Wind Power Curtailment Research Articles

Optimized Dispatching for Integrated Energy System With Wind Power

The intermittency and unpredictability are the obstacles to absorb all wind power. The combined heat and power (CHP) system has been widely utilized as a heat and electricity source, and the heating load limits the regulatory capacity of CHP to reduce curtailed wind power. This study proposes an optimization model of an integrated power and heating network system to fully utilize curtailed wind power. The thermal inertia in the primary heating network, thermal comfort of end-users, and electric boiler in the secondary heating network are included in multiple scenarios to analyze different operation strategies. The simulation results show that considering the heating network, the inertia and thermal comfort of end-users could reduce the curtailed wind power ratio from 15% to 9%. Adding a peak-shaving electric boiler on the user side can increase the system electric load and reduce the curtailed wind power, while reducing the heat output of the CHP unit during the peak and low periods of the heat load at night. Moreover, there is less than 1% of the curtailed wind power ratio if the peak-shaving capacity ratio is higher than 10%, which means the application recommendations of installed electric boiler capacity are obtained to increase enough flexibility of the power and heating network system.

Winding down the wind power curtailment in China: What made the difference?

The carbon neutrality goal requires significant acceleration of the renewable energy transition. In China, this acceleration is hampered because of the concerns regarding the recent high-rate wind power curtailment. However, post-2016, wind power curtailment shows notable reductions. Therefore, it is vital to understand the fundamental factors driving such improvements, the curtailment reduction patterns across the countries and key regions, the experiences from the regional heterogeneity of curtailment reductions, and the policy implications available to strengthen wind power curtailment mitigation strategies in the short and long term to accelerate China's clean energy transition. This study constructs an evaluation framework based on the logarithmic mean Divisia index approach to investigate these concerns. Furthermore, it reveals that the eminent contributors at the national scale are local power demand, power exports, and power structures. However, the regional factor patterns oscillate significantly. In northwest China, power transmission is vital to reduce wind power curtailment. In north China, thermal power remains dominant because of its importance to national energy security, impeding its curtailment. Northeast China implements the peak-shaving auxiliary service market, promoting the power grid's capability to consume more local wind power. The fundamental settlement of the curtailment issue calls for market-oriented energy structure reforms to ensure sustainable low-carbon development. This study explains the essence of China's renewable energy development and seeks reliable paths to accelerate wind power integration with policy measures and technical transformations to enhance the adaptability of the power grid system.

Operation and optimal sizing of combined P2G-GfG unit with gas storage for frequency regulation considering curtailed wind power

The increase in penetration of wind turbines in the power system has caused curtailment of wind power due to the stochastic nature of wind, which results in loss of revenue. Consequently, power to gas (P2G) has emerged as a device which can utilize curtailed wind power through the electrolysis of water to hydrogen or further processing to obtain methane. In addition, due to technical, economic and environmental benefits, the share of gas fired generating (GfG) units in power systems has been rising by replacing fossil fuel fired generating units. This paper proposes a coordinated operation and novel optimal sizing of a combined P2G-GfG system (CPGS) with gas storage tank (GST) for participating in frequency regulation. Firstly, the coordination operation of CPGS is presented in which P2G utilizes the curtailed wind power to facilitate the methanation process to produce methane. The methane is stored in a gas storage tank, then a GfG unit is supplied with the gas to participate in frequency regulation. Secondly, a novel optimization model using particle swarm optimization is presented considering the operating strategy and operation constraints to maximize net benefit when CPGS participates in frequency regulation market. Finally, case studies were conducted to verify the proposed coordinated operation and optimal sizing strategy considering actual AGC commands and curtailed wind power data.

Dynamic Modeling and Flexibility Analysis of an Integrated Electrical and Thermal Energy System With the Heat Pump–Thermal Storage

The integration of high-efficiency heat pump and thermal storage devices is of great significance to realize the synergy between efficient and flexible operation of the integrated electric and thermal energy systems. This article proposed an integrated electric–thermal energy system with heat pump and thermal storage devices and introduced the heat current method for constructing its overall dynamic power flow model by considering energy transfer, conversion, and storage processes. On this basis, we derived the overall system constraint and component constraint equations. Under the objective of wind power curtailment minimization, the comprehensive effects of the dynamic characteristics of the heat pump, thermal storage capacity, new wind power installation, and new heat load on the electric and thermal output are analyzed. The results show that the dispatching accuracy of wind power output can be improved by up to 8% by taking into account the dynamic characteristics of the heat pump. The combination of heat pump and thermal storage device results in a leverage factor of 3.06 and 0.17 for the storage and release processes, respectively, effectively increasing the flexibility of system scheduling. The coordination between the newly added wind power installation and the added new heat load, with the higher operating temperature of the heat pump, is more conducive to promote wind power accommodation and improve the system’s overall flexibility. These results provide the necessary basis for the development of an integrated dispatch plan for the integrated electric and thermal energy systems containing the heat pump–thermal storage.

