronous oscillation using branch oscillation energy distribution coefficient

The large-scale integration of wind power into the power grid can cause a new type of sub-synchronous power oscillation, different from traditional thermal power generation. The oscillation energy will spread extensively in the grid, causing power oscillation and even grid-cascading events. To address this issue, this article proposes a method for quantitatively analyzing the propagation characteristics of oscillation energy based on branch oscillation energy. Firstly, analyzing the oscillation energy shared by different branches in the network based on transient energy function. Next, a method is proposed to identify the dominant propagation path of sub-synchronous oscillation by defining the oscillation energy of branches under the dominant oscillation mode and the oscillation energy distribution coefficient of each branch. The oscillation partition set formed by the dominant propagation path can be used to locate the high-risk oscillation area of the system. Finally, the effectiveness of the method proposed in this paper for studying the wide-area propagation characteristics of sub-synchronous oscillations was verified through time-domain simulation analysis.

Large-scale offshore wind integration by wind-thermal-electrolysis-battery (WTEB) power system: A case study of Yangxi, China

Offshore wind power integration is a grand challenge due to the volatility, randomness and intermittency This work presents a wind-thermal-electrolysis-battery (WTEB) hybrid energy system designed to integrate large-scale offshore wind energy and reduce curtailment. The system composes of offshore wind farms (OWF), thermal power plants (TPP), electrolysis station, battery bank. The Qingzhou (geographic name) I-VII offshore wind farms (5000 MW) and the Yangxi (geographic name) coal-fired power plant units #5, #6 (2 × 1240 MW) are taken as a case study. The OWF is the indispensable generator and the renewable fraction increases with integration level. The TPP adjusts the load ratio to meet the changing net load. The electrolysis station consumes the excess electricity and the battery bank time-shifts the electricity to reduce the electrolyzer downtime. The recommended wind-thermal (W-T) capacity ratio is 1.452:1 in the studied case, namely 3600 MW OWF. The WTEB system breaks the trade-off relationship between renewable fraction (50.11%) and curtailment rate (1.48%) compared to traditional wind-thermal (WT) bundled system. The WPEB system improves the electricity shortage rate (4.74%), transmission line capacity factor (61.79%), load following precision (91.79%). The internal rate of return (IRR), levelized cost of electricity (LCOE), levelized cost of hydrogen (LCOH) of WTEB-W3600 are 34.70%, 0.5172 ¥/kWh, 29.76 ¥/kg. The WTEB hybrid energy system for a large-scale offshore wind power integration is first proposed and the techno-economic feasibility analysis proves the feasibility and forward-looking of the system. Collaborative capacity optimization of OWF, TPP, electrolysis station, battery can improve the system performance and economy. The wind-photovoltaic-thermal-electrolysis-battery (WPTEB) system co-locating the OWF and floating photovoltaic (PV) is an important development direction.

Chance-Constrained Joint Dispatch of Generation and Wind Curtailment-Load Shedding Schemes With Large-Scale Wind Power Integration

Wind curtailment (WC) and load shedding (LS) are indispensable measures to mitigate the operational risk in high-renewable power systems. Moreover, WC and LS schemes should be pre-scheduled and confirmed by related entities to make them applicable. In this paper, we propose a novel chance-constrained economic dispatch (CCED) model which can generate optimal WC and LS schemes accounting for the reserve shortage and transmission congestion problems. In the proposed method, WC and LS power are formulated as random decision variables, with which the infeasibility issues of conventional CCED are fully addressed. To solve the proposed model, we first convert the complicated chance constraints into a set of deterministic inequalities equivalently by employing the conditional Value-at-Risk (CVaR) representation and duality theory. Then, a two-layer iterative algorithm is proposed to solve the equivalent problem efficiently, which is based on the generalized Benders decomposition (GBD) framework. Numerical tests demonstrate the effectiveness and efficiency of the proposed method.

Security-constrained Transmission Expansion Planning with N-k Security Criterion and Transient Stability

The integration of large-scale wind power into the power system is a global trend in response to climate change, however, the intermittent nature of wind power presents new challenges for planners. Conventional transmission expansion planning (TEP) methods, which only consider N-1 static security, may not ensure the security of multiple contingencies and transient stability in a large-scale wind power integration scenario. Incorporating N-k-based static constraints and dynamic constraints, such as differential-algebraic-equations-(DAEs)-based transient stability constraints, into TEP can result in a heavy computational burden and render the TEP model intractable. This paper proposes a tractable mathematical model (S&DSTEP) for joint consideration of static and dynamic security in TEP with wind power. The uncertain wind power generation is captured through selected scenarios and the model is divided into a TEP master problem and multiple sub-problems for evaluating static security and transient stability. Static security is determined by the use of fault chain theory (FCT), avoiding the need for enumerating all N-k contingencies, thus reducing the computational burden. Transient stability is analyzed through the use of stabilizing cuts generated from the extended equal-area criteria (EEAC) rather than dynamic constraints expressed as DAEs. The proposed model is verified on the modified New England 39-bus system and IEEE 118-bus system, demonstrating its ability to consider transient stability while being more computationally efficient compared to the robust method that directly incorporates all contingency scenarios.

