An evolutionary algorithm-based optimal allocation method of wind resources under the background of carbon neutralization is proposed in order to better achieve the goal of energy conservation and emission reduction under the background of carbon neutralization, aiming at the current unreasonable allocation of wind resources. The evaluation model of balanced wind resource allocation is designed, and the evaluation index of optimal wind resource allocation is constructed using the evolutionary algorithm. The optimal allocation path of wind energy resources is chosen to achieve the goal of reasonable wind energy resource allocation. Finally, simulation experiments show that using an evolutionary algorithm to solve the problem of poor energy allocation and achieve the research goal, the optimal allocation method of wind energy resources under the background of carbon neutralization can effectively solve the problem of poor energy allocation.

1. Introduction

China’s wind resources and load distribution are inversely distributed. Wind resources are mainly distributed in the West and North, while the load is mainly distributed in the East. As a result, China’s wind industry has high power supply cost, difficult wind power consumption, serious transmission loss, and bloated wind investment [1]. The backdrop of carbon neutralization has a number of negative externalities for society, including a rise in social power costs, a reduction in energy utilization efficiency, and an increase in the degree of air pollution. The major difficulty of low-carbon and sustainable wind industry growth is how to coordinate the distribution of wind resources and load and attain cross-regional optimum allocation of wind resources. In the conventional method, the mode of primary energy transmission determines the cross-regional distribution of wind resources. The advancement of long-distance transmission technologies relieves some of the load on existing energy transportation lines and expands the types of cross-regional wind resource allocation [2]. China is dedicated to constructing cross-regional transmission lines, upgrading the interregional power grid structure, and increasing clean energy use via interregional power generation replacement. The distribution of China's coal resources and wind demand, as well as the type of cross-regional resource allocation, are discussed in this paper [3]. Many domestic and international literature works have presented relevant approaches, such as applying energy storage technology, wind and fire binding, adding reactive power compensation devices, and so on, but these methods do not consider the best distribution of renewable energy resources across a large region.

2. Optimal Allocation of Wind Energy Resources Based on Evolutionary Algorithm

2.1. Wind Energy Resource Distribution Management Model Based on Evolutionary Algorithm

When the supply-demand relationship of wind power tends to balance, the structural emission reduction strategy of China’s wind power sector with “eliminating backward production capacity and improving advanced production capacity” as the core is feasible, and the policy of “pressing the big over the small” has been implemented. Under the background of carbon neutralization, the essence of this structural emission reduction strategy is to use the means of optimal allocation of resources to improve the carbon emission output level and social benefits, measure the structural emission reduction cost with the optimal allocation of resources, and use simplified methods to calculate the dynamic carbon emission reduction cost with the improvement of economic and social development level as much as possible [4]. Under the dual constraints of wind emission reduction and carbon neutralization, the preferred strategy for China’s wind emission reduction is to reasonably allocate the production share among power plants according to the difference in carbon emission output level, from eliminating the most backward capacity to improving the utilization rate of the most advanced capacity, so as to reduce the carbon emission of wind production at a relatively low cost: secondly, introduce demand side management and whole wind system optimization, considering the urban industrial upgrading and the resulting increase in carbon factor productivity, while increasing the output level of wind carbon emission and social benefits, gradually pay the cost of wind emission reduction from low to high, so as to effectively solve the contradiction between carbon emission reduction and economic development in developing countries [5]. The use of equilibrium theory and optimum planning theory to examine the structural emission reduction cost accounting of China’s wind industry based on the optimal allocation of resources is presented in this work, which is based on the aforementioned analysis. The method of improving resource allocation via capacity structure modification in the wind sector is investigated using an evolutionary algorithm [6]. The wind demand side response is introduced into the framework of general equilibrium analysis to expand the research boundary to the wind system and analyze the process of improving carbon factor productivity and reducing carbon emissions from wind output through structural adjustment and optimal resource allocation: under various conditions of optimal resource allocation, the planning theory method and its dual principle are used to solve the social cost arithmetic. Figure 1 depicts a wind energy resource distribution management model based on an evolutionary algorithm.

