Rural electric power pricing transaction strategy

Abstract

The invention discloses a rural electric power pricing transaction strategy, which relates to the technical field of electric power pricing, and comprises the following steps: establishing a peak avoidance response coefficient and an agricultural production excitation coefficient according to the production characteristics of agricultural users and the power generation characteristics of photovoltaic and biomass energy; on the basis of formulating an electricity selling package, power consumers are stimulated and attracted by peak and valley electricity prices to actively transfer an electricity load; aiming at the analysis of the electricity consumption and the electricity consumption time of different users, the designed electricity consumption package comprises a time-of-use electricity price package, a step electricity price package and a fixed discount package, the electricity consumption package is aimed at three types of users, namely agricultural users, mixed agricultural users and non-agricultural users, and the rural electricity pricing transaction strategy has the advantages that the electricity consumption and the electricity consumption time of different users are analyzed; production characteristics of agricultural users are fully considered, a peak-avoiding response coefficient and an agricultural production excitation coefficient are proposed, the power users are excited and attracted by peak-valley electricity prices on the basis of formulating an electricity selling package, and benefits of the power users and benefits of power companies are taken into consideration.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity pricing, and in particular to a rural electricity pricing transaction strategy. Background Art

[0002] With the development of smart electricity consumption technology, the control potential of the user side has been gradually released. As one of the main measures for load demand response, real-time electricity price can reflect the real-time supply and demand status of electric energy and guide the user's electricity consumption behavior. At present, there have been a lot of studies on the pricing measures of real-time electricity price, which are mainly divided into real-time electricity price algorithm based on energy consumption scheduling theory, real-time electricity price algorithm based on statistical demand elasticity model, real-time electricity price algorithm based on benefit model, real-time electricity price algorithm based on congestion management, etc. The above algorithms price real-time electricity price from different angles, but only consider the grid operation status of the corresponding time section. In fact, different real-time electricity prices will produce different load response degrees, and different load curves will affect the operation mode and economic efficiency of the grid. Therefore, real-time electricity price and the economic operation of the grid are mutually affected, and they should be considered in a comprehensive manner when pricing real-time electricity price. However, the current real-time electricity price is based on the real-time supply and demand relationship, and the time scale is mostly hourly, with high flexibility and high electricity elasticity. However, from the analysis of long time scale (week, month, season), the real-time electricity price lacks certain stability. Therefore, a rural electricity pricing transaction strategy is proposed. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention provides a rural electricity pricing transaction strategy, which solves the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a rural electricity pricing and trading strategy, which sets up a peak avoidance response coefficient and an agricultural production incentive coefficient according to the production characteristics of agricultural users and the power generation characteristics of photovoltaic and biomass energy, and uses peak and valley electricity price incentives to attract electricity users to actively transfer their electricity loads on the basis of formulating electricity sales packages; based on the analysis of electricity consumption and electricity consumption time of different users, the designed electricity packages include time-of-use electricity price packages, tiered electricity price packages and fixed discount packages, and the electricity packages are targeted at three types of users, namely agricultural users, mixed agricultural users, and non-agricultural users.

[0005] Optionally, before the peak avoidance response coefficient, the power generation characteristics of photovoltaic and biomass energy are considered, so the photo-biomass peak avoidance response coefficient and the agricultural production peak avoidance response coefficient are proposed:

[0006] The light-induced peak avoidance response coefficient is set as follows:

[0007]

[0008] Among them, CPV-Bio (t): agricultural peak avoidance response coefficient, value at time t; P Bio (t): biomass power generation power at time t (biomass power generation output power, which can be regarded as a threshold); P PV，max (t): the maximum output power of photovoltaic power generation at time t (assuming the maximum value of photovoltaic power generation); α and β are adjustable parameters that control the sensitivity of biomass power generation to photovoltaic power generation regulation;

[0009] The agricultural production peak avoidance response coefficient: the setting formula is as follows:

[0010]

[0011] Among them, C Agriculture (t): Agricultural peak avoidance response coefficient, value at time t; P Agriculture (t): agricultural electricity demand at time t (usually the electricity demand for activities such as irrigation and agricultural production); P Agriculture,max (t): the maximum power demand for agricultural electricity at time t (based on historical data and seasonal demand forecast); P pv (t): photovoltaic power generation at time t; P pv.,max (t): Maximum output power of photovoltaic power generation at time t, P Bio (t): Biomass power generation at time t, P Bio,max (t): maximum output power of biomass power generation at time t, γ1, γ2, γ3, γ4: adjustment parameters to control the impact of various factors on the agricultural peak avoidance coefficient.

