Pricing data scheduling method and terminal based on new energy consumption

Abstract

The application discloses a kind of based on new energy consumption's electricity price data scheduling method and terminal, by equivalent quantification of the power generation cost, transmission and distribution cost and carbon emission cost avoided by new energy consumption into degree electricity avoidable cost, fully consider new energy consumption factor.Based on the first operation and maintenance cost, the first investment cost and incentive cost prediction value required by new energy consumption determine degree electricity fixed cost.When degree electricity avoidable cost exceeds degree electricity fixed cost, it means that the current new energy consumption benefit exceeds cost, at this time, the expected electricity price data of user is obtained through new energy demand response mechanism, to guide user to carry out power consumption in new energy output peak period.In addition, the difference between degree electricity avoidable cost and degree electricity fixed cost is maximized as the target to build the electricity price data scheduling model, and the expected electricity price data of the winning bid is determined based on the model solution result, so as to improve new energy consumption rate, and realize the maximization of economic benefit.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power dispatching, and particularly relates to a power price data dispatching method based on new energy consumption and a terminal. BACKGROUND

[0002] At present, the installed capacity of photovoltaic and the construction amount of charging infrastructure in China show a high-speed growth trend. However, with large-scale photovoltaic on-grid, the phenomenon of consumption difficulty appears, especially in rural areas. At present, the state has introduced a series of favorable policies to encourage new energy vehicles to go to the countryside, so as to speed up the filling of the short board of new energy vehicle consumption and use in rural areas, thereby adding new vitality to the growth of new energy vehicles in rural areas.

[0003] New energy vehicles, as an important adjustable resource for new energy consumption such as photovoltaic in rural areas, have great potential, but there is a problem that the overall matching degree of photovoltaic output and charging load is not high in the current new energy in rural areas. The output of photovoltaic is mainly concentrated in the middle of the day, while the use habit of electric vehicles in rural areas is mainly daytime travel and nighttime charging. This time misalignment makes it difficult to fully play the synergistic effect between the two.

[0004] The demand response mechanism and orderly charging and discharging strategy of new energy vehicles provide a new solution to this challenge. Through the incentive signal of demand response, the charging and discharging behavior of new energy vehicles can be effectively guided and optimized, so as to better match the photovoltaic output. However, the current demand response mechanism and electric vehicle dispatching method still have problems of theoretical and optimization deficiency in promoting new energy consumption. First, the demand response mechanism does not fully consider the new energy consumption factor, and lacks a clear value transfer mechanism. Second, the electric vehicle dispatching method does not closely combine the characteristics of photovoltaic output, resulting in limitations in collaborative optimization. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a power price data dispatching method based on new energy consumption and a terminal, which can effectively utilize the characteristics of new energy output and promote the new energy consumption rate.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is:

[0007] The power price data dispatching method based on new energy consumption comprises:

[0008] The degree of electricity avoidable cost based on new energy consumption is calculated according to the generation cost, transmission and distribution cost and carbon emission cost of non-new energy generation mode;

[0009] The degree of electricity fixed cost based on new energy demand response is calculated according to the first operation and maintenance cost, the first investment cost and the incentive cost prediction value of new energy generation mode;

[0010] If the degree electricity avoidable cost is greater than or equal to the degree electricity fixed cost, the expected electricity price data of the user is obtained through a new energy demand response mechanism, and the expected electricity price data includes electricity quantity and degree electricity price.

[0011] A price data scheduling model is constructed based on the expected electricity price data, with the maximum difference between the degree electricity avoidable cost and the degree electricity fixed cost as the target.

[0012] The price data scheduling model is solved based on operation constraints, and the winning expected electricity price data is determined according to the solving result.

[0013] To solve the above technical problems, another technical solution adopted by the present application is:

[0014] The price data scheduling terminal based on new energy consumption includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements each step in the above price data scheduling method based on new energy consumption when executing the computer program.

[0015] The present application has the following advantages: by quantifying the avoided generation cost, transmission and distribution cost, and carbon emission cost of local new energy consumption as the degree electricity avoidable cost, the new energy consumption factor is fully considered. At the same time, the first operation and maintenance cost, the first investment cost, and the incentive cost prediction value required for local new energy consumption are used to determine the degree electricity fixed cost required for new energy consumption. When the degree electricity avoidable cost is greater than or equal to the degree electricity fixed cost, it means that the current new energy consumption benefit is greater than or equal to the cost, and at this time, the expected electricity price data of the user is obtained through the new energy demand response mechanism to guide the user to use electricity during the peak period of new energy output. In addition, the price data scheduling model is constructed with the maximum difference between the degree electricity avoidable cost and the degree electricity fixed cost as the target, and the winning expected electricity price data is determined based on the model solving result, so as to maximize the economic benefit while improving the new energy consumption rate. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The step flowchart of the price data scheduling method based on new energy consumption provided by the embodiment of the present application is provided.

