Wind Power Consumption Research Articles

Day-ahead scheduling of the electrothermal integrated system considering multiple thermal inertia

For the problem of optimal scheduling of integrated energy systems involving wind power consumption, by mining multiple thermal inertia, a stochastic optimal scheduling strategy with consideration of mixed uncertainties is proposed. Firstly, the network thermal inertia is explored by considering the dynamic characteristics and thermal storage of heat network. Then the heat load flexibility constraint is introduced to model the thermal inertia of buildings. Finally heat pumps and heat storage are used as time-shiftable loads or sources, which are jointly modelled with CHP units to solve the peak-valley mismatch of electric and heat loads, and on this basis, the model is established. In this paper, the Barry Island’s case which is composed of 33-node thermal network coupled with 9-node grid system is used as an example for analysis, and results show that by exploring the multiple thermal inertia, both the complementarity and the flexible dispatch between multiple heterogeneous energy sources in integrated energy system can be realized.

Optimal Scheduling of Combined Electric and Heating Considering the Control Process of CHP Unit and Electric Boiler

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric–thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control of CHP units and the retrofitting of electric boilers can promote the consumption of wind power and improve the overall operating economy of the system while ensuring the feasibility of the CEHS scheduling scheme.

Optimal capacity of variable-speed pumped storage for wind power consumption based on double-layer stochastic programming

Configuring a certain capacity of energy storage for the power system can effectively improve the reliability of the power supply and the level of wind power consumption. This paper takes pumped storage investment cost and wind power consumption demand as the optimization goal, realizes the coordinated operation of pumped storage units and thermal power units, and considers the uncertainty of wind power and load, the multi-timescale characteristics of different types of units and load demand response, and establishes a multi-timescale pumped storage capacity optimization model based on stochastic programming. The model consists of inner and outer layers. The outer layer is investment decision-making, which decides the allocation of pumped storage capacity. The inner layer is for optimization decision-making, and the system optimization operation is carried out in three stages: day-ahead short-term optimization, intraday ultra short-term optimization, and real-time optimization. An example of a typical scenario is established, and the planning results verify the effectiveness of the proposed model and method. The thermal-wind-hydro power system's total cost decreases first and then increases with the increase in the installed capacity of the pumped storage, and the curtailment rate gradually decreases. The total cost increases faster when the pumped-storage installed capacity is larger than optimal. For a pumped-storage power station of the same capacity, variable-speed pumped storage is better than fixed speed pumped storage in reducing the wind curtailment rate. The main reason is that its output is continuously adjustable under the condition of the variable-speed water pump, which is especially suitable for energy storage at night when wind power is high.

Optimal scheduling of park-level integrated energy system considering ladder-type carbon trading mechanism and flexible load

In an attempt to improve the utilization efficiency of multi-energy coupling in park-level integrated energy system (PIES), promote wind power consumption and reduce carbon emissions, a low-carbon economic operation optimization model of PIES integrating flexible load and carbon trading mechanism is constructed. Firstly, according to the characteristics of load response, the demand response is divided into four types: which can be shifted, transferred, reduced and replaced. Secondly, the PIES basic architecture is given by considering the combined heat and power generation coupling equipment, new energy and flexible load in the park. Finally, introducing the ladder-type carbon trading mechanism into the system and minimize the total operating cost, the low-carbon economic operation optimization model of PIES is established. The YALMIP toolbox and CPLEX solver are used to solve the example, the simulation results show that the participation of electrical and thermal coupled scheduling and flexible electric or thermal loads can significantly reduce the system operating cost, reduce the load peak-to-valley difference and relieve peak power consumption pressure.

Short-term Wind Speed Prediction Based on Improved Auto Encoder

In order to improve the wind power consumption level of the new power system with new energy as the main body, it is necessary to accurately predict the wind speed. The key is to refine the dynamic trend of the wind power system and the potential physical structure in the wind speed sequence. Firstly, on the basis of the library Koopman dynamics theory and the encoder structures, a physically constrained spatio-temporal neural network is built, which generates the linear evolution moment of the nonlinear variables of the wind farm. Secondly, the system trend is approximated by the linear evolution matrix, and the forward and backward dynamics are fully considered in the prediction process. Then, the bidirectional correlation prediction mechanism and the cost function adapted to different objects are set to reduce the requirements for the reversibility and stability of the prediction sequence. Meanwhile, the hidden vector of feature space is visualized to show the feature interval dependency in the system. Finally, the effectiveness of the proposed method is verified by the wind speed measurement data of Prince Mountain in Beipiao. The results show that the proposed method has high prediction accuracy, strong generalization ability, and high interpretability for strong random and strong fluctuation wind speed series.

