CN116452325A - Park green power transaction rolling clearing and settlement method based on Nash bargained price - Google Patents

Park green power transaction rolling clearing and settlement method based on Nash bargained price Download PDF

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CN116452325A
CN116452325A CN202111636846.0A CN202111636846A CN116452325A CN 116452325 A CN116452325 A CN 116452325A CN 202111636846 A CN202111636846 A CN 202111636846A CN 116452325 A CN116452325 A CN 116452325A
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贾乾罡
罗祾
许少伦
董真
程凡
刘婧
潘爱强
范莹
田浩毅
严正
平健
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Shanghai Jiaotong University
State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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Abstract

A method for rolling out and settling green electric power trade of a park based on Nash bargained price comprises the steps of constructing a park green electric trade rolling out and settling model based on Nash bargained price after constructing a park green electric trade framework, and conducting inter-park green electric trade rolling out based on Nash bargained price. The invention promotes the production and the digestion of distributed renewable energy sources of the power distribution network. On one hand, the green value of the distributed renewable energy source is reflected, and the production and the consumption of the distributed renewable energy source are promoted; on the other hand, the influence caused by uncertainty of green power output prediction is effectively reduced by using rolling clearing and settlement means.

Description

Park green power transaction rolling clearing and settlement method based on Nash bargained price
Technical Field
The invention relates to a technology in the field of energy configuration, in particular to a park green electric power transaction rolling clearing and settlement method based on Nash bargaining.
Background
With the access of mass distributed renewable energy sources, industrial and commercial parks on the distribution side also aggregate numerous distributed renewable energy sources and green electricity demand users, and the potential of the power distribution side for producing and consuming green electricity is not neglected. However, currently distributed renewable energy sources are still consumed in a "self-service, rest on the net" manner. This rigid price mechanism fails to adequately stimulate the ability of industrial and commercial parks to produce and consume green electricity. On the one hand, for industrial and commercial parks where green electricity supply is over-demanded, the surplus internet power does not exert the green added value; on the other hand, in a commercial park where green electricity supply is not required, green electricity cannot be purchased from the outside to satisfy the green electricity demand of the own load. Therefore, it is necessary to study the green electricity trading mechanism between gardens, and to improve the enthusiasm of the gardens for producing and consuming renewable energy sources.
The prior art focuses on distributed electric energy transaction among micro networks at the power distribution network side, can not fully ensure the production enthusiasm of green electricity producers and the benefits of green electricity consumers, has deviation between the clear result and the actual execution result of electric energy transaction, and is not beneficial to promoting the production and the consumption of distributed renewable energy sources to the maximum extent.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a park green electric power transaction rolling clearing and settlement method based on Nash bargaining, which promotes the production and the consumption of distributed renewable energy sources of a power distribution network. On one hand, the green value of the distributed renewable energy source is reflected, and the production and the consumption of the distributed renewable energy source are promoted; on the other hand, the influence caused by uncertainty of green power output prediction is effectively reduced by using rolling clearing and settlement means.
The park described in the present invention: the industrial and commercial park can be regarded as a small power generation and distribution system formed by integrating a distributed power supply, an energy storage device, an energy conversion device, related loads, a monitoring device and a protection device, is an autonomous system capable of realizing self-control, protection and management, and can be operated in a grid connection mode with an external power grid or in an isolated mode.
The green electricity in the invention is as follows: that is, in the process of producing electric power, the carbon dioxide emission amount is zero or approaches zero, and the influence on environmental impact is lower compared with the electric power produced by other modes (such as thermal power generation). The main sources of green electricity are solar energy, wind power, biomass energy, geothermal energy and the like.
The green electricity transaction described in the present invention: the power transaction using the green power product as a target is used for meeting the requirements of purchasing and consuming green power of power consumers and providing corresponding green power consumption certification.
The invention is realized by the following technical scheme:
the invention relates to a park green electric power transaction rolling clearing and settlement method based on Nash bargained, which comprises the steps of constructing a park green electric power transaction rolling clearing and settlement model based on Nash bargained after constructing a park green electric power transaction framework, and carrying out park green electric power transaction rolling clearing based on Nash bargained.