Connecting this large-scale fluctuating power supply to the power grid will bring many adverse effects to the safe and stable operation of the wind system [7]. In recent years, using the space-time complementary characteristics of wind energy resources in a wide area to smooth the fluctuation of large-scale wind energy output power has attracted extensive attention of scholars at home and abroad. The temporal and geographical complementarity of wind energy resources in China has been explored at the hour level and above, while the temporal and spatial complementarity at the hour level and below has to be investigated further [8]. As a result, this research investigates the spatiotemporal complementarity of wind energy resources on a short time scale across a large area of China, using meteorological wind speed data from the China Meteorological Administration and an evolutionary algorithm and index analysis. The mathematical expression for global equilibrium may be stated as follows, based on the foregoing analysis: let be the social welfare function, be the utility function, be the profit function, the economy is composed of interrelated markets, is the price vector, D (D1, D2) are the market demand vector and market supply vector respectively, and is the consumption vector and equal to ; the solution of the general equilibrium price system can be expressed as follows under the constraint and the producer profit at the same time and realize the optimization of social welfare.

Under the above framework, the evolutionary algorithm can be transformed into an optimal programming problem to solve the equilibrium price of a single market and clear the market and maximize the objective function. Let be the market objective function of , the utility function, r the profit function. The economy is composed of a single market I and other markets, P is the price vector, and are the demand vector and supply vector, respectively, and is the consumption vector equal to ; then the solution of the evolutionary algorithm price system can be expressed as a formula: assuming that other market conditions remain unchanged. Under the constraint that single market supply equals market demand (consumption), solving single market equilibrium price maximizes consumer utility and producer profit at the same time.

By comparing the primal problem with the dual problem of the above symmetric linear programming, the corresponding relationship shown in Table 1 can be listed.

In the short term, a single wind power enterprise can achieve emission reduction through energy efficiency technology innovation, reduction of power generation, and other measures, and in the long term, it can further achieve emission reduction through clean fuel conversion, scale adjustment, and other measures [9]. The structural measures of replacing backward high-carbon emission units with advanced low-carbon emission units in the wind industry may effectively lower the wind industry's carbon emission intensity. The choice variable combination of optimum production in the wind business is addressed using an evolutionary algorithm and optimal planning, using technology, scale, and energy structure as decision variables [10]. It is possible to calculate the marginal emission reduction cost and the emission reduction cost under the conditions of the wind industry evolutionary algorithm. On this foundation, the wind system can improve the power structure, introduce demand side management to create a social sharing mechanism for structural emission reduction costs, and analyze the cost of wind carbon emission reduction with the entire economic system as the object, yielding the marginal cost and cost of wind carbon emission reduction under the condition of social general equilibrium. [11]. For different types of emission reduction possibilities, Table 2 summarizes their scope of application, the meaning of emission reduction costs, and their theoretical basis.

In the research of wind energy emission reduction, how much should be paid for carbon emission is a key and complex problem. Under different emission reduction possibilities, the formation mechanism of marginal emission reduction cost and marginal emission reduction income is different [12]. The marginal emission reduction cost of the evolutionary algorithm is inconsistent with the marginal emission reduction cost of general equilibrium, which shows that there are infinite evolutionary algorithm points fluctuating up and down around the general equilibrium point. Therefore, in reality, it is difficult to set a unified price for carbon emission reduction [13]. The internal relationship and complementary relationship between general equilibrium and evolutionary algorithm provide an important theoretical basis and research ideas for this paper; namely, the Shenzhen wind system is a representative wind system in China [14] based on the evolutionary algorithm and general equilibrium theory. To begin, conduct a local analysis using a microevolutionary algorithm and local optimal planning to determine the carbon emission reduction scheme and cost of the wind industry; after that, broaden the research boundary and assumptions to investigate emission reduction ideas and costs at the system level using a microgeneral equilibrium and the system optimal planning. Finally, using an evolutionary algorithm and general equilibrium analysis, examine the link between the wind industry and wind system emission reduction costs, and propose China’s wind carbon emission reduction plan.