Multi-objective Optimization Method for Hybrid Energy Storage Capacity of Wind Farm Based on Source-load Interaction

The traditional method for multi-objective optimization of a wind farm’s hybrid energy storage capacity does not fully consider the impact of source-load interaction on wind power consumption capacity, resulting in a high total system operation cost. Therefore, a multi-objective optimization method for the hybrid energy storage capacity of the wind farm based on source-load interaction is proposed. According to the topological structure of the system, a multi-objective optimization model is constructed. The constraint conditions of the optimization model are studied. By synthesizing the constraint conditions, multiple objective functions are aggregated into a single objective function. The model is solved by using the ordinal optimization theory to get the optimal solution of the objective function. The experimental demonstration adopted the proposed method to improve the wind power system’s energy storage capacity. The results show that the operation cost of the system is lower after the proposed method is adopted to improve the wind farm’s hybrid energy storage capacity. Therefore, it can be proved that the total operation cost of the system can be decreased by integrating the interactive load into the hybrid energy storage optimization’s wind farm capacity.

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.

Optimization method of wind power consumption based on thermal storage tanks against the background of stepped carbon trading

In the context of Chinese efforts to achieve the goal of “carbon peaking and carbon neutrality” for the “Three North” area heating season, thermal power units, due to their unique operation mode of “fixing electricity by heat” and the resulting problem of insufficient wind power consumption by the night system, were proposed as an optimization method for wind power consumption based on thermal storage tanks in the context of stepped carbon trading. The optimization method is applied to thermal power plants and considers the constraints of cogeneration unit thermoelectric coupling, electrical power balance, and thermal power balance. Taking the lowest total cost of thermal power plants as the objective function, the Cplex solver in Matlab is used to solve the objective function. The wind power consumption by the system, the CO2 emissions of thermal power plants, and the economy of the system are analyzed in different scenarios. The calculation example results show that the model proposed in this article not only improves the wind power consumption rate of the system and reduces the CO2 emission of the thermal power plant but also improves the economy.

Optimal allocation of phase shifting transformer with uncertain wind power based on dynamic programming

Phase Shifting Transformer (PST) can help improve the power flow distribution of the transmission section, which can increase the wind power consumption of the grid. In order to adapt the PST allocation to the grid evolution, this paper presents a dynamic programming method to allocate PST in each planning stage of the grid optimally. The optimal allocation model of PST under a single grid seeks to maximize the wind power consumption and the Total Transfer Capacity (TTC) between areas. A calculation method for TTC of grids containing PST and wind power is proposed. The Non-Dominated Sorting Genetic Algorithm II (NSGA2) is used to solve the Pareto sets under each planning stage of the grid. Then, the optimal planning path of PST is derived based on dynamic programming. The superiority of the proposed method is demonstrated by comparing the IEEE-118 system results of dynamic and static programming.

Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed, a multi-time scale model of coordination optimization is built. An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs, as well as to find the most optimal energy configuration within the system. Lastly, we take an example of regional power grid in Gansu Province for simulation analysis. Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

Control Strategy for Energy-Storage Systems to Smooth Wind Power Fluctuation Based on Interval and Fuzzy Control

The anti-peak shaving characteristics of wind power is an important factor that limits the consumption of wind power. The use of the space-time translation capability of a battery energy storage system is one of the effective means for promoting wind power consumption. Thus, this study proposes an energy storage system smoothing wind power fluctuation control strategy considering wind power consumption to improve the utilization level and economy of an energy storage system. First, the statistical characteristics of wind power are analyzed, and the scene division principle of a multiscenario regulation of an energy storage system is formulated using a correlation coefficient. On this basis, the charging and discharging intervals of the energy storage system in each scenario are determined by comprehensively considering the wind power and the time domain distribution law of load. Then, the control strategy of energy storage system based on inter-zone control is proposed based on the division of charging and discharging intervals. During the charging interval, smoothing the upward fluctuation of wind power or reducing the amount of abandoned wind power is aimed at storing electricity. During the discharge interval, electricity is released to smooth the downward fluctuation of wind power. Finally, considering the real-time state of charge and wind power consumption of the energy storage system, a method of charging and discharging power correction based on fuzzy control is proposed. Taking the actual data of a wind farm as an example, a test is performed on a simulation platform of a combined wind storage power generation system to verify the feasibility and superiority of the proposed control strategy.

