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

Abstract

This invention discloses a community-distribution network energy optimization method that considers flexible resources and internal transactions. It obtains the internal transaction power of community members through a producer-consumer transaction model, and obtains the active and reactive power on the producer-consumer side through a day-ahead optimization scheduling model, thus deriving the day-ahead active and reactive power curves on the producer-consumer side. Furthermore, it obtains the real-time active and reactive power curves of the producer-consumer side through an active and reactive power flexibility boundary model, thereby deriving the active and reactive power flexibility margins. Finally, it minimizes branch current violations, node voltage violations, and the cost of purchasing flexible power through a distribution network system optimization model. This invention reduces community electricity costs and distribution network voltage and current violations through resource sharing optimization, effectively reducing community electricity costs while balancing power factor and operational stability, providing a safe, efficient, and intelligent solution for community-distribution network energy management.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically to a community-distribution network energy optimization method that takes into account flexibility resources and internal transactions. Background Technology

[0002] With the accelerated development of distributed renewable energy, community energy systems, as an organic whole encompassing distributed photovoltaics, electric vehicle clusters, battery storage devices, and adjustable loads, achieve local consumption of distributed energy through sharing and optimizing flexible resources. Traditional community day-ahead optimization models often fail to consider the physical constraints of the distribution network, as well as issues such as neglecting internal transactions, failing to quantify flexibility margins, and ignoring reactive power, resulting in limited optimization effects. A significant shift is the inclusion of voltage and power flow constraints of the distribution network in community optimization models, ensuring optimization effectiveness in practical applications. Simultaneously, the existence of energy transactions among community members presents a significant challenge: determining internal transaction prices to ensure overall benefits while maintaining fairness in transactions among internal members. Furthermore, quantifying the active and reactive power flexibility margins of photovoltaics and energy storage from an economic dispatch perspective and providing flexibility services to the distribution network is also an urgent problem to be solved.

[0003] Existing research on community-day optimized scheduling does not consider the physical constraints of the distribution network, nor does it closely integrate internal trading mechanisms with power flow models. This makes it difficult to ensure the incentive and fairness of transactions while guaranteeing operational constraints such as node voltage and current. Furthermore, existing research does not consider providing reactive power flexibility support to the distribution network. Distribution network operators still lack systematic solutions for optimizing the utilization of flexibility resources, making it difficult to achieve intelligent and flexible optimized operation of the community-distribution network.

[0004] Chinese invention patent CN110400079A discloses a community optimization method for energy sharing among new energy users. This method predicts each user's new energy generation and power load for each time period of the next day, and optimizes scheduling based on these figures to minimize the community's overall energy cost on the next day when users participate in energy sharing. This yields scheduling parameters, and the method calculates the individual energy cost for each user when participating in energy sharing. Furthermore, it optimizes scheduling based on new energy generation and power load to minimize the energy cost for each user on the next day when they do not participate in energy sharing. Based on these energy costs, a settlement price is determined for calculating each user's energy sharing revenue on the next day, ensuring that the total cost for each user participating in energy sharing is lower than the energy cost when not participating. Day-ahead transactions and next-day energy scheduling are then conducted based on the scheduling parameters to achieve energy sharing within the new energy user community. While this scheme can promote energy sharing within the new energy user community, it still has the following technical shortcomings:

[0005] 1) This invention establishes a community energy optimization model, but ignores the physical constraints and power flow constraints of the distribution network. This may lead to the optimization results violating the constraints in practical applications, affecting the safe and stable operation of the distribution network.

[0006] 2) This invention focuses on quantifying the active power margin of the community system, but neglects the quantification of reactive power flexibility, making it difficult to provide comprehensive flexibility services;

[0007] 3) This invention only uses the electricity purchase price given by the power grid to calculate revenue, and does not clarify the calculation method of internal transaction electricity price, making it difficult to balance the community's electricity costs and the interests of its members;

[0008] 4) This invention only involves the optimization of flexible resources within the community, and does not construct a power distribution system optimization scheme to match the community's flexible services, making it difficult to realize the role of the energy community. Summary of the Invention

[0009] To address at least one of the problems existing in the prior art, this invention provides a community-distribution network energy optimization method that considers flexible resources and internal transactions. It minimizes energy costs and reduces power factor penalties through a community-day-ahead optimization scheduling model. The community-internal producer-consumer transaction model considers electricity transactions among members within the same community and uses a shadow price reflecting marginal supply and demand balance as the community-internal transaction price to obtain the power traded among community members. It quantifies the flexibility margins of active and reactive power in photovoltaic and energy storage systems, providing a low-cost method for reducing node voltage and branch current violations in the distribution network. This establishes a distribution network system optimization model utilizing flexible resources, improving the stability of distribution network operation.

