Community-distribution network energy optimization method considering flexible resources and internal transactions

Abstract

The invention discloses a community-distribution network energy optimization method considering flexible resources and internal transactions, and the method comprises the steps: obtaining the internal transaction power of community members through a community internal production consumer transaction model, obtaining the active power and reactive power of a production consumer side through a community day-ahead optimization scheduling model, and obtaining the day-ahead active power and reactive power curves of the production consumer side. The active and reactive power curves of the producer and the consumer during real-time operation are obtained through the active and reactive power flexibility boundary models, then the active and reactive power flexibility margins are obtained, and the branch current violation amount, the node voltage violation amount and the cost for purchasing the flexible power are minimized through the power distribution network system optimization model. According to the community-distribution network energy management method, the violation amount of the community electricity charge and the voltage and current of the distribution network is reduced through resource sharing optimization, the community electricity utilization cost can be effectively reduced, the power factor and the operation stability are considered, and a safe, efficient and intelligent solution is provided for community-distribution network energy management.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a community-distribution network energy optimization method considering flexibility resources and internal transactions. Background Art

[0002] With the rapid development of distributed renewable energy, community energy systems, as integrated systems encompassing distributed photovoltaics, electric vehicle clusters, battery energy storage, and adjustable loads, are enabling local consumption of distributed energy by sharing and optimizing flexible resources. Traditional community day-ahead optimization models often fail to consider the physical constraints of the distribution network, internal transactions, quantify flexibility margins, and neglect reactive power, resulting in limited optimization results. Incorporating voltage and power flow constraints within the distribution network within community optimization models is a significant shift and can ensure effective optimization in practical applications. Furthermore, energy transactions between community members present a significant challenge in determining internal transaction prices to ensure overall efficiency while maintaining fairness among members. Furthermore, quantifying the flexibility margins of active and reactive power from photovoltaic and energy storage systems from an economic dispatch perspective and providing flexibility services for the distribution network is a pressing issue.

[0003] Existing research fails to consider the physical constraints of the distribution network in community day-ahead scheduling optimization, nor does it closely integrate internal trading mechanisms with power flow models. This makes it difficult to ensure incentives and fairness in trading while maintaining operational constraints such as node voltage and current. Furthermore, existing research fails to consider providing reactive power flexibility support for the distribution network. Distribution network operators still lack systematic solutions for optimizing the use of flexibility resources, making it difficult to achieve intelligent and flexible optimization of community-distribution networks.

[0004] Chinese invention patent CN110400079A discloses a community optimization method for energy sharing among new energy users. The method predicts the new energy generation and power load of each user in each time period of the next day, optimizes the scheduling based on the new energy generation and power load, so that the overall energy cost of the community on the next day is the lowest when the user participates in energy sharing, thereby obtaining scheduling parameters, and calculates the energy cost of each user when participating in energy sharing. The method also optimizes the scheduling based on the new energy generation and power load, so that the energy cost of each user on the next day is the lowest when the user does not participate in energy sharing, thereby obtaining the energy cost of each user when not participating in energy sharing. Based on the energy cost, a settlement price is determined for calculating the energy sharing income of each user on the next day, so that the total cost of each user after participating in energy sharing is lower than the energy cost when not participating in energy sharing. Day-ahead transactions and energy scheduling for the next day are performed according to the scheduling parameters to achieve energy sharing within the new energy user community. This solution can promote energy sharing within the new energy user community, but it still has the following technical deficiencies: 1) This invention establishes a community energy optimization model, but ignores the physical and power constraints of the distribution network. This may cause the optimization results to violate the constraints in actual applications, affecting the safe and stable operation of the distribution network. 2) This invention focuses on quantifying the active power margin of the community system, but ignores the quantification of reactive power flexibility, making it difficult to provide comprehensive flexibility services; 3) This invention only uses the electricity purchase price set by the grid to calculate revenue, and does not specify how to calculate the internal transaction price, making it difficult to balance the community's electricity costs with the interests of its members; 4) This invention only involves the optimization of flexibility resources within the community, and does not build a distribution system optimization solution that matches the community flexibility services, making it difficult to play the role of an energy community. Summary of the Invention

[0005] In order to solve at least one of the problems existing in the prior art, the present invention provides a community-distribution network energy optimization method that takes into account flexibility resources and internal transactions, and minimizes energy costs and reduces power factor penalties through a community day-ahead optimization scheduling model; the community internal producer-consumer transaction model considers the electricity transactions of members in the same community and adopts a shadow price that reflects the marginal supply and demand balance as the community internal transaction electricity price to obtain the internal transaction power of community members; quantifies the flexibility margin of active power and reactive power of photovoltaic and energy storage, and provides a low-cost method for reducing node voltage and branch current violations for the distribution network, establishes a distribution network system optimization model that utilizes flexibility resources, and improves the stability of distribution network operation.