2.2. Evaluation Index of Optimal Allocation of Wind Energy Resources

China is rich in wind energy resources. In terms of the reserves of wind energy resources, China’s exploitable wind energy resources are equivalent to those of the United States but far better than those in India, Germany, Spain, and other regions with abundant wind energy resources. In 2012, China added 130 GW of wind power installed capacity and accumulated 753 GW of wind power installed capacity, both ranking first in the world. However, with the rapid development of China’s wind power industry, the problem of wind power abandonment in China has gradually become prominent [15]. In 2012, the annual equivalent utilization hours of wind turbines in some provinces of China were only 1400 hours, far lower than the planned utilization hours of power generation. The national abandoned wind power reached 20 TWh, and the power loss caused by the abandoned wind was equivalent to 7 Me. When wind turbine generating efficiency cannot be guaranteed, generators’ attitudes toward wind power investment have shifted from favorable to wait-and-see, which is not beneficial to the industry’s long-term growth. Wind energy resources in China are mostly concentrated in the northern and eastern coastal regions [16]. However, the expansion of wind power installed capacity in China is mostly centered in the northern and eastern areas, taking into account development circumstances and project economics. The wind power installed capacity of the three provinces with the highest power demand accounts for 2.2%, 3.1%, and 7.6% of the national installed capacity, respectively. Only the wind power installed capacity is compared with the wind power demand [17]. The inconsistency between the distribution of wind energy resources and the distribution of wind demand is an important reason for the difficulty of wind power consumption. In areas rich in wind energy resources, the wind demand is low and lack of sufficient wind power consumption capacity, resulting in serious wind abandonment. Figure 2 shows the Lorentz curve of wind power installation and regional distribution of wind demand.

It is the Lorentz curve between the exploitable amount of wind energy technology and the regional distribution of wind demand as shown in Figure 3.

In recent years, with the rapid growth of the wind power generation market in the world, wind power generation technology has also made great progress. The technology of units above MW has gradually matured and gradually realized commercialization. At present, the average unit capacity in the world is close to 1.5 MW, and 1.5–20 MW wind turbines have become the mainstream models in Europe; offshore wind farms mainly install 25–30 MW wind turbines, and most of the current wind turbines use more advanced doubly fed asynchronous generators or direct drive generators [18]. At the same time, in order to adapt to various wind conditions, in terms of models, they are subdivided into low and medium wind speed area units, inland units, and high wind speed area units and make greater use of wind energy through the development of pitch, speed change, and other technologies. Wind power information includes policies and regulations, technical standards, wind power planning, survey and design, project approval, project construction, completion of final accounts, grid connected operation, equipment manufacturing, and foreign information [19]. Each wind turbine manufacturing enterprise shall directly fill in the manufacturing information of wind turbine equipment on the Internet in accordance with the technical regulations on wind power information collection and submission and enter the wind power engineering information database after being reviewed by the national wind power information management center: each wind turbine manufacturing enterprise shall open the port and meet the demands of the national wind power information management center by obtaining key wind turbine operating data remotely. For the simulation of the output power of the onshore wind farm, firstly, the wind speed observed by the 10 m high standard wind tower is transformed into the wind speed at the height of the typical fan hub by using the evolutionary algorithm law:where is the wind speed at the hub height ; is the wind speed at the height of the meteorological observation tower. For the offshore wind farm in Block B in this paper, firstly, the offshore onshore wind speed is transformed into the offshore wind speed at the same height by the formula:where is the offshore wind speed near the land at the height of the wind tower. The wind speed at the height of the hub of the sea fan is simulated by (3), where l0 = 0.001.

The power of the four areas is recorded as , , , and respectively, and the total output power of the four areas is recorded as pgrid. Wind energy is the kinetic energy generated by airflow. The size of wind energy mainly depends on wind speed and air density. The wind energy expression is as follows:where is the air density (kg/m3); is the time (s); is the sectional area (m2); is wind speed (m/s); E is wind energy (). The magnitude of wind energy is exactly related to the cube of wind speed, the area over which the wind travels, air density, and, of course, time, according to the formula. If the area swept by a wind turbine blade revolving in a circle is a, air with wind speed flows through the wind turbine in unit time, and the wind power transferred to the wind turbine is represented as follows:where is the air density (kg/m3); is the area swept by the wind turbine blade rotating for one circle (m2); is wind speed (m/s); P is the wind power () sweeping across the cross-sectional area of the wind turbine in unit time. Without confusion, P is sometimes referred to as wind energy for simplicity. In order to measure the size of wind energy resources in a location, it is usually expressed by wind power density. Wind power density is the wind power per unit area, that is, the wind power of airflow per unit cross-sectional area, which can be expressed as follows:

