Wind Power Curtailment Research Articles

Optimization Strategy of Wind Power Curtailment Under LVRT Operation

Optimization Strategy of Wind Power Curtailment Under LVRT Operation

Hydrogen for harvesting the potential of offshore wind: A North Sea case study

Economical offshore wind developments depend on alternatives for cost-efficient transmission of the generated energy to connecting markets. Distance to shore, availability of an offshore power grid and scale of the wind farm may impede export through power cables. Conversion to H2 through offshore electrolysis may for certain offshore wind assets be a future option to enable energy export. Here, we analyse the cost sensitivity of offshore electrolysis for harvesting offshore wind in the North Sea using a technology-detailed multi-carrier energy system modelling framework for analysis of energy export. We include multiple investment options for electric power and hydrogen export including HVDC cables, new hydrogen pipelines, tie-in to existing pipelines and pipelines with linepacking. Existing hydropower is included in the modelling, and the effect on offshore electrolysis from increased pumping capacity in the hydropower system is analysed. Considering the lack of empirical cost data on offshore electrolysis, as well as the high uncertainty in future electricity and H2 prices, we analyse the cost sensitivity of offshore electrolysis in the North Sea by comparing costs relative to onshore electrolysis and energy prices relative to a nominal scenario. Offshore electrolysis is shown to be particularly sensitive to the electricity price, and an electricity price of 1.5 times the baseline assumption was needed to provide sufficient offshore energy for any significant offshore electrolysis investments. On the other hand, too high electricity prices would have a negative impact on offshore electrolysis because the energy is more valuable as electricity, even at the cost of increased wind power curtailment. This shows that there is a window-of-opportunity in terms of onshore electricity where offshore electrolysis can play a significant role in the production of H2. Pumped hydropower increases the maximum installed offshore electrolysis at the optimal electricity and H2 prices and makes offshore electrolysis more competitive at low electricity prices. Linepacking can make offshore electrolysis investments more robust against low H2 and high electricity prices as it allow for more variable H2 production through storing excess energy from offshore. The increased electrolysis capacity needed for variable electrolyser operation and linepacking is installed onshore due to its lower CAPEX compared to offshore installations.

Optimization of wind and solar energy storage system capacity configuration based on the Parzen window estimation method

Compressed air energy storage (CAES) effectively reduces wind and solar power curtailment due to randomness. However, inaccurate daily data and improper storage capacity configuration impact CAES development. This study uses the Parzen window estimation method to extract features from historical data, obtaining distributions of typical weekly wind power, solar power, and load. These distributions are compared to Weibull and Beta distributions. The wind–solar energy storage system's capacity configuration is optimized using a genetic algorithm to maximize profit. Different methods are compared in island/grid-connected modes using evaluation metrics to verify the accuracy of the Parzen window estimation method. The results show that it surpasses parameter estimation for real-time series-based configuration. Under grid-connected mode, rated power configurations are 1107 MW for wind, 346 MW for solar, and 290 MW for CAES. The CAES system has a rated capacity of 2320 MW·h, meeting average hourly power demand of 699.26 MW. It saves $6.55 million per week in electricity costs, with a maximum weekly profit of $0.61 million. Payback period for system investment is 5.6 years, excluding penalty costs.

Data‐driven stochastic model predictive control for regulating wind farm power generation with controlled battery storage

AbstractThe uncertainty and fluctuation of the volatile wind power cause more reserve and frequency regulation capacity, so it incurs additional operational cost for the power grid. Therefore, a wind farm (WF) coordinated controller is essential to reduce the power fluctuation and trace the scheduled power generation with minimal wind curtailment and battery degradation cost. This paper proposes a data‐driven stochastic model predictive control (SMPC) method for WFs to realize these objectives. Firstly, to address the non‐linear dynamic model of wind turbines, the dynamic mode decomposition method accompanying Koopman operator is used to learn a lifted linear dynamic model from measurements. Secondary, this paper proposes the relationship between the curtailed wind power of wind turbines and their pitch angle, wind speed, and rotation speed, as well as the corresponding estimation formula. In addition, the prediction errors of wind speeds are characterized using the Gaussian mixture model (GMM), which may significantly affect the controller's performance. Finally, a tractable data‐driven SMPC model is developed and is verified on a DIgSILENT/PowerFactory based simulation platform. Compared with conventional model predictive control algorithms, it can significantly reduce the overall operational cost of WFs.