[0006] To achieve the purpose of the present invention, the present invention provides a community-distribution network energy optimization method that considers flexibility resources and internal transactions, and optimizes community-distribution network energy through a community-distribution network energy optimization model. In the community-distribution network energy optimization model: The intra-community prosumer transaction model considers the electricity transactions of members in the same community and adopts the shadow price reflecting the marginal supply and demand balance as the intra-community transaction price to obtain the intra-community transaction power. The community's day-ahead optimal dispatch model is based on the internal transaction power of community members, distribution network constraints, and considers the power balance of prosumers. By minimizing the total cost of transactions between the community and the grid and the penalty for non-compliant power factors, the active power and reactive power of the prosumer at each moment are obtained, and then the day-ahead active power and reactive power curves of the prosumer side are obtained. The active power flexibility boundary model and reactive power flexibility boundary model take into account the community's electricity purchase cost, low power factor operation penalty and flexibility reward, and obtain the active power curve and reactive power curve of the prosumer in real time. The active power curve and reactive power curve of the prosumer on the day-ahead side are used as reference curves, and the active power flexibility margin and reactive power flexibility margin are obtained based on the active power curve and reactive power curve of the prosumer in real time. The distribution network system optimization model determines the optimal active flexibility and reactive flexibility power based on the active flexibility margin, reactive flexibility margin, branch current maximum flow constraint and node voltage operating range constraint to minimize the branch current violation and node voltage violation of the distribution network system and minimize the cost of purchasing flexibility power.

[0007] Furthermore, the distribution network constraints include coupling constraints, branch constraints, transformer constraints and power balance constraints.

[0008] Furthermore, the objective function of the community's day-ahead optimization scheduling model is expressed as:

[0009] in, for Time Node Total electricity cost in the community, total electricity cost in the community The feasible region of is defined as the difference between the cost of purchasing electricity from the grid and the profit of selling electricity to the grid; To optimize the time period, is the time step; For the community the collection of prosumers within; represents the penalty factor, which converts the non-compliant power factor into a penalty amount; For nodes Where the prosumer is at the moment The reactive power does not meet the standard; The decision variables of the objective function are the active power of the photovoltaic and energy storage systems, the reactive power output of the photovoltaic, energy storage systems and capacitors, the amount of power traded among community members, and the active power traded by the community to the grid.

[0010] Furthermore, the node is obtained through the objective function Where the prosumer is at the moment The reactive power is not in compliance with the standard , based on the node Where the prosumer is at the moment The reactive power is not in compliance with the standard The boundaries of active power and reactive power on the prosumer side are determined by the sum of the reactive power decision variables including the reactive power of photovoltaic and energy storage systems, the power amount traded internally by community members, the reactive power provided by capacitors, and the reactive power traded by the community to the grid. The boundaries of reactive power are determined by the active power on the prosumer side, the reactive power of the prosumer load, and the minimum power factor.

[0011] Furthermore, the intra-community prosumer transaction model includes the prosumer balance power equation, the intra-community transaction power balance equation, and the power equation for non-community prosumers that do not participate in transactions. In the power equation for non-community prosumers that do not participate in transactions, prosumers only exchange power with the power grid.

[0012] Furthermore, the active flexibility boundary model considers the community electricity purchase cost, low power factor operation penalty and flexibility bonus, while the reactive flexibility boundary model considers flexibility bonus based on the community electricity purchase cost, but does not consider the low power factor operation penalty.

[0013] Furthermore, active flexibility is used to reduce the current violation of the distribution network system. The distribution network system optimization model that considers minimizing the current violation and the cost of purchasing flexibility resources is: ; ; in, For the moment, To optimize the time period; is the penalty factor for the current violation; is the sum of the branch current violations; Indicates the unit price of unit active flexibility service; is the sum of the available active power flexibility, is the time step, For nodes, Representing the community The collection of producers and consumers within and are the optimal upward and downward active flexibility powers determined by the distribution network system optimization model, , , represents the upward active power flexibility margin obtained from the active power flexibility boundary model, represents the downward active power flexibility margin obtained from the active power flexibility boundary model; Indicates the interval of flexibility service provision; and Represents a binary variable.

[0014] Furthermore, reactive flexibility is used to reduce the voltage violation at the distribution system nodes. Considering minimizing the voltage violation and the purchase flexibility cost, the distribution network system optimization model is: ; ; in, For the moment, To optimize the time period, A penalty factor representing the amount of voltage violation; is the sum of node voltage violations; is the sum of the available reactive power flexibility, is the time step, For nodes, Representing the community The collection of producers and consumers within means that prosumers provide upward reactive power flexibility, means that the prosumer provides downward reactive power flexibility, Indicates the interval of flexibility service provision, represents the optimal upward reactive flexibility power determined by the distribution network system optimization model, represents the optimal downward reactive flexibility power determined by the distribution network system optimization model, , , represents the upward reactive flexibility margin obtained from the reactive flexibility boundary model, Represents the downward reactive flexibility margin obtained from the reactive flexibility boundary model.

[0015] The present invention also provides a device comprising a processor and a memory, wherein the memory is used to store instructions or computer programs, and the processor is used to execute the instructions or computer programs in the memory so that the device performs the steps of the community-distribution network energy optimization method considering flexibility resources and internal transactions.