Research on Multi-Objective Optimization Model of Industrial Microgrid Considering Demand Response Technology and User Satisfaction

In the process of wind power, coal power, and energy storage equipment participating in the operation of industrial microgrids, the stable operation of wind-storage industrial microgrids is guaranteed by considering demand response technology and user satisfaction. This paper firstly sorts out the status quo of microgrid operation optimization, and determines the main requirements for user satisfaction considering three types of load characteristics, demand response technology, power consumption benefit loss, user balance power purchase price and wind power consumption evaluation indicators in the system. Secondly, the operation architecture of the windstorage industrial microgrid is designed, and the multi-objective optimization model of the wind-storage industrial microgrid is established with the comprehensive operating cost and user satisfaction as the target variables, and the corresponding solution method is mentioned. Finally, a typical wind-storage industrial microgrid is selected for simulation analysis, and the results show that, (1) Considering the demand response technology, the comprehensive operating cost of the wind-storage industrial microgrid per day is 5292.63 yuan, the user satisfaction index is 0.953, and the wind power consumption rate reaches 100%. (2) By setting four scenarios, it highlights that the grid-connected operation mode is superior to the off-grid operation mode. Considering the demand response technology, the load curve can be optimized, and the time-of-use electricity price can be fully used to coordinate the operation of each unit, which enhances the wind power consumption capacity. The compromise solution of the system comprehensive operating cost and user satisfaction under the confidence level of 0.95 is obtained, namely (5343.22, 0.94). (3) The frontier curve shows that in the process of model solving, it is impossible to optimize any sub-objective by changing the control variables, which proves that there is a close relationship between the comprehensive operating cost of the system and the confidence level, which can provide effective guidance for the optimal operation of industrial microgrids.

Model Predictive Control Method of Multi-Energy Flow System Considering Wind Power Consumption

Model Predictive Control Method of Multi-Energy Flow System Considering Wind Power Consumption

An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II

The optimal allocation of integrated energy system capacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy, which has attracted many attentions. However, the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters. To solve the above problem, the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms. Firstly, an integrated energy system consisting of the photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and energy storage is established. Meanwhile, the minimum economic cost, the maximum wind and PV power consumption rate, and the minimum load shortage rate are considered to be the objective functions. Then, a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-Ⅱ is proposed to solve the optimal allocation problem. According to the optimal result, the economic cost is 6.3 million RMB, and the load shortage rate is 9.83%. Finally, four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method. The comparative results indicate that the proposed method possesses a stronger merit-seeking ability, resulting in a solution satisfaction rate of 87.37%, which is higher than that of the unimproved non-dominated sorting genetic algorithms-Ⅱ.

Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm

Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area. To improve the wind-power absorption capacity and operating economy of the system, the structure of the system is improved by adding a heat storage device and an electric boiler. First, aiming at the minimum operating cost of the system, the optimal scheduling model of the cogeneration system, including a heat storage device and electric boiler, is constructed. Second, according to the characteristics of the problem, a cultural gene algorithm program is compiled to simulate the calculation example. Finally, through the system improvement, the comparison between the conditions before and after and the simulation solutions of similar algorithms prove the effectiveness of the proposed scheme. The simulation results show that adding the heat storage device and electric boiler to the scheduling optimization process not only improves the wind power consumption capacity of the cogeneration system but also reduces the operating cost of the system by significantly reducing the coal consumption of the unit and improving the economy of the system operation. The cultural gene algorithm framework has both the global evolution process of the population and the local search for the characteristics of the problem, which has a better optimization effect on the solution.

Wind energy market in USA

This paper reviews the development and the perspectives of the wind energy market in USA, particularly to identify its potential growth as renewable energy source for electric generation, and to provide researchers, and various agencies a better understanding of wind energy market opportunities and barriers in USA within global context. The history of wind energy development, the cost analysis of wind energy compared to others renewable resources and fossil fuels, the renewable energy barriers, the prospect, and cost of the wind energy for USA market and legal acts are all reviewed. USA renewable wind and solar energy sources are used today to generate direct electric power for direct use by utility power companies, industrial, commercial, residential, and transportation sectors. In 2021, USA produced 92.9 quadrillion Btu from different types of energy resources including fossil fuels (35% petroleum, 34% natural gas, and 10% coal), 9% nuclear energy, and 12% renewable sources of energy. USA wind turbine capacity showed a growth rate of 13.4% with 117.7 Gigawatts in 2020, and 11.7% growth rate over 2009-2010. The European and Asia Pacific wind capacities were higher in 2020 than US with 216.6 and 341.9 Gigawatts, respectively. However, the European wind capacity growth rate decreased to 5.9% in 2020 as compared to their 10.3% growth during 2009-2019. The Asia Pacific’s wind capacity experienced firm growth at rate of 28.6% in 2020 and 23.0% during 2009-2019. Furthermore, the literature studies found that USA has become the world’s second largest wind power consumption (approximately 21%) country in 2020. The geographic distribution of wind resources are 20 times greater than the total global energy consumption. The average construction cost of windmill is being declined due to the tax incentives, utility demand, and better technology. This study suggests that the government regulatory policies and their commitment for harnessing wind energy should be stable and clear. Feed-in-tariff and long-term financial subsidies will also promote the diffusion of wind power development.