[0012] Optionally, the peak avoidance response coefficient:

[0013]

[0014] Where: μ′ i is the peak-avoidance response coefficient of user i before package optimization; a is the weighted coefficient of electricity consumption during the off-peak period; q′ i,f ,q′ i,p ,q′ i,g are the electricity consumption of user i in peak hours, normal hours, and valley hours before package optimization;

[0015] In order to obtain package incentives, power users need to adjust their own electricity consumption. As the proportion of electricity consumption during normal and valley periods increases, the peak-avoidance response coefficient of the user also increases accordingly. Therefore, based on the initial electricity load, the power user transfers the electricity consumption during peak periods to normal and valley periods, so that the peak-avoidance response coefficient of power user i after load transfer is equal to the standard value of the peak-avoidance response coefficient of the package selected by the power user, then:

[0016]

[0017] Δq ij,g , Δq ij,p They are the transferred electricity consumption in valley section and flat section respectively.

[0018] r ij =(r f q ij,f +r p q ij,p +r g q ij,g )x ij (5)

[0019] x ij It is the preferential coefficient for peak and valley electricity prices, and the specific value of the peak and valley electricity price peak avoidance coefficient.

[0020] Optionally, the agricultural production incentive coefficient: the daily load curve of each residential user is clustered and analyzed by a density clustering algorithm, and the users are clustered into three categories. The load peak of typical agricultural users is mainly during the day, mixed agricultural users have a peak period in the morning and afternoon, and non-agricultural users have three peak periods in the morning, afternoon and evening. The electricity consumption of non-agricultural users is relatively smooth. The agricultural users have large fluctuations in electricity load and no base load; the mixed agricultural users have a basic electricity consumption of 30kw·h, and the electricity load changes greatly during the agricultural electricity season; the non-agricultural users have a basic daily electricity consumption of 1000kw·h, and the load is small during the agricultural electricity season;

[0021]

[0022] For the electricity consumption during the agricultural electricity consumption period, the electricity price is optimized according to the daily load deviation. During the peak period of agricultural electricity consumption, a reward function is added. For the transfer load Δp in agricultural electricity consumption, the percentage of Δp in the total power is calculated, and the threshold of the package and the electricity price are reduced within the scope permitted by the policy;

[0023] For agricultural quarterly long-term electricity packages, for time-of-use electricity prices: For tiered electricity prices: For fixed discount packages:

[0024] in, are the electricity prices after selecting the reward discount package, β t * It is the conversion factor after the reward is converted.

[0025] Optionally, the time-of-use electricity price package: users are charged for electricity consumption according to different time periods, and the electricity consumption period is divided into three periods: peak, valley and flat, as follows: the peak period is T P={11:00-12:00, 14:00-21:00}; the normal time period is T F ={07:00-11:00, 12:00-14:00, 21:00-23:00}; the valley period is T V = {23:00-07:00}, the peak period electricity price is the highest, the normal period is the second, and the valley period is the lowest. The electricity price in each period is r t Ι for:

[0026]

[0027] Where: r p 、r F 、r V The electricity prices during peak, flat and valley periods are RMB / kWh; T P 、T F 、T V They are peak time, normal time and valley time respectively;

[0028] Among them, the electricity cost of user i choosing the tiered electricity price package is for:

[0029]

[0030] In the formula, Q i.t is the electricity consumption of rural photovoltaic user i during period t;

[0031] The revenue that the time-of-use electricity price package brings to the power grid C U1 for:

[0032]

[0033] Where: n U1 The number of users who choose the time-of-use electricity price package; The electricity cost (yuan) of choosing a time-of-use electricity price package for rural PV user i.

[0034] Optionally, the tiered electricity price package: different electricity consumption intervals are set, and the electricity price in each interval is different, regardless of the electricity consumption period, and the electricity fee is settled according to the electricity consumption. The electricity price of this package is p ΙΙ for:

[0035]

[0036] Where: is the electricity price in different power consumption ranges; Q i is the electricity consumption of user i; Q Ι , Q ΙΙ is the power consumption threshold in different intervals;

[0037] Among them, the electricity cost c of user i choosing the tiered electricity price package is i U2 for:

[0038]

[0039] The income C obtained by the power grid through the tiered electricity price package U2 for:

[0040]

[0041] Where: n U2 The number of users who choose the tiered electricity price package.