[0016] The present invention also provides a computer-readable storage medium, which stores instructions. When the instructions are executed on a device, the device executes the steps of the community-distribution network energy optimization method considering flexibility resources and internal transactions.

[0017] Compared with the prior art, the present invention can at least achieve the following beneficial effects: (1) The present invention combines the physical constraints of the distribution network and the power flow model in the day-ahead community energy optimization, solving the problem of neglecting the constraints of the distribution network voltage, power flow, etc. in traditional community optimization. The internal transaction electricity price is determined by the shadow price related to active power, ensuring the fairness of the transaction and enhancing the internal resource exchange and energy self-sufficiency rate of the community. From the perspective of economic dispatch, the flexibility margin of active power and reactive power of photovoltaic and energy storage is quantified, and an active flexibility and reactive flexibility boundary model is constructed with the minimum community electricity cost, the minimum low power factor penalty, and the maximum flexibility resource reward. This solves the problem of unquantified flexibility margin and neglect of reactive power in traditional flexibility resource scheduling methods. The quantified flexibility resources are used to reduce the violation of the node voltage and branch current of the distribution network, considering the optimization goals of minimizing the cost of purchasing flexibility resources for the distribution network and minimizing the violation of voltage or current, and taking into account the multiple needs of distribution network operation. The present invention can enable the community-distribution network energy optimization model to have the capabilities of considering distribution network constraints, internal community transactions, quantifying the flexibility of active power and reactive power, and providing distribution network flexibility services, and can improve the local consumption capacity of new energy.

[0018] (2) The present invention can solve the problems of existing community energy optimization in terms of distribution network coupling constraints, internal transactions, and the lack of flexibility resource quantification capabilities, as well as the problem of flexible coordination with the distribution network.

[0019] (3) The present invention reduces the violation of community electricity charges and distribution network voltage and current through resource sharing optimization, thereby achieving low-cost and high self-sufficiency energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the process of a community-distribution network energy optimization method considering flexibility resources and internal transactions in an embodiment of the present invention.

[0021] Figure 2 Schematic diagram of the composition of the community energy optimization model in an embodiment of the present invention.

[0022] Figure 3 Schematic diagram of a balanced T-type equivalent circuit used in a distribution network branch in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1The embodiment of the present invention provides a community-distribution network energy optimization method that considers flexibility resources and internal transactions. The method optimizes the community-distribution network energy through a community-distribution network energy optimization model. The optimization is performed through the following steps: Step S1. The intra-community prosumer transaction model considers the electricity transactions of members within the same community and adopts the shadow price reflecting the marginal supply and demand balance as the intra-community transaction price to obtain the intra-community transaction power.

[0025] Step S2. The community day-ahead optimization scheduling model is based on the internal transaction power of community members, distribution network constraints, and takes into account the power balance of producers and consumers. By minimizing the total cost of transactions between the community and the grid and the penalty for non-compliant power factors, the active power and reactive power on the producer-consumer side at each moment are obtained, and then the day-ahead active power curve and reactive power curve on the producer-consumer side are obtained.

[0026] The steps for constructing the community's intra-prosumer transaction model and the community's day-ahead optimization scheduling model include: Based on the balanced T-type model equivalent to the distribution network branch (it is understandable that other models can also be used to equivalent the distribution network branch), the DistFlow method is used to construct the distribution network flow model, analyze the distribution network constraints, and consider the power balance of producers and consumers to minimize the total cost of transactions between the community and the grid and the penalty for non-compliant power factors, thereby establishing a community day-ahead optimal scheduling model for community day-ahead optimal scheduling.

[0027] In one embodiment of the present invention, a balanced T-type equivalent distribution network branch is as follows: Figure 3 As shown in the figure, the balanced T-type equivalent distribution network branch includes input impedance, output impedance, parallel admittance and transformer, and the series impedances at both ends are , represents the complex impedance, is the resistance of the series impedance at both ends, is the reactance of the series impedance at both ends, is the imaginary part, and the intermediate parallel admittance is , is the middle parallel susceptance. The square value of the input voltage is The square value of the input branch current is , the active power at the input is , the reactive power at the input is The transformer is located on the right and is a common transformer or an ideal on-load tap-changer transformer (OLTC). The tap ratio of the ideal on-load tap-changer transformer on the right is , is the transformer tap ratio. The manager wants the square value of the output voltage after the on-load tap changer transformer is , the square value of the output branch current is , the output active power is , the output reactive power is , Indicates time.

[0028] The distribution network constraints include coupling constraints, branch constraints, transformer constraints and power balance constraints.

[0029] The coupling constraint is that according to Kirchhoff's voltage and current laws, the voltage and power on the branches connected to the same bus in the distribution network will affect each other, that is, the voltages of multiple branches on the same bus must be consistent, and the total power input and output of all branches must be equal. In addition, when the node (node For branch When the corresponding node (the number of nodes is equal to the number of buses) is used as an input or output node, the node The square of the voltage is , therefore, the node voltage is expressed as follows: (1); (2); in, The output voltage is The square value of the moment; The input voltage is The square value of the moment; For nodes Voltage The square value of the moment; For the A branch collection of output buses, For the A branch collection of input buses; For nodes Entry The actual active power at any moment, For nodes Entry Actual reactive power at all times; For nodes Output The actual active power at any moment, For nodes Output The actual reactive power at any moment.