The invention relates to a system for realizing the method, which comprises the following steps: the micro-grid green electricity market rolling out clear unit and micro-grid green electricity settlement unit based on Nash bargaining, wherein: the micro-grid green electricity market rolling clearing unit based on Nash bargaining carries out market clearing processing according to the micro-grid information participating in green electricity transaction to obtain a micro-grid green electricity transaction result; and the micro-grid green electricity settlement unit performs settlement processing according to the calculated micro-grid green electricity transaction amount and price information to obtain a settlement result.
Drawings
FIG. 1 is a schematic diagram of a green electricity trading mechanism among parks;
FIG. 2 is a schematic of green power output and load levels in different campuses on a typical day;
FIG. 3 is a schematic view of green electricity trading volume in a garden section;
FIG. 4 is a schematic diagram of green electricity trade prices among parks;
FIG. 5 is a schematic diagram of cost variation for different campuses;
fig. 6 is a schematic diagram of the balance of different parks.
Detailed Description
Under the inter-park green electricity trading mechanism provided by the embodiment, for parks where green electricity supply is greater than demand, i.e. local renewable energy source output is greater than load demand, the distributed energy internet electricity price can sell rich electric energy to power grid enterprises, or green electricity can be sold to other parks in the inter-park green electricity trading market; for parks where supply is short, i.e. where local renewable energy is less than load demand, electricity can be purchased from grid enterprises at terminal sales prices, or green electricity from other parks in the inter-park green electricity trading market. As shown in fig. 1, this embodiment relates to a method for rolling out and settling green power transaction in a park based on nash bargained price, comprising the following steps:
s1, constructing a garden interval green electricity transaction mechanism, which comprises the following steps: clearing and settlement of the transaction:
s1.1, clearing out transaction: in period v, the campus participating in the green electricity transaction first submits its own quotation information from period v+1 to period v+t, and deals with the transaction deposit. And then, the trading platform solves by taking T as a clearing window to obtain a clearing result of the green electricity trading in the garden area. And finally, returning the clearing result to a park participating in the transaction by the platform, and delivering green electricity according to the clearing result in the period v+1 after the park confirms the result.
S1.2, transaction settlement: the park performs transaction settlement in the v+2 period, the transaction platform calculates the green electricity transaction deviation amount between the parks and performs settlement according to the measurement result submitted by the measurement equipment, and then returns the settlement result to the park participating in the transaction, wherein: the core algorithm of the trading platform is an S2 inter-park green electricity trading model.
S2, constructing a green electricity transaction model among parks, which specifically comprises the following steps:
s2.1, green electricity transaction clearing: in order to determine the clearing result of green electricity transaction among the parks, an optimized operation model of a single park is firstly established under the transaction mechanism of the embodiment. And then, providing a rolling out model and a solving method for green electricity trading among parks based on Nash bargained price theory, and ensuring the resource allocation efficiency optimality and the trading bonus allocation fairness of the market.
S2.1.1 a single campus operation model is constructed, comprising:
1) The objective function specifically includes:
(1) the objective function of a single park is to minimize its overall energy costWherein: />Representing green electricity deficiency costs, costs of electricity purchased from the grid enterprise, benefits of selling electricity to the grid enterprise, and benefits of selling green electricity to other campuses, respectively (benefits are negative representing costs of green electricity purchased from other campuses).
(2) Green electricity demand exists for both business and residential areas in the campus: for industrial users, the use of green energy can bring higher added value to the commodity; for commercial residential users, the green energy can be used to meet the enthusiasm of energy conservation and emission reduction. Green electricity shortage cost of park iWherein: t is the run time period; Δt is the length of the period; t is a time window for park rolling optimization; v is the current time period; a, a i Is a green electricity shortage cost coefficient of the park i and represents utility loss of unit load brought by the green electricity shortage in the park to users; />Is the load level of the t-slot park i; mu (mu) i Is the green electricity demand duty cycle in the load; />Is the green electricity self-generating partial power of park i.
(3) Cost of campus i purchasing electricity from large gridWherein: selling electricity price for the terminal; />Is the electricity purchasing quantity of the park i from the large power grid in the period t.
(4) When the green electricity output in park i is higher than the green electricity demand of load, the surplus electricity can be sold to the power grid enterprises to obtain benefitsWherein: gamma is the online electricity price of the distributed energy source; />Is the green margin internet partial power of park i.