Optimal Configuration of Power-to-Heat Equipment Considering Peak-Shaving Ancillary Service Market

The serious problem of wind power curtailment in northern China has created a pressing need to enhance the peak-shaving ability of the power system. As the main source of power supply in northern China, combined heat and power (CHP) units have significant potential for peak-shaving. Currently, the Chinese government encourages CHP plants to increase their peak-shaving capacity by installing power-to-heat (P2H) equipment. In addition, the government has implemented auxiliary service market policies to encourage CHP plants to provide peak-shaving services. In order to maximize economic benefits for CHP plants, this paper proposes an optimal configuration method of P2H equipment with the static payback time (SPT) as the objective function. Cost and income models of installing the P2H equipment are constructed by taking into account the auxiliary service market policies. The peak-shaving income model of the CHP plant is derived emphatically as a key part of the proposed method. Finally, the district heating region in Jilin province is used as a case study example. The results show that adding the P2H equipment is significantly effective in improving the peak-shaving ability of CHP units, and investing in heat pumps is more cost-effective than electric boilers. The proposed method can be applied to other northern regions relying on CHP units for central heating, providing a valuable solution to the problem of wind power curtailment in these regions.

Low-carbon economic dispatch of integrated energy systems that incorporate CCPP-P2G and PDR considering dynamic carbon trading price

To address the challenge of global warming stemming from excessive fossil fuel combustion, the exploration of low-carbon technologies and improvements to carbon trading mechanisms stand out as vital strategies. An effective conduit for these strategies is the integrated energy system (IES), which has garnered increasing attention for its potential. To realize low-carbon advancements within energy systems, this study proposes a low-carbon economic scheduling framework for the IES that integrates the coupled operation of carbon-capture power plants, power-to-gas (CCPP–P2G) unit and the price-based demand response (PDR) considering dynamic carbon trading mechanisms. The proposed approach firstly concurrently considers source-side CCPP-P2G and load-side PDR to achieve coordinated low-carbon operation, elucidating the underlying mechanisms. Subsequently, a dynamic carbon trading model for the IES is formulated, flexibly capturing the ebb and flow of supply and demand in the carbon trading market. Amidst the uncertainty of the system, a bi-level optimization model is constructed to minimize the operational cost. Finally the effectiveness of the proposed method is demonstrated on an IES consisting of the IEEE-30 bus power system, 6-bus natural gas system and 6-bus district heating system. The findings indicate that the proposed dynamic carbon trading mechanism is able to effectively provide time-varying carbon trading price signals to fully reflect the supply and demand relationship of carbon emission allowance. Furthermore, the synchronized operation of CCPP-P2G and PDR yields substantial benefits, compared to the benchmark. Specifically for the test case, the proposed framework reduces system operational costs, wind power curtailment, and carbon emissions by $87,160, 1,410 MWh, and 4,849t, respectively.

Optimal energy management of an integrated energy system with multiple hydrogen sources

Hydrogen is considered a promising alternative to fossil fuels in an integrated energy system (IES). In order to reduce the cost of hydrogen energy utilization and the carbon emissions of the IES, this paper proposes a low-carbon dispatching strategy for a coordinated integrated energy system using green hydrogen and blue hydrogen. The strategy takes into account the economic and low-carbon complementarity between hydrogen production by water electrolysis and hydrogen production from natural gas. It introduces the green hydrogen production–storage–use module (GH-PSUM) and the blue hydrogen production–storage–use module (BH-PSUM) to facilitate the refined utilization of different types of hydrogen energy. Additionally, the flexibility in hydrogen load supply is analyzed, and the dynamic response mechanism of the hydrogen load supply structure (DRM-HLSS) is proposed to further reduce operating costs and carbon emissions. Furthermore, a carbon trading mechanism (CTM) is introduced to constrain the carbon emissions of the integrated energy system. By comprehensively considering the constraints of each equipment, the proposed model aims to minimize the total economic cost, which includes wind power operation and curtailment penalty costs, energy purchase costs, blue hydrogen purification costs, and carbon transaction costs. The rationality of the established scheduling model is verified through a comparative analysis of the scheduling results across multiple operating scenarios.