Because wind speed fluctuates all the time, the most common wind speed measuring technique offers the average wind speed over a period of time. The wind speed with a 10-minute average period is the most widely utilized among them. By combining the data, the hourly average wind speed, monthly average wind speed, yearly average wind speed, and other information may be produced. In a nutshell, the average wind speed is computed using the formula:where is the wind speed (m/s); is the number of data; is the average wind speed (m/s). In order to calculate the average wind power density in a certain period of time, it is necessary to sum and average the wind power density in each segment, and the calculation formula is as follows:

For the convenience of calculation, the weighted average wind speed is defined.

Then the average wind power density can be expressed as , and the average wind power density can be expressed as follows:where Is the air density (kg/m3); is the weighted average wind speed (m/s); is the average wind power density (w/m2). Wind energy is proportional to air density. The air density changes with air pressure, temperature, and humidity. From the gas equation:where , is the density of dry air in other states and standard states (kg/m3); , is the air pressure in other states and standard states (kPa); , is the thermodynamic temperature (k) of air in other states and standard states, respectively. When t0 = 273k and P0 = 101.3 kPa in the standard state, the density of dry air with normal composition is taken ρ0 = 1.293 kg/m3. By substituting these values into the formula, the dry air density calculation formula is as follows:

When using the above formula to calculate the dry air density, pay attention to the values of pressure and temperature. Where is the absolute pressure of air, in kPa, is the thermodynamic temperature of air (k), t = 273 + T, t is the Celsius temperature of air (°C). For wet air, according to the gas equation and Dalton’s partial pressure law, the calculation formula of wet air density can be deduced as follows:where is the density of wet air (kg/m3); ψ Is air relative humidity (%); is the saturated steam pressure (kPa), which can be determined by looking up the table or by function fitting. The commonly used fitting function formula of saturated steam pressure is proposed by the ASHRAE organization in the United States, which is suitable for the range of 0–200°C.where is the saturated steam pressure, in PA; T is the absolute temperature of wet air, in K; Coefficient K1=5800.2206; k2 =1.3914993; k3= 4.8640239 × 10–2; k4 = 4.1764768 × 10–5; k5 = 1.4452093 × 10–8; k6 = 6.5459673. In this paper, the saturated steam pressure is calculated by the fitting formula method, and then the wet air density is calculated by formula as the basis for calculating the size of wind energy.

2.3. Realization of Optimal Allocation of Wind Energy Resources

In order to achieve the goal of sustainable development, we must achieve energy conservation, emission reduction, and greenhouse gas emission reduction. One of the important measures is to vigorously develop new or renewable energy. The second characteristic of the distribution of wind energy resources in China is that wind conditions are inversely distributed with local economic development levels; i.e., China’s economic development level shows a stepwise decreasing trend from east to west, while the regions with the most abundant wind energy resources are distributed in economically underdeveloped or even underdeveloped areas like Xinjiang, Inner Mongolia, and Qinghai Tibet P. Although there are abundant wind energy resources along the coast of Bohai Bay, the east coast, and around the Leizhou Peninsula, the exploitable area is limited, and extreme weather such as typhoons is common, so the development efficiency and effect of wind energy resources cannot be compared to those of the Western and Northern provinces. In the western region, the current situation of reverse resource distribution and the market has created a return period of abundant wind energy resources, low wind power consumption, and difficult power transmission, while in the economically developed eastern region, there are phenomena such as continuous rises in power coal prices, wind shortages, and staggered peak power consumption. Affected by many factors such as the distribution of wind energy resources, the current situation of economic development, land, and region, the “resource-oriented” model of wind energy resources development in China has produced the coexistence of “surplus” of wind power and shortage of thermal power. It can be seen that the macroselection of wind energy resources development should also fully consider the impact of wind consumption market factors. Therefore, the various special attributes of wind power generation determine that the macrolocation of wind energy resources development should mainly consider factors such as wind energy resources distribution, wind load location, supporting equipment manufacturing, government attention, and so on (see chart) in Figure 4.