(5) Benefits of green electricity sales from campus i to other parksWherein: pi ij T is the clearing price of green electricity transactions between campus i and campus j in the t period; p (P) ij,t Δt is the period tGreen electricity sold by zone i to campus j. And has pi ij,t ＝π ji,t ；P ij,t ＝-P ji,t
2) The constraint conditions specifically include:
(1) charging and discharging constraints of energy storage device of park iWherein: />The energy stored by the energy storage device in the park i in the period t is stored; />And->Respectively charging power and discharging power of the energy storage device in the park i in the t period; />And->The charging and discharging efficiencies of the energy storage devices in the park i are respectively.
(2) Green power output in park i is the sum of the self-service partial power and the rest of the internet partial power:
(3) electric energy supply and demand balance constraint of park i
(4) Charging and discharging power constraint and stored energy constraint of energy storage device in park iWherein: />Is the upper energy limit of the energy storage device in park i; />Is the maximum charge and discharge power of the energy storage device in park i;and in order to represent the 0-1 variable of the charge and discharge states of the energy storage, the value of 1 represents that the energy storage device of the park i is in the charge state in the t period, and otherwise, the energy storage device is in the discharge state.
S2.1.2 Rolling out green electricity transactions among parks based on nash bargained: the premise that the reasonable park participates in the inter-park transaction is that the green electricity transaction can reduce the comprehensive energy cost of the green electricity transaction, so that the cost of the park before and after the green electricity transaction needs to meet personal rationality constraint:wherein: />The comprehensive energy cost of the park i calculated according to the steps is expressed when the park i does not participate in the inter-park transaction, and in the Nash bargaining theory, the cost is also called a negotiation breaking point of the park participating in the transaction and is substituted as a constant into a rolling-out model to be solved; />Nash bargained-based inter-campus green electricity transaction rolling clear model +.>Wherein: m is the number of parks participating in green electricity transaction; is the clearing variable of green electricity transaction among parks, namely { pi } ij,t ,P ij,t }。
The green electricity transaction rolling model among parks based on Nash bargained price is obtained through a two-stage solving method. Since the purpose of Nash bargaining is to fairly increase social benefits, the model is converted into two sub-problems of minimizing social costs and maximizing payment benefits, and then solved in turn to get the result of the inter-campus transaction. The optimal solution equivalent of the result sum obtained by the two-stage optimization specifically comprises the following steps:
1) The total social cost is minimized: according to the arithmetic-geometric mean inequality, when the objective function obtains the optimal solution, the following needs to be satisfied:which is equivalent to the overall energy cost minimization problem for all parks involved in the transaction: />Since the sales of green electricity in the garden are opposite to each other and the trading prices of green electricity are equal, the trading profits of the whole garden cancel each other, i.e.)>Thereby simplifying the problem of minimizing the total comprehensive energy consumption cost of the park>Further determining the optimal trade volume of the original rolling out model and the corresponding local optimal scheduling plan of the park, i.e +.>Wherein: the underlined variables represent their optimal solutions.
2) Maximizing payment benefit: after determining the inter-campus green electricity optimal trading volume, the optimization variables need to be back-substituted and logarithmically taken to determine the green electricity trading price. In the rolling clearing mode, the clearing power of the v+1 to v+T time periods is obtained in the v time period, but only the clearing result of the v+1 time period is actually executed, so that the payment benefit maximization sub-problem also only needs to solve the clearing price of the v+1 time period, namelyPrice clearing by green electricity transaction between parks in v+1 periodAnd (5) finishing the clearing process.
S2.2, green electricity transaction settlement: the actual values of the load and green electricity output in the park often deviate from the predicted values. In this case, the actual green electricity generation is specified to meet the local demand and the inter-campus trade delivery demand in turn, and the remainder is purchased by the grid enterprise. Meanwhile, when a certain park sells green electricity to a plurality of parks at the same time, the actual delivery amount of the green electricity is distributed according to the transaction amount proportion. When the green electricity actually delivered by seller campus i to buyer campus j is less than its amount, then campus i needs to pay compensation for campus j, consisting of two parts: firstly, green electricity deficiency loss compensation (taking green electricity utility coefficient of park j as a reference) due to green electricity deficiency, and secondly, supply and demand unbalance compensation (taking terminal selling electricity price purchased from a power grid enterprise as a reference) due to electric energy deficiency are specifically as follows:wherein: />Is the green electric power actually delivered by park i to park j.
