Retail side electricity price optimization method considering distributed photovoltaic and energy storage flexible interaction

Abstract

The invention belongs to the technical field of power systems, and relates to a retail side electricity price optimization method considering distributed photovoltaic and energy storage flexible interaction, which comprises the following steps: 1, establishing a power utilization decision balance optimization model of power consumers; 2, establishing a traditional Distflow power flow model of the power distribution network; 3, establishing a retail-side electricity price optimization decision model by taking the lowest total cost of a power distribution network operator as an objective function; according to the method, the influence of the power loss of the power distribution network, the power purchasing behavior of the power distribution network operator from the superior wholesale market and the transaction behavior of the power distribution network operator and the power consumer on the cost of the power distribution network operator is fully considered, and the power distribution network operator decision optimization model is established; the optimal retail side electricity price is decided on the premise that the total operation cost of a power distribution network operator in a working day is lowest, so that the income of the power distribution network operator is increased as much as possible.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems and relates to a method for optimizing retail-side electricity prices by taking into account the flexible interaction between distributed photovoltaics and energy storage. Background Art

[0002] As the core operator of the distribution network and the distribution side market and an important entity that coordinates the power generation and consumption sides, the distribution network operator not only undertakes the responsibility of optimizing the operation within the distribution system, but also plays a bridging role between the distribution side market and the transmission side market and the wholesale power market. Power retail trading refers to power trading activities carried out between distribution network operators and small and medium-sized terminal power users. Its core purpose is to promote users' rational use of electricity and optimize the allocation of power resources through a reasonable retail side electricity price mechanism.

[0003] In the process of electricity trading, in order to alleviate the imbalance of electricity demand between peak hours and off-peak hours, the retail side electricity price usually adopts a time-of-use electricity price mechanism. This price signal can encourage users to adjust their own electricity demand, for example, by shifting electricity load to low-price periods, thereby reducing the overall power system operating costs and improving the flexibility and economy of power grid operation. In this process, distribution network operators purchase electricity from the upper wholesale market and resell it to downstream power users to obtain profits. Their profit model is directly affected by the price fluctuations in the electricity market and the electricity consumption behavior of users.

[0004] Under the background of "dual carbon" goals and the construction of new power systems, the rapid development of technologies such as distributed photovoltaics, energy storage equipment and demand response has provided power users with more opportunities to participate in the power market. Users can use their own energy storage equipment to optimize power purchase and sales decisions, reasonably allocate power purchase and sales at different times, and minimize their own operating costs. At the same time, the active participation of users provides a basis for distribution network operators to optimize the power purchase and sales price strategy, thereby creating conditions for improving economic benefits.

[0005] The profit model of distribution network operators is mainly based on the price difference between purchasing electricity from the upper wholesale market and reselling it to users. In order to balance the supply and demand of electricity while increasing profits, operators need to formulate reasonable retail-side electricity price strategies and guide users to adjust their electricity consumption and sales behaviors through price signals.

[0006] Therefore, how to utilize the user's response characteristics to price signals and reasonably formulate retail-side electricity price strategies to maximize the profits of distribution network operators while ensuring the safe and stable operation of the distribution network has become an issue that needs to be studied urgently. Summary of the invention

[0007] The present invention provides a retail-side electricity price optimization method that considers the flexible interaction between distributed photovoltaics and energy storage, and provides an important reference for promoting the efficient operation of the power market and improving the economic benefits of distribution network operators: considering the cost of electricity users purchasing and selling electricity from distribution network operators, the cost of distributed photovoltaic output of power users, and the cost of energy storage operation, an electricity user electricity consumption balance decision optimization model is established to achieve optimal electricity consumption behavior under the premise of the lowest total operating cost of power users within a working day. Fully considering the impact of distribution network power loss, distribution network operators' electricity purchase behavior from the upper wholesale market, and distribution network operators' transaction behavior with power users on the cost of distribution network operators, a distribution network operator decision optimization model is established to decide the optimal retail-side electricity price under the premise of the lowest total operating cost of distribution network operators within a working day, so as to maximize their own profits.

