Industrial park resource operation optimization method based on electric carbon space-time distribution characteristics

Abstract

The invention relates to an industrial park resource operation optimization method based on power-carbon space-time distribution characteristics, and the method comprises the steps: firstly obtaining historical load data, future power utilization growth rate prediction data and carbon emission monitoring data of an industrial park; calculating future power consumption demands based on the data, and constructing a power and electric quantity balance constraint condition; then, establishing carbon emission space-time distribution and power space-time distribution according to the carbon emission monitoring data and the generated power of the distributed power supply; constructing a comprehensive optimization objective function according to the planning cost, the investment operation cost, the carbon emission objective and the new energy consumption proportion, and processing the comprehensive optimization objective function through a Lagrange multiplier method; solving the comprehensive optimization objective function to obtain an optimal output distribution strategy of the industrial park distributed power supply; according to the method, the spatial and temporal distribution characteristics of electric power and carbon emission of the park are comprehensively considered, and the obtained configuration strategy for optimizing distributed energy resources can effectively improve the energy utilization efficiency of the park.

Description

Technical Field

[0001] The present invention relates to an industrial park resource operation optimization method based on the spatiotemporal distribution characteristics of electricity and carbon, and belongs to the technical field of low-carbon transformation of power systems. Background Art

[0002] With the continuous increase in global energy demand and increasingly stringent carbon emission control targets, energy management in industrial parks and large commercial areas faces new challenges. In order to achieve a balance between economic development and environmental protection, how to effectively utilize distributed energy resources in the park, reduce carbon emissions and improve energy efficiency has become an important research direction. At present, with the development of renewable energy technologies such as photovoltaics and wind power, a variety of flexible energy supply methods have gradually been introduced in the park. However, the intermittent and instability of distributed energy has brought challenges to the stability of the park's energy supply, especially during high-load periods. How to coordinate the interaction between new energy generation and traditional energy has become a key issue.

[0003] Existing industrial park energy management solutions usually focus on the optimization of a single energy source, fail to fully consider the temporal and spatial distribution characteristics of electricity and carbon emissions within the park, and are still not perfect for the use of multi-energy complementarity and flexible resources. In addition, carbon emission policies and carbon trading mechanisms have gradually become important factors in energy planning. Traditional park resource optimization methods are difficult to cope with complex carbon emission constraints and to achieve a balance between multiple goals, such as the comprehensive optimization of economic benefits, new energy consumption, and carbon emissions. Summary of the invention

[0004] The purpose of the present invention is to solve the problems existing in the prior art and to provide an industrial park resource operation optimization method that takes carbon emission constraints into consideration and balances multiple objectives.

[0005] In order to achieve the above-mentioned purpose, the technical solution proposed by the present invention is: a method for optimizing the operation of industrial park resources based on the spatiotemporal distribution characteristics of electric carbon, comprising:

[0006] Obtain historical load data, future electricity consumption growth rate forecast data and carbon emission monitoring data of the industrial park; and calculate future electricity demand based on the above data;

[0007] Construct power balance constraints based on the power generation of distributed power sources in the industrial park and future power demand;

[0008] Establish the spatiotemporal distribution of carbon emissions and the spatiotemporal distribution of electricity based on carbon emission monitoring data and the power generation of distributed power sources; calculate the carbon emission target based on the spatiotemporal distribution of carbon emissions, and calculate the planning cost, investment and operation cost, and new energy consumption ratio based on the spatiotemporal distribution of electricity;

[0009] Construct a comprehensive optimization objective function based on planning cost, investment and operation cost, carbon emission target and new energy consumption ratio, and process the comprehensive optimization objective function through Lagrange multiplier method;

[0010] Solve the comprehensive optimization objective function to obtain the optimal output distribution plan of distributed power sources in the industrial park.

[0011] The further design of the above technical solution is: the future electricity demand is:

[0012] D(t)=f(P hist (t),γ)

[0013] Where D(t) represents the electricity demand at future time t, P hist (t) is the historical power load data, and γ is the future electricity consumption growth rate.

[0014] The power balance constraint condition h i For each constraint h i for:

[0015]

[0016] Among them, P i (t) represents the power generation of the i-th distributed generation at time t, P max,i is the maximum output power of the ith distributed power source, and N is the number of distributed power sources.