The trial environment of the algorithm contained 4 independently operated parks where the equipment contained distributed renewable energy sources, energy storage devices and power consumers with the parameter settings shown in table 1. And the rolling optimization time window T is 24 hours, and each optimization period length Δt is 1 hour.
TABLE 1 setting of operating parameters for different campuses
The actual load curve and the distributed green power output curve of the campus on a typical day are shown in fig. 2, and the deviation between the predicted value and the actual value is set to be satisfied:wherein: />Is the actual green power output; />Is the actual load level; />And->Is the predicted degree of deviation of green electricity and load; epsilon i,t Is the upper limit of the predicted deviation degree, and epsilon is set by the method i,t ＝ε initial ++ (t-v) δ, wherein: epsilon initial Is the initial value of the upper limit of the predicted deviation degree; delta is the step size of the change. Representing that the upper limit will increase with increasing predicted and current time interval (epsilon of the campus initial All 0.01 and delta all 0.01).
The power trade situation between parks is shown in figure 3. In the inter-campus trading market, campus 1 and campus 4 purchase 206.44MWh and 148.99MWh green electricity, respectively, for green electricity buyers in the trade because their own green electricity capacity is insufficient to meet the local green electricity demand. Park 2 and park 3 sell 205.48MWh and 149.95MWh green electricity, respectively, to green electricity sellers in the trade because there is still room for their own green electricity capacity after meeting the local green electricity demand. Therefore, the green electricity trading mechanism among parks provided by the method realizes reasonable configuration of green electricity resources in a wider range.
The green electricity trade price in the garden section is shown in fig. 4. Green electric prices for campsite 1 and 3 are between $14.26/MW and $18.60/MW, and green electric prices for campsite 2 are between $14.05/MW and $16.12/MW; green electric prices for campsite 4 and campsite 2 are between $10.38/MW and $13.52/MW, and green electric prices for campsite 3 are between $9.99/MW and $14.25/MW. Wherein: the price of electricity purchased from campus 12, 3 is higher than the price of electricity purchased from campus 4, 2, 3 because the green electricity shortage cost factor for campus 1 is higher than that for campus 4, and the green electricity shortage (load level times green electricity demand ratio) is greater, thus it is desirable to pay higher fees to meet the local green electricity demand.
The change of the comprehensive energy cost of each park after the green electricity trade is cleared is shown in figure 5. As can be seen, the costs of campuses 1-4 are reduced to the same extent during different time periods in the method of rolling out green electronic transactions between campuses based on Nash bargained. The total daily costs for parks are shown in table 2, with the total costs for parks 1-4 each reduced by $451 after the transaction. The result shows that the clearing method provided by the method realizes pareto improvement of all market members, and all parks fairly distribute social benefit increment brought by green electricity trading.
Table 2 cost improvement levels for different campuses
The balance of each campus to participate in the transaction is shown in figure 6. The results indicate that green electricity-rich parks (parks 2 and 3) can receive additional revenue through inter-park green electricity transactions, while green electricity-deficient parks (parks 1 and 4) satisfy local green electricity demand by participating in transactions.
In green electricity trading, campus 2 and campus 3 are green electricity sellers, and their settlement bias needs to be checked. To verify the effectiveness of the rolling clearing method in reducing the loss of campus revenue caused by uncertainty, the present example compares the penalty results of the method in the campus deviation with the multi-period unified clearing method, as shown in table 3.
TABLE 3 transaction settlement deviation penalty results
As can be seen from table 3, the bias penalty for campus 2 and 3 using the rolling purge method was reduced by $14.2 (13.7%) and $82.5 (33.4%), respectively, as compared to the multi-slot unified purge method.
Compared with the prior art, the method can improve the accuracy of the delivery of the park in the environment of uncertainty of renewable energy power generation when the park participates in green electricity transaction, and maintain the enthusiasm of the park in the green electricity transaction.
The foregoing embodiments may be partially modified in numerous ways by those skilled in the art without departing from the principles and spirit of the invention, the scope of which is defined in the claims and not by the foregoing embodiments, and all such implementations are within the scope of the invention.