[0042] Optionally, the fixed discount package: when the electricity consumption is greater than Q0, the portion less than the preferential electricity consumption Q0 can enjoy a discount, and the portion greater than Q0 is charged at the original price p ΙIΙ If the electricity consumption is less than Q0, the electricity fee will be paid according to the electricity consumption of Q0;

[0043]

[0044] Electricity cost of rural photovoltaic user i choosing a fixed discount package for:

[0045]

[0046] Where: κ is the discount coefficient for the part of electricity consumption not exceeding Q0;

[0047] Fixed discount package brings revenue to the grid C U3 for:

[0048]

[0049] Where: n U3 The number of users who choose the fixed discount package.

[0050] The present invention provides a rural electricity pricing transaction strategy, which has the following beneficial effects:

[0051] This rural electricity pricing and trading strategy fully considers the production characteristics of agricultural users, proposes a peak avoidance response coefficient and an agricultural production incentive coefficient, and uses peak and valley electricity price incentives on the basis of formulating electricity sales packages to attract electricity users to actively transfer their electricity loads, which not only protects the interests of electricity users, but also takes into account the interests of power companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a typical agricultural user electricity consumption characteristic diagram of the present invention;

[0053] Figure 2 It is the electricity consumption characteristic diagram of mixed agricultural users of the present invention;

[0054] Figure 3 This is the electricity consumption characteristic diagram of non-agricultural users in the present invention. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present invention will be described clearly and completely 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 present invention, rather than all the embodiments.

[0056] See also Figures 1 to 3 The present invention provides a technical solution: a rural electricity pricing and trading strategy, which sets up a peak avoidance response coefficient and an agricultural production incentive coefficient according to the production characteristics of agricultural users and the power generation characteristics of photovoltaic and biomass energy, and uses peak and valley electricity price incentives to attract electricity users to actively transfer their electricity loads on the basis of formulating electricity sales packages; based on the analysis of electricity consumption and electricity consumption time of different users, the designed electricity packages include time-of-use electricity price packages, tiered electricity price packages and fixed discount packages, and the electricity packages are targeted at three types of users, namely agricultural users, mixed agricultural users, and non-agricultural users.

[0057] Before the peak avoidance response coefficient, the power generation characteristics of photovoltaic and biomass energy are considered, so the photo-biomass peak avoidance response coefficient and the agricultural production peak avoidance response coefficient are proposed:

[0058] Photo-induced peak response coefficient: The setting formula is as follows:

[0059]

[0060] Among them, C PV-Bio (t): agricultural peak avoidance response coefficient, value at time t; P Bio (t): biomass power generation power at time t (biomass power generation output power, which can be regarded as a threshold); P PV，max (t): the maximum output power of photovoltaic power generation at time t (assuming the maximum value of photovoltaic power generation); α and β are adjustable parameters that control the sensitivity of biomass power generation to photovoltaic power generation regulation;

[0061] Agricultural production peak avoidance response coefficient: The setting formula is as follows:

[0062]

[0063] Among them, C Agriculture (t): Agricultural peak avoidance response coefficient, value at time t; P Agriculture (t): agricultural electricity demand at time t (usually the electricity demand for activities such as irrigation and agricultural production); P Agriculture,max (t): the maximum power demand for agricultural electricity at time t (based on historical data and seasonal demand forecast); Ppv (t): photovoltaic power generation at time t; P pv.,max (t): Maximum output power of photovoltaic power generation at time t, P Bio (t): Biomass power generation at time t, P Bio,max (t): maximum output power of biomass power generation at time t, γ1, γ2, γ3, γ4: adjustment parameters to control the impact of various factors on the agricultural peak avoidance coefficient.

[0064] Peak avoidance response coefficient:

[0065]

[0066] Where: μ′ i is the peak-avoidance response coefficient of user i before package optimization; a is the weighted coefficient of electricity consumption during the off-peak period; q′ i,f ,q′ i,p ,q′ i,g are the electricity consumption of user i in peak hours, normal hours, and valley hours before package optimization;

[0067] In order to obtain package incentives, power users need to adjust their own electricity consumption. As the proportion of electricity consumption during normal and valley periods increases, the peak-avoidance response coefficient of the user also increases accordingly. Therefore, based on the initial electricity load, the power user transfers the electricity consumption during peak periods to normal and valley periods, so that the peak-avoidance response coefficient of power user i after load transfer is equal to the standard value of the peak-avoidance response coefficient of the package selected by the power user, then:

[0068]

[0069] Δq ij,g , Δq ij,p They are the transferred electricity consumption in valley section and flat section respectively.