[0017] Figure 2 The quantitative link diagram of the degree electricity avoidable cost provided by the embodiment of the present application is provided.

[0018] Figure 3 Another flowchart of the price data scheduling method based on new energy consumption provided by the embodiment of the present application is provided.

[0019] Figure 4A structure schematic diagram of a price data scheduling terminal based on new energy consumption provided by the embodiment of the present application is provided.

[0020] Label description:

[0021] 100, a price data scheduling terminal based on new energy consumption; 101, a memory; 102, a processor. DETAILED DESCRIPTION

[0022] To explain the technical content, the purpose and the effect of the present application in detail, the following is described in conjunction with the embodiments and the accompanying drawings.

[0023] The embodiment of the present application provides a price data scheduling method based on new energy consumption, comprising:

[0024] According to the power generation cost, the power transmission and distribution cost and the carbon emission cost of the non-new energy power generation method, the degree of electricity avoidable cost based on new energy consumption is calculated;

[0025] According to the first operation and maintenance cost, the first investment cost and the incentive cost prediction value of the new energy power generation method, the degree of electricity fixed cost based on new energy demand response is calculated;

[0026] If the degree of electricity avoidable cost is greater than or equal to the degree of electricity fixed cost, the expected price data of the user is obtained through the new energy demand response mechanism, and the expected price data includes the power and the degree of electricity price;

[0027] The difference between the degree of electricity avoidable cost and the degree of electricity fixed cost is maximized as the target, and the price data scheduling model is constructed based on the expected price data;

[0028] The price data scheduling model is solved based on the operation constraint condition, and the expected price data of the winning bid is determined according to the solving result.

[0029] From the above description, the beneficial effects of the present application are that the equivalent quantification of the power generation cost, the power transmission and distribution cost and the carbon emission cost avoided by the local consumption of new energy into the degree of electricity avoidable cost is realized, and the new energy consumption factor is fully considered. At the same time, the degree of electricity fixed cost required by the local consumption of new energy is determined based on the first operation and maintenance cost, the first investment cost and the incentive cost prediction value required by the local consumption of new energy. When the degree of electricity avoidable cost is greater than or equal to the degree of electricity fixed cost, it means that the benefit of the current new energy consumption is greater than or equal to the cost, and at this time, the expected price data of the user is obtained through the new energy demand response mechanism to guide the user to use electricity during the peak period of new energy output. In addition, the difference between the degree of electricity avoidable cost and the degree of electricity fixed cost is maximized as the target to construct the price data scheduling model, and the expected price data of the winning bid is determined based on the model solving result, so as to improve the new energy consumption rate and maximize the economic benefit.

[0030] Further, the power generation cost of the non-new energy power generation mode includes fuel cost, second operation and maintenance cost, and second investment cost, and the power transmission and distribution cost of the non-new energy power generation mode includes power transmission investment cost and power distribution investment cost;

[0031] The degree of electricity avoidable cost based on new energy consumption is calculated according to the power generation cost, power transmission and distribution cost, and carbon emission cost of the non-new energy power generation mode.

[0032] The energy consumption avoidable cost based on new energy consumption is calculated according to the fuel cost, second operation and maintenance cost, and second investment cost.

[0033] The power transmission and distribution avoidable cost based on new energy consumption is calculated according to the power transmission investment cost and power distribution investment cost.

[0034] The carbon emission avoidable cost based on new energy consumption is calculated according to the power transmission and distribution avoidable cost and the carbon emission cost.

[0035] The degree of electricity avoidable cost based on new energy consumption is calculated according to the energy consumption avoidable cost, power transmission and distribution avoidable cost, and carbon emission avoidable cost.

[0036] From the above description, from the power generation link, by reducing the abandoned light and wind, the consumption of other energy such as thermal power, hydropower and nuclear power is avoided; from the power transmission and distribution link, by promoting local consumption of new energy, the power grid power transmission and distribution capacity investment can be reduced; from the environmental benefits, the use of new energy replaces the high carbon emission power generation output of thermal power, and reduces the carbon reduction cost. Therefore, based on the avoided cost of new energy power generation mode in the power generation link, power transmission and distribution link, and environmental benefits, the degree of electricity avoidable cost brought by new energy consumption is determined, that is, the benefit, so as to realize new energy consumption while ensuring the maximization of economic benefit.

[0037] Further, the energy consumption avoidable cost based on new energy consumption calculated according to the fuel cost, second operation and maintenance cost, and second investment cost is specifically:

[0038]

[0039] Wherein, C ener represents the energy consumption avoidable cost, n1 represents the number of non-new energy power generation modes, j represents the type of non-new energy power generation mode, j=1 represents thermal power generation mode, j=2 represents nuclear power generation mode, j=3 represents hydropower generation mode, j=4 represents other non-new energy power generation mode, a represents the power generation capacity ratio of different power generation modes, C fuelj represents the fuel cost of the jth non-new energy power generation mode, C opj represents the second operation and maintenance cost of the jth non-new energy power generation mode, Cgenj Denote the second investment cost of the j-th non-new energy power generation method.