Stochastic optimal dispatch of combined heat and power integrated AA-CAES power station considering thermal inertia of DHN

In order to improve the peak shaving capability of combined heat and power system and cope with uncertainties, a stochastic optimal dispatch model of combined heat and power system integrated Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) power station considering the thermal inertia of District Heating Network (DHN) is proposed in this paper. The proposed model has the following characteristics: 1) AA-CAES power stations are configured in combined heat and power system, i.e., AA-CAES can supply both power and heat to the system, and the linearized model of AA-CAES power stations is established in this paper; 2) considering the thermal inertia of heating pipelines and buildings, the DHN model is established; 3) in order to deal with uncertainties in the system, the stochastic optimal (SO) dispatch model is established. The case study shows that compared with AA-CAES power stations which only provides power for the system, the combined heat and power supply of AA-CAES power stations can reduce the operation cost and the wind power curtailment. The thermal inertia of DHN cause the time shift of heat load, which effectively improves the operation flexibility of the combined electric-thermal system. In addition, configuring AA-CAES power station and considering the thermal inertia of DHN can enhance the ability of the system to cope with uncertainties.

Low-carbon economic planning of integrated electricity-gas energy systems

The energy system carbon reduction is an inevitable trend to deal with the global warming problem. The power industry, being a vital part of energy system, faces severe challenges. To decarbonize the power sectors, the implementation of low-carbon technologies and integration of high penetrated renewable power generation would be an effective solution. Therefore, given the existence of multi-type low-carbon technologies including the flexibility reformation of coal-fired units, construction of gas-fired units and installation of energy storage systems, a low-carbon economic planning model of integrated electricity-gas systems with high penetration of wind generation is proposed. The carbon tax and carbon capture technology are integrated to accelerate carbon reductions. In particular, the strategic planning cases contributed by different scenarios are formulated. The simulation results on an electricity-gas test system demonstrate the feasibility and effectiveness of the proposed model in carbon reduction as well as wind curtailment decrease. Compared with the initial state, the implementations of the proposed three planning strategies can reduce carbon emissions by 9.8%, 32.5% and 9.3%, respectively. Meanwhile, the wind power curtailment ratio is decreased by 63.2%, 38.9%, and 63.7%, respectively. Moreover, a sensitivity analysis of carbon tax price and wind power penetration level are performed to investigate the low-carbon transition of the integrated electricity-gas systems.

Operation Optimization of Integrated Energy System considering Power-to-Gas Technology and Carbon Trading

Application of power-to-gas (P2G) technology can implement bidirectional energy flow between power network and gas network, which contribute to improved energy coupling, better operational flexibility, and high economic efficiency in the integrated energy system (IES). This study proposes a detailed IES model based on the refined P2G model according to the characteristics of P2G equipment. An optimal efficiency matching coefficient is proposed to increase the rate of energy equipment utilization. To address the carbon allocation problem, the carbon trading mechanism is employed in an optimization model with the consideration of economic benefits and costs (i.e., sales benefits, operating cost, carbon trading cost, wind power, and photovoltaic curtailment punishments). Case studies verify the advantage of the proposed optimization model. Furthermore, the results show that the P2G with gas tank mode has an obvious advantage in a comprehensive operating capability of IES.

Collection System Topology for Deep-Sea Offshore Wind Farms Considering Wind Characteristics

DC series-parallel collection system has recently been widely discussed for integrating deep-sea OWFs because it saves the offshore HVDC stations. However, comparing with traditional offshore electrical collection systems, there is a unique operational problem introducing by the DC series-parallel system that is the wind power curtailment caused by the wind power fluctuations. In this paper, a rigorous mathematical analysis of wind power curtailment is conducted, and an optimization model considering wind power curtailment for DC series-parallel collection systems is proposed. To address the particularities of the series-parallel collection system in graph theory, an improved parthenogenetic algorithm (PGA) is suggested to address the optimization problem. The proposed model and optimization algorithms are tested based on a deep-sea OWF with 56 wind turbines (WTs). The simulation results show that considering wind power curtailment during the DC series-parallel system planning helps to save the levelized cable cost of energy (LCCOE) in the long term, and putting the wiring direction of the series as perpendicular as possible to the prevailing wind direction of the OWF helps to increase the power generation of the OWF and reduce LCCOE.