[0070] r ij =(r f q ij,f +r p q ij,p +r g q ij,g )x ij (5)

[0071] x ij It is the preferential coefficient for peak and valley electricity prices, and the specific value of the peak and valley electricity price peak avoidance coefficient.

[0072] Agricultural production incentive coefficient: The power grid's subsidies for agricultural electricity are very high, which are already lower than the grid's minimum recycling price. From the perspective of the cost-effectiveness of power generation, the price of agricultural electricity is optimized, and the subsidy is replaced by the electricity price after the incentive coefficient, and a quarterly package is proposed for rural agricultural producers;

[0073] The density clustering algorithm is used to cluster the daily load curves of each residential user, and the users are clustered into three categories. The load peak of typical agricultural users is mainly during the day, mixed agricultural users have a peak period in the morning and afternoon, and non-agricultural users have three peak periods in the morning, afternoon and evening. The electricity consumption of non-agricultural users is relatively smooth. For agricultural users, the electricity load fluctuates greatly and there is no base load; for mixed agricultural users, the basic electricity consumption is 30kw·h, and the electricity load changes greatly during the agricultural electricity season; for non-agricultural users, the basic electricity consumption is 1000kw·h per day, and the load is small during the agricultural electricity season;

[0074]

[0075] For the electricity consumption during the agricultural electricity consumption period, the electricity price is optimized according to the daily load deviation. During the peak period of agricultural electricity consumption, a reward function is added. For the transfer load Δp in agricultural electricity consumption, the percentage of Δp in the total power is calculated, and the threshold of the package and the electricity price are reduced within the scope permitted by the policy;

[0076] For agricultural quarterly long-term electricity packages, for time-of-use electricity prices: For tiered electricity prices: For fixed discount packages:

[0077] in, are the electricity prices after selecting the reward discount package, β t * is the conversion factor after the reward is converted;

[0078] Combined with meteorological data such as rainfall, the general behavior of agricultural irrigation is summarized. For example, the overall correlation between agricultural irrigation electricity consumption and rainfall is strong. When it rains, the photovoltaic output is low or there is no photovoltaic output, and agriculture does not need irrigation. On sunny days, when the photovoltaic output is strong, agriculture generally needs irrigation, which is almost consistent with the output characteristics of photovoltaic power generation. The fuzzy membership function is used to calculate the peak and valley membership of each type of user in each time period. The peak and valley time division of the load curves of the three types of users is obtained through density clustering. According to the demand response division results, the following peak and valley electricity prices are obtained in the peak season of agricultural electricity consumption;

[0079] 1. Typical agricultural users: Typical agricultural users consume electricity intensively in March, July and November, and consume relatively less electricity in other months. There is no base load;

[0080] 2. Mixed agricultural users: Mixed agricultural users use more than 1,000 kWh of electricity per month, with relatively more electricity consumption from March to June and less electricity consumption in February;

[0081] 3. Non-agricultural users: Non-agricultural users consume more than 2000 kWh of electricity per month, with a relatively stable monthly electricity consumption, and electricity consumption in summer is slightly higher than in other months;

[0082] Time-of-use electricity price package: users are charged for electricity consumption according to different time periods, and the electricity consumption period is divided into three periods: peak, valley and flat, as follows: Peak period is T P ={11:00-12:00, 14:00-21:00}; the normal time period is T F ={07:00-11:00, 12:00-14:00, 21:00-23:00}; the valley period is T V = {23:00-07:00}, the peak period electricity price is the highest, the normal period is the second, and the valley period is the lowest. The electricity price in each period is r t Ι for:

[0083]

[0084] Where: r p 、r F 、r V The electricity prices during peak, flat and valley periods are RMB / kWh; T P 、T F 、T V They are peak time, normal time and valley time respectively;

[0085] Among them, the electricity cost of user i choosing the tiered electricity price package is for:

[0086]

[0087] In the formula, Q i.t is the electricity consumption of rural photovoltaic user i during period t;

[0088] The revenue that the time-of-use electricity price package brings to the power grid C U1 for:

[0089]

[0090] Where: n U1 The number of users who choose the time-of-use electricity price package; The electricity cost (yuan) of choosing a time-of-use electricity price package for rural PV user i.

