Rural area new energy and charging and storage collaborative planning method considering comprehensive power supply cost

Abstract

The invention discloses a rural area new energy and charging and storage collaborative planning method considering comprehensive power supply cost. The method comprises the following steps: establishing a time-of-use electricity price sensitivity model of different types of electric vehicles in a rural area; putting forward charging strategies of different types of electric vehicles in rural areas under the driving of the electricity price sensitivity model and multi-temporal-spatial-scale charging load characteristics under different time-of-use electricity prices; establishing an energy storage capacity standby calculation model for promoting multi-time scale self-balancing of the source charging and storage system; an energy storage charging and discharging strategy daily rolling and real-time optimization calculation model and a solving method for promoting new energy consumption and electric vehicle access under the multi-time scale self-balance of the rural source charging and storage system are provided; proposing a new energy and charging and storage collaborative planning optimization model and a solving method taking the minimum comprehensive power supply cost as a target; according to the invention, energy supply in rural areas can be optimized, the power supply cost is reduced, efficient consumption of new energy is realized, and the power supply reliability and economy are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of coordinated development of new energy and charging and storage in rural areas, and specifically relates to a method for coordinated planning of new energy and charging and storage in rural areas taking into account comprehensive power supply costs. Background Art

[0002] In recent years, distributed renewable energy power generation has been widely used in rural areas, and the penetration rate of renewable energy power generation in some rural areas has increased significantly. However, renewable energy generates more electricity during the day, while rural load electricity consumption is relatively low, resulting in severe absorption difficulties for distributed renewable energy power generation in rural areas. For example, the output of rural renewable energy, which is mainly distributed photovoltaics, is concentrated at noon, while the charging load of electric vehicles, which is mainly distributed charging, is concentrated at night. At the same time, considering that the annual renewable energy power generation and charging volume of typical users differ by about 10 times, the output characteristics of the two are poorly matched, and the source-load mismatch problem in rural areas is more prominent, which will bring about a large number of grid transformation and upgrading and energy storage installation needs, and greatly increase the comprehensive power supply cost in rural areas. During this period, the more prominent source-load mismatch problem caused severe reverse transmission of renewable energy power generation, which had a huge impact on the operation and management of the distribution network.

[0003] On the other hand, as the new energy vehicle going to the countryside continues to deepen, rural areas will face a large number of electric vehicle charging pile access needs in the future. However, the rural power grid is relatively weak and has limited power supply capacity. In order to meet the charging pile access requirements, the rural power grid will face huge investment and transformation needs. At the same time, as new energy gradually becomes the main power source in rural areas, if the electric vehicle charging load surges, the evening peak power balance will face great challenges. Based on the forecast results of the number of electric vehicles in rural areas, combined with the average annual mileage and power consumption per 100 kilometers of various models, it is estimated that by 2030, the peak load of electric vehicles will reach about 290 million kilowatts, more than 20 times that of 2020, and most of it is concentrated during the evening peak of the power grid load, which has a prominent source-load matching problem with distributed photovoltaics.

[0004] In order to solve the above problems, it is necessary to develop a coordinated planning method for new energy and charging and storage in rural areas that takes into account the comprehensive power supply cost. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a method for coordinated planning of new energy and charging and storage in rural areas that takes into account the comprehensive power supply cost, which can optimize the energy supply in rural areas, reduce the power supply cost, achieve efficient consumption of new energy, and improve power supply reliability and economy.

[0006] The object of the present invention is achieved by: a method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs, comprising the following steps:

[0007] S1, analyze the impact of time-of-use electricity prices on different types of electric vehicles in rural areas, and establish a sensitivity model of different types of electric vehicles in rural areas to time-of-use electricity prices;

[0008] S2, proposes charging strategies for different types of electric vehicles in rural areas driven by electricity price sensitivity models and their multi-temporal and spatial-scale charging load characteristics under different time-of-use electricity prices;

[0009] S3, establish a storage capacity reserve calculation model to promote multi-time scale self-balancing of the source-charging-storage system;

[0010] S4, proposes a daily rolling and real-time optimization calculation model and solution method for energy storage charging and discharging strategies to promote new energy consumption and electric vehicle access under multi-time scale self-balancing of rural source charging and storage systems;

[0011] S5, analyzes the comprehensive power supply cost of the rural source-charging-storage self-balancing system considering power supply cost, energy storage equipment cost, power restriction cost, etc., and proposes an optimization model and solution method for the coordinated planning of new energy and charging and storage with the goal of minimizing the comprehensive power supply cost.