Claims (6)

1. The park green electric trade rolling clearing and settlement method based on the Nash bargained is characterized in that after a park green electric trade framework is constructed, a park green electric trade rolling clearing and settlement model based on the Nash bargained is constructed, and park green electric trade rolling clearing based on the Nash bargained is carried out.
2. The method for rolling out and settling green electric power transaction based on Nash bargained price according to claim 1, characterized in that it comprises the following steps:
s1, constructing a garden interval green electricity transaction mechanism, which comprises the following steps: clearing and settlement of the transaction:
s1.1, clearing out transaction: in the period v, firstly submitting quotation information from v+1 period to v+T period by a park participating in green electricity transaction, and delivering transaction deposit; then, the trading platform solves by taking T as a clearing window to obtain a clearing result of green electricity trading in the garden area; finally, the platform returns the clearing result to the park participating in the transaction, and after the park confirms the result, the park delivers green electricity according to the clearing result in the period v+1;
s1.2, transaction settlement: the park performs transaction settlement in the v+2 period, the transaction platform calculates the green electricity transaction deviation amount between the parks and performs settlement according to the measurement result submitted by the measurement equipment, and then returns the settlement result to the park participating in the transaction, wherein: the core algorithm of the transaction platform is an S2 inter-park green electricity transaction model;
s2, constructing a green electricity transaction model among parks, which specifically comprises the following steps:
s2.1, green electricity transaction clearing: in order to determine the clearing result of green electricity transaction among the parks, an optimized operation model of a single park is firstly established under the transaction mechanism of the embodiment; secondly, providing a green electricity trade rolling out model and a solving method between parks based on Nash bargained theory, and guaranteeing the resource allocation efficiency optimality of the market and the trade bonus allocation fairness;
s2.2, green electricity transaction settlement: the actual values of the load and the green electricity output in the park often deviate from the predicted values; in this case, the actual green electricity generating capacity is regulated to sequentially meet the local demand and the inter-campus trade delivery demand, and the rest part is purchased by the power grid enterprise; meanwhile, when a certain park sells green electricity to a plurality of parks at the same time, the actual delivery quantity of the green electricity is distributed according to the transaction quantity proportion; when the green electricity actually delivered by seller campus i to buyer campus j is less than its amount, then campus i needs to pay compensation for campus j, consisting of two parts: firstly, green electricity deficiency loss compensation due to green electricity deficiency is based on a green electricity utility coefficient of a park j, secondly, supply and demand unbalance compensation due to electric energy deficiency is based on a terminal selling electricity price purchased from a power grid enterprise, and specifically, the method comprises the following steps:wherein: />Is the green electric power actually delivered by park i to park j.
3. The method for rolling out and settling green power transaction based on nash bargained price according to claim 2, wherein said step S2.1 comprises:
s2.1.1 a single campus operation model is constructed, comprising:
1) The objective function specifically includes:
(1) the objective function of a single park is to minimize its overall energy costWherein: respectively representing insufficient green electricity cost, electricity purchasing cost from a power grid enterprise, electricity selling benefits to the power grid enterprise and green electricity selling benefits to other parks, namely, negatively representing the green electricity purchasing cost from other parks;
(2) green electricity demand exists for both business and residential areas in the campus: for industrial users, the use of green energy can bring higher added value to the commodity; for commercial residential users, the green energy is used to meet the enthusiasm of energy conservation and emission reduction; green electricity shortage cost of park iWherein: t is the run time period; Δt is the length of the period; t is a time window for park rolling optimization; v is the current time period; a, a i Is a green electricity shortage cost coefficient of the park i and represents utility loss of unit load brought by the green electricity shortage in the park to users; />Is the load level of the t-slot park i; mu (mu) i Is the green electricity demand duty cycle in the load; />The power is the self-power-generation and self-use partial power of green electricity of park i;
(3) cost of campus i purchasing electricity from large gridWherein: selling electricity price for the terminal; />The electricity purchasing quantity of the park i from the large power grid in the period t;
(4) when the green power output in park i is higher than negativeWhen green electricity is required, the surplus electricity is sold to the power grid enterprises, and the obtained benefits are obtainedWherein: the method comprises the steps of surfing the internet for distributed energy sources; />Is the green electricity allowance internet surfing part power of park i;