An ICEEMDAN-based collaborative optimization control for wind-hydrogen-electrochemical energy storage under multiple application scenarios

An improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-based collaborative optimization control strategy of wind-hydrogen-electrochemical energy storage coupled system with the interconversion characteristics between hydrogen with electricity under multiple application scenarios is introduced in this paper. After identifying the grid-connected wind power based on the ICEEMDAN algorithm, the normalized maximum discrepancy of the modal functions divides the high-frequency modal components into the fluctuating power smoothed by lithium iron phosphate batteries and hydrogen storage, with wind power curtailment from grid connection being consumed by electrolysis of water for hydrogen in alkaline electrolyzers. Another novelty is a collaborative optimization strategy for hydrogen-electrochemical energy storage under two application scenarios, comparing the smoothing effect and the ability to eliminate wind curtailment with different energy storage schemes. Demonstrate the method's effectiveness through the certain operational data from a Chinese wind farm. Simulation results indicate that the coupled system results in 19.45% and 7.79% cost reduction compared to other schemes, and the collaborative optimization control strategy achieves complete wind curtailment, which further improves the capacity of consuming curtailed wind power while smoothing fluctuations and providing certain engineering application value.

Efficient Operation of Integrated Electrical-Water System for Wind Power Accommodation

This paper investigates the operation of an integrated electrical-water system for wind power accommodation. The efficient operation can be considered as how well to handle the binary variables in a water distribution system (WDS) model. The optimization problem of WDS is NP-hard, and it often takes hours to find the optimal solution. Since wind power varies in minutes, long computation time may hinder the WDS from being fully used by power system to deal with the wind power fluctuation and uncertainties. This paper develops a three-step optimization model to answer the challenges of the WDS optimization problem. The solution hinges on the calculation of the binary variable values in advance. The first step is to construct a linear power-WDS co-optimization (LPWCO) model to find the optimal electrical demand of WDS. The second step develops a tank flow allocation based method and a greedy-based algorithm to determine the binary variable values of WDS. This allows the WDS optimization model to be efficiently solved by commercial solvers in the third step. The proposed three-step model converts the NP-hard optimization problem of WDS into a polynomial-time problem which can be solved quickly. This allows the wind power system to leverage the energy flexibility provided by WDS in the real-time operation. Results of case study show that, compared with existing methods, the proposed method can reduce the wind power curtailment and the operation cost by about 55% and 53%. More importantly, the solution time is reduced dramatically from hours to seconds, suitable for practical application.

Benefit Evaluation of Hydrogen Production System Harnessing Curtailed Wind Considering Integrated Demand Response

With the continuous increase of wind power installed capacity in China in recent years, the problem of wind curtailment has become increasingly serious. Using curtailed wind power for hydrogen production is an effective way to reduce wind curtailment. In this paper, based on the complementary characteristics of various energies in the electric-hydrogen system, a capacity planning model of the energy system considering wind curtailment wind-to-hydrogen technology and integrated demand response is established. The proposed model utilizes a fast unit commitment technology, with the function of solving the optimum capacity of various equipment in the electrolytic hydrogen production system under different wind curtailment utilization rates and different wind power installed capacities. A case study based on Xinjiang province, which has rich wind energy resources, is conducted to obtain the optimal composition of new-built capacities of the electrolytic hydrogen production system. In light of the simulation results, the levelized cost of hydrogen is proportional to the wind power installed capacity and inversely proportional to the utilization rate of wind curtailment.

