Utilization Rate Of Wind Power Research Articles

Power synergistic control between of power‐to‐hydrogen and SCESS‐DFIG considering “source–load” turbulence

AbstractHydrogen production from wind power can reduce wind abandonment and improve the utilization rate of wind power. However, both “source” turbulence caused by unstable wind speed and “load” turbulence caused by sudden load changes will adversely affect the stable operation of power‐to‐hydrogen systems and the grid connection stability of wind farms. A synergistic control strategy between a supercapacitor and power‐to‐hydrogen system is proposed to solve this problem. The strategy is based on the ensemble empirical‐mode decomposition algorithm. By using the supercapacitor to stabilize the high‐frequency component in the “source–load” turbulence, the stability of the power‐to‐hydrogen system is improved. An optimal allocation method of supercapacitor capacity suitable for the control strategy is then proposed. Considering the different states of each fan and supercapacitor, the unit inertia time constant variance is reduced by reasonably distributing the output of each unit. This effectively improves the inertia time constant and grid connection stability of the wind farm. Finally, the effectiveness of the strategy is verified by Simulink simulations.

Harmony search meta-heuristic algorithm based on the optimal sizing of wind-battery hybrid micro-grid power system with different battery technologies

Wind power is a clean and renewable energy source that can contribute to sustainable development. To improve the utilization rate of wind power and reduce the cost of its use in grid-independent areas, this study performs an in-depth evaluation and analysis of the optimal design of wind-battery hybrid micro-grid power schemes with different battery technologies such as Nickel-Iron, Lithium-Ion, and Lead-Acid (LA). First, a mathematical model of a stand-alone wind-battery hybrid micro-grid power scheme is established, to evaluate the overall operation cost and reliability and estimate the optimal design of the micro-grid power scheme with different battery technologies. Then, an improved harmony search meta-heuristic algorithm is presented to optimize the system cost and reliability as the output, which was conceptualized using the musical process. The proposed meta-heuristic algorithm is compared using the original harmony search algorithm. Finally, the effects of wind speed, load demands, interest rate, battery lifetime, and the battery's depth of discharge are investigated for the overall operation cost and reliability. Experimental results show that the proposed method is suitable for solving optimal design problems and can reduce the total cost by 13.5 % and can reduce the average simulation time by 1.5 % compared to the original harmony search method. In different scenarios, when an LA battery is selected for the hybrid micro-grid power system; it is suitable for solving the power supply problem and can reduce the total cost and can increase the reliability of the scheme compared to the other case. Finally, system optimization can effectively contribute to the expansion of wind energy use and sustainable development.

The pricing method for abandoned wind power contract between wind power enterprises and desalination plants in bilateral transactions

In Northeast China’s electric power auxiliary service market, guiding interruptible load users to participate in bilateral transactions is an effective measure to ease the difficulty of grid peak regulation and improve the utilization rate of wind power. Aiming at the lack of theoretical guidance in the current bilateral transactions, this paper proposes a two-stage pricing (TSP) method. Firstly, the freshwater supply and demand balance model of the desalination plant is constructed. The upper-level optimization model of bilateral transaction for profit maximization of the desalination plant is constructed. Then, considering the profit distribution among transaction parties and constraints on wind power utilization, a low-level calculation model of wind power pricing is constructed. Finally, the TSP method is applied to the numerical analysis of bilateral transaction between a desalination plant and a wind power company. The TSP method jointly optimizes the dispatching scheme between the variable speed pump and the water storage tank of the desalination plant. Under the constraints of reasonable profit distribution and wind power utilization, the transaction price finally converges to a range acceptable to both parties. The TSP method can effectively improve the utilization rate of wind power and optimize the flexibility of energy utilization. • A detailed mixed-integer linear programming (MILP) model is developed for desalination plants. • A model for seawater desalination plants to participate in bilateral trading markets is proposed. • A TSP method for calculating the price of bilateral transactions is proposed. • The TSP method can effectively optimize the flexibility of energy utilization.