According to the above four aspects, the macrosite selection index system for wind energy resource development is designed from the perspectives of resource conditions, consumption capacity, manufacturing capacity, and support capacity (see Table 3).

As the most potential new energy, wind energy has great advantages in energy consumption and pollution emission. However, wind power generation is uncertain and intermittent, so a large load capacity is needed to stabilize its impact on the wind system; At the same time, China’s wind energy supplies and load are inversely distributed, making wind power growth more difficult to achieve. In China’s wind business, wind power consumption has become a major and tough issue. The optimization approach for cross-regional wind power consumption is discussed in this chapter, with the objective of cross-regional optimum wind energy resource allocation. Wind power consumption may be separated into three dimensions according to the industrial chain: There are three types of electricity generation: on-grid, off-grid, and demand side. The ways of promoting wind power consumption in each dimension are shown in Figure 5.

Wind power technology includes aerodynamics, material properties, computer control, structural mechanics, and other disciplines. It consists of a collection of high-tech apps. It has greater technical support needs than typical power production projects. Strong wind power scientific research capabilities serve to improve the technical support and management level of wind power projects, hence promoting their growth. A large number of studies show that the wind speed is generally positive skew distribution, and multiple models can be used to fit the wind speed distribution. The fitting effect of evolutionary algorithm distribution is the best. The probability density function and distribution function can be expressed as follows:where is the wind speed, and are the scale parameters and shape parameters of evolutionary algorithm distribution, respectively. According to the principle of the inverse transformation method, if is [0, the probability distribution of random variables evenly distributed in the interval is f (x), then let , i.e., , the variable with distribution function of can be obtained. As of now,


When x and are random variables uniformly distributed in the interval [0, 1], X can be used to replace , and the following can be obtained:

According to the probability distribution of wind speed, the hourly wind speed sampling value can be generated by an evolutionary algorithm random number generator . The wind turbine’s output power is computed by assessing the fan operating status at a certain wind speed. Due to the indirectness and instability of wind power production, it is required to restrict the percentage of wind power installed capacity in the area to the total installed capacity in the event of low predicted wind power output or inadequate capacity of peak shaving units. The transmission line, for example, may disperse wind power across areas; therefore, transmission line capacity must be included into the wind power installed capacity restriction. In addition, the gas turbine has the ability to start up and stop down quickly. The increase of the proportion of gas turbine assembled units can increase the proportion of wind power installed capacity.where is the limit of the total installed capacity of wind power, which is determined by the wind energy resources in the region. The difficulty of wind power consumption is not only affected by the instability of its own output but also constrained by the rapid development of wind power as shown in Figure 6.

There is a negative correlation between the growth rate of wind power installed capacity and wind power grid connection rate. The faster the growth of wind power installed capacity, the lower the grid connection rate. Therefore, in order to orderly develop new energy and avoid the waste of new energy investment caused by the uncoordinated development of power grid and new energy, the growth rate of the new energy power supply must be controlled.

3. Analysis of Experimental Results

The decision variables of generation scheduling optimization include continuous variables and 0-1 variables, which is a mixed integer optimization problem. Further, the real-time output of the unit is taken as the decision variable, and the relationship between it and coal consumption is expressed as a quadratic function. As a result, the optimization model’s decision variable contains quadratic terms, making the generation scheduling optimization a mixed integer quadratic constraint programming issue. Mixed integer quadratic constraint programming is difficult to solve and has a sluggish convergence rate. The coal consumption function of coal-fired units is separated into many segments, each with its own linear function. In this way, the quadratic optimization problem is transformed into a linear optimization problem (as shown in Figure 7).

The first level zoning index mainly considers two aspects: the effective wind energy density and the cumulative hours of annual effective wind speed. According to the corresponding indicators, the wind energy resources throughout the country are divided into four wind energy resource areas, namely, wind energy resource rich area I, wind energy resource rich area II, wind energy resource available (general) area III, and wind energy resource poor area IV. See Table 4 for details.