[0091] Ladder electricity price package: set different electricity consumption intervals, different electricity prices for each interval, regardless of the time of electricity consumption, and settle electricity charges based on electricity consumption. The electricity price of this package is p ΙΙ for:

[0092]

[0093] Where: is the electricity price in different power consumption ranges; Q i is the electricity consumption of user i; Q Ι , Q ΙΙ is the power consumption threshold in different intervals;

[0094] Among them, the electricity cost of user i choosing the tiered electricity price package is for:

[0095]

[0096] The income C obtained by the power grid through the tiered electricity price package U2 for:

[0097]

[0098] Where: n U2 The number of users who choose the tiered electricity price package.

[0099] Fixed discount package: When the electricity consumption is greater than Q0, the portion less than the preferential electricity consumption Q0 can enjoy a discount, and the portion greater than Q0 is charged at the original price p ΙIΙ If the electricity consumption is less than Q0, the electricity fee will be paid according to the electricity consumption of Q0;

[0100]

[0101] Electricity cost of rural photovoltaic user i choosing a fixed discount package for:

[0102]

[0103] Where: κ is the discount coefficient for the part of electricity consumption not exceeding Q0;

[0104] Fixed discount package brings revenue to the grid C U3 for:

[0105]

[0106] Where: n U3 The number of users who choose the fixed discount package.

[0107] Table 1 Package incentive coefficient values

[0108]

[0109] Table 2 Electricity consumption thresholds for tiered electricity prices and fixed discount packages

[0110]

[0111] Table 3 Electricity price package settings of power sales companies

[0112]

[0113]

[0114] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A rural electricity pricing and trading strategy, characterized by: According to the production characteristics of agricultural users, as well as the power generation characteristics of photovoltaic and biomass energy, peak-avoidance response coefficients and agricultural production incentive coefficients are established. On the basis of formulating electricity sales packages, peak-valley electricity price incentives are used to attract electricity users to actively shift their electricity loads. Based on the analysis of electricity consumption and electricity consumption time of different users, the designed electricity packages include time-of-use electricity price packages, tiered electricity price packages and fixed discount packages. The electricity packages are aimed at three types of users, namely agricultural users, mixed agricultural users and non-agricultural users.

2. A rural electricity pricing transaction strategy according to claim 1, characterized in that: Before the peak avoidance response coefficient is proposed, the power generation characteristics of photovoltaic and biomass energy are considered, so the photo-biomass peak avoidance response coefficient and the agricultural production peak avoidance response coefficient are proposed: The light-induced peak avoidance response coefficient is set as follows: Among them, C PV-Bio (t): agricultural peak avoidance response coefficient, value at time t; P Bio (t): biomass power generation power at time t (biomass power generation output power, which can be regarded as a threshold); P PV，max (t): the maximum output power of photovoltaic power generation at time t (assuming the maximum value of photovoltaic power generation); α and β are adjustable parameters that control the sensitivity of biomass power generation to photovoltaic power generation regulation; The agricultural production peak avoidance response coefficient: the setting formula is as follows: Among them, C Agriculture (t): Agricultural peak avoidance response coefficient, value at time t; P Agriculture (t): agricultural electricity demand at time t (usually the electricity demand for activities such as irrigation and agricultural production); P Agriculture,max (t): the maximum power demand for agricultural electricity at time t (based on historical data and seasonal demand forecast); P pv (t): photovoltaic power generation at time t; P pv.,max (t): Maximum output power of photovoltaic power generation at time t, P Bio (t): Biomass power generation at time t, P Bio,max (t): maximum output power of biomass power generation at time t, γ1, γ2, γ3, γ4: adjustment parameters to control the impact of various factors on the agricultural peak avoidance coefficient.