[0030] Nodes belonging to a community can conduct internal transactions with other nodes in the same community. In this case, prosumers exchange power through the distribution network, and two additional power balance relationships need to be met: (3); in, For nodes Prosumers in the community The active power output during the transaction, Representation node Prosumers in the community Active power input at the time of transaction; Representation node Prosumers in the community The reactive power output during the transaction. Representation node Prosumers in the community Reactive power input at the moment of trading. In particular, for nodes that do not belong to any community, there is no internal transaction, and there is only the power balance relationship described in formula (2).

[0031] Combining the DistFlow model and the balanced T-type model equivalent distribution network branch, the voltage, current and power balance of the distribution network branch are modeled. The voltage of the branch is expressed as: (4); in, For nodes The midpoint of the branch is The square value of the voltage at the moment, For nodes The input voltage is The square value of the moment; For nodes Entry The actual active power at any moment, For nodes Entry Actual reactive power at all times; The input branch current is The square value of the moment; The output branch current is The square value of the moment; For the uncorrected OLTC transformer Branch output voltage at time; node The series impedances at both ends of the branch are , is the resistance of the series impedance at both ends, is the reactance of the series impedance at both ends, and the parallel admittance in the middle is , It is the middle parallel susceptance.

[0032] The power balance constraint is: (5); in, For nodes Entry The actual active power at any moment, For nodes Entry Actual reactive power at all times; For nodes Output The actual active power at any moment, For nodes Output Actual reactive power at all times; for Time Node Active power on the prosumer side, for Reactive power on the producer-consumer side at the moment; The series resistance across the branch is , the reactance is , the intermediate parallel admittance is .

[0033] The current is expressed as: (6); in, For nodes The input end of the branch is The estimated value of active power at the moment, is a node The input end of the branch is The estimated value of reactive power at the moment, For nodes The midpoint of the branch is The estimated value of active power at the moment, is a node The midpoint of the branch is Estimated value of reactive power at the moment; For nodes Entry The actual active power at any moment, For nodes Entry Actual reactive power at all times; For nodes The midpoint of the branch is The actual active power at any moment, is a node The midpoint of the branch is Actual reactive power at any moment.

[0034] Assuming the bus voltage per unit value is 1.0 pu and ignoring the effects of OLTC and prosumer-side equipment, the estimated power at the branch input can be calculated by adding the power demand of the downstream bus it feeds. The estimated power at the branch midpoint can be calculated by averaging the estimated power at the branch input and the estimated power at the branch output, expressed as: (7); in, For nodes The input end of the branch is The estimated value of active power at the moment, is a node The input end of the branch is The estimated value of reactive power at the moment, For nodes The midpoint of the branch is The estimated value of active power at the moment, is a node The midpoint of the branch is Estimated value of reactive power at the moment; For nodes The output end of the branch is The estimated value of active power at the moment, is a node The output end of the branch is Estimated value of reactive power at the moment; For the The downstream busbar collection of the branch power supply, For downstream Busbars in The active load on the prosumer side at the moment, For downstream Busbars in Reactive power at the moment.

[0035] Branch constraints refer to voltage and current constraints. The expressions of branch constraints include formula (4), formula (5), formula (6) and formula (7), and formula (4), formula (5), formula (6) and formula (7) are the distribution network power flow model expressions.

[0036] For the transformer, the input voltage has been set to However, for the output voltage, we will discuss the OLTC transformer and the ordinary transformer. For the OLTC transformer, it can adjust the voltage under load or excitation state, and its output voltage is subject to the tap ratio range. The transformer constraint is expressed as: (8); in, For the uncorrected The square value of the branch output voltage at the moment; The output voltage is The square value of the moment; is the minimum tap ratio of the transformer, is the maximum tap ratio of the transformer.

[0037] In particular, for ordinary transformers, the voltage can only be adjusted by manually switching the tap in a power outage state. In a normal distribution network, the output voltage is Ordinary transformers can only switch voltage during power outages. In normal operation, they are equivalent to a transformer with a tap ratio of K=1, which is a special case of an ideal OLTC.

[0038] On the prosumer side, ignoring the active power loss in the transformer and cables inside the prosumer, there are photovoltaic power generation power, energy storage charging and discharging power, and load power. The power balance of the prosumer needs to be considered, which can be expressed as follows: , (9); in, for Time Node Active power on the prosumer side, for Time Node Reactive power on the prosumer side; for Time Node Active power of the community load, for Time Node Active power of the community energy storage battery, for Time Node Active power of photovoltaic power generation in the community; when the battery is discharged , charging is the opposite, the entire charging and discharging process must meet the maximum power limit, that is , is the maximum power of the battery; for Time Node The reactive power of the community load, for Time Node The reactive power of the community energy storage battery, for Time Node The reactive power of photovoltaic power generation in the community, Reactive power supplied to the capacitor; for Time Node The loss of charging and discharging of the community energy storage battery is expressed as: (10); in, 、 is the coefficient, when the battery is charging ,on the contrary ; is the battery discharge efficiency; Battery charging efficiency; for Time Node The active power of the community’s energy storage battery.