[0010] To achieve the objectives of this invention, the present invention provides a community-distribution network energy optimization method that considers flexible resources and internal transactions. This method optimizes community-distribution network energy through a community-distribution network energy optimization model, wherein:

[0011] The intra-community producer-consumer transaction model considers the electricity transactions of members within the same community and uses the shadow price, which reflects the marginal supply and demand balance, as the intra-community transaction electricity price to obtain the intra-community transaction power.

[0012] The community-based day-ahead optimization scheduling model is based on the power trading within the community members, distribution network constraints, and the power balance of producers and consumers. By minimizing the total cost of transactions between the community and the power grid and the penalty for non-compliant power factors, it obtains the active and reactive power on the producer and consumer side at each time point, and thus obtains the day-ahead active and reactive power curves on the producer and consumer side.

[0013] The active power flexibility boundary model and the reactive power flexibility boundary model take into account the community electricity purchase cost, low power factor operation penalty and flexibility reward to obtain the active power curve and reactive power curve of the producer and consumer in real time. The active power curve and reactive power curve of the producer and consumer side in day-ahead 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 producer and consumer in real time.

[0014] The distribution network system optimization model determines the optimal active and reactive power flexibility based on active power flexibility margin, reactive power flexibility margin, maximum branch current flow constraint, and node voltage operating range constraint, in order to minimize the branch current violation, node voltage violation, and cost of purchasing flexibility power in the distribution network system.

[0015] Furthermore, the distribution network constraints include coupling constraints, branch constraints, transformer constraints, and power balance constraints.

[0016] Furthermore, the objective function of the community's recently optimized scheduling model is expressed as:

[0017]

[0018] in, for Time Node Total electricity cost of the community The feasible region 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 time slots, For time step; For the community The collection of producers and consumers within; This represents the penalty factor, which converts non-compliant power factors into penalty amounts. For nodes At any time, the consumer is in the production and consumption area The reactive power is non-compliant;

[0019] 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 amount traded within the community members, and the active power traded by the community to the grid.

[0020] Furthermore, nodes are obtained through the objective function. At any time, the consumer is in the production and consumption area Non-compliant reactive power Based on nodes At any time, the consumer is in the production and consumption area Non-compliant reactive power The boundary between active and reactive power on the producer-consumer side is determined by the sum of decision variables including the reactive power of photovoltaic and energy storage systems, the power traded within the community members, the reactive power provided by capacitors, and the reactive power traded by the community to the grid. The boundary of reactive power is determined by the active power on the producer-consumer side, the reactive power of the producer-consumer load, and the minimum power factor.

[0021] Furthermore, the community-based producer-consumer trading model includes a producer-consumer balance power equation, a community-based trading power balance equation, and a producer-consumer power equation for non-community participants who do not participate in trading. In the producer-consumer power equation for non-community participants, producers only exchange power with the power grid.

[0022] Furthermore, the active power flexibility boundary model considers community electricity purchase costs, low power factor operation penalties, and flexibility rewards, while the reactive power flexibility boundary model considers flexibility rewards based on community electricity purchase costs, but does not consider low power factor operation penalties.

[0023] Furthermore, active power flexibility is used to reduce current violations in the distribution network system. The distribution network system optimization model that considers minimizing current violations and the cost of purchasing flexibility resources is as follows:

[0024] ;

[0025] ;

[0026] in, For a moment, To optimize time periods; This is the penalty factor for the current violation. The sum of the branch current violations; Indicates the unit price of active flexibility services; The sum of available active power flexibility, For time step, For nodes, Representing the community The collection of producers and consumers within, and These are the optimal upward and downward active power flexibility determined by the distribution network system optimization model, respectively. , , This represents the upward active power flexibility margin obtained from the active power flexibility boundary model. This represents the downward active power flexibility margin obtained from the active power flexibility boundary model. Indicates the range of flexible services provided; and Represents a binary variable.

