A design method of deep valley electricity price mechanism for new energy consumption

Abstract

The application relates to a design method of a deep valley electricity price mechanism for new energy consumption, which comprises the following steps: obtaining new energy power generation of an electric network in each period; obtaining basic data of a power user, including user load demand in each period, price elasticity of the power user, power limitation of a flexible charging and discharging service provided by an energy storage system, and total capacity limitation of an energy storage battery; establishing a design optimization model of the deep valley electricity price mechanism for new energy consumption; solving the design optimization model of the deep valley electricity price mechanism by using a multi-objective genetic algorithm to obtain an optimization result; and setting period division of the deep valley electricity price and electricity prices in each period according to the optimization result. The application provides a setting mode of the deep valley electricity price mechanism under the condition of large-scale new energy power generation access in the future, gives a method for playing a price incentive role of the electricity price and increasing new energy consumption under the premise of meeting user power demand, and provides a universal calculation strategy which can be widely applied.

Description

Technical Field

[0001] This invention relates to the field of deep-valley electricity pricing mechanism design technology, and in particular to a design method for a deep-valley electricity pricing mechanism oriented towards the consumption of new energy. Background Technology

[0002] With the acceleration of social modernization, the development and utilization of new energy power generation is an important measure for my country to optimize its energy structure and ensure energy security. In recent years, my country's new energy power generation has made significant progress. However, the development of power grid infrastructure construction and power transmission, distribution, and sales mechanisms has lagged behind, leading to increasingly serious problems of wind and solar power curtailment. Therefore, to address this issue, the National Development and Reform Commission (NDRC) pointed out in the "Notice on Further Improving the Time-of-Use Pricing Mechanism (NDRC Price

[2021] No. 1093)" that in areas where combined heat and power (CHP) units and renewable energy capacity account for a large proportion and where the power system experiences a significant period of supply exceeding demand, a deep-valley pricing mechanism can be established, referencing the peak-hour pricing mechanism. The NDRC also emphasized strengthening the connection and coordination between peak-hour and deep-valley pricing mechanisms and electricity demand-side management policies to fully tap the demand-side adjustment capacity.

[0003] With the worsening of environmental problems and the development of related technologies, the large-scale popularization of new energy power generation has become an inevitable trend. New energy power generation has its own unique characteristics, such as the nocturnal nature of photovoltaic power generation and the anti-peak-shaving characteristics of wind power, which increase the likelihood of high generation during midday and evening hours. What were originally peak electricity consumption periods become off-peak periods to accommodate new energy consumption. Therefore, establishing a deep-valley electricity pricing mechanism design and optimization model for new energy consumption has broad prospects. However, how to establish a deep-valley electricity pricing model and how to optimize the time period division and prices for each time period remain unsolved. Summary of the Invention

[0004] The purpose of this invention is to provide a design method for a deep-valley electricity price mechanism oriented towards renewable energy consumption, which fully utilizes price incentive signals to maximize renewable energy consumption while meeting user needs.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a design method for a deep off-peak electricity price mechanism for renewable energy consumption, the method comprising the following sequential steps:

[0006] (1) Obtain the amount of new energy power generation in each time period of the power grid, including wind power generation and photovoltaic power generation;

[0007] (2) Obtain basic data of electricity users: including the load demand of users in each time period, the price elasticity of electricity users, the power limit of energy storage systems willing to provide flexible charging and discharging services, and the total capacity limit of energy storage batteries;

[0008] (3) Based on the data obtained in steps (1) and (2), establish a design optimization model for the deep valley electricity price mechanism for new energy consumption;

[0009] (4) Use a multi-objective genetic algorithm to solve the design optimization model of the valley electricity price mechanism, obtain the optimization results, and set the time period division of valley electricity price and the electricity price of each time period according to the optimization results.

[0010] In step (2), the formula for calculating the price elasticity is:

[0011]

[0012] In the formula, and These are the time periods before and after the implementation of the deep off-peak electricity pricing mechanism. User load, For the time period Electricity price change and Before and after the implementation of the deep off-peak electricity pricing mechanism, during the time period Electricity price, and All of these are price elasticity coefficients.

[0013] The specific step (3) refers to: based on the data obtained in steps (1) and (2), using clustering methods to divide the load membership into deep valley electricity pricing mechanism time periods, the deep valley electricity pricing mechanism time period division includes peak time period, normal time period, valley time period and deep valley time period; and establishing a deep valley electricity pricing mechanism design optimization model based on the deep valley electricity pricing mechanism time period division results and the price elasticity of electricity users.