[0017] The power spatiotemporal distribution model constructed based on the power generation and spatial coordinates of each distributed power source, and the carbon emission spatiotemporal distribution model constructed based on the spatial coordinates and emissions of each carbon emission source in the carbon emission monitoring data are:

[0018]

[0019] Among them, (x, y) is the spatial coordinate, P(x, y, t) represents the temporal and spatial distribution of power, and P i (x, y, t) represents the power generation of the ith distributed generation at the corresponding spatial position, C(x, y, t) represents the spatiotemporal distribution of carbon emissions, and C i (x, y, t) represents the emission of the i-th carbon emission source at the corresponding spatial position, and N and M are the number of distributed power sources and carbon emission sources, respectively.

[0020] The comprehensive optimization objective function is:

[0021] minF=w1C plan +w2C inv +w3C carbon -w4R renewable

[0022] Among them, C plan is the planning cost, C inv is the investment and operation cost, C carbon For carbon emission targets, R renewable is the proportion of new energy consumption, w1, w2, w3, w4 are the weights of each optimization objective.

[0023] The comprehensive optimization objective function after processing is:

[0024]

[0025] Among them, λ i is the Lagrange multiplier corresponding to each constraint condition.

[0026] The steps of solving the comprehensive optimization objective function are as follows:

[0027] S1. Convert the comprehensive optimization objective function into the following Lagrange multiplier equation:

[0028]

[0029] S2. Update the system solution set X using the gradient descent method i :

[0030]

[0031] Among them, α is the learning rate and k is the number of iterations;

[0032] S3. Update constraints:

[0033]

[0034] Among them, β is the step size parameter;

[0035] S4, iterate until the following stop condition is met:

[0036] And|h i |<∈2

[0037] Among them, ∈1,∈2 are tolerance errors;

[0038] Final solution set X i =P i (x, y, t), i = 1 ~ N is the optimal output distribution strategy of distributed power sources in industrial parks.

[0039] The beneficial effects of the present invention are:

[0040] The technical solution of the present invention comprehensively considers the temporal and spatial distribution characteristics of the park's electricity and carbon emissions by calculating the temporal distribution and spatial distribution of the two elements of electricity and carbon, while also comprehensively considering factors such as the park's energy demand, carbon emissions, investment planning costs, and new energy consumption, so as to optimize the configuration and operation strategies of various distributed energy resources in the park, aiming to achieve efficient utilization of distributed energy resources and significant reduction of carbon emissions in the park through dynamic regulation of flexible resources; this optimization method has important application value in improving the energy supply security of the park, increasing the utilization rate of new energy, and reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of the park resource operation optimization of the present invention.

[0042] Figure 2 It is the energy supply and demand diagram of each month in the park of the present invention.

[0043] Figure 3 This is a graph showing the reduction in carbon emissions before and after the optimization of the present invention.

[0044] Figure 4 It is a comparison chart of operating costs before and after the optimization of the present invention.

[0045] Figure 5 This is a graph showing the changing trend of the renewable energy consumption ratio before and after the optimization of the present invention. DETAILED DESCRIPTION

[0046] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Embodiment 1

[0048] like Figure 1 As shown, a method for optimizing the operation of industrial park resources based on the spatiotemporal distribution characteristics of electricity and carbon in this embodiment includes:

[0049] Obtain historical load data, future electricity consumption growth rate forecast data and carbon emission monitoring data of the industrial park; and calculate future electricity demand based on the above data;

[0050] Construct power balance constraints based on the power generation of distributed power sources in the industrial park and future power demand;

[0051] Establish the spatiotemporal distribution of carbon emissions and the spatiotemporal distribution of electricity based on carbon emission monitoring data and the power generation of distributed power sources; calculate the carbon emission target based on the spatiotemporal distribution of carbon emissions, and calculate the planning cost, investment and operation cost, and new energy consumption ratio based on the spatiotemporal distribution of electricity;

[0052] Construct a comprehensive optimization objective function based on planning cost, investment and operation cost, carbon emission target and new energy consumption ratio, and process the comprehensive optimization objective function through Lagrange multiplier method;

[0053] Solve the comprehensive optimization objective function to obtain the optimal output distribution plan of distributed power sources in the industrial park.

[0054] Embodiment 2

[0055] The optimization method of this embodiment is further designed based on the first embodiment. Figure 1 As shown:

[0056] S1. Obtain the historical load data, future electricity consumption growth rate forecast data and carbon emission monitoring data of the industrial park; and calculate the future electricity demand based on the above data.

