Gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction

Abstract

The invention discloses a gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction, and relates to the technical field of power systems. The method comprises the following steps: determining an operation area, collecting environmental data, selecting a time period to construct a new energy output data model to fit output data, clustering load power consumption data through an improved k-means algorithm to obtain typical daily data, classifying power consumption sources, constructing a carbon emission income cost model and a carbon emission intensity index, and calculating the power consumption of the load. And determining a capacity configuration mathematical model of the gravity energy storage system, and solving by adopting a frost ice algorithm to obtain a capacity configuration result of the gravity energy storage system. Under the condition that the construction cost economy of the gravity energy storage system and the new energy consumption income cost are optimal, through simulation of wind-solar-energy-storage combined operation of an operation area, optimization is carried out in combination with k-means and frost ice algorithms, and a reliable capacity configuration scheme of the gravity energy storage system can be obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for configuring gravity energy storage capacity taking into account new energy consumption and carbon emission reduction. Background Art

[0002] In recent years, with the large-scale development and utilization of wind and solar energy, the installed capacity of new energy has continued to increase. However, the output of new energy has uncertainty, randomness and anti-peak characteristics, which makes consumption difficult and causes the phenomenon of wind and solar power abandonment. Energy storage technology can effectively realize the time shifting, storage and utilization of electric energy, which helps to promote the consumption of new energy in the power system. However, due to the site selection and construction scale, existing energy storage systems such as battery energy storage systems and compressed air energy storage systems are limited by terrain, cost and technology.

[0003] As a unique energy storage method, the gravity energy storage system can rely on the surrounding environment of the construction area and has obvious natural advantages. The gravity energy storage system uses height difference to store energy. Its energy storage structure can adapt to changes in terrain, and the energy storage medium can be made from local materials. It can not only be recycled, but also has almost no pollution to the environment. At the same time, it has the ability to participate in power grid regulation on a long-term scale. Its unique advantages and economic performance have broad application prospects.

[0004] Configuring a certain amount of energy storage capacity in a high-proportion new energy power system can not only effectively improve the consumption capacity of wind and solar power generation, but also improve the reliability and economy of wind-photovoltaic hybrid systems. Capacity configuration affects the reliability and economy of the system. Too small capacity leads to a mismatch between load demand and too large capacity leads to a significant increase in investment costs.

[0005] Based on the above analysis, in-depth research on how to reasonably configure energy storage capacity, reduce the system's wind and solar power abandonment phenomenon, and at the same time ensure the system and economy is a key technical issue to test the feasibility of large-scale energy storage systems such as gravity energy storage. Summary of the invention

[0006] In order to address the shortcomings of the background technology, the present invention provides a gravity energy storage capacity configuration method that takes into account new energy consumption and carbon emission reduction. It takes into account the cost-effectiveness of the construction of the gravity energy storage system and the optimal consumption benefits of the new energy power generation system. By simulating the joint operation of wind, solar and storage in the operating area, combined with k-means and frost and ice algorithms for optimization, a reliable gravity energy storage system capacity configuration plan can be obtained.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for configuring gravity energy storage capacity considering new energy consumption and carbon emission reduction, comprising the following steps:

[0008] S1. Determine the operating area, where a new energy power generation system jointly operated by a wind farm and a photovoltaic power station is set up and equipped with a gravity energy storage system, collect environmental data of the area, select a time period T and build a new energy output data model for the area according to the fitting formula, calculate the output data, and cluster the load power consumption data through the k-means algorithm to obtain typical daily data;

[0009] S2. Classify the electricity sources according to the load electricity consumption data of the operating area, and construct a carbon emission benefit-cost model and carbon emission intensity index;

[0010] S3. According to the new energy output data model of the operating area, combined with the load power consumption in the area, taking into account the carbon emission benefits, taking the minimum comprehensive cost of the gravity energy storage system as the objective function, carbon emission intensity as the evaluation index, and taking the output power of the new energy power generation system and the charge and discharge power of the gravity energy storage system as the constraints, determine the capacity configuration mathematical model of the gravity energy storage system;

[0011] S4. The frost-ice algorithm is used to solve the capacity configuration mathematical model of the gravity energy storage system to obtain the capacity configuration result of the gravity energy storage system.