[0012] Preferably, step S1 is specifically as follows: first, basic data of electric vehicles in rural areas are collected, including type, quantity and charging habits, as well as specific policies of time-of-use electricity prices in the area; second, through data analysis, the response degree of different types of electric vehicles to time-of-use electricity prices is studied, that is, how changes in electricity prices affect the charging time and charging amount of electric vehicles; finally, based on the response degree analysis, a sensitivity model of electric vehicles to time-of-use electricity prices is established, which can predict the charging behavior of electric vehicles under different electricity prices.

[0013] Preferably, an electricity price elasticity matrix is ​​introduced to describe the user's sensitivity to electricity prices in different time periods, and a sensitivity model of electric vehicles to time-of-use electricity prices is established based on user responsiveness analysis and the electricity price elasticity matrix;

[0014] Among them, user responsiveness refers to the user's sensitivity to changes in electricity prices, which is used to calculate the change in user charging capacity under different electricity prices. The calculation formula is:

[0015]

[0016] That is, calculate the percentage by which the charging capacity decreases when the electricity price increases by 1%;

[0017] The electricity price elasticity matrix is ​​a two-dimensional matrix, one dimension of which represents the time period, including peak, normal and valley, and the other dimension represents the change in electricity price. For each time period and electricity price change, the electricity price elasticity coefficient is calculated. The electricity price elasticity coefficient is used to indicate the impact of electricity price changes on the charging amount in this time period. The calculation formula is:

[0018]

[0019] In the above formula, ∈ ij is the electricity price elasticity coefficient under the i-th time period and the j-th electricity price change;

[0020] Among them, the sensitivity model is used to predict the changes in electric vehicle charging demand under different electricity price policies. The model is expressed as:

[0021] Q = f(P, E, ∈);

[0022] In the above formula, Q is the charging amount, P is the electricity price, E is the user responsiveness, and ∈ is the electricity price elasticity matrix.

[0023] Preferably, step S2 specifically comprises: using the sensitivity model established in step S1 to propose a charging strategy for electric vehicle users in rural areas, including charging during low electricity price periods and avoiding charging during peak hours, and analyzing the multi-temporal and spatial scale charging load characteristics of the charging strategy under different time-of-use electricity prices, that is, the charging load distribution in different time periods and different locations.

[0024] Preferably, step S3 specifically comprises: establishing a storage system capacity reserve calculation model for balancing the grid load based on the volatility of electric vehicle charging load and the intermittent factor of renewable energy, solving the model, and obtaining an optimal storage capacity configuration scheme to meet the supply and demand balance requirements of the grid.

[0025] Preferably, the capacity reserve calculation model is used to ensure that in the power system, while taking into account the charging demand of electric vehicles and the fluctuation of renewable energy power generation, sufficient power generation capacity is maintained to meet the power demand at any point in time; for each time period, the net load is first calculated, and the net load is the total power demand minus the renewable energy power generation; then a confidence level is set to calculate the reserve demand to ensure that at this confidence level, the power system can cope with the fluctuations in demand and power generation; then the total required capacity is calculated, and the total required capacity is the sum of the net load and the reserve capacity; the total adjustable output of the existing renewable energy units is compared with the calculated total required capacity, and when the existing capacity is insufficient, consider adding additional power generation capacity or reducing the demand side load.

[0026] Preferably, step S4 is specifically as follows: first, on the basis of step S3, a charging and discharging strategy for the energy storage system is proposed, and the charging strategy is daily rolling and real-time, and can be quickly adjusted according to the actual situation of the current power grid; secondly, an optimization calculation model is established, which includes minimizing the comprehensive cost as the objective function, power balance and the state of the energy storage device as constraints, and charging and discharging power and energy storage capacity as decision variables; finally, the model is solved by an optimization algorithm to obtain the optimal charging and discharging strategy.

[0027] Preferably, step S5 is specifically as follows: first, analyzing the comprehensive power supply cost of the entire rural source-charging-storage self-balancing system, including power supply cost, energy storage equipment cost and power restriction cost; second, comprehensively considering the power generation of renewable energy, the charging demand of electric vehicles, and the charging and discharging strategy factors of the energy storage system, and establishing a new energy and charging-storage coordinated planning optimization model with the goal of minimizing the comprehensive power supply cost; finally, solving the model through an optimization algorithm to obtain the optimal new energy and charging-storage coordinated planning scheme, so as to realize the economical and efficient operation of the power system in rural areas.