[0039] In particular, assuming The battery capacity at this moment is , the charging and discharging process can be described by the energy storage battery capacity model: (11); in, is the time step; the entire charging and discharging process must meet the capacity limit, that is, , is the minimum battery capacity, The maximum battery capacity; at the start of charge and discharge and the end moment , the charge is ; for Time Node The active power of the community energy storage battery.

[0040] The community day-ahead optimization dispatch model takes into account the electricity costs generated by transactions between the community and the external power grid, as well as the penalty for non-compliant power factors. The objective function is expressed as: (12); in, for Time Node The total cost of electricity in the community; To optimize the time period, is the time step; For the community the collection of prosumers within; represents the penalty factor, which converts the non-compliant power factor into a penalty amount; For nodes Where the prosumer is at the moment The reactive power is not in compliance with the standard. In one embodiment of the present invention, Set to 24h, Set to 0.25h.

[0041] The decision variables of the objective function are the active power of the photovoltaic and energy storage systems, the reactive power output of the photovoltaic, energy storage systems and capacitors, the power traded among community members, and the power traded by the community to the grid.

[0042] Includes the active power of photovoltaic and energy storage systems, the active power of internal transactions among community members, and the active power decision variables of the community's transactions with the grid. for: (13); in, for Time Node Active power of the community energy storage battery, for Time Node Active power of photovoltaic power generation in the community; for Time Node Active power exchange within the community; for Time Node The active power traded between the community and the grid, when When , it indicates that power flows from the grid to the prosumer.

[0043] Generally speaking, the price at which the grid sells electricity to prosumers is higher than the price at which the grid buys electricity from prosumers. Total cost of electricity in the community The feasible region of is defined as the difference between the cost of purchasing electricity from the grid and the profit of selling electricity to the grid: (14); in, is the price of electricity sold by the grid to prosumers, The price at which the grid purchases electricity from prosumers; for Time Node The active power traded between the community and the grid; is the time step.

[0044] Expressed as: (15); in, for Time Node Active power of the community load; for Time Node Active power exchange within the community, for Time Node Active power of the community energy storage battery, for Time Node Active power of photovoltaic power generation in the community; for Time Node The active power traded between the community and the grid.

[0045] It includes the reactive power of photovoltaic and energy storage systems, the reactive power traded among community members, the reactive power provided by capacitors, and the reactive power decision variables traded by the community to the grid. for: (16); in, for Time Node The reactive power of the community energy storage battery is limited by the minimum power factor. for Time Node The reactive power of photovoltaic power generation in the community, for Time Node Reactive power exchange within the community, Reactive power provided by capacitors. Capacitors are connected at certain nodes, where there is a maximum reactive power available. By balancing the reactive power provided by the energy storage battery with the reactive power of the capacitor, the reactive power non-compliance in the objective function of the community day-ahead optimization scheduling model is minimized.

[0046] Reactive power limit and Defined by the active power on the prosumer side, the reactive power of the prosumer load and the minimum power factor: , (17); in, for Time Node Reactive power of the community load; for Time Node Active power on the prosumer side; Indicates the minimum power factor; is the maximum value of reactive power, is the minimum value of reactive power.

[0047] Therefore, the reactive power non-compliance quantity can be expressed as: (18); in, is the non-compliant quantity of reactive power; is a symbolic function defined as: (19); in, for Time Node Active power on the prosumer side.

[0048] Get the node through the objective function Where the prosumer is at the moment The reactive power is not in compliance with the standard , based on the node Where the prosumer is at the moment The reactive power is not in compliance with the standard The boundaries of active power and reactive power on the prosumer side are used to determine the sum of the decision variables including the reactive power of photovoltaic and energy storage systems, the power volume traded within the community members, the reactive power provided by the capacitors, and the reactive power traded by the community to the grid. .

[0049] In step S1, the intra-community prosumer transaction model, which includes the prosumer balance power equation, the intra-community transaction power balance equation, and the power equation for non-community prosumers not participating in the transaction, is as follows: In addition to trading electricity with the grid, prosumers in the same community can conduct internal transactions. Therefore, the power balance equation for prosumers is: (20); in, for Time Node Active power on the prosumer side, for Time Node Reactive power on the prosumer side, for Time Node The active power traded between the community and the grid, for Time Node Active power exchange within the community, for The grid sends the node Reactive power provided by prosumers in the community, for Time Node Reactive power exchange within the community. In order to avoid prosumers absorbing reactive power from the grid for internal transactions within the community, and also to avoid injecting reactive power into the grid, Set to non-negative.