Linearized AC power flow model based interval total transfer capability evaluation with uncertain renewable energy integration

Total transfer capability (TTC) evaluation plays an essential role in guaranteeing reliable power transactions in a deregulated electricity market. However, the large-scale integration of wind power brings significant challenges to TTC calculations. The conventional probabilistic TTC evaluation methods are highly dependent on the pre-defined probabilistic distributions of the input random variables, which could not be accurately predicted in practical situations. This paper proposes an interval TTC evaluation method based on the linearized AC power flow (LACPF) model, which can provide intuitive TTC boundary information without any prior distributions. The adoption of LACPF also contributes to yielding more accurate TTC values than those DC power flow (DCPF) based methods. The proposed interval optimization model is equivalently translated into an optimistic model and a pessimistic model. The optimistic model is formulated as a max-max problem and can be directly converted to a single-level linear programming model. The pessimistic model is formulated as a min-max problem that cannot be solved directly. Hence, the strong duality theory and the big-M method are introduced to transfer the original bilevel model into a single-level mixed integer linear programming (MILP) model for an efficient solution. The proposed method is validated on the IEEE 118-bus system and a modified actual power grid in Northwest China. Numerical results demonstrate its computational efficiency.

Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions

With the large-scale integration of wind and solar power in China, the consumption of these intermittent renewable energies is severely restricted by the capacity of the transmission channel, which leads to massive renewable energy curtailment. Therefore, it would be beneficial to use limited transmission channels to absorb as much renewable energy as possible. In this paper, we propose a chance constraint-based multistage nested hydro-wind-solar coordinated optimal scheduling model to aid peak shaving while ensuring maximum power generation. First, multistage partitioned section chance constraints are introduced to mitigate the power congestion of the transmission network. Then, based on the principle of using hydropower to complement the uncertainty of wind and solar power, the compensation chance constraints are considered. To quantify the uncertainty of wind and solar power, their prediction errors are analyzed using the Gaussian mixture model. Finally, the model is recast and linearized into a mixed integer linear programming model. A case study of a hydro-wind-solar base in Southwest China demonstrates that the proposed model can effectively leverage the regulation ability of hydropower to coordinate multiple power sources with the restrictions of multistage transmission sections, effectively alleviating the congestion of transmission channels and reducing the curtailment of renewable energy.

Nonlinear rotor kinetic energy control strategy of DFIG-based wind turbine participating in grid frequency regulation

Large-scale wind power integration reduces the level of inertia in power systems, significantly deteriorating their frequency stability. To address this issue, a nonlinear rotor kinetic release control strategy is designed for wind power grid-connected systems based on an extended state observer (ESO) and nonlinear feedforward state feedback transformation (NFSFT) theories. First, the affine nonlinear system of a DFIG-based wind turbine participating in grid frequency regulation is established. The system is transformed into a basic equivalent linear system based on the NFSFT theory, and a nonlinear control law is obtained. Second, the ESO is used to observe the generalized disturbance composed of the complex parameter operation required by the nonlinear control law, which circumvents the huge computational burden of the nonlinear control law and the unknown uncertainty of the system. Finally, a limiting link and speed control link are added to prevent excessive release of the kinetic energy of the wind turbine rotor and complete the wind turbine speed recovery. A wind power grid-connected system is built using MATLAB / SIMULINK for simulation verification. The results show that the proposed strategy exhibits a better frequency regulation effect than other strategies.

A dual-layer cooperative control strategy of battery energy storage units for smoothing wind power fluctuations

The large-scale integration of wind power with intermittent characteristics into grids brings a challenge to the power system. Installation of the battery storage energy system (BESS) in a wind farm (WF) can effectively smooth wind power fluctuation. However, the BESS units may face the problem of over-charge/over-discharge if the power dispatch strategy for multiple units of BESS is improper, which results in lifetime degradation. In this paper, a dual-layer cooperative control strategy of multiple BESS units is proposed. In the first layer, the time-varying characteristics of wind power are introduced to define a power fluctuation coefficient. The coefficient can adjust the time constant of the first-order low pass filter (FLF) to avoid the deep energy utilization of BESS and decrease the over-charge/over-discharge possibility. To facilitate more power output for units with high SOC and absorb more energy for units with low SOC, the second layer calculates a SOC distribution factor by a function of the SOC of each unit to allocate the power command. The two layers of control operate cooperatively to reduce the SOC variation and unbalance degrees, thus the over-charge/over-discharge can be avoided, which is good for prolonging the lifetime of BESS with multiple units. Taking an actual WF in the Hunan province of China as the research object, the effectiveness of the proposed strategy is verified by the co-simulation of DIgSILENT/PowerFactory and Matlab/Simulink.

Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks

Wind energy represents one of the leading renewable energy sectors and is considered instrumental in the ongoing decarbonization process. Accurate forecasts are essential for a reliable large-scale wind power integration, allowing efficient operation and maintenance, planning of unit commitment, and scheduling by system operators. However, due to non-stationarity, randomness, and intermittency, forecasting wind power is challenging. This work investigates a multi-modal approach for wind power forecasting by considering turbine-level time series collected from SCADA systems and high-resolution Numerical Weather Prediction maps. A neural architecture based on stacked Recurrent Neural Networks is proposed to process and combine different data sources containing spatio-temporal patterns. This architecture allows combining the local information from the turbine’s internal operating conditions with future meteorological data from the surrounding area. Specifically, this work focuses on multi-horizon turbine-level hourly forecasts for an entire wind farm with a lead time of 90 h. This work explores the impact of meteorological variables on different spatial scales, from full grids to cardinal point features, on wind power forecasts. Results show that a subset of features associated with all wind directions, even when spatially distant, can produce more accurate forecasts with respect to full grids and reduce computation times. The proposed model outperforms the linear regression baseline and the XGBoost regressor achieving an average skill score of 25%. Finally, the integration of SCADA data in the training process improved the predictions allowing the multi-modal neural network to model not only the meteorological patterns but also the turbine’s internal behavior.

Research on large-scale wind power consumption in the electricity market considering demand response and energy storage systems

Large-scale wind power integration brings great challenges to power system operation. The use of large-scale wind power in the electricity market has become a concern for many researchers. Demand response (DR) and energy storage systems (ESSs) play crucial roles in the consumption of large-scale wind power. In this paper, a detailed DR model is established, including price-based demand response (PBDR) and incentive-based demand response (IBDR). IBDR contains load shifting (LS) and load curtailment (LC). The IBDR model in this study not only includes its bidding and market clearing but also contains relevant constraints: maximum/minimum duration time, shifting/curtailment gap time, and shifting/curtailment frequency. A two-stage trading method, including a day-ahead (DA) market and a real-time (RT) market, is proposed. The method contains various market participants: conventional units (CUs), rapid adjustment units (RAUs), wind power, ESS, and multiple types of DR. The roles and economic benefits of various market participants in the consumption of large-scale wind power are analyzed in an IEEE 30 bus system, verifying the accuracy and validity of the model. The best DR scale and the suggestions of ESS are given. The results show that the proposed method can effectively utilize wind power and decrease system costs.

Real-time low-carbon scheduling for the wind–thermal–hydro-storage resilient power system using linear stochastic robust optimization

With the large-scale wind power integration, power systems have to address not only the conventional power demand fluctuations but also the wind uncertainty. To improve the economical effectiveness, resilience, and environmental protection of power systems in the source-load uncertainty, a real-time low-carbon scheduling for the wind–thermal–hydro-storage integrated system is proposed. The power imbalance caused by the uncertainty is neutralized by the synergetic linear decision of multiple resources. To address the source-load uncertainty, a stochastic robust optimization is introduced, which establishes the system constraints by robust optimization for the resilience operation, while optimizing the expected operation cost in the empirical uncertainty distribution for economic efficiency. Moreover, a multi-point estimation is applied to formulate the expected operation cost precisely and quickly. By using the dual theory, the proposed real-time power scheduling is derived as a mixed integer bilinear constrained programming. A multi-step sequential convexified solution is developed to solve the complex scheduling problem, which linearizes the bilinear constraints with alternate optimization and relaxes the state variables of energy storages with an “estimation–correction” strategy. Finally, case studies show the superiority of the proposed scheduling method and convexified solution.

Wind power interval prediction based on variational mode decomposition and the fast gate recurrent unit

Large-scale wind power integration is difficult due to the uncertainty of wind power, and therefore the use of conventional point prediction of wind power cannot meet the needs of power grid planning. In contrast, interval prediction is playing an increasingly important role as an effective approach because the interval can describe the uncertainty of wind power. In this study, a wind interval prediction model based on Variational Mode Decomposition (VMD) and the Fast Gate Recurrent Unit (F-GRU) optimized with an improved whale optimization algorithm (IWOA) is proposed. Firstly, the wind power series was decomposed using VMD to obtain several Intrinsic Mode Function (IMF) components. Secondly, an interval prediction model was constructed based on the lower upper bound estimation. Finally, according to the fitness function, the F-GRU parameters were optimized by IWOA, and thefinal prediction interval was obtained. Actual examples show that the method can be employed to improve the interval coverage and reduce the interval bandwidth and thus has strong practical significance.