[0008] The technical solution adopted by the present invention to solve the technical problem is: a method for optimizing retail electricity prices considering the flexible interaction between distributed photovoltaics and energy storage, comprising the following steps:

[0009] Step 1: Establish a balanced optimization model for power consumption decisions of power users;

[0010] The electricity consumption decision-making equilibrium optimization model takes the lowest individual cost of each user as the objective function to meet their respective load demands. The cost of each user includes the cost of purchasing and selling electricity from the distribution network, the operating cost of energy storage and distributed photovoltaics;

[0011] Step 2: Establish the traditional Distflow power flow model of the distribution network;

[0012] Step 3: Establish a retail-side electricity price optimization decision model with the lowest total cost of distribution network operators as the objective function;

[0013] The retail-side electricity price optimization decision model takes the minimization of the total cost of the distribution network operator as its objective function, including the cost of electricity transactions between the distribution network operator and users, the cost of active power loss in the distribution network, the cost of purchasing electricity from the upper wholesale market, and the cost of purchasing electricity from distributed renewable energy sources.

[0014] Preferably, in step 1, the objective function of the electricity consumption decision-making balance optimization model is:

[0015] (1)

[0016] In formula (1), Indicates user Costs; , They represent the electricity purchase and sales prices of users at time t respectively; represents the amount of electricity traded between user k and the distribution network operator at time t; represents a binary variable that characterizes the user's role in purchasing and selling electricity. When it is, it means that the user buys electricity from the distribution network operator, and vice versa, it means that the user sells electricity to the distribution network operator; , Represent the unit operating costs of energy storage and distributed photovoltaics respectively; , Represents electricity users Energy storage devices in Charging and discharging power at all times; Represents the output of distributed photovoltaic of user k at time t.

[0017] Preferably, in step 1, the constraints of the electricity consumption decision-making balance optimization model include:

[0018] (2)

[0019] (3)

[0020] (4)

[0021] (5)

[0022] (6)

[0023] (7)

[0024] (8)

[0025] In formula (2) to formula (8), represents the load of user k at time t; represents the maximum allowed value of electricity purchased by electricity user k from the distribution network operator; Indicates the maximum predicted output of distributed photovoltaic equipment; , Respectively represent the maximum value of the electric power of the energy storage device; Represents the storage capacity of the energy storage device at time t; , Respectively represent the energy storage charging and discharging efficiency; Indicates the initial storage capacity of the energy storage device, Indicates the storage capacity of the energy storage device at the last moment.

[0026] Preferably, in step 2, the traditional Distflow power flow model of the distribution network includes:

[0027] (9)

[0028] (10)

[0029] (11)

[0030] (12)

[0031] (13)

[0032] (14)

[0033] In formula (9) to formula (14), represents the square of the current amplitude of branch ij at time t; represents the square of the voltage amplitude of node i at time t; , They represent the active and reactive power flowing from node i through branch ij respectively; , They represent the resistance and reactance of branch ij respectively; , denote the net active and reactive power injected into node i, respectively; Represents the set of all branches; express The distributed renewable energy grid power at node i at time; represents the active power flowing from node i through branch ik; , They represent the minimum and maximum square values ​​of the voltage amplitude at node i respectively; Indicates branch The maximum value of the square of the current amplitude.

[0034] Preferably, in step 3, the objective function of the retail-side electricity price optimization decision model is:

[0035] (15)

[0036] In formula (15), represents the total cost of the distribution network operator; represents the active power loss cost coefficient; express The electricity purchase price of the distribution network operator in the upper wholesale market at that moment; express The amount of electricity purchased by distribution network operators in the upper wholesale market at any given moment; Represents the on-grid electricity price of distributed renewable energy.