Nexus between Renewable Energy, Credit Gap Risk, Financial Development and R&D Expenditure: Panel ARDL Approach

In the study, we investigate the relationships between renewable energy consumption sub-indicators of G-8 countries and financial development, credit gap risk, and R&D expenditure from 1996 to 2018. The relationships among the variables in the study are analyzed by employing the Panel ARDL method and the Dumitrescu–Hurlin panel causality test. The cointegration relationships between the variables have been analyzed using the bounds test approach, and an unrestricted error correction model has been established. Contrary to previous studies in the renewable energy literature, this study employed the variable of credit gap risk. Therefore, we believe that this study will fill the gap in the literature and attract the attention of researchers and policymakers. The results indicate that increases in total demand for renewable energy positively affect the financial development of countries. Moreover, R&D expenditures increase as the demand for hydro energy and solar energy increases. This result indicates that wind power consumption has a short-term impact on R&D expenditure, and such an impact ceases to exist in the long run. According to the empirical research findings, the rise in demand for renewable energy may be a factor mitigating the credit gap risk of countries. In other words, the credit gap risk, which is considered a leading indicator of systemic banking crises, can be mitigated by the rise in the demand for renewable energy.

Research on surplus wind power consumption mechanism based on grid demand response

A method of using the demand response mechanism of the power grid to promote the consumption of surplus wind power is proposed for the consumption of new energy generation. In the proposed method, the replacement of the output of the gas turbine is used as an important way to consume the surplus power, and the consumption of the surplus wind power is realized by using the surplus wind power to replace part of the output of the gas turbine. With the objective function of the maximum consumable surplus wind power and the constraints of the maximum replaceable output of the gas turbine, the cost of the replacement output and the balance of supply and demand of the grid, a model of the maximum consumable surplus wind power considering the cost of the replacement output is constructed. In addition, a cost optimal allocation model considering the different quotations of surplus wind power resource owners is constructed to obtain the optimal surplus power resource consumption scheme.

Double-layer Optimization Study of Virtual Power Plants with Source-load-storage

In order to effectively solve the supply-demand imbalance of the grid caused by intermittent new energy penetration and improve the scheduling flexibility of virtual power plants, a two-layer optimization model of virtual power plants with source-load-storage is proposed. The upper layer of the model is designed to reduce the forecast error of wind power output and improve the revenue of the virtual power plant. On the basis of the maximum wind power consumption, distributed energy storage and fuel cells are used to coordinate and compensate for the deviation between the planned and actual output of renewable energy. The active output of the upper layer is transferred to the lower layer. The lower layer of the model is solved by using the improved linear decreasing inertia weight particle swarm algorithm for the output of the gas turbine unit. The analysis results show that the model can absorb the excess new energy generation in the grid, achieve the balance of supply and demand regulation in the grid, and effectively reduce the economic operation cost of the system.

A demand side controller of electrolytic aluminum industrial microgrids considering wind power fluctuations

Direct wind power purchase for large industrial users is a meaningful way to improve wind power consumption and decrease industrial production costs. Short-term wind power fluctuations may lead to large-scale wind power curtailment problems. To promote use of wind energy, a demand side control method is proposed based on output regulator theory for a grid-connected industrial microgrid with electrolytic aluminum loads to continuously track and respond to wind power fluctuations. The control model of the EALs and the dominant frequencies of the wind power fluctuation signals are analyzed and incorporated into the demand side control plant. The feedback control signals with active power deviations on the tie-line are used to design the demand side controller. Simulations are conducted for an actual industrial microgrid to validate the feasibility and effectiveness of the proposed method. The results demonstrate that the proposed controller based on output regulator theory is able to effectively track wind power fluctuations.