[0012] Furthermore, the environmental data in step S1 includes a historical data set consisting of wind speed, temperature, humidity, solar irradiance, and solar irradiance angle.

[0013] Furthermore, in the step S1, the construction of the new energy output data model adopts Weibull distribution and Beta distribution for fitting wind power generation and photovoltaic power generation respectively.

[0014] Furthermore, the k-means algorithm in step S1 includes:

[0015] 1) The load power consumption data of the operating area is converted into a matrix as follows:

[0016]

[0017] In the formula, Q m×n It is an m×n dimensional matrix, a represents the power consumption element, subscript m represents the corresponding mth day, and subscript n represents the corresponding nth power consumption device;

[0018] 2) Select the load power consumption data in the zth column with the largest power consumption and assign weights to it, and simplify it as follows:

[0019]

[0020] In the formula, μ represents the weight, q represents the power consumption of n electrical devices in a single day, and the subscript m represents the corresponding mth day;

[0021] 3) Randomly generate L cluster centers C l , where l = 1, 2, ..., L;

[0022] 4) According to the Euclidean distance formula, calculate the distance from each q to each cluster center C l distance and assigned to the nearest cluster center;

[0023] 5) Iterate and repeat steps 3) and 4) to get the updated cluster center C l ', until C l -C l The iteration stops when '≤ε, where ε represents the clustering decision threshold and the maximum number of iterations is set to avoid infinite iterations;

[0024] 6) Get the final cluster center as the typical day data.

[0025] Furthermore, the carbon emission benefit cost model and the carbon emission intensity index in step S2 are respectively expressed as:

[0026]

[0027] In the formula, C em represents the carbon emission benefit cost, β i represents the carbon emission benefit coefficient, AD i Indicates the total amount of electricity used, EF i represents the electricity supply emission factor;

[0028]

[0029] In the formula, r represents the carbon emission intensity, and G represents the total carbon emissions in the operating area.

[0030] Furthermore, the capacity configuration mathematical model of the gravity energy storage system in step S3 is expressed as:

[0031] 1) Objective function

[0032] C tatol =min(C capacity +C power +C om )

[0033] In the formula, C tatol represents the comprehensive cost of the gravity energy storage system, C capacity represents the capacity cost of the gravity energy storage system, C power represents the power cost of the gravity energy storage system, C om represents the operation and maintenance cost of the gravity energy storage system;

[0034] 2) Power generation constraints of wind farms

[0035] P wd (t)≤P w (t)≤P wu (t)

[0036] P wu (t) = (1 + α)P w (t)

[0037] P wd (t) = (1-α)P w (t)

[0038] Where P w (t) represents the wind power output power at time t, P wd (t) represents the lower limit of wind power generation at time t, P wu (t) represents the upper limit of wind power generation at time t, and α represents the output limit value range;

[0039] 3) Power generation constraints of photovoltaic power plants

[0040]

[0041] Where P pv (t) represents the photovoltaic power output power at time t, P STC Indicates the maximum rated power of photovoltaic under standard conditions, G c (t) represents the actual solar radiation at time t, G STC represents the standard solar radiation intensity, k represents the power temperature coefficient, T c (t) represents the working temperature at time t, T STC Indicates the operating temperature under standard conditions;

[0042] 4) Charging and discharging model of gravity energy storage system

[0043] System Charging:

[0044]

[0045] System discharge:

[0046]

[0047] In the formula, H(t) represents the height of the energy storage weight at time t, H(t-1) represents the height of the energy storage weight at time t-1, and P ch (t) and P dis (t) represents the charging and discharging power, Δt represents the charging and discharging time, η ch and η dis Represent the charging and discharging efficiency, H max Indicates the maximum height of the energy storage weight;

[0048] Charging power:

[0049] P ch (t) = F ch υ=(mg+f ch )

[0050] Discharge power:

[0051] P dis (t) = F dis υ=(mg-f dis )

[0052] Theoretical capacity of gravity energy storage system:

[0053] W=mgH max

[0054] In the formula, F ch and F dis They represent the traction force of the charging and discharging motors, υ represents the running speed of the energy storage weight, m ​​represents the mass of the energy storage weight, g represents the acceleration of gravity, and f ch and f dis They represent the sum of the motion loss and transmission mechanism loss during the rising and falling process of the energy storage weight, respectively, and W represents the theoretical capacity of the gravity energy storage system;

[0055] 5) Constraints

[0056] P(t)+P pv (t)+P w (t) = P load (t)

[0057] In the formula, P(t) represents the charge and discharge power of the gravity energy storage system at time t, P(t) = δ ch P ch (t)+δ dis P dis (t), δ ch and δ dis Represent the state parameters of charging and discharging respectively, satisfying P load (t) represents the load power consumption at time t.

[0058] Furthermore, the frost algorithm in step S4 includes:

[0059] 1) The calculated output data is used as the input of the capacity configuration mathematical model of the gravity energy storage system, and the population size and the maximum number of cycles are determined. The objective function of the capacity configuration mathematical model of the gravity energy storage system is used as the fitness function, and the capacity and power of the gravity energy storage are used as the particle position, and the constraints are considered for operation;

[0060] 2) Initialize the particle speed and position of the population and calculate the initial fitness value;

[0061] 3) Calculate the initial global optimal solution. For each particle, compare the fitness value of its current position with the fitness value corresponding to its historical optimal position. If the fitness value of the current position is higher, use the current position to update the historical optimal position. For each particle, compare the fitness value of its current position with the fitness value corresponding to its global optimal position. If the fitness value of the current position is higher, use the current position to update the global optimal position.

[0062] 4) Update the location according to the search strategy

[0063] When r1<E, execute the soft frost search strategy

[0064] R ij =R best,j +r1×cos(θ)×γ×(h×(B up -B low )+B low )

[0065]

[0066]

[0067] When r2<E, the hard frost piercing mechanism is used to jump directly to the global optimal position

[0068] R ij =R best,j

[0069] In the formula, R ij is the current particle position, R best,j is the optimal position of the current particle, r1, r2 and h are random numbers, θ is the angle change parameter, γ is the environmental factor, B up is the search upper bound, B low is the search lower bound, E is the loop number factor, X is the maximum loop number, x is the current loop number, and ω is the parameter of the environmental factor;

[0070] Recalculate the fitness and update the global optimal solution;

[0071] 5) Repeat step 4) until the program reaches the maximum number of cycles;

[0072] 6) Output the global optimal solution and obtain the optimal capacity configuration result of the gravity energy storage system.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] 1. In the process of processing load power consumption data, the k-means algorithm is optimized. By giving weights to the power load data of the equipment with the largest power consumption, the algorithm can focus on the data with larger power consumption, making the clustering results more consistent with the power consumption situation. It also generates wind farm and photovoltaic power station output scenarios and load power consumption scenarios, helping to understand the distribution characteristics and resource conditions of different energy outputs in the operating area, and providing basic data for subsequent energy storage capacity configuration;

[0075] 2. Construct a capacity configuration mathematical model for the gravity energy storage system, consider the carbon emission benefit-cost model, and take the minimum comprehensive cost of the gravity energy storage system as the objective function to improve the economy of the system and ensure the reliability of the system;

[0076] 3. Implement the frost-ice algorithm to optimize the wind and solar storage capacity, imitate the physical characteristics of frost, and introduce a soft frost search strategy and a hard frost piercing mechanism. These unique mechanisms help the algorithm to strike a balance between global and local searches, effectively identify the relationship between energy storage capacity configuration and economy, provide important reference for subsequent operational decisions, and improve the utilization rate of energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flowchart of the method of the present invention;

[0078] Figure 2 It is a system topology diagram of the operation area in the method of the present invention;

[0079] Figure 3 It is a flow chart of solving the mathematical model in the method of the present invention;

[0080] Figure 4 It is a data chart of annual wind and solar power generation new energy output in the operating area in the embodiment;

[0081] Figure 5 are four sets of typical daily data of load power consumption in the operating area in the embodiment;

[0082] Figure 6 It is a charging and discharging operation data diagram of the gravity energy storage system applied in the operation area in the embodiment. DETAILED DESCRIPTION