[0057] (1) Historical load data: includes the power load curve of the park in the past few years, usually recorded on an hourly or finer time granularity, and the data comes from power monitoring equipment.

[0058] (2) Future growth forecast data: Based on factors such as regional economic growth trends, population growth rate, and industrial development plans, the future growth rate of the park’s electricity demand is predicted.

[0059] (3) Carbon emission monitoring data: including carbon emission data of various equipment, vehicles and process in the park, which are monitored on a monthly or quarterly basis and come from the carbon emission monitoring system.

[0060] After obtaining the above data, the power demand of the park in a certain period in the future is calculated by the following energy demand forecasting model:

[0061] D(t)=f(P hist (t),γ)

[0062] Where D(t) represents the electricity demand at future time t, P hist (t) is the historical power load data, and γ is the future power consumption growth rate. The prediction model can be implemented through linear regression, time series analysis or deep learning algorithm.

[0063] S2. Construct power balance constraints based on the power generation capacity of distributed power sources in the industrial park and future power demand to ensure the balance of power supply and demand at each time point.

[0064] The constraints are:

[0065]

[0066] P i (t)≤P max,i,i=1~N

[0067] Convert the above constraints into a constraint group containing N+1 conditions, where h i For each constraint:

[0068]

[0069] Among them, P i (t) represents the power generation of the i-th distributed generation at time t, P max,i is the maximum output power of the ith distributed generation, N is the number of distributed generation, and T is the optimization time period.

[0070] S3. The power spatiotemporal distribution model constructed based on the power generation and spatial coordinates of each distributed power source, and the carbon emission spatiotemporal distribution model constructed based on the spatial coordinates and emissions of each carbon emission source in the carbon emission monitoring data are respectively:

[0071]

[0072] Among them, (x, y) is the spatial coordinate, P(x, y, t) represents the temporal and spatial distribution of power, and P i (x, y, t) represents the power generation of the ith distributed generation at the corresponding spatial position, C(x, y, t) represents the spatiotemporal distribution of carbon emissions, and C i (x, y, t) represents the emission of the i-th carbon emission source at the corresponding spatial position, and N and M are the number of distributed power sources and carbon emission sources, respectively.

[0073] Then, the carbon emission target is calculated based on the spatiotemporal distribution of carbon emissions, and the planning cost, investment and operation cost, and new energy consumption ratio are calculated based on the spatiotemporal distribution of electricity. Different spatial distributions of distributed power sources have a coefficient for the corresponding planning cost and operation cost per unit power. The total planning cost and investment and operation cost can be obtained by summing the cost of each distributed power source with this coefficient; the new energy consumption ratio can be obtained based on the proportion of new energy in each distributed power source.

[0074] S4. Construct a comprehensive optimization objective function based on planning costs, investment and operating costs, carbon emission targets and new energy consumption ratio, and process the comprehensive optimization objective function through the Lagrange multiplier method.

[0075] The constructed comprehensive optimization objective function is:

[0076] minF=w1C plan +w2C inv +w3C carbon -w4R renewable

[0077] Among them, C planis the planning cost, C inv is the investment and operation cost, C carbon For carbon emission targets, R renewable is the proportion of new energy consumption, w1, w2, w3, w4 are the weights of each optimization target. According to the actual needs of the park, these weight coefficients can be dynamically adjusted to achieve the optimal combination of economic benefits and environmental protection goals.

[0078] The process of solving the optimization strategy for the above objective function is based on dynamic programming and Lagrange multiplier method to deal with multi-objective optimization problems. The optimization strategy first constructs the Lagrangian function, adds the comprehensive optimization objective and constraint condition group to the Lagrangian objective function, and the processed comprehensive optimization objective function is:

[0079]

[0080] Among them, h i are the constraints, λ i is the Lagrange multiplier corresponding to each constraint, which represents the marginal contribution or weight of the constraint to the optimization objective.

[0081] This strategy requires the system solution set X to be optimized. i for:

[0082] X i =P i (x,y,t),i=1~N

[0083] S5. Solve the comprehensive optimization objective function to obtain the optimal output distribution strategy of distributed power sources in the industrial park.

[0084] By solving the Lagrangian function, the optimization problem can be transformed into solving the following set of Lagrangian multiplier equations:

[0085]

[0086] Use an iterative process to solve the solution set X i With the constraint h i To update, the solution steps include:

[0087] (1) Use the gradient descent method to update the system solution set:

[0088]

[0089] Among them, α is the learning rate and k is the number of iterations.