[0027] Furthermore, reactive power flexibility is used to reduce voltage violations at distribution system nodes. Considering minimizing voltage violations and the cost of purchasing flexibility, the distribution network system optimization model is as follows:

[0028] ;

[0029] ;

[0030] in, For a moment, To optimize time slots, The penalty factor represents the amount of voltage violation; The sum of node voltage violations; The sum of available reactive power flexibility, For time step, For nodes, Representing the community The collection of producers and consumers within, This indicates that producers and consumers provide upward reactive power flexibility. This indicates that producers and consumers provide downward reactive power flexibility. This indicates the range of flexibility services offered. This represents the optimal upward reactive power flexibility determined by the distribution network system optimization model. This represents the optimal downward reactive power flexibility power determined by the optimization model of the distribution network system. , , This represents the upward reactive flexibility margin obtained from the reactive flexibility boundary model. This represents the downward reactive flexibility margin obtained from the reactive flexibility boundary model.

[0031] The present invention also provides an apparatus comprising a processor and a memory, the memory for storing instructions or computer programs, and the processor for executing the instructions or computer programs in the memory to cause the apparatus to perform the steps of the community-distribution network energy optimization method that takes into account flexible resources and internal transactions.

[0032] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a device, cause the device to perform the steps of the community-distribution network energy optimization method that takes into account flexible resources and internal transactions.

[0033] Compared with the prior art, the present invention can achieve at least the following beneficial effects:

[0034] (1) This invention combines the physical constraints and power flow model of the distribution network in the current community energy optimization, solving the problem of neglecting the constraints of distribution network voltage and power flow in traditional community optimization. By determining the internal transaction price through the shadow price related to active power, the fairness of the transaction is ensured, and the internal resource exchange and energy self-sufficiency rate of the community are enhanced. From the perspective of economic dispatch, the flexibility margin of active and reactive power of photovoltaic and energy storage is quantified, and the active and reactive flexibility boundary models with the minimum community electricity cost, the minimum low power factor penalty, and the maximum flexibility resource reward are constructed, solving the problem of not quantifying the flexibility margin and neglecting reactive power in the traditional flexibility resource dispatch method. The quantified flexibility resources are used to reduce the violation of distribution network node voltage and branch current, considering the optimization objectives of minimizing the cost of purchasing flexibility resources and minimizing voltage or current violation, taking into account the multiple needs of distribution network operation. This invention enables the community-distribution network energy optimization model to have the ability to consider distribution network constraints, conduct internal community transactions, quantify the flexibility of active and reactive power, and provide distribution network flexibility services, which can improve the local consumption capacity of new energy.

[0035] (2) This invention can solve the problems of the lack of existing community energy optimization in terms of distribution network coupling constraints, internal transactions, flexible resource quantification capabilities, and flexible coordination with the distribution network.

[0036] (3) This invention reduces the amount of violations of community electricity fees and distribution network voltage and current through resource sharing optimization, thereby achieving low-cost and high self-sufficiency energy management. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating a community-distribution network energy optimization method that considers flexible resources and internal transactions in an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the composition of the community energy optimization model in an embodiment of the present invention.

[0039] Figure 3 This is a schematic diagram of the balanced T-type equivalent circuit used in the distribution network branch in this embodiment of the invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1The community-distribution network energy optimization method considering flexible resources and internal transactions provided in this embodiment of the invention optimizes community-distribution network energy through a community-distribution network energy optimization model, and performs optimization through the following steps:

[0042] Step S1. The intra-community producer-consumer transaction model obtains the intra-community transaction power by considering the electricity transactions of members within the same community and using the shadow price, which reflects the marginal supply and demand balance, as the intra-community transaction electricity price.

[0043] Step S2. The community-day-ahead optimization scheduling model is based on the power trading within the community members, distribution network constraints, and considering the power balance between producers and consumers. By minimizing the total cost of transactions between the community and the power grid and the penalty for non-compliant power factors, the active and reactive power on the producer-consumer side at each time point are obtained, and thus the active and reactive power curves on the producer-consumer side are obtained.

[0044] The steps for constructing the community-based producer-consumer transaction model and the community-based day-ahead optimization scheduling model include:

[0045] Based on the equivalent distribution network branches of the balanced T-type model (understandably, other equivalent distribution network branches can also be used), the DistFlow method is used to construct a distribution network power flow model, analyze distribution network constraints, and consider producer-consumer power balance to minimize the total cost of community-grid transactions and penalties for non-compliant power factors, thereby establishing a community day-ahead optimal scheduling model for day-ahead community optimal scheduling.