[0028] Due to the adoption of the above technical scheme, the beneficial effects of the present invention are as follows: the present invention adopts a new energy and charging and storage collaborative planning method with the goal of minimizing the comprehensive power supply cost, and encourages users to reduce load power consumption during peak power consumption periods or increase load power consumption during low power consumption periods by studying the demand response strategy of time-of-use electricity prices, thereby optimizing the energy supply in rural areas, reducing power supply costs, achieving efficient consumption of new energy, and improving power supply reliability and economy; the present invention adopts source-charging-storage technology to participate in collaborative planning, which can improve the power quality of new energy power generation, improve the stability of the power grid, weaken the problem of unstable daily power generation capacity of new energy, improve energy utilization efficiency, reduce energy waste, further reduce energy costs, and make energy consumption in rural areas more economical and efficient; BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0031] like Figure 1 As shown, the present invention provides a method for coordinated planning of new energy and charging and storage in rural areas taking into account comprehensive power supply costs, comprising the following steps:

[0032] S1, analyze the impact of time-of-use electricity prices on different types of electric vehicles in rural areas, and establish a sensitivity model of different types of electric vehicles in rural areas to time-of-use electricity prices.

[0033] Firstly, basic data such as the types, quantities and charging habits of electric vehicles in rural areas, as well as the specific policies of time-of-use electricity prices in the area, are collected; secondly, through data analysis, the response degree of different types of electric vehicles to time-of-use electricity prices is studied, that is, how changes in electricity prices affect the charging time and charging amount of electric vehicles; finally, based on the response degree analysis, a sensitivity model of electric vehicles to time-of-use electricity prices is established, which can predict the charging behavior of electric vehicles under different electricity prices.

[0034] The present invention introduces an electricity price elasticity matrix to describe the user's sensitivity to electricity prices in different time periods, and establishes a sensitivity model of electric vehicles to time-of-use electricity prices based on user responsiveness analysis and the electricity price elasticity matrix.

[0035] Among them, user responsiveness refers to the user's sensitivity to changes in electricity prices, which is used to calculate the change in user charging capacity under different electricity prices. The calculation formula is:

[0036]

[0037] That is, calculate the percentage by which the charging amount decreases when the electricity price increases by 1%.

[0038] The electricity price elasticity matrix is ​​a two-dimensional matrix, one dimension of which represents the time period, including peak, normal and valley, and the other dimension represents the change in electricity price. For each time period and electricity price change, the electricity price elasticity coefficient is calculated. The electricity price elasticity coefficient is used to indicate the impact of electricity price changes on the charging amount in this time period. The calculation formula is:

[0039]

[0040] In the above formula, ∈ ij It is the electricity price elasticity coefficient under the i-th time period and the j-th electricity price change.

[0041] Among them, the sensitivity model is used to predict the changes in electric vehicle charging demand under different electricity price policies. The model is expressed as:

[0042] Q = f(P, E, ∈);

[0043] In the above formula, Q is the charging amount, P is the electricity price, E is the user responsiveness, and ∈ is the electricity price elasticity matrix.

[0044] S2, proposes charging strategies for different types of electric vehicles in rural areas driven by an electricity price sensitivity model and their multi-temporal and spatial-scale charging load characteristics under different time-of-use electricity prices.

[0045] Using the sensitivity model established in step S1, charging strategies are proposed for electric vehicle users in rural areas, including charging during low electricity price periods and avoiding charging during peak hours. At the same time, the multi-temporal and spatial scale charging load characteristics of these charging strategies under different time-of-use electricity prices are analyzed, that is, the distribution of charging loads in different time periods and different locations.

[0046] S3, establish a storage capacity reserve calculation model that promotes multi-time scale self-balancing of the source-charging-storage system.

[0047] In order to balance the load of the power grid, a capacity reserve calculation model of the energy storage system is established. This model comprehensively considers factors such as the volatility of electric vehicle charging load and the intermittent nature of renewable energy. By solving the model, the optimal energy storage capacity configuration plan is obtained to meet the supply and demand balance requirements of the power grid.

[0048] Among them, the capacity reserve calculation model is used to ensure that in the power system, taking into account the charging demand of electric vehicles and the fluctuations in new energy power generation, sufficient power generation capacity is maintained to meet the power demand at any point in time; for each time period, the net load is calculated first, and the net load is the total power demand minus the new energy power generation. The total power demand and new energy power generation are predicted by big data analysis technology through historical data of the same period; then a confidence level is set (such as 85%, reserving 15% to cope with fluctuations), and the reserve demand is calculated, and the reserve demand = net load * 15%, to ensure that at this confidence level, the power system can cope with fluctuations in demand and power generation; then the total required capacity is calculated, and the total required capacity is the sum of the net load and the reserve capacity; compare the total adjustable output of the existing new energy units with the calculated total required capacity, and when the existing capacity is insufficient, consider adding additional power generation capacity or reducing the demand side load.