A data-driven multi-energy economic scheduling method with experiential knowledge bases

The high penetrability of renewable energy and the increasing demand for hydrogen energy pose challenges to system scheduling and the accommodation of photovoltaic and wind power. Therefore, a data-driven multi-energy economic scheduling method with experiential knowledge bases is proposed to enhance system operational efficiency and economic performance. Firstly, the Wasserstein generative adversarial network is used to enhance wind power and photovoltaic historical samples. The K-medoids clustering algorithm and ϕ-divergence are applied to achieve scenario reduction and acquire fuzzy sets, eliminating the need for assumptions about their probability distributions. Secondly, a kernel density estimation is used to improve the accuracy of characterizing the probability distribution of energy loads. Based on fuzzy sets, the distributionally robust optimization is used to achieve optimal scheduling in an integrated electricity-hydrogen-heat system. This technique enhances the accommodation capacity of photovoltaic and wind power while reducing operational costs. Finally, a decision-making technique based on an experiential knowledge base is studied, aiming to swiftly match corresponding decision variables by evaluating the similarity of labeled state variables. Simulation results show that the proposed method improves the decision-making speed by about 0.8 times and reduces operating costs and photovoltaic or wind power curtailment by at least 1%. The method can provide fast and efficient day-ahead economic scheduling for digitized integrated energy systems.

Analysis of System Balancing and Wind Power Curtailment Challenges in the Ethiopian Power System under Different Scenarios

In this paper, an hourly dispatch model was developed to analyze the system balancing and wind power curtailment challenges in the future of the Ethiopian electric power grid system. The developed model was validated using historical data and was used for the analysis of the grid system in 2030 with different scenarios. The model was used to examine the impacts of transmission capacity, regulation reserve requirement, and daily minimum generation of hydropower for irrigation with three cases of wind annual energy share of 14.5%, 17.8%, and 25.2%. Thus, the curtailment was found to be below 0.2%, 1.1%, and 9.8% for each case, respectively. The cost of wind energy increases in proportion to the percentage of curtailment and the increase in transmission line capacity. Reducing the minimum hydropower generation results in smaller wind power curtailment and better generation–consumption balancing.

Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors

The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power.

Multi-Time Scale Economic Dispatch of Integrated Electricity and Natural Gas Systems with Flexibility Constraints Based on Chance-Constrained Programming

The connection between various energy types in the integrated power and natural gas system has grown stronger in recent years, as has the penetration rate of clean energy. Wind power generation volatility offers a considerable barrier to power system operation. This research provides a multi-time scale economic dispatch model with flexibility limitations to address this issue. Through chance-constrained programming, the equipment flexibility is described by probability functions and predetermined confidence levels in this model, and the generating cost and wind power consumption are improved through day-ahead and intra-day optimal scheduling. Finally, the effectiveness of the proposed model is verified by two case studies of integrated energy systems, where the results show that about 68.0–72.1% wind power curtailment can be effectively reduced while satisfying all load and system safety requirements.

Multi-objective optimization of distribution network considering battery charging and swapping station

Battery charging and swapping station (BCSS) can provide flexibility for the distribution network due to accumulating a large number of batteries. This paper proposes a multi-objective optimization method of distribution network considering BCSS, aiming to minimize the transmission losses, voltage deviation and curtailment of wind and photovoltaic power. Besides, The present study puts forth a novel integrated modeling methodology for batteries, which is founded upon the State of Charge (SOC) interval. This approach includes the total energy constraint, as well as the charging and discharging priorities of the batteries. Finally, numerical tests are carried out to validate the correctness and rationality of the presented method. The outcomes of the study show that 2% reduction in wasted energy is achieved with BCSS.

Multi-objective reactive power optimization strategy of power system considering large-scale renewable integration

To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies their impact on the power system’s operation. First, the power system model with renewables integration is established using AC power flow. The wind turbines and photovoltaic units are modeled in detail according to their topologies and operating characteristics, and then further simplified according to the feasible region. An improved DC power flow model is adopted to handle the non-linear characteristics of the power system. On this basis, a multi-objective reactive power optimization model is constructed to minimize the power generation cost, wind and solar power curtailment, and voltage offset. Finally, comparisons between two types of models in different scenarios are designed. Numerical simulations demonstrate that the participation of renewables in reactive power regulation can improve the operational economy and voltage stability of power systems.