[0083] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0084] like Figure 1 to Figure 3As shown in the figure, a gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction is proposed. Figure 1 As shown, the following steps are included:

[0085] S1. Determine the operating area and combine Figure 2 As shown in the figure, a new energy power generation system jointly operated by a wind farm and a photovoltaic power station is set up in the area and equipped with a gravity energy storage system. The environmental data of the area is collected, the time period T is selected and the new energy output data model of the area is constructed according to the fitting formula, the output data is calculated, and the load power consumption data is clustered by the k-means algorithm to obtain typical daily data. Specifically:

[0086] S101. Collect environmental data of the area where the operation area is located, including historical data sets consisting of wind speed, temperature, humidity, solar irradiance, solar irradiation angle, etc. After data cleaning, screening and normalization, use the improved k-means algorithm to cluster and select typical daily data for analysis. Wind power generation and photovoltaic power generation are fitted with Weibull distribution and Beta distribution respectively, and a new energy output data model is constructed to calculate the output data of new energy in the area at each moment in the entire time period T;

[0087] S102, collect the load power consumption data of the operation area, and after data cleaning, screening and normalization, use the k-means algorithm for clustering, select typical daily data for analysis, wherein the k-means algorithm is optimized as follows:

[0088] 1) The load power consumption data of the operating area is converted into a matrix as follows:

[0089]

[0090] In the formula, Q m×n It is an m×n dimensional matrix, a represents the power consumption element, subscript m represents the corresponding mth day, and subscript n represents the corresponding nth power consumption device;

[0091] 2) Select the load power consumption data in the zth column with the largest power consumption and assign weights to it, and simplify it as follows:

[0092]

[0093] In the formula, μ represents the weight, q represents the power consumption of n electrical devices in a single day, and the subscript m represents the corresponding mth day;

[0094] 3) Randomly generate L cluster centers C l , where l = 1, 2, ..., L;

[0095] 4) According to the Euclidean distance formula, calculate the distance from each q to each cluster center C l distance and assigned to the nearest cluster center;

[0096] 5) Iterate and repeat steps 3) and 4) to get the updated cluster center C l ', until C l -C l The iteration is stopped when '≤ε, where ε represents the clustering judgment threshold and the maximum number of iterations is set. If the iteration reaches the maximum number of iterations and still does not converge, the iteration is stopped to avoid infinite iterations.

[0097] 6) Get the final cluster center as the typical day data.

[0098] S2. Classify the electricity sources according to the load electricity consumption data of the operating area, and construct a carbon emission benefit cost model and carbon emission intensity index, where:

[0099] The carbon emission benefit-cost model is expressed as:

[0100]

[0101] In the formula, C em represents the carbon emission benefit cost, β i represents the carbon emission benefit coefficient, AD i Indicates the total amount of electricity used, EF i represents the electricity supply emission factor.

[0102] The carbon emission intensity index is expressed as:

[0103]

[0104] In the formula, r represents the carbon emission intensity, and G represents the total carbon emissions in the operating area.

[0105] S3. Based on the new energy output data model of the operating area, combined with the load power consumption in the area, and considering the carbon emission benefits, the minimum comprehensive cost of the gravity energy storage system is used as the objective function, the carbon emission intensity is used as the evaluation index, and the output power of the new energy power generation system and the charging and discharging power of the gravity energy storage system are used as constraints. The capacity configuration mathematical model of the gravity energy storage system is determined as follows:

[0106] 1) The objective function is expressed as:

[0107] C tatol =min(C capacity +C power +C om )

[0108] In the formula, C tatol represents the comprehensive cost of the gravity energy storage system, Ccapacity represents the capacity cost of the gravity energy storage system, C power represents the power cost of the gravity energy storage system, C om represents the operation and maintenance cost of the gravity energy storage system;

[0109] 2) The power generation constraints of the wind farm are:

[0110] P wd (t)≤P w (t)≤P wu (t)

[0111] P wu (t) = (1 + α)P w (t)

[0112] P wd (t) = (1-α)P w (t)