[0049] S4. A daily rolling and real-time optimization calculation model and solution method for energy storage charging and discharging strategies to promote the consumption of new energy and the access of electric vehicles under the multi-time scale self-balancing of rural source-charging-storage systems are proposed.

[0050] Firstly, on the basis of step S3, a charging and discharging strategy for the energy storage system is proposed. The charging strategy is daily rolling and adjusted every day according to the data of the previous day. The charging strategy is also real-time, that is, it can be quickly adjusted according to the actual situation of the current power grid; secondly, an optimization calculation model is established, which includes minimizing the comprehensive cost as the objective function, power balance and energy storage equipment status as constraints, and charging and discharging power and energy storage capacity as decision variables; finally, the model is solved by the optimization algorithm to obtain the optimal charging and discharging strategy.

[0051] This step focuses on the daily rolling and real-time optimization calculation model and solution method of the energy storage charging and discharging strategy. According to the actual operation of the power grid, the charging and discharging strategy of the energy storage system can be dynamically adjusted to promote the effective consumption of system energy and the orderly access of electric vehicles.

[0052] S5, analyzes the comprehensive power supply cost of the rural source-charging-storage self-balancing system considering power supply cost, energy storage equipment cost, power restriction cost, etc., and proposes an optimization model and solution method for the coordinated planning of new energy and charging and storage with the goal of minimizing the comprehensive power supply cost.

[0053] Firstly, the comprehensive power supply cost of the entire rural source-charging-storage self-balancing system is analyzed, including power supply cost (such as electricity purchase cost), energy storage equipment cost (such as investment and maintenance cost) and power restriction cost (such as production loss caused by power shortage). Secondly, considering multiple factors such as renewable energy power generation, electric vehicle charging demand, and energy storage system charging and discharging strategy, an optimization model of new energy and charging-storage coordinated planning is established with the goal of minimizing the comprehensive power supply cost. Finally, the model is solved by optimization algorithm to obtain the optimal new energy and charging-storage coordinated planning scheme, so as to realize the economic and efficient operation of power systems in rural areas.

[0054] This step focuses on the comprehensive power supply cost of the rural source-charging-storage self-balancing system, and proposes a new energy and charging-storage collaborative planning optimization model and solution method with the goal of minimizing the comprehensive power supply cost. It not only considers the investment and maintenance costs of energy storage equipment, but also considers multiple economic indicators such as power supply cost and power restriction cost. It aims to reduce the operating cost of the entire power system by optimizing the collaborative working mode of new energy and energy storage system.

[0055] In summary, the present invention collects and organizes relevant data such as energy demand and supply, time-of-use electricity prices, temperature changes, and energy consumption during key periods of agricultural production in rural areas; quantifies these factors through data analysis and modeling, and studies the correlation and influence between the data; simulates and evaluates the changes in different factors to analyze their impact on collaborative configuration plans and operating strategies; determines key factors, optimizes configuration plans and strategies; optimizes the design of renewable energy and energy storage systems in rural areas by using optimization algorithms, such as linear programming, integer programming, genetic algorithms, etc.; and determines the optimal equipment configuration, energy storage capacity planning, and charging and discharging strategies with the goal of minimizing the comprehensive power supply cost by establishing a mathematical model containing multiple factors.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs, characterized in that: The steps include: S1, analyze the impact of time-of-use electricity prices on different types of electric vehicles in rural areas, and establish a sensitivity model of different types of electric vehicles in rural areas to time-of-use electricity prices; S2, proposes charging strategies for different types of electric vehicles in rural areas driven by electricity price sensitivity models and their multi-temporal and spatial-scale charging load characteristics under different time-of-use electricity prices; S3, establish a storage capacity reserve calculation model to promote multi-time scale self-balancing of the source-charging-storage system; S4, proposes a daily rolling and real-time optimization calculation model and solution method for energy storage charging and discharging strategies to promote new energy consumption and electric vehicle access under multi-time scale self-balancing of rural source charging and storage systems; S5, analyzes the comprehensive power supply cost of the rural source-charging-storage self-balancing system considering power supply cost, energy storage equipment cost, power restriction cost, etc., and proposes an optimization model and solution method for the coordinated planning of new energy and charging and storage with the goal of minimizing the comprehensive power supply cost.