Resilient DC Voltage Control for Islanded Wind Farms Integration Using Cascaded Hybrid HVDC System

To integrate large-scale islanded onshore wind power with different sizes, this paper proposes an integration system based on the cascaded hybrid HVDC transmission system, which consists of LCC and several MMCs in series connection at the DC side of the rectifier. A large-scale wind farm is connected with one LCC and one MMC while several small-scale wind farms are connected with MMCs directly. Owing to the hierarchical integration arrangement, the operating flexibility can be improved with reduced capacity and the number of step-up interfacing transformers. A resilient DC voltage control is proposed for the integration system to adaptively redistribute power among the converters during wind power fluctuations. Firstly, the topology and operating characteristics of the wind power integration system are introduced. Then, a resilient DC voltage control is proposed to ensure stable operation during wind power curtailments. Finally, a simulation model of the hybrid cascaded HVDC transmission system is built in PSCAD/EMTDC to verify the effectiveness. The research results show that the system provides a new option for long-distance transmission of large-scale islanded wind power.

An Optimal Dispatch Framework of Electric and Heating Networks Based on Controllable Electric and Thermostatically Controlled Loads

With the increasing capacity of wind power generators (WTGs), the volatility of wind power could significantly challenge the stability and economy of electric and heating networks. To tackle this challenge, this paper proposes an optimal dispatch framework based on controllable load (including controllable electric load and controllable thermostatically load) to reduce wind power curtailment. A forecasting model is developed for the controllable load, which comprehensively considers autocorrelation, weather factor, and consumers’ behavior characteristics. With adjusting controllable load, an optimal dispatch model of power system is then established and resolved by Sequential Least Squares Programming (SLSQP) method. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the root mean square error with the proposed long short-term memory can be reduced by 0.069 and 0.044, respectively. Compared with conventional method, the peak wind power curtailment with dispatching controllable load is reduced by nearly 10% and 5% in two cases, respectively.

Power-based modelling of renewable variability in dispatch models with clustered time periods

The planning of future power systems with high shares of renewable generation requires modelling large and complex systems over long time periods, resulting in models which are computationally heavy to solve. For this reason methods that can be used to decrease the size of power system dispatch models are needed. A common method in large scale planning models is to decrease the model size by increasing the size of the time steps. However, using larger time steps makes the representation of variability of renewable generation and load less accurate, which can affect the results from the model. In this paper, we investigate the possibility to use a power-based version of an economic dispatch model to decrease the model time resolution while getting results which are close to the original high-resolution model. We implement both power-based and the conventional, energy-based, versions of a dispatch model with different time resolutions, and show that the power-based model has better agreement with the high-resolution model, especially as the model time step increases. For example, using the power-based model gives more accurate results for wind power curtailment in a high-renewable scenario.

Modeling and optimal dispatch of a carbon-cycle integrated energy system for low-carbon and economic operation

Energy efficiency and greenhouse gas emissions mitigation are important topics in modern energy systems research. In this study, power-to-gas (P2G), carbon capture, supercritical CO2 (S–CO2) cycle systems are integrated into a carbon cycle system, and the role of the carbon cycle system in the electricity-heat integrated energy system (IES) is explored. The carbon capture system collects the CO2 produced by coal-fired and gas-fired units, which can be recycled in two ways. One is sent to P2G device to react with the hydrogen produced by electrolysis of water. The other part is sent to the S–CO2 unit as the supplementary working fluid. In addition, the waste heat of the gas-fired unit and the heat released in P2G are collected as additional heat supply. This work focuses on the optimal dispatch in an IES with carbon cycle system connection. The optimization results show that with the orderly regulation of the carbon cycle subsystem, the wind power and photovoltaics' curtailment rates can be reduced by 3.1% and 10.8%, respectively. And the system's carbon emissions are reduced by 89.5%. Besides, IES's energy and exergy efficiencies increase by 1% and 0.9% after applying the carbon cycle system.

What is the appropriate pricing mechanism for China’s renewable energy in a new era?