[0113] Where P w (t) represents the wind power output power at time t, P wd (t) represents the lower limit of wind power generation at time t, P wu (t) represents the upper limit of wind power generation at time t, and α represents the output limit value range;

[0114] 3) The power generation constraints of the photovoltaic power station are:

[0115]

[0116] Where P pv (t) represents the photovoltaic power output power at time t, P STC Indicates the maximum rated power of photovoltaic under standard conditions, G c (t) represents the actual solar radiation at time t, G STC represents the standard solar radiation intensity, k represents the power temperature coefficient, T c (t) represents the working temperature at time t, T STC Indicates the operating temperature under standard conditions;

[0117] 4) The charging and discharging model of the gravity energy storage system is:

[0118] System Charging:

[0119]

[0120] System discharge:

[0121]

[0122] In the formula, H(t) represents the height of the energy storage weight at time t, H(t-1) represents the height of the energy storage weight at time t-1, and Pch (t) and P dis (t) represents the charging and discharging power, Δt represents the charging and discharging time, η ch and η dis Represent the charging and discharging efficiency, H max Indicates the maximum height of the energy storage weight;

[0123] Charging power:

[0124] P ch (t) = F ch υ=(mg+f ch )

[0125] Discharge power:

[0126] P dis (t) = F dis υ=(mg-f dis )

[0127] Theoretical capacity of gravity energy storage system:

[0128] W=mgH max

[0129] In the formula, F ch and F dis They represent the traction force of the charging and discharging motors, υ represents the running speed of the energy storage weight, m ​​represents the mass of the energy storage weight, g represents the acceleration of gravity, and f ch and f dis They represent the sum of the motion loss and transmission mechanism loss during the rising and falling process of the energy storage weight, respectively, and W represents the theoretical capacity of the gravity energy storage system;

[0130] 5) Constraints:

[0131] P(t)+P pv (t)+P w (t) = P load (t)

[0132] In the formula, P(t) represents the charge and discharge power of the gravity energy storage system at time t, P(t) = δ ch P ch (t)+δ dis P dis (t), δ ch and δ dis Represent the state parameters of charging and discharging respectively, satisfying P load (t) represents the load power consumption at time t.

[0133] S4. Use the frost-ice algorithm to solve the capacity configuration mathematical model of the gravity energy storage system, combined with Figure 3 As shown, the capacity configuration results of the gravity energy storage system are obtained, specifically:

[0134] 1) The output data calculated according to the new energy output data model in step S1 is used as the input of the capacity configuration mathematical model of the gravity energy storage system in step S3, and the population size and the maximum number of cycles are determined. The objective function of the capacity configuration mathematical model of the gravity energy storage system is used as the fitness function, and the capacity and power of the gravity energy storage are used as the particle position, and the operation is performed considering the constraints;

[0135] 2) Initialize the particle speed and position of the population and calculate the initial fitness value;

[0136] 3) Calculate the initial global optimal solution. For each particle, compare the fitness value of its current position with the fitness value corresponding to its historical optimal position. If the fitness value of the current position is higher, use the current position to update the historical optimal position. For each particle, compare the fitness value of its current position with the fitness value corresponding to its global optimal position. If the fitness value of the current position is higher, use the current position to update the global optimal position.

[0137] 4) Update the location according to the search strategy

[0138] When r1<E, execute the soft frost search strategy

[0139] R ij =R best,j +r1×cos(θ)×γ×(h×(B up -B low )+B low )

[0140]

[0141]

[0142] When r2<E, the hard frost piercing mechanism is used to jump directly to the global optimal position

[0143] R ij =R best,j

[0144] In the formula, R ij is the current particle position, R best,j is the optimal position of the current particle, r1, r2 and h are random numbers, θ is the angle change parameter, γ is the environmental factor, B up is the search upper bound, B low is the search lower bound, E is the loop number factor, X is the maximum loop number, x is the current loop number, and ω is the parameter of the environmental factor;

[0145] Recalculate the fitness and update the global optimal solution;

[0146] 5) Repeat step 4) until the program reaches the maximum number of cycles;

[0147] 6) Output the global optimal solution and obtain the optimal capacity configuration result of the gravity energy storage system, and calculate the new energy electricity consumed by the energy storage based on this, so as to calculate the carbon emission reduction benefits and carbon emission intensity.