Dispatching method of high energy consuming load participating in peak shaving based on variational modal decomposition

With the rapid development of our country, the construction and development of power system has shown the continuous growth of load, and the proportion of new generation energy has also expanded. The impact of these problems on our life cannot be underestimated. Therefore, the development of new energy has become a necessary way to realize the transformation of energy production to green and renewable energy, which is an important direction and necessary decision-making Energy and economy can achieve sustainable development goals all over the world. In order to improve the peak shaving capacity of the power system, this paper proposes to introduce the load side into high energy consuming enterprises to participate in the regulation, and carry out joint optimal dispatching with conventional thermal power units. The optimization goal is to minimize the sum of all day waste air volume of the next day, and the simulation software is used to optimize it. Compared with the regulation of a single thermal power unit, the effect is better, it plays a positive role in reducing wind abandonment, improves the utilization rate of wind power, and has high economic and environmental benefits.

Chance-constrained optimization of distributed power and heat storage in integrated energy networks

Energy storage is widely recognized as an effective alternative to match the instantaneous imbalance between distributed renewable energy sources and loads. However, suitable installation sites and capacities are necessarily determined in integrated energy networks. This paper constructs a two-layer optimization model of integrated power and heat networks to obtain the optimal installation nodes and capacities of electric energy storage (EES) and thermal energy storage (TES) units. In the optimization model, the interchange of power to heat with storage units is collaborated to unitize distributed renewable energy sources and decrease comprehensive costs, including investment cost of storage units and operation cost. A chance-constrained optimization model is proposed to transform the uncertain variables of electric and heat loads into a deterministic optimization problem. The impacts of confidence levels involved in load probability distributions on the optimal nodes and capacities are analyzed. The results demonstrate that the optimal installation nodes are determined by the distributions of distributed power sources and load ratios in networks. The increased confidence level globally raises the optimal installation capacities of EES and TES. When the confidence level increases from 0.50 to 0.99, the utilization rate of wind power increases by 3.91 %, whereas the comprehensive cost increases by 22.34 %. The hybrid integrations of EES and TES consume more uncertain renewable power and improve the economic performances by the interchange of power to heat.

Super-resolution perception for wind power forecasting by enhancing historical data

As an important part of renewable energy, wind power is crucial to the realization of carbon neutrality. It is worth studying on how to accurately predict the wind output so that it can be integrated into the power grid as much as possible to enhance its utilization rate. In this article, a data enhancement method and a framework are proposed to assist wind power forecasting. The proposed method uses the super-resolution perception technology to first detect the completeness and correctness of historical meteorological and wind power data collected by industrial devices. Then, the detected errors are corrected and the missing data are recovered to make the data complete. The frequency of the data is then increased using the proposed method so that the data become complete high-frequency data. Based on the enhanced complete high-frequency data with more detailed characteristics, more accurate forecasts of wind power can be achieved, thereby improving the utilization rate of wind power. Experiments based on public datasets are used to demonstrate the effectiveness of the proposed method and framework. With the proposed method and framework, higher frequency data with more detailed information can be achieved, thereby providing support for accurate wind power prediction that was not possible before.

Thermodynamic investigations on an integrated heat pump with thermal storage for wind-solar hybrid heating

Heating with wind and solar energy is an effective way to reduce carbon dioxide emissions. Vapor Compression Heat Pumps (VCHP) were generally used to improve the utilization rate of wind power. However, normal VCHP driven by wind power or solar energy has low performances in low temperature environment and low supply temperature of heating water, and it is also difficult to achieve continues and stable heating because of the intermittence of wind power. The present paper introduces a new integrated heat pump system, in which a VCHP absorbs heat from the environment and sends it to an Absorption Heat Pump (AHP) as its low temperature heat source after temperature lift. Thermal storage system is also equipped for continues heating and it serves as the driving force of the AHP. The results show that the integrated heat pump can produce heating water at 50 °C or even higher with a COP of about 1.42 when the environment temperature is as low as −30 °C. Also, the power consumption of the VCHP accounts for only 5%–18% of the total energy input. This indicates that the energy consumed by the integrated heat pump can be mainly from off-peak electricity produced by the wind turbine.