[0090] (2) Update constraints:

[0091]

[0092] Among them, β is the step size parameter.

[0093] (3) Stop conditions:

[0094] Iterate until the following stopping condition is met:

[0095] And|h i |<∈2

[0096] Among them, ∈1,∈2 are the tolerance errors.

[0097] The final X i That is, it is the optimal output strategy for park resources and the optimal output plan for distributed power sources in industrial parks.

[0098] Application Examples

[0099] This article takes an industrial park as an example. The park includes multiple distributed photovoltaic power generation systems, wind power generation equipment, traditional gas generators and energy storage systems. The energy demand in the park fluctuates significantly with the seasons and production needs, and the carbon emissions of the park are also affected by the energy supply structure. In order to minimize carbon emissions and optimize energy costs, the park adopts the resource optimization method of the present invention.

[0100] The steps of implementing the optimization method of the above embodiment are as follows:

[0101] (1) Data Collection

[0102] Historical load data and future growth forecast data: Based on the hourly electricity demand of the park in the past three years, the load curve of the park is obtained.

[0103] Carbon emission monitoring: Combine the carbon emission data of gas-fired generators and other energy consumption processes in the park to monitor the total carbon emissions on a monthly basis.

[0104] Environmental and meteorological data: Obtain data such as wind speed and light intensity to predict the power generation capacity of photovoltaic and wind power.

[0105] like Figure 2 As shown, it shows the electricity demand in the park in each month, as well as the contribution of traditional energy, renewable energy and energy storage systems.

[0106] like Figure 3 As shown, the total carbon emissions, carbon emissions from traditional energy, and carbon emissions from renewable energy in different months before and after optimization are displayed to verify the reduction in carbon emissions.

[0107] like Figure 4 As shown, a comparison of operating costs in different categories before and after optimization is shown, including electricity procurement costs, renewable energy operating costs, carbon emission trading costs, and operating costs of energy storage systems.

[0108] like Figure 5 As shown, the changing trend of the proportion of renewable energy consumption before and after optimization in each month is displayed, indicating the improvement of the utilization rate of new energy after optimization.

[0109] (2) Energy demand forecast

[0110] Through the linear regression and time series analysis model of the park's historical load data, the park's energy demand for the next year is predicted, and the new energy power generation is predicted in combination with meteorological data.

[0111] The monthly data are shown in Table 1.

[0112] Table 1 Monthly energy supply and demand list of the park

[0113]

[0114] (3) Analysis of temporal and spatial distribution characteristics of electric carbon

[0115] Based on the source-grid-load-storage system, calculate the temporal and spatial distribution of electricity and carbon emissions. Use the formula:

[0116]

[0117] Among them, P(x,y,t) and C(x,y,t) are the spatiotemporal distribution of electricity and carbon emissions, respectively, showing the energy supply and emissions in different time periods and regions.

[0118] (4) Optimization goal setting and solution

[0119] The optimization model comprehensively considers the planning cost C plan , operating costs C inv 、Carbon emissions carbon And the proportion of new energy consumption R renewable , the objective function is:

[0120] minF=w1C plan +w2C inv +w3C carbon -w4R renewable

[0121] The Lagrange multiplier method and dynamic programming method are used to solve the optimal strategy for each time period and adjust the operation of distributed power sources and energy storage systems.

[0122] (5) Optimization results analysis

[0123] The following table shows the comparison of carbon emissions, renewable energy utilization and operating costs of the park before and after optimization:

[0124] Table 2 Energy carbon emission results after optimization

[0125]

[0126] Table 3 Comparison of costs before and after optimization

[0127] Cost Type Cost before optimization ($) Optimized cost ($) Cost reduction (%) Electricity procurement costs 120,000 100,000 16.70％ Renewable energy operating costs 40,000 35,000 12.50％ Carbon emissions trading costs 30,000 20,000 33.30％ Energy storage system operating costs 10,000 8,000 20.00％ Total Cost 200,000 163,000 18.50％

[0128] From the above analysis, it can be seen that this solution significantly improves the utilization rate of new energy in the park and reduces carbon emissions. With the synergy of distributed energy systems and energy storage systems, the power supply and demand in the park is more balanced, and the operating costs are significantly reduced. Especially when the fluctuation of new energy power generation is large, the optimized scheduling of the energy storage system effectively smooths the load fluctuation and ensures the stability and economy of power supply. Therefore, this solution has shown high effectiveness and practical value in the application of actual parks, and can provide optimization reference for more industrial parks and commercial areas in the future.

