A satisfaction-based day-ahead electricity market user transaction power allocation method

Abstract

The application discloses the technical field of power market electricity distribution, and relates to a user transaction electricity distribution method based on satisfaction in a day-ahead power market, which comprises the following steps: step one, collecting the quotation of a power producer i in a day-ahead market at time t by a day-ahead power market transaction system; step two, calculating the power purchase cost of a user j in the day-ahead market; step three, defining the power consumption satisfaction of the user j in the day-ahead power market at time t; step four, defining the carbon emission index of the user j at time t; step five, obtaining the power purchase distribution of each user participating in the day-ahead power market transaction; and step six, when the distribution is not completed, t becomes t+1, and the above operation is repeated continuously. The method can reasonably distribute the transaction electricity of each user through a user transaction electricity distribution optimization algorithm in the day-ahead power market, maximally improve the satisfaction of each user participating in the power market transaction, efficiently adjust the flexible load resource, and reduce the overall carbon emission of the power system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power market electricity allocation, and particularly relates to a day-ahead power market user transaction electricity allocation method based on satisfaction. BACKGROUND

[0002] With the increasing complexity of power grid structure and the increasing risk of operation control, it is urgent to deepen the development of power grid regulation resources and promote the transformation of the traditional "source following load" mode to the collaborative mode of "source-load interaction". At present, China's medium and long-term power market transactions are steadily advancing, and the power spot market transaction has entered the pilot stage. The power spot market includes electricity transactions mainly in day-ahead, intraday and real-time, and auxiliary service transactions mainly in reserve and frequency modulation. Due to the existence of a large number of flexible load resources with regulation potential on the distribution network side, there are many characteristics such as large quantity, small capacity, low voltage level and diverse subjects, including electric vehicles, distributed energy storage and intelligent building air conditioners and other types of loads. Each power purchasing user selects the optimal electricity in the power market transaction according to the price offered by the power supplier and the callable capacity of the flexible load resources they have. The existing power market user transaction electricity allocation method generally considers reducing the power purchase cost, without considering the minimum carbon emission, the maximum power user satisfaction and the influence of emerging loads such as electric vehicles and energy storage systems. SUMMARY

[0003] In view of the deficiencies of the prior art, the purpose of the present application is to provide a day-ahead power market user transaction electricity allocation method based on satisfaction, so as to solve the problems raised in the background art.

[0004] The purpose of the present application can be achieved by the following technical solutions:

[0005] A day-ahead power market user transaction electricity allocation method based on satisfaction, the day-ahead power market user transaction electricity allocation method comprising the following steps:

[0006] Step 1: The day-ahead power market transaction system collects the price offered by power supplier i in the day-ahead market at time t i∈M, j∈N;

[0007] Step 2: According to the price offered by the power supplier i in the day-ahead market collected in step 1 Calculate the power purchase cost of user j in the day-ahead market The power purchase cost is the sum of the costs of user j purchasing electricity from M power suppliers, as follows:

[0008]

[0009] In the formula, is the decision variable of user j in the day-ahead market;

[0010] Step 3: Define the power consumption satisfaction of user j at time t in the day-ahead power market As follows:

[0011]

[0012] In the formula, is the initial electricity demand predicted by user j in the day-ahead market;

[0013] Step four: define the carbon emission index of user j at time t As follows:

[0014]

[0015] In the formula, γ is a control factor;

[0016] Step five: according to the objective function and constraint conditions of user j, the decision of the electricity purchase quantity of user j at time t is calculated by genetic algorithm, and the electricity purchase quantity distribution of each user participating in the day-ahead electricity market transaction is obtained:

[0017] The objective function of user j is:

[0018]

[0019] The constraint conditions include transaction electricity quantity constraint, user satisfaction constraint, upper limit constraint of power generation company output, discharging capacity constraint of dispatchable electric vehicles, adjustable air conditioning load regulation capacity constraint, energy storage system regulation capacity constraint and day-ahead market power constraint;

[0020] Step six: when the electricity distribution ends, the process ends, and when the electricity distribution does not end, t becomes t+1 and the above operation is repeated.

[0021] Preferably, in the step one, M is a set of power generation companies, and N is a set of users.

[0022] Preferably, in the step three, the numerical value of describes the electricity satisfaction of user j after the flexible resource adjustment, The smaller the value is, the higher the satisfaction is.

[0023] Preferably, the constraint in the constraint condition is as follows:

[0024] The transaction electricity quantity constraint is:

[0025] The user satisfaction constraint is:

[0026] The upper limit constraint of power generation company output is:

[0027] The discharging capacity constraint of dispatchable electric vehicles is:

[0028] The dispatchable air conditioner load regulation constraint:

[0029] The energy storage system regulation constraint:

[0030] The day-ahead market power constraint:

[0031] Advantages of the present application:

[0032] 1、The method in the present application considers the satisfaction degree after adjusting the flexible resource, and reasonably allocates the transaction power of each user through the day-ahead power market user transaction power allocation optimization algorithm, maximizes the satisfaction degree of each user participating in the power market transaction, and can efficiently adjust the flexible load resource, and better reduces the overall carbon emission of the power system;

[0033] 2、The objective function in the present application minimizes the power purchase cost of each user and the carbon emission index of each user, and maximizes the user satisfaction degree, that is, the power purchase demand of the user is maximized on the premise of meeting the carbon emission requirement;

[0034] 3、The constraint condition in the present application considers the important load at the present stage, such as the electric vehicle, the air conditioner load and the energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0036] Figure 1 It is a flowchart of the method. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0038] Please refer to Figure 1 The present application proposes a day-ahead power market user transaction power allocation method based on satisfaction degree, which comprises:

[0039] 1) The day-ahead power market transaction system collects the offer of power supplier i in the day-ahead market at time t where M is the set of power suppliers, and N is the set of users.