[0046] In one embodiment of the present invention, the balanced T-type equivalent distribution network branch is as follows: Figure 3 As shown, a balanced T-type equivalent distribution network branch includes input impedance, output impedance, parallel admittance, and a transformer. The series impedances at both ends are respectively... , Represents complex impedance. It is the resistance in the series impedance at both ends. It is the reactance in the series impedance at both ends. It is the imaginary part, and the admittance in the middle is... , It is the intermediate parallel susceptance. For the first The square of the input voltage of the branch is The square of the input branch current is The active power at the input end is The reactive power at the input end is The transformer is located on the right and is either a conventional transformer or an ideal on-load tap changer (OLTC). The tap ratio of the ideal on-load tap changer located on the right is... , Given the transformer tap changer ratio, the manager wants the square of the output voltage after the on-load tap changer transformer has been transformed to be... The square of the current in the output branch is The active power at the output end is The reactive power at the output end is , Indicates the time.

[0047] The distribution network constraints include coupling constraints, branch constraints, transformer constraints, and power balance constraints.

[0048] The coupling constraint is that, according to Kirchhoff's voltage and current laws, the voltage and power on branches connected to the same busbar in a distribution network will affect each other. That is, the voltages of multiple branches on the same busbar need to be consistent, and the sum of the power input and output of all branches must be equal. Furthermore, when the node... (node branch road When the corresponding node (the number of nodes equals 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:

[0049] (1);

[0050] (2);

[0051] in, For the output voltage at The square of the time; For the input voltage at The square of the time; For nodes Voltage at The square of the time; For the first The set of branches of each output bus. For the first The set of branches of each input bus; For nodes Input end at The actual active power at any given time. For nodes Input end at The actual reactive power at any given time; For nodes The beginning is at The actual active power at any given time. For nodes The beginning is at The actual reactive power at any given moment.

[0052] For a node belonging to a certain community, it can conduct internal transactions with other nodes in the same community. In this case, when prosumers exchange power through the distribution network line, two additional power balance relationships need to be satisfied:

[0053] (3);

[0054] in, For nodes Consumers in the community The active power output during each transaction. Represents a node Consumers in the community Active power input during each transaction; Represents a node Consumers in the community The reactive power output during real-time transactions. Represents a node Consumers in the community The reactive power input during a transaction.

[0055] In particular, for nodes that do not belong to any community, there is no internal transaction situation, and only the power balance relationship described in formula (2) applies.

[0056] By combining the DistFlow model and the balanced T-type model to represent equivalent distribution network branches, the voltage, current, and power balance of the distribution network branches are modeled. For the first... The voltage of each branch is expressed as follows:

[0057] (4);

[0058] in, For nodes The midpoint of the branch road is at The square of the voltage at time t. For nodes Input voltage at The square of the time; For nodes Input end at The actual active power at any given time. For nodes Input end at The actual reactive power at any given time; For the input branch current in The square of the time; For the output branch current in The square of the time; For those not modified by OLTC transformers Branch output voltage at any time; node The series impedances at both ends of the branch are respectively , It is the resistance in the series impedance at both ends. It is the reactance in the series impedance at both ends, and the admittance in the parallel connection in the middle is... , It is the intermediate parallel susceptance.

[0059] The power balance constraint is:

[0060] (5);

[0061] in, For nodes Input end at The actual active power at any given time. For nodes Input end at The actual reactive power at any given time; For nodes The beginning is at The actual active power at any given time. For nodes The beginning is at The actual reactive power at any given time; for Time Node Active power on the producer-consumer side, for Reactive power at any given moment on the producer-consumer side; node The series resistance at both ends of the branch is Reactance is The intermediate parallel admittance is .

[0062] Current is represented as:

[0063] (6);

[0064] in, For nodes The input terminal of the branch is at The estimated value of active power at any given time. It is a node The input terminal of the branch is at The estimated value of reactive power at any given time. For nodes The midpoint of the branch road is at The estimated value of active power at any given time. It is a node The midpoint of the branch road is at Estimated value of reactive power at any given time; For nodes Input end at The actual active power at any given time. For nodes Input end at The actual reactive power at any given time; For nodes The midpoint of the branch road is at The actual active power at any given time. It is a node The midpoint of the branch road is at Actual reactive power at any given moment.