2. The method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs according to claim 1 is characterized in that: The step S1 is specifically as follows: first, collect basic data of electric vehicles in rural areas, including type, quantity and charging habits, as well as specific policies of time-of-use electricity prices in the area; second, through data analysis, study the response degree of different types of electric vehicles to time-of-use electricity prices, that is, how changes in electricity prices affect the charging time and charging amount of electric vehicles; finally, based on the response degree analysis, establish a sensitivity model of electric vehicles to time-of-use electricity prices, which can predict the charging behavior of electric vehicles under different electricity prices.

3. The method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs according to claim 2 is characterized by: The electricity price elasticity matrix is ​​introduced to describe the user's sensitivity to electricity prices in different time periods. Based on the user responsiveness analysis and the electricity price elasticity matrix, a sensitivity model of electric vehicles to time-of-use electricity prices is established. Among them, user responsiveness refers to the user's sensitivity to changes in electricity prices, which is used to calculate the change in user charging capacity under different electricity prices. The calculation formula is: That is, calculate the percentage by which the charging capacity decreases when the electricity price increases by 1%; The electricity price elasticity matrix is ​​a two-dimensional matrix, one dimension of which represents the time period, including peak, normal and valley, and the other dimension represents the change in electricity price. For each time period and electricity price change, the electricity price elasticity coefficient is calculated. The electricity price elasticity coefficient is used to indicate the impact of electricity price changes on the charging amount in this time period. The calculation formula is: In the above formula, ∈ ij is the electricity price elasticity coefficient under the i-th time period and the j-th electricity price change; Among them, the sensitivity model is used to predict the changes in electric vehicle charging demand under different electricity price policies. The model is expressed as: Q = f(P, E, ∈); In the above formula, Q is the charging amount, P is the electricity price, E is the user responsiveness, and ∈ is the electricity price elasticity matrix.

4. The method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs according to claim 1 is characterized in that: The step S2 specifically includes: using the sensitivity model established in step S1, proposing a charging strategy for electric vehicle users in rural areas, including charging during low electricity price periods and avoiding charging during peak hours, and analyzing the multi-temporal and spatial scale charging load characteristics of the charging strategy under different time-of-use electricity prices, that is, the distribution of charging loads in different time periods and different locations.

5. The method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs according to claim 1 is characterized in that: The step S3 is specifically as follows: based on the volatility of electric vehicle charging load and the intermittent factor of renewable energy, a storage system capacity reserve calculation model for balancing the grid load is established, and the model is solved to obtain the optimal storage capacity configuration plan to meet the supply and demand balance requirements of the grid.

6. The method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs according to claim 5 is characterized by: The capacity reserve calculation model is used to ensure that in the power system, sufficient power generation capacity is maintained to meet the power demand at any point in time, taking into account the charging demand of electric vehicles and the fluctuation of renewable energy power generation. For each time period, the net load is calculated first, which is the total power demand minus the renewable energy power generation; Then, a confidence level is set and the reserve requirement is calculated to ensure that the power system can cope with fluctuations in demand and generation at this confidence level. The total required capacity is then calculated, which is the sum of the net load and the reserve capacity. Compare the total adjustable output of existing renewable energy units with the calculated total required capacity. When the existing capacity is insufficient, consider adding additional generating capacity or reducing the demand-side load.

7. The method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs according to claim 1 is characterized in that: The step S4 is specifically as follows: first, on the basis of step S3, a charging and discharging strategy for the energy storage system is proposed, and the charging strategy is daily rolling and real-time, and can be quickly adjusted according to the actual situation of the current power grid; secondly, an optimization calculation model is established, which includes minimizing the comprehensive cost as the objective function, power balance and the state of the energy storage device as constraints, and charging and discharging power and energy storage capacity as decision variables; finally, the model is solved by an optimization algorithm to obtain the optimal charging and discharging strategy.

8. The method for coordinated planning of new energy and charging and storage in rural areas considering comprehensive power supply costs according to claim 1 is characterized in that: The step S5 is specifically as follows: first, analyzing the comprehensive power supply cost of the entire rural source-charging-storage self-balancing system, including power supply cost, energy storage equipment cost and power restriction cost; second, comprehensively considering the power generation of renewable energy, the charging demand of electric vehicles, and the charging and discharging strategy factors of the energy storage system, and establishing a new energy and charging-storage coordinated planning optimization model with the goal of minimizing the comprehensive power supply cost; finally, solving the model through an optimization algorithm to obtain the optimal new energy and charging-storage coordinated planning scheme, so as to realize the economical and efficient operation of the power system in rural areas.

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