[0040] 2) Collect the data of step 1 Calculate the electricity purchase cost of user j in the day-ahead market The electricity purchase cost is the sum of the cost of user j purchasing electricity from M power suppliers, i.e. where is the decision variable of user j in the day-ahead market.

[0041] 3) Define the electricity consumption satisfaction of user j in the day-ahead market at time t The value describes the electricity consumption satisfaction of user j after flexible resource adjustment, and the smaller the value, the higher the satisfaction, where is the initial electricity purchase amount predicted by user j in the day-ahead market.

[0042] 4) Define the carbon emission index of user j at time t to represent the contribution of user j, where γ is a control factor and is a constant value.

[0043] 5) Calculate the decision of user j purchasing electricity from power supplier i at time t according to the following objective function and constraint conditions, and obtain the electricity purchase amount distribution of each user participating in the day-ahead electricity market transaction.

[0044] 6) The objective function of user j is:

[0045]

[0046] 7) The constraint conditions include transaction electricity quantity constraint, user satisfaction constraint, power supplier output upper limit constraint, dispatchable electric vehicle discharge quantity constraint, dispatchable air conditioner load regulation quantity constraint, energy storage system regulation quantity constraint, and day-ahead market power constraint;

[0047] Transaction electricity quantity constraint:

[0048] User satisfaction constraint:

[0049] Power supplier output upper limit constraint:

[0050] Dispatchable electric vehicle discharge quantity constraint:

[0051] Dispatchable air conditioner load regulation quantity constraint:

[0052] Energy storage system regulation quantity constraint:

[0053] Recent market power constraints:

[0054] wherein, is the power output of the power producer i in the day-ahead market at time t, is the upper limit of the discharging power of the dispatchable charging and discharging electric vehicle in the day-ahead market at time t, is the discharging power of the dispatchable charging and discharging electric vehicle in the day-ahead market at time t, is the upper limit of the regulating power of the dispatchable air conditioning load in the day-ahead market at time t, is the regulating power of the dispatchable air conditioning load in the day-ahead market at time t, is the upper limit of the power output of the energy storage system in the day-ahead market at time t, storage,0 is the upper limit of the power storage of the energy storage system, is the regulating power of the energy storage system in the day-ahead market at time t, is the charging power of the dispatchable and disorderly charging and discharging electric vehicle in the day-ahead market at time t.

[0055] 8) When the power distribution ends, the process ends, and when the power distribution does not end, t becomes t+1 to continue repeating the above operation.

[0056] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0057] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only illustrative of the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A method for allocating electricity trading volume to users in the day-ahead electricity market based on satisfaction, characterized in that, The method for allocating electricity traded by users in the day-ahead electricity market includes the following steps: Step 1: The day-ahead electricity market trading system collects the day-ahead price quotes from generator i at time t. i∈M, j∈N; Step Two: Based on the data collected in Step One Calculate the market electricity purchase cost for user j days ago. The cost of purchasing electricity is the sum of the costs incurred by user j in purchasing electricity from M power generators, as shown in the following formula: In the formula, Let j be the market decision variables for user j days ago; Step 3: Define the electricity satisfaction of user j at time t in the day-ahead electricity market. As shown in the following formula: In the formula, The initial electricity demand for user j as predicted by the market today; Step 4: Define the carbon emission index of user j at time t. As shown in the following formula: In the formula, γ is the control factor; Step 5: Based on user j's objective function and constraints, calculate user j's decision to purchase electricity from generator i at time t using a genetic algorithm, thus obtaining the electricity purchase allocation for each user participating in the day-ahead electricity market transaction: The objective function for user j is: The constraints include trading volume constraints, user satisfaction constraints, power generator output upper limit constraints, dispatchable electric vehicle discharge volume constraints, dispatchable air conditioning load regulation volume constraints, energy storage system regulation volume constraints, and day-ahead market power constraints. Step 6: When the power allocation is finished, the process ends. If the power allocation is not finished, t becomes t+1 and the above operation is repeated.

2. The method for allocating day-ahead electricity trading volume based on satisfaction in the electricity market according to claim 1, characterized in that, In step one, M is the collection of e-commerce sellers and N is the collection of users.

3. The method for allocating day-ahead electricity trading volume based on satisfaction in the electricity market according to claim 1, characterized in that, In step three The numerical value describes user j's satisfaction with electricity usage after flexible resource adjustments. The smaller the value, the higher the satisfaction level.

4. The method for allocating day-ahead electricity trading volume based on satisfaction in the electricity market according to claim 1, characterized in that, The constraints in the aforementioned constraints are as follows: The transaction power constraint: The user satisfaction constraint: The power generation operator's output limit constraint: The schedulable electric vehicle discharge constraint: The constraint on the controllable air conditioning load: The energy storage system control constraints: The aforementioned market power constraint: in, This contributes to the market forecast of e-commerce at the time of t. The upper limit of the day-ahead discharge volume of the schedulable charging and discharging electric vehicle market at time t. Let t be the day-ahead discharge volume of the market for dispatchable charging and discharging electric vehicles. The upper limit of the day-ahead market regulation volume for dispatchable air conditioning load at time t. The amount of air conditioning load that can be scheduled at time t is the amount of market regulation available on a given day. G represents the day-ahead output ceiling of energy storage systems at time t. storage,0 This represents the upper limit of the energy storage capacity of the energy storage system. The amount of regulation in the energy storage system market at time t is the amount of regulation in the day-ahead market. The day-ahead charging volume of the electric vehicle market at time t, which is both schedulable and unordered.

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