[0148] In summary, by comprehensively considering multiple factors such as new energy consumption, carbon emission reduction, and cost-effectiveness, an innovative method for gravity energy storage capacity configuration is provided, which has broad application prospects and practical value. The specific advantages are as follows:

[0149] This method can reasonably configure the capacity of gravity energy storage, effectively reduce the phenomenon of wind and solar power abandonment, and improve the capacity for new energy consumption; optimize the reliability and economy of the new energy power generation system jointly operated by wind farms and photovoltaic power stations, and ensure that the gravity energy storage system avoids over-investment while meeting the load demand through precise capacity configuration; it has the ability to participate in grid regulation on a long time scale, which helps to enhance the flexible dispatching efficiency of regional power grids; takes the minimum comprehensive cost of the gravity energy storage system as the objective function, ensures that while meeting the needs of new energy consumption and reducing carbon emissions, it maximizes cost-effectiveness, and uses the particle swarm algorithm to solve the capacity configuration mathematical model of the gravity energy storage system, which improves the efficiency and accuracy of the solution; responds to the national energy conservation and emission reduction policy, and meets the market demand for clean energy and green development.

[0150] Example

[0151] This embodiment uses a steel production enterprise park as an example to specifically illustrate the method of this patent. A high-energy-consuming steel production enterprise in a certain area, including surrounding wind and solar new energy power generation devices, is selected. The working day power load and wind and solar power generation characteristics are used as data. The embedded gravity energy storage of the steel enterprise is considered for capacity configuration. The method of the present invention is applied. The specific process is as follows:

[0152] The power generation process is simulated based on the wind and solar data. The annual wind and solar power generation in the region is obtained through the constructed new energy output model. Figure 4 As shown. It can be seen that the regional photovoltaic resources have obvious characteristics: on a daily scale, solar energy resources are abundant at noon, and on an annual scale, solar energy resources are abundant in summer, which is the peak period of photovoltaic output; regional wind resources also have obvious characteristics: wind power generation fluctuates rapidly and with large amplitudes, with strong randomness and intermittent type. From a seasonal perspective, wind power generation output is higher in spring, but it also fluctuates within a certain range. At the same time, the collected data is clustered according to the improved k-means algorithm to obtain four groups of common load power characteristics as follows Figure 5As shown, the industrial electricity load curve does not change much, the load on weekdays and weekends remains basically consistent, and energy use is relatively stable and concentrated.

[0153] According to the above new energy output data and load power consumption data, the energy storage capacity is configured. Among them, the objective function is set to minimize the cost of the energy storage system, taking into account the operation and maintenance costs and capacity power costs. At the same time, the reduction of carbon emission costs is added as part of the benefits to the objective function, where the carbon emission factor is taken as β = 1.15kg / kWh. In terms of constraints, in addition to the constraints of new energy power generation, a mathematical model of gravity energy storage is constructed. In the process of the weight rising and falling driven by the mechanism, the electrical energy and gravitational potential energy are converted into each other. Among them, the capacity cost of gravity energy storage is much smaller than other common new energy storage, far lower than 5000￥ / kWh, usually around 1200￥ / kWh. This is because the weight of gravity energy storage can be selected from existing resources in the area for reuse, such as a large number of steel blocks in steel enterprises, or some recycled waste. This can greatly reduce the construction cost and achieve a higher economy. According to the algorithm for energy storage capacity configuration, its parameters are sorted as shown in Table 1:

[0154] Table 1. Park energy storage capacity configuration results

[0155]

[0156]

[0157] Energy storage operation combined Figure 6 As shown, the parameters constructed by this patent can clearly characterize the capacity state and charging and discharging state of gravity energy storage, and can operate well. The curve changes continuously within a day, indicating that the system efficiency is high, and the available capacity is maintained between 10% and 90%, indicating that nearly 80% of the system capacity can be used to adjust the load, and it has the ability to store a large amount of electrical energy, can participate in the electricity market to obtain benefits, and has high economic performance.