[0037] Preferably, the constraints of the retail-side electricity price optimization decision model include:

[0038] (16)

[0039] (17)

[0040] (18)

[0041] (19)

[0042] (20)

[0043] In formula (16) to formula (20), represents the maximum value of the distributed renewable energy output at node i; , and , Respectively represent the highest and lowest electricity purchase and sale prices for users; , Respectively represent the average value of purchase and sale electricity prices.

[0044] The beneficial effects of the present invention are:

[0045] The present invention takes users into consideration, fully considers the impact of distributed photovoltaic and energy storage equipment output and power purchase and sale constraints, and considers the impact of distribution network power loss, distribution network operator's power purchase behavior from the upper wholesale market, and distribution network operator's transaction behavior with users on the distribution network operator's cost, and establishes a distribution network operator's retail-side electricity price optimization decision model that considers user participation in transactions, thereby minimizing the distribution network operator's cost. Compared with existing methods, the present invention takes into account the user's power purchase and sale transaction behavior under distributed power sources and energy storage equipment, and provides an important method for optimizing the decision-making of the retail-side electricity price of distribution network operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a user load level diagram of the retail-side electricity price optimization method considering the flexible interaction between distributed photovoltaic and energy storage in the present invention;

[0047] Figure 2 It is the electricity price map of the upper-level wholesale power grid of the present invention;

[0048] Figure 3 It is a diagram of the amount of electricity purchased by the distribution network operator from the upper wholesale market of the present invention;

[0049] Figure 4 It is a diagram of electric power purchase and sale transaction of electric power users of the present invention;

[0050] Figure 5 It is the retail side electricity price strategy diagram of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the relevant technologies in the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] refer to Figures 1 to 5 In this implementation, the retail electricity price optimization method considering the flexible interaction between distributed photovoltaic and energy storage includes the following steps:

[0053] Step 1: Establish a balanced optimization model for power consumption decisions of power users, including objective functions and constraints.

[0054] Objective function: The objective function is to meet the load demands of each user with the lowest individual cost. The cost of each user includes the cost of purchasing and selling electricity from the distribution network, the operating cost of energy storage and distributed photovoltaics.

[0055] (1)

[0056] In the formula, For users Costs; , are the electricity purchase and sales prices of users at time t respectively; is the amount of electricity traded between user k and the distribution network operator at time t; is a binary variable that characterizes the user's role in purchasing and selling electricity. When it is, it means that the user buys electricity from the distribution network operator, and vice versa, it means that the user sells electricity to the distribution network operator; , They are the unit operating costs of energy storage and distributed photovoltaics respectively; , Electricity users Energy storage devices in Charging and discharging power at all times; is the output of distributed photovoltaic of user k at time t.

[0057] Constraints:

[0058] (2)

[0059] (3)

[0060] (4)

[0061] (5)

[0062] (6)

[0063] (7)

[0064] (8)

[0065] In the formula, is the load of user k at time t; is the maximum allowed value of electricity purchased by electricity user k from the distribution network operator; Maximum predicted output for distributed photovoltaic equipment; , are the maximum values ​​of the electric power of the energy storage device respectively; is the storage capacity of the energy storage device at time t; , are the energy storage charging and discharging efficiency respectively; is the initial storage capacity of the energy storage device, It is the storage capacity of the energy storage device at the last moment.

[0066] Step 2: Establish the traditional Distflow power flow model of the distribution network.

[0067] (9)

[0068] (10)

[0069] (11)

[0070] (12)

[0071] (13)

[0072] (14)

[0073] In the formula, is the square of the current amplitude of branch ij at time t; is the square of the voltage amplitude of node i at time t; , are the active and reactive powers flowing from node i through branch ij respectively; , are the resistance and reactance of branch ij respectively; , are the net active and reactive powers injected into node i, respectively; is the set of all branches; for The distributed renewable energy grid power at node i at time; is the active power flowing from node i through branch ik; , are the minimum and maximum square values ​​of the voltage amplitude at node i, respectively; For branch The maximum value of the square of the current amplitude.

[0074] Step 3: Establish a retail-side electricity price optimization decision model with the minimum total cost of distribution network operators as the objective function, including the objective function and constraints.