[0040] As described above, in the power generation link, the avoidable cost of energy consumption represents the fuel cost, generator operation and maintenance cost, and construction investment cost avoided per degree of electricity when using new energy power generation methods such as wind power and photovoltaic power generation instead of other power generation methods such as thermal power, hydropower, and nuclear power. When the user's electricity consumption is determined, based on the avoidable cost of energy consumption, the energy consumption benefit brought by current new energy power generation can be known.

[0041] Furthermore, the calculation of the avoidable cost of power transmission and distribution based on new energy consumption according to the power transmission investment cost and the distribution investment cost is specifically as follows:

[0042]

[0043] where, C tr_dis Denote the avoidable cost of power transmission and distribution, α denote the ratio of the power generation amounts of different power generation methods, C transj Denote the power transmission investment cost of the j-th non-new energy power generation method, C disj Denote the distribution investment cost of the j-th non-new energy power generation method.

[0044] As described above, in the power transmission and distribution link, the avoidable cost of power transmission and distribution represents the investment cost of power grid transmission and distribution facilities avoided per degree of electricity when using new energy power generation methods such as photovoltaic power generation and wind power for nearby consumption instead of other power generation methods such as thermal power, hydropower, and nuclear power. When the user's power generation amount and electricity consumption are determined, based on the avoidable cost of power transmission and distribution, the power transmission and distribution benefit brought by current new energy power generation can be known.

[0045] Furthermore, the calculation of the avoidable carbon emission cost based on new energy consumption according to the avoidable cost of power transmission and distribution and the carbon emission cost is specifically as follows:

[0046]

[0047] where, C car Denote the avoidable carbon emission cost, C tr_dis Denote the avoidable cost of power transmission and distribution, m denote the calculation period, δ j，a Denote the carbon emission intensity of the j-th non-new energy power generation method in the a-th year, C cj，a Denote the carbon emission cost of the j-th non-new energy power generation method in the a-th year.

[0048] Furthermore, the calculation of the avoidable cost per degree of electricity based on new energy consumption according to the avoidable cost of energy consumption, the avoidable cost of power transmission and distribution, and the avoidable carbon emission cost is specifically as follows: [[ID=:43]]

[0049] CACC =C ener +C tr_dis +C car ;

[0050] wherein, C ACC represents the degree of electricity avoidable cost, C ener represents the energy consumption avoidable cost, C tr_dis represents the transmission and distribution avoidable cost, C car represents the carbon emission avoidable cost.

[0051] From the above description, in the environmental benefits, the carbon emission avoidable cost represents the carbon emission cost avoided per degree of electricity when using photovoltaic power generation, wind power and other new energy power generation methods for nearby consumption instead of other power generation methods such as thermal power, hydropower, nuclear power, etc. In the case of determining the user's power generation and power consumption, the environmental benefits brought by the current new energy power generation can be known based on the carbon emission avoidable cost.

[0052] Further, the degree of electricity fixed cost based on new energy demand response calculated according to the first operation and maintenance cost, the first investment cost and the incentive cost prediction value of the new energy power generation method is specifically:

[0053]

[0054] wherein, C DE represents the degree of electricity fixed cost, n2 represents the number of new energy power generation methods, k represents the type of new energy power generation method, k=1 represents photovoltaic power generation method, k=2 represents wind power generation method, and a represents the power generation capacity ratio of different power generation methods. C opk represents the first operation and maintenance cost of the kth new energy power generation method, C genk represents the first investment cost of the kth new energy power generation method, and R de represents the incentive cost prediction value.

[0055] From the above description, the degree of electricity fixed cost represents the sum of the operation and maintenance, construction investment of new energy power generation unit and the incentive cost given to the user participating in new energy consumption type demand response per degree of electricity when using new energy power generation method. Among them, the incentive cost is calculated as the degree of electricity fixed cost with the prediction value, which is used as the condition for judging whether to start new energy demand response to obtain expected electricity price data, so as to promote the new energy consumption rate based on the expected electricity price data of the user, while ensuring the economic benefits.

[0056] Further, the difference between the degree of electricity avoidable cost and the degree of electricity fixed cost is maximized as the target, and the electricity price data scheduling model is constructed based on the expected electricity price data, which is specifically:

[0057]

[0058] wherein f represents the electricity price data scheduling model, P j represents the power generated by the jth non-new energy power generation method in the time period t, P k represents the power generated by the kth new energy power generation method in the time period t, C ACC represents the avoidable cost per kilowatt-hour, C opk represents the first operation and maintenance cost of the kth new energy power generation method, C genk represents the first investment cost of the kth new energy power generation method, P EV represents the charging load of the electric vehicle charging station node, R de_ac represents the actual value of the incentive cost of the user, which is based on the expected electricity price data, and t represents the time period.