Along with the cost reduction and the scaling up of renewable energy, China is phasing out its feed-in tariff (FIT) approach, a fixed pricing mechanism which has been applied to China’s wind and solar power for over a decade. Meanwhile, electricity price deregulation is an important part of China’s ongoing power sector reform and is very likely to have a significant impact on renewable energy development in the country. This paper studies the appropriate electricity pricing mechanism for renewable energy in the aforesaid new era from both theoretical and empirical perspectives by taking wind power as a case study. The main conclusions are that, with decreasing wind power cost, the premium pricing mechanism was appropriate for China’s wind power during the period between 2010 and 2018, and a fully competitive pricing mechanism can be adopted for wind power in China after 2019 when wind power cost can be fully recovered. It is also found that China’s electricity market reform has positive impacts on mitigating wind power curtailment and increasing social welfare. Policy implications are provided at the end of the paper.

Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach

Rather than using individually distributed energy storage frameworks, shared energy storage is being exploited because of its low cost and high efficiency. However, proper sizing and operations approaches are still required to take advantage of shared energy storage in distribution networks. This paper proposes a bi-level model to optimize the size and operations of shared energy storage in hybrid renewable-resource power generation systems. The upper-level model maximizes the benefits of sharing energy storage for the involved stakeholders (transmission and distribution system operators, shared energy storage operators and the various power plant owners) and the lower-level model minimizes the hybrid system operating costs. The benefits of this system were found to be: (1) reductions in wind and solar power curtailment and coal-fired generation costs; (2) peak shaving; (3) frequency regulation; and (4) a deferral of upgrades to power transmission and distribution facilities. To fully realize the long-term planning and short-term operational interactions of shared energy storage, a bi-level nested genetic algorithm was designed to solve the proposed model. By continuously updating the solutions, a set of optimal solutions were generated. To validate the approach, numerical tests were conducted, with the results showing that by properly sizing and operating the shared energy storage in distribution networks, the wind curtailment rate was reduced by about 10.2%, the solar curtailment rate was reduced by 14.2%, and the stakeholder benefits were around 154 million dollars. Further discussion is given on the benefits of shared energy storage investments.

Correlational broad learning for optimal scheduling of integrated energy systems considering distributed ground source heat pump heat storage systems

Renewable energies play an irreplaceable role in building an environment-friendly society; nevertheless, the curtailment phenomenon of renewable energies is serious. To improve the application of renewable energies and save economic costs, an optimal scheduling model based on distributed ground source heat pump heat storage system (DGSHPHSS) is established in this paper. The DGSHPHSS operates as an electrical load to store thermal energy during the valley load period, reducing the curtailment of wind power generation; DGSHPHSS provides thermal energy to the users during the peak load period, reducing the power cost. Besides, to more accurately and faster predict the load curve, this paper proposes a correlational broad learning (CBL) prediction model. The maximum wind power curtailment and economic costs with DGSHPHSSs under the low wind power curves are reduced by 50% and $254,500 than no DGSHPHSS, respectively; the peak values of wind power curtailment and economic costs with DGSHPHSSs under the high wind power curves are reduced by 40% and $65,300, respectively. The prediction model is inspired by sequence characteristics and external factors such as ambient temperature and time-of-use electrical prices. The prediction error obtained by the CBL prediction model can be reduced to 4.52% in simulating integrated energy systems (IESs).

A Nash bargaining-based cooperative planning and operation method for wind-hydrogen-heat multi-agent energy system

Hydrogen production and clean heating from renewable power are promising methods to promote renewable energy consumption and reduce carbon emission. This paper is concentrated on the cooperative planning and operation problems of Wind-Hydrogen-Heat multi-agent energy system. A cooperative planning and operative model for the Wind-Hydrogen-Heating multi-agent energy system is proposed based on the Nash bargaining game theory. This cooperative model is transformed into two subproblems on the alliance planning and operation cost minimization problem and the payment bargaining problem. To protect the privacy of each participant, two distributed algorithms are proposed based on the Alternating Direction Method of Multipliers to solve the two subproblems. Finally, the effectiveness of the proposed cooperative model and distributed algorithms are demonstrated. Simulation results demonstrate that the proposed cooperative model can improve the benefits of both each participant and the cooperative alliance significantly. Furthermore, the wind power curtailment can be reduced through cooperation. And the decline of feed-in tariff of wind power will incentivize the cooperation of Wind-Hydrogen-Heat agents to improve their own benefits.