[0075] Objective function: The objective function is to minimize the total cost of the distribution network operator, including the cost of electricity transactions between the distribution network operator and users, the cost of active power loss in the distribution network, the cost of purchasing electricity from the upper wholesale market, and the cost of purchasing electricity from distributed renewable energy sources.

[0076] (15)

[0077] In the formula, is the total cost for the distribution network operator; is the active power loss cost coefficient; for The electricity purchase price of the distribution network operator in the upper wholesale market at that moment; for The amount of electricity purchased by distribution network operators in the upper wholesale market at any given moment; It is the on-grid electricity price of distributed renewable energy.

[0078] Constraints:

[0079] (16)

[0080] (17)

[0081] (18)

[0082] (19)

[0083] (20)

[0084] In the formula, is the maximum value of the distributed renewable energy output at node i; , and , They are the highest and lowest electricity purchase and sale prices for users respectively; , are the average purchase and sale prices of electricity respectively.

[0085] The implementation process of this method is explained below using the IEEE 33-node power distribution network as an example.

[0086] In this case analysis, 10 users are located at nodes 6, 8, 9, 11, 16, 18, 23, 26, 31, and 32, respectively, with a base voltage of 12.66 kV. The energy storage charge and discharge rates are all 0.95, and the initial energy storage capacity is assumed to be the minimum value. The user load level is as follows: Figure 1 As shown in the figure, the electricity price of the upper wholesale power grid is as follows: Figure 2 shown.

[0087] By using the method of the present invention and implementing each step, the amount of electricity purchased by the distribution network operator from the upper wholesale market can be obtained. Figure 3 As shown in the figure, the amount of electricity purchased and sold by electricity users is as follows: Figure 4 As shown in Figure 2, the retail price strategy of distribution network operators is as follows: Figure 5 As shown. It can be seen that the amount of electricity purchased and sold by users depends on their overall net load level. Users purchase more electricity during the period from 8:00 to 11:00 and from 17:00 to 21:00. From 11:00 to 16:00, the output of distributed photovoltaic power generation is higher than the load, so electricity is sold. Limited by the influence of the average value of the electricity purchase price of electricity users, distribution network operators provide lower electricity purchase prices during periods when electricity users have lower electricity purchase demand, and then increase the electricity price during periods when electricity users have high electricity purchase demand to increase their own profits. By providing higher electricity prices during periods when electricity users have low electricity sales, the electricity sales prices during periods when electricity users have high electricity sales are balanced. Therefore, distribution network operators can maximize their own profits by reasonably adjusting the electricity purchase and sales prices of electricity users during different periods of electricity purchase and sales.

[0088] In summary, the present invention starts from the user, fully considers the impact of the output of distributed photovoltaic and energy storage equipment and the constraints of power purchase and sale, and considers the impact of distribution network power loss, distribution network operator's power purchase behavior from the upper wholesale market, and distribution network operator's transaction behavior with users on the distribution network operator's cost, and establishes a distribution network operator's retail-side electricity price optimization decision model that takes into account user participation in transactions, so as to minimize the cost of distribution network operators. Compared with existing methods, the present invention takes into account the user's power purchase and sale transaction behavior under distributed power sources and energy storage equipment, and provides an important method for optimizing the decision-making of the retail-side electricity price of distribution network operators. Therefore, the present invention has broad application prospects in the field of retail-side electricity prices of distribution network operators.

[0089] It should be emphasized that the above are only preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Any simple modification of the above embodiments based on the technical essence of the present invention also falls within the protection scope of the present invention. Other equivalent changes and modifications still fall within the scope of the technical solution of the present invention.