[0065] Assuming a per-unit bus voltage of 1.0 pu, and ignoring the effects of OLTC and producer-consumer side equipment, the estimated power at the branch inlet can be calculated by superimposing the downstream bus power demand fed by it. The estimated power at the branch midpoint can be calculated by averaging the estimated power at the branch inlet and the estimated power at the branch outlet, expressed as:

[0066] (7);

[0067] in, For nodes The input terminal of the branch is at The estimated value of active power at any given time. It is a node The input terminal of the branch is at The estimated value of reactive power at any given time. For nodes The midpoint of the branch road is at The estimated value of active power at any given time. It is a node The midpoint of the branch road is at Estimated value of reactive power at any given time; For nodes The output end of the branch is at The estimated value of active power at any given time. It is a node The output end of the branch is at Estimated value of reactive power at any given time; For the first The downstream busbar assembly of the branch power supply, For downstream busbars in Active load on the producer-consumer side at any given time For downstream busbars in Reactive power at any given moment.

[0068] Branch constraints refer to voltage and current constraints. The expressions for branch constraints include formulas (4), (5), (6), and (7), and formulas (4), (5), (6), and (7) are expressions for the power flow model of the distribution network.

[0069] For the transformer, its input voltage has been set to However, regarding the output voltage, we will discuss it by classifying OLTC transformers and ordinary transformers. For OLTC transformers, voltage regulation is possible under load or excitation conditions, and their output voltage is affected by the tap ratio range. The influence, then the transformer constraint is expressed as:

[0070] (8);

[0071] in, For those not modified by transformers The square value of the output voltage of the branch at any given moment; For the output voltage at The square of the time; This is the minimum tap ratio of the transformer. This represents the maximum tap ratio of the transformer.

[0072] Specifically, for ordinary transformers, the voltage can only be adjusted by manually switching the taps during a power outage. In a normally operating distribution network, its output voltage is... Ordinary transformers can only switch voltages during power outages. Under normal operation, they are equivalent to transformers with a tap ratio of K=1, which is a special case of ideal OLTC.

[0073] On the producer-consumer side, ignoring the active power losses in transformers and cables within the producer-consumer's internal devices, there are photovoltaic power generation, energy storage charging and discharging power, and load power consumption. Therefore, producer-consumer power balance needs to be considered, as shown below:

[0074] ,

[0075] (9);

[0076] in, for Time Node Active power on the producer-consumer side, for Time Node Reactive power on the consumer side; for Time Node The active power load of the community. for Time Node The active power of the energy storage batteries in the community for Time Node The active power of the photovoltaic power generation in the community; the battery discharge time Conversely, during charging, the maximum power limit must be met throughout the entire charging and discharging process. , This is the battery's maximum power. for Time Node The reactive power of the community load, for Time Node The reactive power of the energy storage batteries in the community for Time Node The reactive power of the photovoltaic power generation in the community The reactive power provided to the capacitor; for Time Node The charging and discharging losses of the community's energy storage batteries are expressed as:

[0077] (10);

[0078] in, , It's a coefficient, used when the battery is charging. ,on the contrary ; For battery discharge efficiency; Improve battery charging efficiency; for Time Node The active power of the energy storage batteries in the community.

[0079] Specifically, assuming At any given time, the battery capacity is The charging and discharging process can be described by the energy storage battery capacity model:

[0080] (11);

[0081] in, The time step; the entire charge and discharge process must meet the capacity limit, i.e. , This represents the minimum battery capacity. This represents the maximum battery capacity; at the start of charging / discharging. and the end time The charge is ; for Time Node The active power of the energy storage batteries in the community.

[0082] The community recently optimized its scheduling model, taking into account the electricity costs generated from transactions between the community and the external power grid, as well as penalties for non-compliant power factors. The objective function is then expressed as:

[0083] (12);

[0084] in, for Time Node Total electricity cost of the community; To optimize time slots, For time step; For the community The collection of producers and consumers within; This represents the penalty factor, which converts non-compliant power factors into penalty amounts. For nodes At any time, the consumer is in the production and consumption area The reactive power is non-compliant, in one embodiment of the present invention. Set to 24h Set to 0.25h.