[0014] The constraints of the deep valley electricity pricing mechanism design optimization model include price constraints and energy storage system charging and discharging constraints. The price constraint of the deep valley electricity pricing mechanism design optimization model is as follows:

[0015]

[0016] in, , , and These are the four electricity prices: off-peak electricity price, low-peak electricity price, normal electricity price, and peak electricity price. Each of these four prices has its own upper and lower limits. The upper and lower limits for the off-peak electricity price are respectively... and The upper and lower limits of off-peak electricity prices are respectively and The upper and lower limits of electricity prices are as follows: and The upper and lower limits of peak electricity prices are respectively and ;

[0017] The charging and discharging constraints of the energy storage system are:

[0018]

[0019] In the formula, and These represent the upper and lower limits of energy constraint for the battery under safe operating conditions. and These represent the upper limits for discharging and charging the energy storage system, respectively; the second formula, or the second constraint, represents the upper limits for the energy storage system during the time period. Simultaneous charging and discharging are not allowed. The third formula, or third constraint, indicates that the energy storage system cannot perform charging and discharging simultaneously during a given time period. The discharge limiting conditions, among which This indicates that the state of charge of the energy storage system is not lower than The fourth formula, or fourth constraint, represents the energy storage system during a given time period. The charging limitations, among which, This indicates that the state of charge of the energy storage system is not higher than ; Improve battery energy storage charging efficiency; For battery energy storage discharge efficiency;

[0020] Energy storage system during time period State of charge The calculation formula is:

[0021]

[0022] The optimization objective of the deep valley electricity pricing mechanism design optimization model is as follows:

[0023] Maximize user satisfaction index:

[0024]

[0025] The user satisfaction index is a weighted sum of the electricity usage habit satisfaction index and the electricity cost expenditure habit satisfaction index; where, These are the weighting coefficients. and Before and after the implementation of off-peak electricity pricing, respectively, during the time period Electricity expenses;

[0026] Minimize wind and solar curtailment rates:

[0027]

[0028] In the formula, and They are respectively in the time period Photovoltaic power generation and wind power generation.

[0029] The clustering method is fuzzy mean C-clustering.

[0030] The load membership degree includes peak membership degree and valley membership degree, and the calculation formula is as follows:

[0031]

[0032]

[0033] In the formula, For peak membership degree, For the valley value membership degree, The load during the trough period, The load during peak hours. and These are the minimum and maximum values ​​of the load, respectively.

[0034] As can be seen from the above technical solution, the beneficial effects of the present invention are as follows: First, the present invention proposes a method for setting up a deep-valley electricity price mechanism in the case of large-scale new energy power generation access in the future; Second, the present invention provides a method to increase the consumption of new energy by leveraging the price incentive effect of electricity prices while meeting users' electricity demand; Third, the present invention provides a universal calculation strategy, which enables the method to be widely applied. Attached Figure Description

[0035] Figure 1 This is a flowchart of the method of the present invention;

[0036] Figure 2 This is the result of the time period division for off-peak electricity pricing in Example 1;

[0037] Figure 3 This is the Pareto frontier for multi-objective optimization of valley electricity pricing in Example 1;

[0038] Figure 4 This is a comparison chart of user load before and after the implementation of off-peak electricity pricing in Example 1. Detailed Implementation

[0039] like Figure 1 As shown, a design method for a deep off-peak electricity pricing mechanism for renewable energy consumption is presented. This method includes the following sequential steps:

[0040] (1) Obtain the amount of new energy power generation in each time period of the power grid, including wind power generation and photovoltaic power generation;

[0041] (2) Obtain basic data of electricity users: including the load demand of users in each time period, the price elasticity of electricity users, the power limit of energy storage systems willing to provide flexible charging and discharging services, and the total capacity limit of energy storage batteries;

[0042] (3) Based on the data obtained in steps (1) and (2), establish a design optimization model for the deep valley electricity price mechanism for new energy consumption;

[0043] (4) Use a multi-objective genetic algorithm to solve the design optimization model of the valley electricity price mechanism, obtain the optimization results, and set the time period division of valley electricity price and the electricity price of each time period according to the optimization results.