[0059] As can be seen from the above description, the avoidable cost per kilowatt-hour represents the benefit of new energy consumption of the power grid, the fixed cost per kilowatt-hour represents the cost of new energy consumption of the power grid, the maximum difference between the avoidable cost per kilowatt-hour and the fixed cost per kilowatt-hour represents the maximization of economic benefits of new energy consumption of the power grid, and the electricity quantity at the maximization of economic benefits can be solved based on the electricity price data scheduling model. Based on the solved electricity quantity and the actual value of the incentive cost of the user, the final winning electricity quantity and electricity price can be determined, so as to promote the new energy consumption rate and maximize the economic benefits.

[0060] Further, the operation constraints include power constraints, output constraints and safety constraints of the electric vehicle charging station;

[0061] The power constraints are specifically:

[0062]

[0063] wherein P l represents the active power flowing into the electric vehicle charging station node, n1 represents the number of non-new energy power generation methods, n2 represents the number of new energy power generation methods, P EV represents the charging load of the electric vehicle charging station node;

[0064] The output constraints are specifically:

[0065] 0≤P l ≤P ra_gen ;

[0066] 0≤P EV ≤P ra_EV ;

[0067] wherein P ra_gen represents the rated power of the jth power generation method, P ra_EV represents the maximum charging power of the electric vehicle;

[0068] The safety constraints are specifically:

[0069] U min ≤U pu ≤U max ;

[0070] f min ≤f l ≤f max ;

[0071] wherein, U pu represents the unit value of the electric vehicle charging station node voltage amplitude, U min and U max respectively represent the lower limit value and the upper limit value of the allowed voltage offset, f1 represents the electric vehicle charging station node frequency, f min and f max respectively represent the lower limit value and the upper limit value of the allowed frequency offset.

[0072] As can be known from the above description, in order to maximize economic benefits, the new energy is reasonably consumed by scheduling the orderly charging of electric vehicles. Since the price is declared, in order to ensure clearing and profitability, the user will usually declare a price close to the generation cost, so that the lower-cost new energy generation method is preferentially used to replace the higher-cost other generation method, which can reduce the cost of purchasing electricity from the user, and is helpful to reduce carbon emissions and increase environmental benefits. At the same time, the user with lower expectation is preferentially given incentive to reduce the cost of incentive users and promote the local consumption of new energy.

[0073] Another embodiment of the application provides a kind of based on new energy consumption's electric price data scheduling terminal, including memory, processor and the computer program stored in the memory and running on the processor, the processor executes the computer program when realizing each step in the above based on new energy consumption's electric price data scheduling method.

[0074] From the above description, the beneficial effects of the present application are that: by quantifying the avoided generation cost, transmission and distribution cost and carbon emission cost as the degree of electricity avoidable cost when avoiding new energy on-site consumption, fully considering the new energy consumption factor. At the same time, the first operation and maintenance cost, the first investment cost and the incentive cost prediction value required for the on-site consumption of new energy are determined to determine the degree of electricity fixed cost required for the on-site consumption of new energy. When the degree of electricity avoidable cost is greater than or equal to the degree of electricity fixed cost, it means that the benefit of current new energy consumption is greater than or equal to the cost, at this time, the expected price data of the user is obtained through the new energy demand response mechanism to guide the user to use electricity during the peak period of new energy output. In addition, the difference between the degree of electricity avoidable cost and the degree of electricity fixed cost is maximized to build a price data scheduling model, and the expected price data of the winning bid is determined based on the model solution result, so as to maximize the economic benefit while improving the new energy consumption rate.

[0075] The method and terminal for price data scheduling based on new energy consumption provided by the embodiments of the present application can be applied to the optimal scheduling scenario of new energy power system, can effectively utilize the output characteristics of new energy, and promote the new energy consumption rate. The following will be described through specific embodiments:

[0076] Please refer to Figures 1 to 3 , the first embodiment of the present application is:

[0077] As shown in Figure 1 and Figure 3 , a method for price data scheduling based on new energy consumption includes:

[0078] S1, calculate the degree of electricity avoidable cost based on new energy consumption according to the generation cost, transmission and distribution cost and carbon emission cost of non-new energy generation method. The generation cost of the non-new energy generation method includes fuel cost, second operation and maintenance cost and second investment cost, and the transmission and distribution cost of the non-new energy generation method includes transmission investment cost and distribution investment cost.

[0079] Specifically, as shown in Figure 2 , the step S1 includes:

[0080] S11, calculate the energy consumption avoidable cost based on new energy consumption according to the fuel cost, second operation and maintenance cost and second investment cost.