[0158] In this embodiment, the energy storage system mainly considers the gravity energy storage system established by high-carbon emission enterprises such as steel enterprises. Among them, steel enterprises are usually located in industrial parks with high electricity prices. By storing electricity during the low electricity price period through industrial and commercial energy storage systems and releasing electricity during the peak electricity price period, it can help enterprises smooth the electricity consumption curve and avoid high electricity bills during peak hours, thereby greatly reducing the overall energy cost. And by configuring energy storage to alleviate practical problems, significantly enhance the flexible dispatching efficiency of regional power grids, improve the absorption and storage capacity of enterprises for renewable energy, promote the widespread application of green electricity, effectively reduce the dependence on traditional fossil energy, and it is expected to reduce carbon emissions every year. Using the products of steel enterprises as counterweights can reduce the construction cost of gravity energy storage systems, because these materials themselves are by-products of the enterprise and do not need to be purchased additionally, thereby reducing the overall investment of the energy storage system. In addition, gravity energy storage is often configured at high-load nodes, and its capacity is mainly used for node load regulation, and participates in the recovery of new energy power generation, and usually has a large capacity.

[0159] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other forms of assembly without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and range of equivalents of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0160] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction, characterized by: The following steps are involved: S1. Determine the operating area, where a new energy power generation system jointly operated by a wind farm and a photovoltaic power station is set up and equipped with a gravity energy storage system, collect environmental data of the area, select a time period T and build a new energy output data model for the area according to the fitting formula, calculate the output data, and cluster the load power consumption data through the k-means algorithm to obtain typical daily data; S2. Classify the electricity sources according to the load electricity consumption data of the operating area, and construct a carbon emission benefit-cost model and carbon emission intensity index; S3. According to the new energy output data model of the operating area, combined with the load power consumption in the area, taking into account the carbon emission benefits, taking the minimum comprehensive cost of the gravity energy storage system as the objective function, carbon emission intensity as the evaluation index, and taking the output power of the new energy power generation system and the charge and discharge power of the gravity energy storage system as the constraints, determine the capacity configuration mathematical model of the gravity energy storage system; S4. The frost-ice algorithm is used to solve the capacity configuration mathematical model of the gravity energy storage system to obtain the capacity configuration result of the gravity energy storage system.

2. A gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction according to claim 1, characterized in that: The environmental data in step S1 includes a historical data set consisting of wind speed, temperature, humidity, solar irradiance, and solar irradiance angle.

3. A gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction according to claim 1, characterized in that: In the step S1, the new energy output data model is constructed by fitting the wind power generation and the photovoltaic power generation using the Weibull distribution and the Beta distribution respectively.

4. A gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction according to claim 1, characterized in that: The k-means algorithm in step S1 includes: 1) The load power consumption data of the operating area is converted into a matrix as follows: In the formula, Q m×n It is an m×n dimensional matrix, a represents the power consumption element, subscript m represents the corresponding mth day, and subscript n represents the corresponding nth power consumption device; 2) Select the load power consumption data in the zth column with the largest power consumption and assign weights to it, and simplify it as follows: In the formula, μ represents the weight, q represents the power consumption of n electrical devices in a single day, and the subscript m represents the corresponding mth day; 3) Randomly generate L cluster centers C l , where l = 1, 2, ..., L; 4) According to the Euclidean distance formula, calculate the distance from each q to each cluster center C l distance and assigned to the nearest cluster center; 5) Iterate and repeat steps 3) and 4) to get the updated cluster center C l ', until ||C l -C l Stop the iteration when ||≤ε, where ε represents the clustering determination threshold, and set the maximum number of iterations to avoid infinite iterations; 6) Get the final cluster center as the typical day data.

5. A gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction according to claim 4, characterized in that: The carbon emission benefit cost model and the carbon emission intensity index in step S2 are respectively expressed as: In the formula, C em represents the carbon emission benefit cost, β i represents the carbon emission benefit coefficient, AD i Indicates the total amount of electricity used, EF i represents the electricity supply emission factor; In the formula, r represents the carbon emission intensity, and G represents the total carbon emissions in the operating area.