[0044] In step (2), the formula for calculating the price elasticity is:

[0045]

[0046] In the formula, and These are the time periods before and after the implementation of the deep off-peak electricity pricing mechanism. User load, For the time period Electricity price change and Before and after the implementation of the deep off-peak electricity pricing mechanism, during the time period Electricity price, and All of these are price elasticity coefficients.

[0047] The specific step (3) refers to: based on the data obtained in steps (1) and (2), using clustering methods to divide the load membership into deep valley electricity pricing mechanism time periods, the deep valley electricity pricing mechanism time period division includes peak time period, normal time period, valley time period and deep valley time period; and establishing a deep valley electricity pricing mechanism design optimization model based on the deep valley electricity pricing mechanism time period division results and the price elasticity of electricity users.

[0048] The constraints of the deep valley electricity pricing mechanism design optimization model include price constraints and energy storage system charging and discharging constraints. The price constraint of the deep valley electricity pricing mechanism design optimization model is as follows:

[0049]

[0050] in, , , and These are the four electricity prices: off-peak electricity price, low-peak electricity price, normal electricity price, and peak electricity price. Each of these four prices has its own upper and lower limits. The upper and lower limits for the off-peak electricity price are respectively... and The upper and lower limits of off-peak electricity prices are respectively and The upper and lower limits of electricity prices are as follows: and The upper and lower limits of peak electricity prices are respectively and ;

[0051] The charging and discharging constraints of the energy storage system are:

[0052]

[0053] In the formula, and These represent the upper and lower limits of energy constraint for the battery under safe operating conditions. and These represent the upper limits for discharging and charging the energy storage system, respectively; the second formula, or the second constraint, represents the upper limits for the energy storage system during the time period. Simultaneous charging and discharging are not allowed. The third formula, or third constraint, indicates that the energy storage system cannot perform charging and discharging simultaneously during a given time period. The discharge limiting conditions, among which This indicates that the state of charge of the energy storage system is not lower than The fourth formula, or fourth constraint, represents the energy storage system during a given time period. The charging limitations, among which, This indicates that the state of charge of the energy storage system is not higher than ; Improve battery energy storage charging efficiency; For battery energy storage discharge efficiency;

[0054] Energy storage system during time period State of charge The calculation formula is:

[0055]

[0056] The optimization objective of the deep valley electricity pricing mechanism design optimization model is as follows:

[0057] Maximize user satisfaction index:

[0058]

[0059] The user satisfaction index is a weighted sum of the electricity usage habit satisfaction index and the electricity cost expenditure habit satisfaction index; where, These are the weighting coefficients. and Before and after the implementation of off-peak electricity pricing, respectively, during the time period Electricity expenses;

[0060] Minimize wind and solar curtailment rates:

[0061]

[0062] In the formula, and They are respectively in the time period Photovoltaic power generation and wind power generation.

[0063] The clustering method is fuzzy mean C-clustering.

[0064] The load membership degree includes peak membership degree and valley membership degree, and the calculation formula is as follows:

[0065]

[0066]

[0067] In the formula, For peak membership degree, For the valley value membership degree, The load during the trough period, The load during peak hours. and These are the minimum and maximum values ​​of the load, respectively.

[0068] Example 1

[0069] This embodiment considers an energy storage system. Its charge / discharge constraints mainly include maximum state of charge (MOC), minimum state of charge (MOC), initial MOC, upper charge / discharge limits, and charge / discharge coefficients, as shown in Table 1. The price constraints and initial price settings for the four time periods of the off-peak pricing mechanism are shown in Table 2. The calculation period is one day, i.e., 24 hours. Data for photovoltaic power generation, wind power generation, and user load for each time period are shown in Table 3. Price elasticity coefficient. and Set to 500 and -150 respectively. User satisfaction weighting coefficient. Set it to 0.5.

[0070] Table 1 Charge and discharge constraints of energy storage system

[0071]

[0072] Table 2. Valley Electricity Price Constraints

[0073]

[0074] Table 3 Data on New Energy Power Generation and User Load

[0075]

[0076] The time-sharing results of the deep valley electricity pricing mechanism obtained using this embodiment are as follows: Figure 2 As shown, the time period division using clustering methods has a certain degree of rationality.