Claims

1. A retail electricity price optimization method considering the flexible interaction between distributed photovoltaics and energy storage, characterized in that: The following steps are involved: Step 1: Establish a balanced optimization model for power consumption decisions of power users; The electricity consumption decision-making equilibrium optimization model takes the lowest individual cost of users as the objective function to meet their respective load demands. The cost of each user includes the cost of purchasing and selling electricity from the distribution network, the operating cost of energy storage and distributed photovoltaics; Step 2: Establish the traditional Distflow power flow model of the distribution network; Step 3: Establish a retail-side electricity price optimization decision model with the lowest total cost of distribution network operators as the objective function; The retail-side electricity price optimization decision model takes the minimization of the total cost of the distribution network operator as the objective function, including the cost of electricity transactions between the distribution network operator and users, the cost of active power loss in the distribution network, the cost of electricity purchased by the distribution system operator from the upper wholesale market, and the cost of electricity purchased from distributed renewable energy sources.

2. The retail-side electricity price optimization method considering the flexible interaction between distributed photovoltaic and energy storage according to claim 1 is characterized in that: In step 1, the objective function of the electricity consumption decision-making balance optimization model is: （1） In formula (1), Indicates user Costs; , They represent the electricity purchase and sales prices of the user at time t respectively; represents the amount of electricity traded between user k and the distribution network operator at time t; represents a binary variable that characterizes the user's role in purchasing and selling electricity. When it is, it means that the user buys electricity from the distribution network operator, and vice versa, it means that the user sells electricity to the distribution network operator; , Represent the unit operating costs of energy storage and distributed photovoltaics respectively; , Represents electricity users Energy storage devices in Charging and discharging power at all times; Represents the output of distributed photovoltaic of user k at time t.

3. The method for optimizing retail electricity prices considering the flexible interaction between distributed photovoltaic and energy storage according to claim 2 is characterized in that: In step 1, the constraints of the electricity consumption decision-making balance optimization model include: （2） （3） （4） （5） （6） （7） （8） In formula (2) to formula (8), represents the load of user k at time t; represents the maximum allowed value of electricity purchased by electricity user k from the distribution network operator; Indicates the maximum predicted output of distributed photovoltaic equipment; , Respectively represent the maximum value of the electric power of the energy storage device; Represents the storage capacity of the energy storage device at time t; , Respectively represent the energy storage charging and discharging efficiency; Indicates the initial storage capacity of the energy storage device, Indicates the storage capacity of the energy storage device at the last moment.

4. The method for optimizing retail electricity prices considering the flexible interaction between distributed photovoltaic and energy storage according to claim 1 is characterized in that: In step 2, the traditional Distflow power flow model of the distribution network includes: （9） （10） （11） （12） （13） （14） In formula (9) to formula (14), represents the square of the current amplitude of branch ij at time t; represents the square of the voltage amplitude of node i at time t; , They represent the active and reactive power flowing from node i through branch ij respectively; , They represent the resistance and reactance of branch ij respectively; , denote the net active and reactive power injected into node i, respectively; Represents the set of all branches; express The distributed renewable energy grid power at node i at time; represents the active power flowing from node i through branch ik; , They represent the minimum and maximum square values ​​of the voltage amplitude at node i respectively; Indicates branch Maximum value of the square of the current amplitude.

5. The method for optimizing retail electricity prices considering the flexible interaction between distributed photovoltaic and energy storage according to claim 1 is characterized in that: In step 3, the objective function of the retail-side electricity price optimization decision model is: （15） In formula (15), represents the total cost of the distribution network operator; represents the active power loss cost coefficient; express The electricity purchase price of the distribution network operator in the upper wholesale market at that moment; express The amount of electricity purchased by distribution network operators in the upper wholesale market at any given moment; Represents the on-grid electricity price of distributed renewable energy.

6. The method for optimizing retail electricity prices considering the flexible interaction between distributed photovoltaics and energy storage according to claim 5 is characterized in that: The constraints of the retail-side electricity price optimization decision model include: （16） （17） （18） （19） （20） In formula (16) to formula (20), represents the maximum value of the distributed renewable energy output at node i; , and , Respectively represent the highest and lowest electricity purchase and sale prices for users; , Respectively represent the average value of purchase and sale electricity prices.

Images