6. A gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction according to claim 5, characterized in that: The capacity configuration mathematical model of the gravity energy storage system in step S3 is expressed as: 1) Objective function C tatol =min(C capacity +C power +C om ) In the formula, C tatol represents the comprehensive cost of the gravity energy storage system, C capacity represents the capacity cost of the gravity energy storage system, C power represents the power cost of the gravity energy storage system, C om represents the operation and maintenance cost of the gravity energy storage system; 2) Power generation constraints of wind farms P wd (t)≤P w (t)≤P wu (t) P wu (t)=(1+α)P w (t) P wd (t)=(1-α)P w (t) Where P w (t) represents the wind power output power at time t, P wd (t) represents the lower limit of wind power generation at time t, P wu (t) represents the upper limit of wind power generation at time t, and α represents the output limit value range; 3) Power generation constraints of photovoltaic power plants Where P pv (t) represents the photovoltaic power output power at time t, P STC Indicates the maximum rated power of photovoltaic under standard conditions, G c (t) represents the actual solar radiation at time t, G STC represents the standard solar radiation intensity, k represents the power temperature coefficient, T c (t) represents the working temperature at time t, T STC Indicates the operating temperature under standard conditions; 4) Charging and discharging model of gravity energy storage system System Charging: System discharge: In the formula, H(t) represents the height of the energy storage weight at time t, H(t-1) represents the height of the energy storage weight at time t-1, and P ch (t) and P dis (t) represents the charging and discharging power, Δt represents the charging and discharging time, η ch and η dis Represent the charging and discharging efficiency, H max Indicates the maximum height of the energy storage weight; Charging power: P ch (t)=F ch υ=(mg+f ch )υ Discharge power: P dis (t)=F dis υ=(mg-f dis )υ Theoretical capacity of gravity energy storage system: W=mgH max In the formula, F ch and F dis They represent the traction force of the charging and discharging motors, υ represents the running speed of the energy storage weight, m ​​represents the mass of the energy storage weight, g represents the acceleration of gravity, and f ch and f dis They represent the sum of the motion loss and transmission mechanism loss during the rising and falling process of the energy storage weight, respectively, and W represents the theoretical capacity of the gravity energy storage system; 5) Constraints P(t)+P pv (t)+P w (t)=P load (t) In the formula, P(t) represents the charge and discharge power of the gravity energy storage system at time t, P(t) = δ ch P ch (t)+δ dis P dis (t), δ ch and δ dis Represent the state parameters of charging and discharging respectively, satisfying P load (t) represents the load power consumption at time t.

7. A gravity energy storage capacity configuration method considering new energy consumption and carbon emission reduction according to claim 6, characterized in that: The frost algorithm in step S4 includes: 1) The calculated output data is used as the input of the capacity configuration mathematical model of the gravity energy storage system, and the population size and the maximum number of cycles are determined. The objective function of the capacity configuration mathematical model of the gravity energy storage system is used as the fitness function, and the capacity and power of the gravity energy storage are used as the particle position, and the constraints are considered for operation; 2) Initialize the particle speed and position of the population and calculate the initial fitness value; 3) Calculate the initial global optimal solution. For each particle, compare the fitness value of its current position with the fitness value corresponding to its historical optimal position. If the fitness value of the current position is higher, use the current position to update the historical optimal position. For each particle, compare the fitness value of its current position with the fitness value corresponding to its global optimal position. If the fitness value of the current position is higher, use the current position to update the global optimal position. 4) Update the location according to the search strategy When r1<E, execute the soft frost search strategy R ij =R best,j +r1×cos(θ)×γ×(h×(B up -B low )+B low ) When r2<E, the hard frost piercing mechanism is used to jump directly to the global optimal position R ij =R best,j In the formula, R ij is the current particle position, R best,j is the optimal position of the current particle, r1, r2 and h are random numbers, θ is the angle change parameter, γ is the environmental factor, B up is the search upper bound, B low is the search lower bound, E is the loop number factor, X is the maximum loop number, x is the current loop number, and ω is the parameter of the environmental factor; Recalculate the fitness and update the global optimal solution; 5) Repeat step 4) until the program reaches the maximum number of cycles; 6) Output the global optimal solution and obtain the optimal capacity configuration result of the gravity energy storage system.

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