[0081] It should be noted that with the development of new power system, the future energy structure will be mainly based on non-fossil fuels, and new energy will be consumed through new energy consumption type demand response mechanism to reduce the investment and operation and maintenance cost of thermal power, hydropower and nuclear power units, and even further reduce the investment of peak power generation units during the peak power consumption period. Therefore, the degree of electricity cost avoidable by promoting new energy consumption through new energy demand response in the power generation link includes fuel cost Cfuelj Second maintenance cost C opj And the second investment cost C genj .

[0082] Step S11 specifically involves:

[0083]

[0084] Among them, C ener Let n1 represent the number of non-renewable energy generation methods, j represent the types of non-renewable energy generation methods (j=1 for thermal power, j=2 for nuclear power, j=3 for hydropower, j=4 for other non-renewable energy generation methods), α represent the ratio of power generation from different generation methods, and C represent the energy consumption avoidable costs. fuelj Let C represent the fuel cost of the j-th non-renewable energy power generation method. opj C represents the second operation and maintenance cost of the j-th non-new energy power generation method. genj This represents the second investment cost of the j-th non-new energy power generation method.

[0085] In some embodiments, α satisfies

[0086] In some embodiments, C fuelj Specifically: C fuelj =β j C βj , where β j C represents the energy consumption per kilowatt-hour of the j-th non-renewable energy generation method. βj This represents the unit fuel cost of the j-th type of non-new energy generator set.

[0087] In some embodiments, C opj Specifically: Where m represents the calculated lifespan, and C pj,a h represents the annual operating cost and annual maintenance cost per unit power for the j-th non-new energy power generation method in year a. j,a This represents the annual utilization hours of the j-th non-new energy power generation method in year a.

[0088] In some embodiments, C genj Specifically: Among them, C inj,a This represents the unit power investment cost of the j-th type of non-new energy generator set in year a.

[0089] S12. Calculate the avoidable transmission and distribution costs based on renewable energy consumption, according to the transmission investment cost and distribution investment cost.

[0090] It should be noted that, due to the promotion of local consumption of new energy, on the one hand, the capacity of power transmission and distribution lines is reduced, thereby significantly reducing the capacity of power transmission and distribution lines, on the other hand, the local power balance of the power grid is improved, the power allocation is reduced, the operation efficiency of the power grid is improved, and the dependence on large and centralized power grid facilities is reduced, which helps to reduce the investment cost of power transmission and distribution facilities. Therefore, the avoidable electricity cost of promoting new energy consumption through new energy demand response in the power transmission and distribution link includes the power transmission investment cost C transj and the power distribution investment cost C disj .

[0091] The step S12 is specifically:

[0092]

[0093] Wherein, C tr_dis represents the avoidable cost of power transmission and distribution, a represents the power generation ratio of different power generation methods, C transj represents the power transmission investment cost of the jth non-new energy power generation method, and C disj represents the power distribution investment cost of the jth non-new energy power generation method.

[0094] In some embodiments, C transj is specifically: Wherein, C tj,a represents the unit power transmission investment cost of the jth non-new energy power generation method in the a year.

[0095] In some embodiments, C disj is specifically: Wherein, C dj,a represents the unit power distribution investment cost of the jth non-new energy power generation method in the a year.

[0096] S13, calculate the carbon emission avoidable cost based on new energy consumption according to the avoidable cost of power transmission and distribution and the carbon emission cost.

[0097] It should be noted that, using new energy power generation does not produce greenhouse gases, while thermal power generation is one of the main sources of greenhouse gas emissions, and burning fossil fuels will produce a large amount of carbon dioxide, and by replacing thermal power generation with new energy, greenhouse gas emissions can be greatly reduced, thereby reducing the carbon reduction cost. Therefore, the avoidable electricity cost of promoting new energy consumption through new energy demand response in the environmental benefit part includes the carbon emission cost C cj,a .

[0098] The step S13 is specifically:

[0099]

[0100] Wherein, C carrepresents carbon emission avoidable cost, C tr_dis represents power transmission and distribution avoidable cost, m represents calculation period, δ j,a represents carbon emission intensity of the jth non-new energy power generation mode in the ath year, C cj,a represents carbon emission cost of the jth non-new energy power generation mode in the ath year.

[0101] S14, calculate degree electricity avoidable cost based on new energy consumption according to the energy consumption avoidable cost, the power transmission and distribution avoidable cost, and the carbon emission avoidable cost.

[0102] The step S14 specifically includes:

[0103] C ACC =C ener +C tr_dis +C car ;

[0104] wherein, C ACC represents degree electricity avoidable cost, C ener represents energy consumption avoidable cost, C tr_dis represents power transmission and distribution avoidable cost, C car represents carbon emission avoidable cost.

[0105] It should be noted that the C ener , C tr_dis and C car respectively represent energy consumption avoidable cost, power transmission and distribution avoidable cost, and carbon emission avoidable cost per degree electricity.

[0106] S2, calculate degree electricity fixed cost based on new energy demand response according to the first operation and maintenance cost, the first investment cost, and the incentive cost prediction value of the new energy power generation mode.