Artificial intelligence-based prediction and analysis of the oversupply of wind and solar energy in power systems

The economic viability of renewable energy is deteriorating due to its curtailment in power systems. Therefore, it is imperative to forecast curtailments for more effective utilization. To alleviate this issue, in this paper, we propose artificial intelligent-based models to accurately predict wind and solar power curtailments (WSPCs), which have not been investigated before. In this regard, a prediction methodology is developed using different types of machine learning (ML) methods and evaluated based on both hold-out (HO) and cross-validation (CV) approaches. The ML methods considered include regression trees (RT), gradient boosting trees (GBT), random forest (RF), feed-forward artificial neural networks (ANN), long short-term memory (LSTM), and support vector machines (SVR). The prediction models are trained based on eight input features, including load demands, the output power of thermal power plants, nuclear units, solar farms, wind turbines, biomass/geothermal units, large hydro units, power imports, and WSPC as two target variables. Based on historical data, i.e., hourly records of California independent system operator (ISO), the predictive models are validated, and the optimal hyperparameters are chosen using Bayesian optimization for each model to attain the best results. Among all the models, the RF model results in the minimum prediction errors and thus the best performance by implementing the proposed CV approach. The obtained results demonstrate the effectiveness of the proposed models in the prediction of WSPCs.

Discussions on the real potential of district heating networks in improving wind power accommodation with temperature feedback as one consideration

The wind curtailment has been serious in China, reaching 1.66e + 10 kW·h in 2020, since the “heat-led” operation mode of the combined heat and power unit restrains the flexibility of the power systems. The district heating networks, due to thermal storage ability, have a great potential in providing flexibility of the integrated electricity and heat energy systems as well as improving wind power accommodation. However, this paper finds that analysis deviations of evaluating the district heating networks’ real potential may exist in the previous literature since they neglect the temperature feedback mechanism. The mechanism can compensate for the heat supply if the district heating networks’ model is inaccurate, which is a basic method to guarantee the users’ thermal comfort. Ignorance of the compensation results in serious analysis deviations of the systems modelling and optimization. In order to correct the deviations, this paper offers a reliable integrated heat and power dispatch model that simultaneously considers the temperature feedback mechanism and the thermal inertia of the district heating networks, to investigate the actual influences of the district heating networks’ thermal inertia on wind power promotion. Results show that the consideration of the temperature feedback mechanism causes the wind power curtailment to increase by 3.4% to 4.1% in comparison with that when the mechanism is not considered.

A multi-objective optimization framework for integrated electricity and natural gas networks considering smart homes in downward under uncertainties

This paper presents a multi-objective optimization framework for day-ahead scheduling of integrated electricity and natural gas networks in the presence of five sets of smart homes. The study system includes a 69-bus electricity distribution network and a 14-node natural gas network, equipped with gas turbines, wind turbines, photovoltaic (PV) panels, electrical energy storage (EES) systems and power-to-gas (P2G) technologies. The scheduling problem is modeled as a two-objective optimization problem and its objectives include minimizing the operation cost and CO2 emissions. In order to model the two-objective optimization problem, the epsilon-constraint method has been adopted. Finally, the proposed model has been solved in the form of 3 case studies by CPLEX solver in general algebraic modeling system (GAMS) software. The simulation results demonstrate that the two-objective modeling of the scheduling problem leads to a 2.87% reduction in CO2 emissions despite a 0.75% increase in operating costs. The results also illustrate that a 21.93% increase in the customer's comfort index leads to a 41% increase in annual operating costs. Finally, the results substantiate that the installation of P2G technologies along with wind turbines prevents wind power curtailment in some hours.

A new flexibility based probabilistic economic load dispatch solution incorporating wind power

Nowadays, by the growing in renewable energies in the power system, the uncertainty of power output related to these resources increases. This needs to improve power system flexibility to reduce the risk of load shedding or renewable curtailment due to the mentioned challenge. So, nowadays power system flexibility is an important characteristic especially in power system operation which should be evaluated continuously and maintained in the desired value. In operation horizon time such as operational planning or real time operation the system generation flexibility should be monitored, and sufficient system flexibility should be provided to avoid unacceptable generation/load unbalance cause unwanted renewable curtailment or load shedding.This paper presents a new flexibility based risk limiting dynamic economic load dispatch solution incorporating wind power. In the proposed method the generation system flexibility index, introduced by the authors is converted to economic value and included in the total operation cost function. Then by the suitable economic trade-off between system flexibility level and risk of load shedding and wind power curtailment, the best reduction in these two unwanted risks are obtained. Increasing the system generation flexibility comes from the generation rearrangement of thermal generation units imposes more cost to the generation cost but is balanced by reduction in unwanted renewable curtailment or load shedding.