Optimized operation of micro grid electric heating integrated system based on PMV

The characteristics of wind power, such as peak inversion, lead to serious wind abandonment in heating season. With the implementation and use of the heat storage electric boiler (HSEB), the utilization rate of wind power can be effectively improved by establishing the electric heat combined operation system with the HSEB. When the thermal comfort of human body is considered, the heat load will be changed into elastic range, and the regulation range of heating unit will be increased. Based on the analysis of the heat transfer state of the house on the user’s side, the relationship model between the outdoor temperature and the indoor heat supply is established in this paper. On this basis, the micro grid electric heating comprehensive system with the HSEB is constructed. Taking the minimum operation cost of the system as the objective function, the economic optimal operation model of the micro grid electric heating system based on PMV is established. The feasibility of the four schemes is verified by an example. The results show that the micro grid electric heating system based on the PMV contained HSEB can accommodate the abandoned wind power, reduce the fuel cost and reduce the environmental pollution on the premise of ensuring the indoor comfort, which has a good application prospect.

A two-layer model to dispatch electric vehicles and wind power

In this paper, the optimal charging and discharging schedules of electric vehicle (EV) are studied considering wind power under the condition of distribution network. In view of the uncertainty of EV charging-discharging demand and wind power output, the Markov decision process is adopted to model the randomness of supply and demand. Considering the dimensional disaster caused by dispatching a large number of EVs’ charging and discharging behavior in a centralized way, this paper proposes the two-layer dispatching model based on Markov decision process. First, the lower EV agents are responsible for collecting the real-time charging-discharging demands for EV and report to the upper dispatching center. Then the upper dispatching center gives the optimal charging and discharging power according to the real-time distribution operating status, wind power output and the EV information reported by each EV agent. Last, the lower agent gives the optimal charging-discharging sequence of each EV according to the upper optimal power. The goal of the upper dispatching center considers the power losses in the distribution network, load variance and the matching degree between EV charging-discharging and wind power output. The goal of the lower EV agent considers the EV charging-discharging fees and costs by EV battery losses. When deciding the optimal charging strategy, we design the two-layer Rollout algorithm to decide the optimal charging-discharging strategy considering the impact on future strategy decisions by current strategy decisions. Finally, the optimal results under four different strategies are simulated on the IEEE 30-bus distribution network system. The simulation results show that the proposed model and strategy can effectively reduce the distribution network losses and load variance, and greatly improve the utilization rate of wind power. Compared to the cost of uncoordinated EV charging, EV charging-discharging fees and battery loss costs by the proposed strategy have been greatly reduced.

A Novel Hybrid Model for Wind Speed Prediction Based on VMD and Neural Network Considering Atmospheric Uncertainties

Considering the strong fluctuation and the nonlinearity of wind speed, and atmospheric uncertainties, wind speed prediction based on the hybrid model is presented, which is composed of the neural network, variational mode decomposition (VMD), and Lorenz disturbance. First, the VMD is used to process the data to get several intrinsic mode functions (IMFs). Second, the neural network model (NN model) can be established by these IMFs of the training set, and the validation set is used to adjust the model parameters. Subsequently, given the nonlinearity of wind speed, Lorenz disturbance is added to determine the finial model, and the best Lorenz disturbance parameter and the best Lorenz disturbance sequence can be obtained by minimizing the mean absolute error of validation set. At last, the wind speed can be forecasted by the hybrid model. Taking Sotavento wind farm in Spain as an example, the results show that, the hybrid model has stable prediction performance, and the distribution characteristics of its results are consistent with the actual wind speed. The general model only focuses on improving prediction accuracy. However, on the basis of improving the forecasting accuracy, the proposed model not only enhances the prediction stability, but also restores the characteristics of wind speed. This research work provides a more scientific basis for wind power dispatching arrangement, and it is of great significance to improve the utilization rate of wind power.