[0107] The step S2 specifically includes:

[0108]

[0109] wherein, C DE represents degree electricity fixed cost, n2 represents the number of new energy power generation modes, k represents the type of new energy power generation mode, k=1 represents photovoltaic power generation mode, k=2 represents wind power generation mode, α represents power generation capacity ratio of different power generation modes, C opk represents the first operation and maintenance cost of the kth new energy power generation mode, C genk represents the first investment cost of the kth new energy power generation mode, R de represents incentive cost prediction value.

[0110] In some embodiments, the calculation principle of C opk is the same as that of Copj The calculation principle of C is the same as that of C wherein m represents the life cycle of calculation, C pk,a represents the annual operation cost and annual maintenance cost per unit power of the kth new energy power generation mode in the ath year, h k，a represents the annual utilization hours of the kth new energy power generation mode in the ath year.

[0111] In some embodiments, C genk The calculation principle of C is the same as that of C genj The calculation principle of C is the same as that of C wherein C ink，a represents the investment cost per unit power of the kth new energy power generation equipment in the ath year.

[0112] In some embodiments, R de represents the predicted value of the degree price incentive cost of the user participating in the new energy demand response mechanism. If the user participates in the new energy demand response mechanism for the first time, the average value of the past ten winning prices of the new energy demand response mechanism is taken as the predicted value of the degree price incentive cost. If the user does not participate in the new energy demand response mechanism for the first time, the average value of the past five winning prices of the user is taken to determine the predicted value of the degree price incentive cost.

[0113] S3, if the degree avoidable cost is greater than or equal to the degree fixed cost, obtaining the expected price data of the user through the new energy demand response mechanism, the expected price data including the electricity quantity and the degree price.

[0114] In some embodiments, when the degree avoidable cost C ACC of new energy consumption exceeds the degree fixed cost C DE of new energy consumption, the benefit brought by new energy power generation is higher than the power generation cost, which is beneficial to reduce the social power generation cost. At this time, the new energy demand response mechanism is started, and the new energy power generation user and the electric vehicle user and other load aggregators are notified of the requirements and notices, and the new energy power generation user voluntarily reports the new energy power generation quantity and the power generation price according to the invitation of the power grid enterprise, and at the same time the electric vehicle user voluntarily reports the electric vehicle power consumption and the expected degree price incentive price according to the invitation of the power grid enterprise. The power grid enterprise determines the final winning new energy power generation quantity, power generation price, electric vehicle power consumption and expected degree price incentive price through market bidding, comprehensively considering the price, bidding time and regional load prediction, i.e. through the price data scheduling model of steps S5 to S6 of the present application, to promote new energy consumption.

[0115] It should be noted that the final winning new energy power generation quantity, power generation price, electric vehicle power consumption and expected degree price incentive price will affect the degree fixed cost C DE based on new energy demand response.

[0116] S4, maximizing a difference between the degree of electricity avoidable cost and the degree of electricity fixed cost as an objective, constructing a price data scheduling model based on the expected price data.

[0117] S5, solving the price data scheduling model based on the operation constraints, and determining the winning expected price data according to a solving result.

[0118] Embodiment two of the present application is:

[0119] A price data scheduling method based on new energy consumption, which is different from embodiment one in that the specific steps of step S4 and step S5 are limited.

[0120] The step S4 is specifically:

[0121]

[0122] Wherein, f represents the price data scheduling model, P j represents the power generation of the jth non-new energy power generation method in the time period t, P k represents the power generation of the kth new energy power generation method in the time period t, C ACC represents the degree of electricity avoidable cost, C opk represents the first operation cost of the kth new energy power generation method, C genk represents the first investment cost of the kth new energy power generation method, P EV represents the charging load of the electric vehicle charging station node, R de_ac represents the actual value of the incentive cost of the user, which is obtained based on the expected price data, t represents the clearing time period, and t is generally 15 minutes.

[0123] It should be noted that, since the electric vehicle user reports the price to ensure clearing and profitability, the electric vehicle user usually reports the price close to the power generation cost, so the new energy power generation method with lower cost is preferred to replace the power generation method with higher cost, so as to reduce the cost of purchasing electricity from the power generation user, and help to reduce carbon emissions and increase environmental benefits. In addition, the electric vehicle user with lower expected degree of electricity incentive price is preferred to reduce the incentive user cost and promote the local consumption of new energy. In summary, the cost of the power grid enterprise is minimized from the purchasing side and the power consumption side, and the new energy vehicle consumption is promoted.