Second level zoning index: considering the seasonal variation of wind, the size of wind energy density in four seasons, and the cumulative hours of effective wind. Spring, summer, autumn, and winter are represented by 1, 2, 3, and 4, respectively. See Table 5 for details. The expression method is represented by the word code with the most accumulated hours of effective wind energy, such as “14” in spring and winter, and so on.

Level III zoning index: the maximum design wind speed of wind turbine is mainly considered, and the local maximum wind speed is usually used. According to relevant indicators, it is divided into four levels, i.e., extra strong pressure type A, strong pressure type B, medium pressure type C, and weak pressure type D, as shown in Table 6.

According to the four wind energy resource areas divided according to the above first level zoning indicators and combined with the evaluation and research of other wind energy resources, this paper summarizes the regional distribution of wind energy resources and the main characteristics and advantages of each region in China. It should be emphasized, however, that the split of these four resource zones only applies to the average situation of wind energy resources in the general pattern and does not reflect the situation of wind energy resources in each region under unique topography. The system dynamics simulation is used in conjunction with the aforementioned data to investigate the semiequilibrium condition of supply and demand for regional wind resources. First, pay attention to the supply and demand of coal, as shown in Figure 8.

Wind power generation showed a good growth momentum in the middle and early stage of the simulation time, and remained stable when it reached the upper limit of regional wind power development in the 13th year. Driven by the load scale and wind power permeability, the consumption of wind power at the transmission end increases steadily. The growth rate of cross-regional transmission wind power is higher than that of local consumption in the middle and early stages, and gradually decreases after the wind power generation reaches the upper limit of development in the later stage, as shown in Figure 9.

The coal supply in the sending end area cannot entirely fulfill the whole demand of the two regions due to existing coal production capacity. After a number of years, coal demand will outstrip supply, necessitating resource allocation from other areas. Although the supply and demand imbalance is reasonably steady over the simulated year, it nonetheless exhibits a growing tendency. The development of wind power helps to curb the rise of coal prices but from Figure 10.

The equivalent utilization hours of wind power have no impact on several indexes in the system before the installed capacity of wind power reaches the upper limit of technical development. The reason is that the constraints on wind power generation in the system during this time period are the wind power consumption capacity at the transmission end and the proportion of wind power transmitted across regions. When the wind power installed capacity reaches the upper limit of technical development, the total installed capacity of wind power becomes a link restricting the growth of wind power generation, and the increase of equivalent utilization hours continues the growth trend of wind power generation to a certain extent.

4. Conclusion

China’s wind load is mainly distributed in the eastern region, while the main wind resources, such as coal, hydropower, and wind energy, are mainly distributed in the western and northern regions. The inverse distribution of wind load and wind resources makes it necessary to optimize the allocation of cross-regional resources in China. The consistency between coal, hydropower, wind energy, and load distribution is described by the Lorentz curve, and the Gini coefficient is used for quantitative calculation. The research shows that there is a high degree of disharmony in the distribution of coal, hydropower, and wind energy. Comparing the Gini coefficient of various energy sources, firstly, the coal distribution is relatively coordinated with the load distribution; secondly, hydropower, and finally, wind energy. This paper discusses the development status and development planning of coal transportation and power transmission. Coal transportation and transmission mode have advantages and disadvantages in economic benefits, transportation energy consumption, floor area, environmental benefits, and so on. In the long run, the two will still be important components of China’s cross-regional allocation of wind resources, but the proportion of power transmission should be increased.

Data Availability

Data used to support this study are available in supplementary information files.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


This work was supported by Innovation Engineering Scientific Research Consulting Project of Party School of Gansu Provincial Party Committee of the CPC (Gansu Institute of Public Administration), “Study on Carbon Peak and Carbon Neutralization in Gansu Province,” Soft Science Project of Gansu Provincial Department of Science And Technology, “Mechanism of Social Forces Participating in Emergency Management of Major Emergencies,” National Natural Science Foundation of China (No. 41930101).