(5) benefits of green electricity sales from campus i to other parksWherein: pi ij,t The price of green electricity transaction between park i and park j in the period of t is clear; p (P) ij,t Δt is the green electricity amount sold by park i to park j in period t; and has pi ij,t ＝π ji,t ；P ij,t ＝-P ji,t ；
2) The constraint conditions specifically include:
(1) charging and discharging constraints of energy storage device of park iWherein: />The energy stored by the energy storage device in the park i in the period t is stored; />And->Respectively charging power and discharging power of the energy storage device in the park i in the t period; />And->Respectively charging and discharging efficiency of the energy storage device in the park i;
(2) green power output in park i is the sum of the self-service partial power and the rest of the internet partial power:
(3) electric energy supply and demand balance constraint of park i
(4) Charging and discharging power constraint and stored energy constraint of energy storage device in park iWherein:is the upper energy limit of the energy storage device in park i; p (P) i es,max Is the maximum charge and discharge power of the energy storage device in park i; />In order to represent 0-1 variable of the charge and discharge states of the energy storage, the value of 1 represents that the energy storage device of the park i is in a charge state in a t period, and otherwise, the energy storage device is in a discharge state;
s2.1.2 Rolling out green electricity transactions among parks based on nash bargained: the premise that the rational park participates in the inter-park transaction is that the green electricity transaction reduces the comprehensive energy cost of the environment, so that the cost of the park before and after the green electricity transaction needs to meet personal rational constraint:wherein: />Indicating that the calculated campus i does not participate in the campusThe comprehensive energy cost during transaction is also called negotiation breaking point of the park for participating in the transaction in Nash bargaining theory, and the cost is substituted as constant into the rolling-out model for solving; />Nash bargained inter-park green electricity transaction rolling modelWherein: m is the number of parks participating in green electricity transaction; is the clearing variable of green electricity transaction among parks, namely { pi } ij,t ,P ij,t }。
4. The method for rolling out and settling green power transaction on a nano-bargained basis according to claim 3, wherein the green power transaction rolling out model between the nano-bargained basis is obtained by a two-stage solving method; since the purpose of Nash bargaining is to fairly increase social benefits, the model is converted into two sub-problems of minimizing social costs and maximizing payment benefits, and then solved in turn to get the result of the inter-campus transaction.
5. The method for rolling out and settling green power transaction based on nash bargained price according to claim 4, wherein the two-stage optimization results and the optimal solution equivalent thereof specifically comprises:
1) The total social cost is minimized: according to the arithmetic-geometric mean inequality, when the objective function obtains the optimal solution, the following needs to be satisfied:which is equivalent to the overall energy cost minimization problem for all parks involved in the transaction: />Since the sales of green electricity in the round section are opposite to each other, and green electricityThe trade prices are equal, so that trade gains of all parks cancel each other, i.e. +.>Thereby simplifying the problem of minimizing the total comprehensive energy consumption cost of the park>Further determining the optimal trade volume of the original rolling out model and the corresponding local optimal scheduling plan of the park, i.e +.>Wherein: the underlined variables represent their optimal solutions;
2) Maximizing payment benefit: after determining the optimal trading volume of green electricity among parks, the optimal variables are replaced and logarithm is taken to determine the trading price of green electricity; in the rolling clearing mode, the clearing power of the v+1 to v+T time periods is obtained in the v time period, but only the clearing result of the v+1 time period is actually executed, so that the payment benefit maximization sub-problem also only needs to solve the clearing price of the v+1 time period, namelyThe clearing process is completed by obtaining the green electricity trade clearing price between parks in the v+1 period.
6. A campus green power transaction roll-out and settlement system implementing the method of any one of claims 1-5, comprising: the micro-grid green electricity market rolling out clear unit and micro-grid green electricity settlement unit based on Nash bargaining, wherein: the micro-grid green electricity market rolling clearing unit based on Nash bargaining carries out market clearing processing according to the micro-grid information participating in green electricity transaction to obtain a micro-grid green electricity transaction result; and the micro-grid green electricity settlement unit performs settlement processing according to the calculated micro-grid green electricity transaction amount and price information to obtain a settlement result.
CN202111636846.0A 2021-12-29 2021-12-29 Park green power transaction rolling clearing and settlement method based on Nash bargained price Pending CN116452325A (en)

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