[0129] The technical solutions of the present invention are not limited to the above-mentioned embodiments, and any technical solutions obtained by equivalent replacement methods fall within the scope of protection required by the present invention.

Claims

1. A method for optimizing the operation of industrial park resources based on the temporal and spatial distribution characteristics of electricity and carbon, characterized in that: include: Obtain historical load data of industrial parks, forecast data on future electricity consumption growth rates, and carbon emission monitoring data; and calculate future electricity demand based on the above data; Construct power balance constraints based on the power generation of distributed power sources in the industrial park and future power demand; Establish the spatiotemporal distribution of carbon emissions and power based on carbon emission monitoring data and the power generation of distributed power sources; Calculate carbon emission targets based on the temporal and spatial distribution of carbon emissions, and calculate planning costs, investment and operating costs, and new energy consumption ratios based on the temporal and spatial distribution of electricity; Construct a comprehensive optimization objective function based on planning cost, investment and operation cost, carbon emission target and new energy consumption ratio, and process the comprehensive optimization objective function through Lagrange multiplier method; Solve the comprehensive optimization objective function to obtain the optimal output distribution plan of distributed power sources in the industrial park.

2. The industrial park resource operation optimization method based on the spatiotemporal distribution characteristics of electric carbon according to claim 1 is characterized by: The future electricity demand is: D(t)=f(P hist (t),γ) Where D(t) represents the electricity demand at future time t, P hist (t) is the historical power load data, and γ is the future electricity consumption growth rate.

3. The method for optimizing the operation of industrial park resources based on the temporal and spatial distribution characteristics of electric carbon according to claim 2 is characterized in that: The power balance constraint condition h i For each constraint h i for: Among them, P i (t) represents the power generation of the i-th distributed generation at time t, P max,i is the maximum output power of the ith distributed power source, and N is the number of distributed power sources.

4. The method for optimizing the operation of industrial park resources based on the temporal and spatial distribution characteristics of electric carbon according to claim 3 is characterized by: The power spatiotemporal distribution model constructed based on the power generation and spatial coordinates of each distributed power source, and the carbon emission spatiotemporal distribution model constructed based on the spatial coordinates and emissions of each carbon emission source in the carbon emission monitoring data are: Among them, (x, y) is the spatial coordinate, P(x, y, t) represents the temporal and spatial distribution of power, and P i (x, y, t) represents the power generation of the ith distributed generation at the corresponding spatial position, C(x, y, t) represents the spatiotemporal distribution of carbon emissions, and C i (x, y, t) represents the emission of the i-th carbon emission source at the corresponding spatial position, and N and M are the number of distributed power sources and carbon emission sources, respectively.

5. The method for optimizing the operation of industrial park resources based on the temporal and spatial distribution characteristics of electric carbon according to claim 4 is characterized by: The comprehensive optimization objective function is: minF=w1C plan +w2C inv +w3C carbon -w4R renewable Among them, C plan is the planning cost, C inv is the investment and operation cost, C carbon For carbon emission targets, R renewable is the proportion of new energy consumption, w1, w2, w3, w4 are the weights of each optimization objective.

6. The method for optimizing the operation of industrial park resources based on the temporal and spatial distribution characteristics of electric carbon according to claim 5 is characterized by: The comprehensive optimization objective function after processing is: Among them, λ i is the Lagrange multiplier corresponding to each constraint condition.

7. The method for optimizing the operation of industrial park resources based on the temporal and spatial distribution characteristics of electric carbon according to claim 6 is characterized by: The steps of solving the comprehensive optimization objective function are as follows: S1. Convert the comprehensive optimization objective function into the following Lagrange multiplier equation: S2. Use gradient descent method to update the system solution set X i : Among them, α is the learning rate and k is the number of iterations; S3. Update constraints: Among them, β is the step size parameter; S4, iterate until the following stop condition is met: And|h i |<∈2 Among them, ∈1,∈2 are tolerance errors; Final solution set X i =P i (x, y, t), i = 1 ~ N is the optimal output distribution strategy of distributed power sources in industrial parks.

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