Improving the Utilization Rate of Wind Power Based on the Thermal-Electricity Combined Operation Technology

As large scale integration of wind power generation into grid, the fluctuation of wind power must be stabilized with fast response complementary power source such as gas and oil power generation. However entire power system is dominated by thermal power plant in China, moreover the proportion of heating units is more and more high. Therefore, expanding the peak regulating capacity of heating units to take in wind power on a large-scale is an inevitable choice. This research proposed the heat load and wind power forecast approach, meanwhile, the analysis strategy of forecasting uncertainty is also be put forward. Then, a thermal electricity combined (TEC) operation technology, which based on making the best use of the heat transfer and thermal storage characteristics of heating network and electric heating, is proposed in this paper to enhance the utilization of wind power. Final, a practical system block diagram is proposed for practical application.

Analytical Study on the Effect of Coordinated Application of Distributed Grid Connection and SVG on Wind Farms to Increase Power Quality in Fuxin Rural Power Grid

power quality problems such as high line loss, low voltage exist commonly in rural power grid of China due to few supply points, long power transmission and distribution line. Huge fund is required to increase the quantity of supply points in rural power grid; installation of var compensators on line nodes could not solve power quality problems on the whole line. At the same time, as distributed power supply, wind power is also restricted by peak regulation, insufficient line output capacity and other factors in large scale centralized grid-connected output, wind curtailment often occurs. In order to solve the above-mentioned two problems simultaneously, electric power of wind farm may be output via nearby rural power grid line, thus not only utilization rate of wind power is increased, but also power quality of rural power grid is improved.This paper takes an example of a district in Fuxin City, summarizes power quality change data of relevant rural power grid before and after the distributed grid connection of wind farm is made, the analysis shows that distributed grid connection of wind power could improve load flow distribution of the power system, reduce system transmission loss in this district obviously, increase economical efficiency of the system operation effectively. At the same time, effectiveness of static var generator in substation to overcome the impact of such grid connection mode on power grid is verified.

Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method

Accurate regional wind power forecasting guarantees the security and economics of the power system integrated with large scale of wind power. Aiming at the gross wind power output of the whole regional grid area, existing regional wind power forecasting methods fails to characterize the locally gross output power of the wind farm aggregation forming a power flow interface with specified flow restraints. In this paper, the work flow of the power flow oriented regional wind power forecasting method based on whole-grid regional wind power forecasting methods was presented first. Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented. Finally, the case study proved the feasibility and effectiveness of our method. The conclusion indicates that the method presented in this paper implements a multiple temporal and spatial scale regional win power forecasting technology, which can obviously improve the accuracy of regional wind power forecasting, relieve the pressure for the grid side and improve the utilization rate of wind power.

Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method

Accurate regional wind power forecasting guarantees the security and economics of the power system integrated with large scale of wind power. Aiming at the gross wind power output of the whole regional grid area, existing regional wind power forecasting methods fails to characterize the locally gross output power of the wind farm aggregation forming a power flow interface with specified flow restraints. In this paper, the work flow of the power flow oriented regional wind power forecasting method based on whole-grid regional wind power forecasting methods was presented first. Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented. Finally, the case study proved the feasibility and effectiveness of our method. The conclusion indicates that the method presented in this paper implements a multiple temporal and spatial scale regional win power forecasting technology, which can obviously improve the accuracy of regional wind power forecasting, relieve the pressure for the grid side and improve the utilization rate of wind power.

Data Acquisition and Power Forecasting of Trans-Regional Wind Farm

For the geographical dispersion of wind farms, an effective and reliable platform of data transmission and storage based on VPN private tunnel and PI database is built to solve information sharing and centralized power forecasting. Then the real-time data which wind speed is predicted by ARMA model and predictive power through the wind-power model is built by neural networks is published. The simulation results indicate the forecasting system is conducive to ensuring the stable and secure running of the grid and improving the utilization rate of wind power.