Cause Analysis and Countermeasures of Wind Power Curtailment

The current world has numerous issues, such as insufficient electrical supply, excessive fossil fuel consumption, and environmental contamination. As a rapidly increasing renewable energy in recent years, wind energy plays a significant role in mitigating these issues. Nevertheless, the rapidly expanding wind energy business has led to the emergence of the problem of wind power delays. Using the existing state of wind power in China as a starting point, this article examines the causes of curtailment of wind power and the obstacles that must be overcome to improve the situation. The objective of this study is to examine the current problem of wind curtailment in China, its causes, and the problems it faces in order to give readers with further suggestions for solving wind power curtailment problem and promoting the wind power industry's continued progress toward low-cost, high-quality mature energy.

Multi-objective two-stage stochastic unit commitment model for wind-integrated power systems: A compromise programming approach

In this paper, we introduce a compromise programming (CP) framework for solving a multi-objective two-stage stochastic unit commitment problem characterized by high penetration of wind power. The proposed framework aims at finding best-compromise Pareto efficient on/off schedules, accounting for wind and power demand uncertainties: such solutions must trade off the three objectives of operating cost, CO2 emissions, and wind power curtailment in accordance to the decision maker preferences. To achieve this, we introduce a practical procedure to compute the ideal and Nadir points associated to the multi-objective two-stage stochastic unit commitment problem and propose a linearized ℓ1 norm-based compromise program to design best-compromise on/off schedules that correspondingly minimize and maximize their weighted distances to the ideal point and to the Nadir point, considering the preference weights assigned by the decision maker to each of the three objective functions. The proposed CP framework is applied to a case study related to the New England IEEE-39 bus test system. The results show that, compared to the schedule obtained through the traditional minimization of operating cost, the designed best-compromised schedules considerably improve CO2 emissions reduction and wind power curtailment performance by conservatively sacrificing operating cost performance.

Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading

As a promising solution to realize the energetic complementarity and energy cascade utilization, integrated energy system with a large-scale integration of renewable energy garners considerable attention recently. However, the renewable energy curtailment and uncertainty become two prominent challenges. To the end, we establish a hydrogen-based integrated energy system (HIES) operation framework comprising a refined power to gas (P2G) and carbon capture system (CCS) under carbon trading. Subsequently, a Wasserstein metric based distributional robust optimization method (WDRO) is developed to address wind power uncertainty. Finally, numerical analysis is performed. Simulations results show that: (1) the proposed approach can realize a low-carbon electric energy supply and consumption mode and satisfies the hydrogen demand through internal equipment of the system; (2) compared with the benchmark, the proposed model reveals outstanding economic, environmental and energy performance by obtaining 31.81% decline in operation cost, a 20.19% mitigation in CO2 emission, 7.89% reduction of wind power curtailment and a higher energy efficiency; (3) the validation of WDRO model is demonstrated by the data-driven and robustness test. Moreover, the model exhibits advantages due to its trade-off between conservativeness and computational complexity. The optimization results provide a good reference for the HIES investment strategy and government carbon pricing decision.

Research on collaborative control and optimization of energy storage units under the high proportion of wind power infiltration

With the growing maturity of technology and strong support for national policies, the proportion of new energy power generation systems represented by wind power in the power network is increasing. Under the high proportion of wind power penetration in the power network, the power generation burden of thermal power generation unit is greatly reduced, and the running costs of the electricity grid system can be reduced while obtaining environmental value benefits. However, the stochastic and uncertain output of wind power poses a challenge to grid dispatch under a high proportion of wind power penetration. The energy storage power system responds quickly and can provide certain heat reserves and virtual rotational inertia for the power network, and the wind power generation system can effectively smooth the wind power output and reduce the wind power curtailment rate. This paper proposes an optimal allocation method that takes into account the operating cost of the energy storage plant, the operating cost of the thermal power generation group, the cost of wind turbine abandonment, the branch capacity constraint and the standby capacity constraint. A four-unit 14-node model is built to simulate the cooperative control of energy storage under the penetration of a high proportion of wind power in summer and winter, and verified the effectiveness of the strategy.