[0050] There is a balance between active power and reactive power within the community, that is, the power balance equation within the community is: (twenty one); in, Representing the community the collection of prosumers within; for Time Node Active power exchange within the community, for Time Node Reactive power exchange within the community.

[0051] Since reactive power does not generate direct economic benefits, assuming that reactive power is compensated free of charge, the intra-community transaction price can be defined as the shadow price related to active power: (twenty two); in, To provide electricity prices for intra-community transactions; is the shadow price; is the time step.

[0052] For prosumers that do not belong to any community and cannot participate in internal transactions, they only exchange power with the grid, that is, non-community prosumers do not participate in transactions. The power equation for prosumers is: (twenty three); in, for Time Node Active power on the prosumer side, for Time Node Reactive power on the prosumer side; for Time Node The active power traded between the community and the grid, for The grid sends the node Reactive power provided by prosumers in the community.

[0053] S3. The active power flexibility boundary model and the reactive power flexibility boundary model take into account the community's electricity purchase cost, low power factor operation penalty, and flexibility reward to obtain the active power curve and reactive power curve of the prosumer during real-time operation. The active power curve and reactive power curve of the prosumer on the day-ahead side are used as reference curves. The active power flexibility margin and reactive power flexibility margin are derived based on the active power curve and reactive power curve of the prosumer during real-time operation.

[0054] Specifically, taking the active power curve on the prosumer side of the day before as the reference curve, the active power curve of the prosumer based on real-time operation is determined as the upper limit or lower limit of flexibility, and the difference between the upper limit or lower limit of flexibility and the reference curve is defined as the active flexibility margin; taking the reactive power curve on the prosumer side of the day before as the reference curve, the reactive power curve of the prosumer based on real-time operation is determined as the upper limit or lower limit of flexibility, and the difference between the upper limit or lower limit of flexibility and the reference curve is defined as the reactive flexibility margin.

[0055] In this step, the community's electricity purchase costs, low power factor operation penalties, and flexibility incentives are considered to construct the intraday active and reactive flexibility boundary models for the prosumer side, including photovoltaic and energy storage resources. (Intraday refers to the day) The active power flexibility boundary model takes into account the community power purchase cost, low power factor operation penalty and flexibility bonus, and is defined as: (twenty four); in, Indicates the interval of flexibility service provision, starting at , the duration is ; Cost of electricity for the community; For the community the collection of prosumers within; represents the penalty factor, which converts the non-compliant power factor into a penalty amount; For producers and consumers at all times The reactive power does not meet the standard; is the time step; Representation node The reward for the community to provide upstream active flexibility resources to the distribution grid, The reward for the community to provide downstream active flexibility resources to the distribution grid is: (25); in, The unit price of the unit active power flexibility service is determined by the distribution system operator. , The price at which the grid sells electricity to prosumers; is the time step; Indicates the upward active power flexibility margin, Indicates the downward active power flexibility margin. At the same time, the prosumer can only choose to provide either upward or downward flexibility: , (26); in, is the active power curve of the prosumer side on the day before, and the active power of the prosumer side at each moment is obtained by the community day-ahead optimization scheduling model. Represents the active power curve of the prosumer during real-time operation.

[0056] Assume that the reactive power output of the flexibility resource at a given moment is limited only by its power range and is not affected by the reactive power at any historical moment. Furthermore, since reactive output does not affect active power output, this embodiment of the present invention assumes that reactive flexibility has a limited impact on active power output. Furthermore, since the distribution network primarily focuses on the reactive flexibility margin that prosumers can provide, penalties for low power factor operation are not considered. In other words, the reactive flexibility boundary model considers flexibility incentives based on the community's electricity purchase cost, defined as: (27); in, Cost of electricity for the community; For the community the collection of prosumers within; Indicates the interval of flexibility service provision, starting at , the duration is ; Representation node The reward for the community to provide upstream reactive flexibility resources to the grid, Representation node The reward for the community providing downstream reactive flexibility resources to the grid is: (28); in, Indicates the unit price of unit reactive flexibility service; is the time step; represents the upward reactive flexibility margin, It represents the downward reactive power flexibility margin, which is expressed as: (29); in, Represents the reactive power curve of prosumers during real-time operation; is the reactive power curve of the prosumer side on the day-ahead, which is obtained by the reactive power of the prosumer side at each moment through the community day-ahead optimization scheduling model.

[0057] S4. The distribution network system optimization model determines the optimal active and reactive flexibility powers based on the active flexibility margin, reactive flexibility margin, branch current maximum flow constraints, and node voltage operating range constraints to minimize branch current violations, node voltage violations, and the cost of purchasing flexibility power in the distribution network system.

[0058] By analyzing the maximum flow constraint of distribution network branch current and the node voltage operating range constraint, the flexibility resources of prosumers are used to reduce the violations of distribution network branch current and node voltage, and a distribution network system optimization model is established to obtain the minimum current violation and the cost of purchasing flexibility power, the minimum voltage violation and the minimum cost of purchasing flexibility power during optimization.