[0124] In the step S5, the operation constraints include power constraints, output constraints and safety constraints of the electric vehicle charging station, so that

[0125] (1) The power constraint is specifically:

[0126]

[0127] wherein P l represents the active power flowing into the electric vehicle charging station node, n1 represents the number of non-new energy power generation methods, n2 represents the number of new energy power generation methods, P EV represents the charging load of the electric vehicle charging station node;

[0128] (2) The output constraint is specifically:

[0129] 0≤P l ≤P ra_gen ;

[0130] 0≤P EV ≤P ra_EV ;

[0131] wherein P ra_gen represents the rated power of the i-th power generation method, P ra_EV represents the maximum charging power of the electric vehicle;

[0132] (3) The safety constraint is specifically:

[0133] U min ≤U pu ≤U max ;

[0134] f min ≤f l ≤f max ;

[0135] wherein U pu represents the per-unit value of the voltage amplitude of the electric vehicle charging station node, U min and U max respectively represent the lower limit value and the upper limit value of the allowed voltage offset, f l represents the frequency of the electric vehicle charging station node, f min and f max respectively represent the lower limit value and the upper limit value of the allowed frequency offset.

[0136] In some embodiments, the electricity price data scheduling model is solved based on the above operation constraints, and the winning electric vehicle electricity consumption and the expected electricity price can be determined according to the solving result, and the electric vehicle scheduling curve can be determined according to the electric vehicle electricity consumption and the expected electricity price, wherein the electric vehicle scheduling curve comprises data such as the charging time and the charging load of the electric vehicle.

[0137] Please refer to Figure 4 , embodiment three of the present application is:

[0138] The application provides a new energy consumption-based electricity price data scheduling terminal 100, which comprises a memory 101, a processor 102 and a computer program stored in the memory 101 and running on the processor 102, and the processor 102 implements each step in the new energy consumption-based electricity price data scheduling method of the above-mentioned embodiment I and embodiment II when executing the computer program.

[0139] In conclusion, the application provides a new energy consumption-based electricity price data scheduling method and terminal, which quantifies the avoided generation cost, power transmission and distribution cost and carbon emission cost of local new energy consumption into an avoided cost per kilowatt-hour, fully considers the new energy consumption factor. Meanwhile, the first operation and maintenance cost, the first investment cost and the incentive cost prediction value required by local new energy consumption are used to determine the fixed cost per kilowatt-hour required by new energy consumption. When the avoided cost per kilowatt-hour is greater than or equal to the fixed cost per kilowatt-hour, it indicates that the current new energy consumption benefit is greater than or equal to the cost, at this time, the expected electricity price data of users is obtained through the new energy demand response mechanism, and an electricity price data scheduling model is constructed by taking the maximum difference between the avoided cost per kilowatt-hour and the fixed cost per kilowatt-hour as the target. Based on the electricity price data scheduling model, a lower-cost new energy generation method can be considered on the electricity purchase side to reduce the electricity purchase cost of users, and the incentive cost of electric vehicle users can be considered on the electricity consumption side to reduce the incentive cost of users, so as to maximize the economic benefit of the power grid. In addition, the electricity price data scheduling model guides part of the users to charge at the peak of new energy output based on the actual declared incentive price per kilowatt-hour of the users, further promotes the new energy consumption rate.

[0140] The above-mentioned embodiments are only examples of the application, and do not limit the patent scope of the application, and any equivalent transformation or direct or indirect application in the related technical field based on the content of the specification and drawings of the application is also included in the patent protection scope of the application.

Claims

1. A method for electricity price data scheduling based on renewable energy consumption, characterized in that, include: The avoidable cost per kilowatt-hour based on renewable energy consumption is calculated based on the generation cost, transmission and distribution cost, and carbon emission cost of non-renewable energy power generation methods. Calculate the fixed cost per kilowatt-hour based on the demand response of new energy sources by considering the first operation and maintenance cost, the first investment cost, and the predicted incentive cost of the new energy power generation method. If the avoidable cost per kilowatt-hour is greater than or equal to the fixed cost per kilowatt-hour, then the user's expected electricity price data is obtained through the new energy demand response mechanism. The expected electricity price data includes the amount of electricity and the price per kilowatt-hour. With the goal of maximizing the difference between the avoidable cost per kilowatt-hour and the fixed cost per kilowatt-hour, an electricity price data scheduling model is constructed based on the expected electricity price data; The electricity price data scheduling model is solved based on the operational constraints, and the expected electricity price data for the winning bid is determined based on the solution results. The power generation cost of the non-new energy power generation method includes fuel cost, second operation and maintenance cost, and second investment cost; the power transmission and distribution cost of the non-new energy power generation method includes power transmission investment cost and power distribution investment cost. The calculation of the avoidable cost per kilowatt-hour based on renewable energy consumption, based on the generation cost, transmission and distribution cost, and carbon emission cost of non-renewable energy power generation methods, includes: Calculate the energy consumption avoidable cost based on the new energy consumption based on the aforementioned fuel cost, second operation and maintenance cost, and second investment cost; Calculate the avoidable transmission and distribution costs based on renewable energy consumption, according to the aforementioned transmission investment costs and distribution investment costs. Calculate the carbon emission avoidable cost based on the avoidable cost of power transmission and distribution and the carbon emission cost; The avoidable cost per kilowatt-hour based on renewable energy consumption is calculated based on the avoidable costs of energy consumption, transmission and distribution, and carbon emission. The operational constraints include power constraints, output constraints, and safety constraints for electric vehicle charging stations. The power constraint is specifically as follows: ； in, This represents the active power flowing into the electric vehicle charging station node, where n1 represents the number of non-renewable energy generation methods and n2 represents the number of new energy generation methods. P EV This indicates the charging load of an electric vehicle charging station node. The output constraint is specifically as follows: ； ； in, Indicates the first l The rated power of each power generation method This indicates the maximum charging power of the electric vehicle; The security constraints are specifically as follows: ； ； in, U pu Per-unit value representing the voltage amplitude at electric vehicle charging station nodes. U min and U max These represent the lower and upper limits of the allowable voltage offset, respectively. f l This indicates the frequency of electric vehicle charging station nodes. f min and f max These represent the lower and upper limits of the allowed frequency offset, respectively.