3. A rural electricity pricing transaction strategy according to claim 1, characterized in that: The peak avoidance response coefficient: Where: μ′ i is the peak-avoidance response coefficient of user i before package optimization; a is the weighted coefficient of electricity consumption during the off-peak period; q′ i,f ,q′ i,p ,q′ i,g are the electricity consumption of user i in peak hours, normal hours, and valley hours before package optimization; In order to obtain package incentives, power users need to adjust their own electricity consumption. As the proportion of electricity consumption during normal and valley periods increases, the peak-avoidance response coefficient of the user also increases accordingly. Therefore, based on the initial electricity load, the power user transfers the electricity consumption during peak periods to normal and valley periods, so that the peak-avoidance response coefficient of power user i after load transfer is equal to the standard value of the peak-avoidance response coefficient of the package selected by the power user, then: Δq ij,g , Δq ij,p They are the transferred electricity consumption in valley section and flat section respectively. r ij =(r f q ij,f +r p q ij,p +r g q ij,g )x ij (5) x ij It is the preferential coefficient for peak and valley electricity prices, and the specific value of the peak and valley electricity price peak avoidance coefficient.

4. A rural electricity pricing transaction strategy according to claim 1, characterized in that: The agricultural production incentive coefficient: the daily load curves of each residential user are clustered and analyzed through the density clustering algorithm, and the users are clustered into three categories. The load peak of typical agricultural users is mainly during the day, mixed agricultural users have a peak period in the morning and afternoon, and non-agricultural users have three peak periods in the morning, afternoon and evening. The electricity consumption of non-agricultural users is relatively smooth. The agricultural users have large fluctuations in electricity load and no base load; the mixed agricultural users have a basic electricity consumption of 30kw·h, and the electricity load changes greatly during the agricultural electricity season; the non-agricultural users have a basic daily electricity consumption of 1000kw·h, and the load is small during the agricultural electricity season; For the electricity consumption during the agricultural electricity consumption period, the electricity price is optimized according to the daily load deviation. During the peak period of agricultural electricity consumption, a reward function is added. For the transfer load Δp in agricultural electricity consumption, the percentage of Δp in the total power is calculated, and the threshold of the package and the electricity price are reduced within the scope permitted by the policy; For agricultural quarterly long-term electricity packages, for time-of-use electricity prices: For tiered electricity prices: For fixed discount packages: in, are the electricity prices after selecting the reward discount package, β t * It is the conversion factor after reward conversion.

5. A rural electricity pricing transaction strategy according to claim 1, characterized in that: The time-of-use electricity price package: users are charged for electricity consumption according to different time periods, and the electricity consumption period is divided into three periods: peak, valley and flat, as follows: the peak period is T P ={11:00-12:00, 14:00-21:00}; the normal time period is T F ={07:00-11:00, 12:00-14:00, 21:00-23:00}; the valley period is T V = {23:00-07:00}, the peak period electricity price is the highest, the normal period is the second, and the valley period is the lowest. The electricity price in each period is r t Ι for: Where: r p 、r F 、r V The electricity prices during peak, flat and valley periods are RMB / kWh; T P , T F , T V They are peak time, normal time and valley time respectively; Among them, the electricity cost of user i choosing the tiered electricity price package is for: In the formula, Q i.t is the electricity consumption of rural photovoltaic user i during period t; The revenue that the time-of-use electricity price package brings to the power grid C U1 for: Where: n U1 The number of users who choose the time-of-use electricity price package; The electricity cost (yuan) of choosing a time-of-use electricity price package for rural PV user i.

6. A rural electricity pricing transaction strategy according to claim 1, characterized in that: The tiered electricity price package: different electricity consumption intervals are set, and the electricity price in each interval is different. The electricity fee is settled according to the electricity consumption regardless of the electricity consumption period. The electricity price of this package is p ΙΙ for: Where: is the electricity price in different power consumption ranges; Q i is the electricity consumption of user i; Q Ι , Q ΙΙ is the power consumption threshold in different intervals; Among them, the electricity cost of user i choosing the tiered electricity price package is for: The income C obtained by the power grid through the tiered electricity price package U2 for: Where: n U2 The number of users who choose the tiered electricity price package.

7. A rural electricity pricing transaction strategy according to claim 1, characterized in that: The fixed discount package: when the electricity consumption is greater than Q0, the portion less than the preferential electricity consumption Q0 can enjoy a discount, and the portion greater than Q0 is charged at the original price p ΙIΙ If the electricity consumption is less than Q0, the electricity fee will be paid according to the electricity consumption of Q0; Electricity cost of rural photovoltaic user i choosing a fixed discount package for: Where: κ is the discount coefficient for the part of electricity consumption not exceeding Q0; Fixed discount package brings revenue to the grid C U3 for: Where: n U3 The number of users who choose the fixed discount package.

Images