[0059] The steps for establishing the distribution network system optimization model with flexible resource regulation include: When the load current of the distribution network exceeds its allowable current carrying capacity, the line voltage drop increases, resulting in low node voltage, which ultimately degrades the power quality and shortens the life of the equipment. The maximum current allowed for each branch is , then the violation of the branch input and output current can be expressed as: (30); in, Representative Node exist The amount of current violation at the input end at the moment, Representative Node exist The amount of current violation at the output end at any moment; Representative Node exist The actual input current at the moment, Representation node exist The actual output current at the moment; For the The maximum current allowed for each branch.

[0060] Assume that The maximum voltage allowed for each busbar is , the minimum allowable voltage is , then the voltage violation of the node where the bus is located can be expressed as: (31); in, represents the upper bound of the node voltage violation, Represent the lower bound of the node voltage violation, Represents the actual voltage of the node.

[0061] When the distribution network system obtains flexibility services from community prosumers to reduce the violation of branch current and node voltage in the distribution network system, optimization models are established according to different objectives.

[0062] Active flexibility is used to reduce the current violation of the distribution network system. The distribution network system optimization model that considers minimizing the current violation and the cost of purchasing flexibility resources is: (32); in, To optimize the time period; is the penalty factor for the current violation; is the sum of the branch current violations, satisfying , Representative Node exist The amount of current violation at the input end at the moment, Representative Node exist The amount of current violation at the output end at any moment, Indicates all buses; Indicates the unit price of unit active flexibility service; is the sum of the available active power flexibility, that is (33); in, and are the optimal upward and downward active flexibility powers determined by the distribution network system optimization model, , , represents the upward active power flexibility margin obtained from the active power flexibility boundary model, represents the downward active power flexibility margin obtained from the active power flexibility boundary model; Indicates the interval of flexibility service provision, starting at , the duration is ; and represents a binary variable; when the prosumer provides upward active power flexibility service, ; When prosumers provide downward active power flexibility services, ; The two binary variables satisfy the following relationship, indicating that flexibility service only Continuous time periods ( ) is activated within: (34); in, In the time interval Neidi A binary variable at a certain moment, For the time interval The binary variable of the previous moment.

[0063] At this time, the distribution network Active power of nodes for: (35); in, The active power curve of the day-ahead prosumer side; Indicates the upward active power flexibility margin, Indicates the downward active power flexibility margin; Indicates the Nodes connected A collection of branches.

[0064] Reactive flexibility is used to reduce the voltage violation at the distribution system nodes. The distribution network optimization model that considers minimizing the voltage violation and the cost of purchasing flexibility is: (36); in, To optimize the time period; A penalty factor representing the amount of voltage violation; is the sum of the node voltage violations, satisfying , Representative Node exist The amount of violation of the input voltage at the moment, Representative Node exist The amount of violation of the output voltage at any moment; Indicates the unit price of unit reactive flexibility service; is the sum of the reactive power flexibility that can be provided, that is: (37); in, For the community the collection of prosumers within; Indicates the interval of flexibility service provision, starting at , the duration is ; represents the optimal upward reactive flexibility power determined by the distribution network system optimization model, represents the optimal downward reactive flexibility power determined by the distribution network system optimization model, , , represents the upward reactive flexibility margin obtained from the reactive flexibility boundary model, represents the downward reactive flexibility margin obtained from the reactive flexibility boundary model; means that prosumers provide upward reactive power flexibility, The prosumer provides downward reactive power flexibility, satisfying the following relationship, ensuring that flexibility services are only provided when Activated during consecutive time periods: (38); in, Indicates the time interval Neidi A binary variable at a certain moment, For the time interval The binary variable of the previous moment.

[0065] At this time, the distribution network Reactive power of nodes for (39); in, is the reactive power curve of the producer and consumer side on the day before; represents the upward reactive flexibility margin, Indicates the downward reactive power flexibility margin.

[0066] In one embodiment of the present invention, a device is provided, comprising a processor and a memory, wherein the memory is used to store instructions or computer programs, and the processor is used to execute the instructions or computer programs in the memory so that the device performs the steps of the aforementioned method.

[0067] In one embodiment of the present invention, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium. When the instructions are executed on a device, the device executes the steps of the aforementioned method.

[0068] The embodiments of the present invention reduce the community electricity charges and the amount of violations of distribution network voltage and current through resource sharing optimization, thereby achieving low-cost, high self-sufficiency energy management. Based on the power flow model, considering the distribution network constraints, with low electricity charges and low power factor penalties as the optimization goals, a community day-ahead optimization scheduling model is established. Shadow prices are proposed as transaction electricity prices, and a producer-consumer transaction model within the community is constructed to ensure fairness in internal transactions. The flexibility margin of active and reactive power is defined, and a power flexibility boundary model containing photovoltaics and energy storage is proposed to promote optimal resource allocation. In addition, based on the flexibility boundary, the amount of voltage and current violations is reduced, a distribution network system optimization model is established, and the operational stability of the distribution network is improved. The solution provided by the embodiments of the present invention can effectively reduce the electricity costs of the community, while taking into account the power factor and operational stability, providing a safe, efficient, and intelligent solution for community-distribution network energy management.