2. The method according to claim 1, characterized in that, The calculation of avoidable energy consumption costs based on renewable energy consumption, calculated according to the fuel cost, the second operation and maintenance cost, and the second investment cost, specifically includes: ； in, This indicates the avoidable cost of energy consumption. n1 represents the number of non-new energy power generation methods, and j represents the type of non-new energy power generation method: j=1 represents thermal power generation, j=2 represents nuclear power generation, j=3 represents hydropower generation, and j=4 represents other non-new energy power generation methods. α This represents the ratio of electricity generated by different power generation methods. C fuelj This represents the fuel cost of the j-th non-new energy power generation method. This represents the second operation and maintenance cost of the j-th non-new energy power generation method. This represents the second investment cost of the j-th non-new energy power generation method.

3. The method according to claim 1, characterized in that, The calculation of avoidable transmission and distribution costs based on renewable energy consumption, according to the transmission investment cost and distribution investment cost, specifically involves: ； in, This indicates that power transmission and distribution can avoid costs. α This represents the ratio of electricity generated by different power generation methods. This represents the transmission investment cost of the j-th non-new energy power generation method. This represents the power distribution investment cost for the j-th non-new energy power generation method.

4. The method according to claim 1, characterized in that, The calculation of the carbon emission avoidance cost based on the avoidable power transmission and distribution costs and the carbon emission costs specifically involves: ； in, This indicates that carbon emissions can avoid costs. This indicates that power transmission and distribution can avoid costs, and m represents the calculation period. This indicates that the j-th non-new energy power generation method is in the first... a Annual carbon emission intensity C cj,a This indicates that the j-th non-new energy power generation method is in the first... a Annual carbon emission costs.

5. The method according to claim 1, characterized in that, The calculation of the avoidable cost per kilowatt-hour based on renewable energy consumption, the avoidable cost of power transmission and distribution, and the avoidable cost of carbon emissions is specifically as follows: ； in, This indicates that costs can be avoided per kilowatt-hour. This indicates that energy consumption can avoid costs. This indicates that power transmission and distribution can avoid costs. This indicates that carbon emissions can avoid costs.

6. The method according to claim 1, characterized in that, The calculation of the fixed cost per kilowatt-hour based on the new energy demand response, using the predicted values ​​of the first operation and maintenance cost, the first investment cost, and the incentive cost of the new energy power generation method, is specifically as follows: ； in, Let n represent the fixed cost per kilowatt-hour, n² represent the number of renewable energy generation methods, and k represent the type of renewable energy generation method, where k=1 represents photovoltaic power generation and k=2 represents wind power generation. α This represents the ratio of electricity generated by different power generation methods. This represents the first operation and maintenance cost of the k-th renewable energy power generation method. This represents the first investment cost of the k-th new energy power generation method. This represents the predicted value of incentive costs.

7. The method according to claim 6, characterized in that, The specific steps for constructing an electricity price data scheduling model based on the expected electricity price data, with the objective of maximizing the difference between the avoidable cost per kilowatt-hour and the fixed cost per kilowatt-hour, are as follows: ； in, This represents the electricity price data scheduling model. This represents the power generation of the j-th non-new energy power generation method within time period t. This represents the power generation capacity of the k-th new energy power generation method within time period t. This indicates that costs can be avoided per kilowatt-hour. This represents the first operation and maintenance cost of the k-th renewable energy power generation method. This represents the first investment cost of the k-th new energy power generation method. P EV This indicates the charging load of electric vehicle charging station nodes. R de_ac This represents the actual value of the user's incentive cost, which is obtained based on the expected electricity price data, and t represents the clearing period.

8. A data dispatch terminal for electricity pricing based on renewable energy consumption, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements each step of the electricity price data scheduling method based on renewable energy consumption as described in any one of claims 1-7.

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