[0077] In this embodiment, the multi-objective optimization Pareto front for valley electricity pricing is as follows: Figure 3As shown, the optimized design model of the deep-valley electricity pricing mechanism for renewable energy consumption reduces wind and solar curtailment rates while maintaining relatively high user satisfaction, achieving real-time renewable energy consumption. A comparison of user load before and after the implementation of deep-valley electricity pricing is shown below. Figure 4 As shown, the implementation of the deep valley electricity price has improved the user load characteristics, including load factor and peak-valley difference.

[0078] In summary, this invention proposes a method for setting up a deep-valley electricity price mechanism in the context of large-scale renewable energy power generation in the future; this invention provides a method that leverages the price incentive effect of electricity prices and increases renewable energy consumption while meeting users' electricity demand; this invention provides a universal calculation strategy, enabling the method to be widely applied.

Claims

1. A design method of a deep valley electricity price mechanism for new energy consumption, characterized in that: The method comprises the following steps in sequence: (1) obtaining the new energy power generation of each time period of the power grid, including wind power generation and photovoltaic power generation; (2) obtaining the basic data of the power user: including the load demand of each time period of the user, the price elasticity of the power user, the power limit of the energy storage system for providing flexible charging and discharging service, and the total capacity limit of the energy storage battery; (3) establishing a deep valley electricity price mechanism design optimization model according to the data obtained in steps (1) and (2); (4) solving the deep valley electricity price mechanism design optimization model by using a multi-objective genetic algorithm to obtain an optimization result, and setting the time period division of the deep valley electricity price and the electricity price of each time period according to the optimization result; In step (2), the calculation formula of the price elasticity is: ， wherein, and are the user load in time period before and after the implementation of the deep valley electricity price mechanism, respectively, is the electricity price change amount in time period , and are the electricity prices in time period before and after the implementation of the deep valley electricity price mechanism, respectively, and are both price elasticity coefficients. The step (3) specifically refers to: according to the data obtained in steps (1) and (2), using a clustering method to perform deep valley electricity price mechanism time period division on the load membership degree, and the deep valley electricity price mechanism time period division includes a peak time period, a flat time period, a valley time period and a deep valley time period; By the deep valley electricity price mechanism time period division result and the price elasticity of the power user, a deep valley electricity price mechanism design optimization model is established; The constraint conditions of the deep valley electricity price mechanism design optimization model include price constraints and energy storage system charging and discharging constraints, wherein the price constraints of the deep valley electricity price mechanism design optimization model are: ， wherein, , , and are deep valley electricity price, low valley electricity price, flat time electricity price and peak electricity price respectively; the upper and lower limits of the four electricity prices are respectively and ; the upper and lower limits of the low valley electricity price are respectively and ; the upper and lower limits of the flat time electricity price are respectively and ; the upper and lower limits of the peak electricity price are respectively and ; The energy storage system charging and discharging constraints are: ， wherein, and are the upper and lower limits of the energy constraint for the battery under safe operation, respectively, and are the upper limits of discharging and charging of the energy storage system, respectively; the second formula, i.e., the second constraint, indicates that the energy storage system cannot simultaneously charge and discharge in the time period ; the third formula, i.e., the third constraint, indicates the discharging limit condition of the energy storage system in the time period , wherein indicates that the state of charge of the energy storage system is not lower than ; the fourth formula, i.e., the fourth constraint, indicates the charging limit condition of the energy storage system in the time period , wherein, indicates that the state of charge of the energy storage system is not higher than ; is the charging efficiency of the battery energy storage; is the discharging efficiency of the battery energy storage; The energy storage system is in a state of charge at a time period The formula for calculating the state of charge is: ， The optimization objectives of the deep valley electricity price mechanism design optimization model are as follows: Maximizing the user satisfaction index: ， The user satisfaction index is a weighted accumulation of the electricity usage habit satisfaction index and the electricity fee expenditure habit satisfaction index; in the formula, is a weight coefficient, and respectively are the electricity fee expenditure habit satisfaction indexes before and after the implementation of the deep valley electricity price in the time period electricity fee expenditure; Minimizing the wind and light curtailment rate: ， wherein and are the photovoltaic and wind power generation, respectively, in the time period photovoltaic and wind power generation.

2. The method of claim 1, wherein the method is characterized by: The clustering method is fuzzy mean C clustering.

3. The method of claim 1, wherein the method is characterized by: The load membership degree includes a peak value membership degree and a valley value membership degree, and the calculation formula is as follows: ， ， wherein is the peak membership, is the valley membership, is the load for the valley period, is the load for the peak period, and are the minimum and maximum values of the load, respectively.

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