[0069] The aforementioned embodiments of the present invention consider distribution network constraints in community optimization, formulate internal energy trading strategies, and define flexibility boundaries, thereby realizing internal energy flow and flexibility resource utilization at the community level, and achieving dual optimization of economy and minimization of voltage and current violations at the distribution network level, providing an innovative path for the intelligent development of community-distribution network structure.

[0070] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A community-distribution network energy optimization method considering flexibility resources and internal transactions, characterized by: The community-distribution network energy is optimized through a community-distribution network energy optimization model, wherein: The intra-community prosumer transaction model considers the electricity transactions of members in the same community and adopts the shadow price reflecting the marginal supply and demand balance as the intra-community transaction price to obtain the intra-community transaction power. The community's day-ahead optimal dispatch model is based on the internal transaction power of community members, distribution network constraints, and considers the power balance of prosumers. By minimizing the total cost of transactions between the community and the grid and the penalty for non-compliant power factors, the active power and reactive power of the prosumer at each moment are obtained, and then the day-ahead active power and reactive power curves of the prosumer side are obtained. The active power flexibility boundary model and reactive power flexibility boundary model take into account the community's electricity purchase cost, low power factor operation penalty and flexibility reward, and obtain the active power curve and reactive power curve of the prosumer in real time. The active power curve and reactive power curve of the prosumer on the day-ahead side are used as reference curves, and the active power flexibility margin and reactive power flexibility margin are obtained based on the active power curve and reactive power curve of the prosumer in real time. The distribution network system optimization model determines the optimal active flexibility and reactive flexibility power based on the active flexibility margin, reactive flexibility margin, branch current maximum flow constraint and node voltage operating range constraint to minimize the branch current violation and node voltage violation of the distribution network system and minimize the cost of purchasing flexibility power.

2. The community-distribution network energy optimization method considering flexibility resources and internal transactions according to claim 1 is characterized in that: The distribution network constraints include coupling constraints, branch constraints, transformer constraints and power balance constraints.

3. The community-distribution network energy optimization method considering flexibility resources and internal transactions according to claim 1 is characterized in that: The decision variables of the objective function of the community day-ahead optimization scheduling model are the active power of the photovoltaic and energy storage systems, the reactive power output of the photovoltaic, energy storage systems and capacitors, the power volume traded among community members, and the active power traded by the community to the grid.

4. The community-distribution network energy optimization method considering flexibility resources and internal transactions according to claim 3 is characterized in that: Get the node through the objective function Where the prosumer is at the moment The reactive power is not in compliance with the standard , based on the node Where the prosumer is at the moment The reactive power is not in compliance with the standard The boundaries of active power and reactive power on the prosumer side are determined by the sum of the reactive power decision variables including the reactive power of photovoltaic and energy storage systems, the power amount traded internally by community members, the reactive power provided by capacitors, and the reactive power traded by the community to the grid. The boundaries of reactive power are determined by the active power on the prosumer side, the reactive power of the prosumer load, and the minimum power factor.

5. The community-distribution network energy optimization method considering flexibility resources and internal transactions according to claim 1 is characterized in that: The intra-community prosumer transaction model includes the prosumer balance power equation, the intra-community transaction power balance equation, and the power equation for non-community prosumers that do not participate in transactions. In the power equation for non-community prosumers that do not participate in transactions, prosumers only exchange power with the power grid.

6. The community-distribution network energy optimization method considering flexibility resources and internal transactions according to claim 1 is characterized in that: The active power flexibility boundary model takes into account the community power purchase cost, low power factor operation penalty and flexibility bonus. The reactive power flexibility boundary model considers flexibility bonus based on the community power purchase cost, but does not consider the low power factor operation penalty.

7. The community-distribution network energy optimization method considering flexibility resources and internal transactions according to any one of claims 1 to 6, characterized in that: Active flexibility is used to reduce the current violation of the distribution network system. The distribution network system optimization model that considers minimizing the current violation and the cost of purchasing flexibility resources is: ； in, For the moment, To optimize the time period; is the penalty factor for the current violation; is the sum of the branch current violations; Indicates the unit price of unit active flexibility service; is the sum of the available active power flexibility, is the time step, For nodes, Representing the community The collection of producers and consumers within.

8. The community-distribution network energy optimization method considering flexibility resources and internal transactions according to any one of claims 1 to 6, characterized in that: Reactive flexibility is used to reduce the violation of voltage at distribution system nodes. Considering minimizing voltage violation and purchasing flexibility cost, the distribution network system optimization model is: ； in, For the moment, To optimize the time period, A penalty factor representing the amount of voltage violation; is the sum of node voltage violations; Indicates the unit price of unit reactive flexibility service; is the sum of the available reactive power flexibility, is the time step.

9. A device, characterized in that The device includes a processor and a memory, wherein the memory is used to store instructions or computer programs, and the processor is used to execute the instructions or computer programs in the memory, so that the device performs the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed on a device, the device is caused to execute the steps of the method according to any one of claims 1 to 8.

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