[0085] 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 of internal transactions among community members, and the power of transactions between the community and the grid.

[0086] It includes the sum of active power from photovoltaic and energy storage systems, active power traded within the community, and active power decision variables for the community's transactions with the grid. for:

[0087] (13);

[0088] in, for Time Node The active power of the energy storage batteries in the community for Time Node The active power of the 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 power grid, when When, it indicates that power flows from the power grid to the producer and consumer.

[0089] Generally, the price the power grid sells electricity to producers and consumers is higher than the price the power grid buys electricity from producers and consumers. Total electricity cost in the community The feasible region is defined as the difference between the cost of purchasing electricity from the grid and the profit from selling electricity to the grid:

[0090] (14);

[0091] in, The electricity price sold by the power grid to producers and consumers. The electricity price that the power grid pays to producers and consumers; for Time Node The active power traded between the community and the power grid; For time step.

[0092] Represented as:

[0093] (15);

[0094] in, for Time Node The active power load of the community; for Time Node Active power exchange within the community for Time Node The active power of the energy storage batteries in the community for Time Node The active power of the photovoltaic power generation in the community; for Time Node The active power traded between the community and the power grid.

[0095] This includes the sum of reactive power from photovoltaic and energy storage systems, reactive power traded within the community, reactive power supplied by capacitors, and decision variables for reactive power traded by the community to the grid. for:

[0096] (16);

[0097] in, for Time Node The reactive power of the community's energy storage batteries is limited by the minimum power factor. for Time Node The reactive power of the photovoltaic power generation in the community for Time Node Reactive power exchange within the community The reactive power provided by the capacitor. The capacitor is connected at certain nodes, and there is a maximum reactive power that can be provided. By making decisions to balance the reactive power provided by the energy storage battery and 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.

[0098] Reactive power boundary and Defined by active power on the producer-consumer side, reactive power of producer-consumer load, and minimum power factor:

[0099] ,

[0100] (17);

[0101] in, for Time Node The reactive power load of the community; for Time Node Active power on the producer-consumer side; Indicates the minimum power factor; This represents the maximum value of reactive power. This represents the minimum reactive power.

[0102] Therefore, non-compliant reactive power can be expressed as:

[0103] (18);

[0104] in, This refers to non-compliant amounts of reactive power. For symbolic functions, it is defined as:

[0105] (19);

[0106] in, for Time Node Active power on the producer-consumer side.

[0107] Nodes are obtained through the objective function. At any time, the consumer is in the production and consumption area Non-compliant reactive power Based on nodes At any time, the consumer is in the production and consumption area Non-compliant reactive power The boundary between active and reactive power on the producer-consumer side is used to determine the sum of decision variables that include reactive power from photovoltaic and energy storage systems, power traded within the community, reactive power supplied by capacitors, and reactive power traded by the community to the grid. .

[0108] In step S1, the community-internal prosumer trading model, which includes the prosumer-consumer equilibrium power equation, the community-internal transaction power equilibrium equation, and the prosumer-consumer power equation for non-community participants, is as follows:

[0109] In addition to trading electricity with the power grid, producers and consumers within the same community can engage in internal trading. Therefore, the producer-consumer power balance equation is:

[0110] (20);

[0111] in, for Time Node Active power on the producer-consumer side, for Time Node The reactive power on the producer-consumer side is located. for Time Node The active power traded between the community and the power grid for Time Node Active power exchange within the community for The power grid is constantly moving to the nodes. The reactive power provided by the producers and consumers in the community for Time Node Reactive power exchange within the community. To prevent producers and consumers from absorbing reactive power from the grid for internal community trading, and also to avoid injecting reactive power into the grid, Set to non-negative.

[0112] There is a balance between active and reactive power within the community; that is, the power balance equation for transactions within the community is:

[0113] (twenty one);

[0114] in, Representing the community The collection of producers and consumers within; for Time Node Active power exchange within the community for Time Node Reactive power exchange within the community.

[0115] Since reactive power does not generate direct economic benefits, assuming that reactive power is compensated without payment, the intra-community electricity trading price can be defined as a shadow price related to active power:

[0116] (twenty two);

[0117] in, Electricity prices for internal community transactions; For shadow prices; For time step.

[0118] For prosumers who do not belong to any community and cannot participate in internal transactions, they only exchange power with the power grid. That is, the power equation for prosumers who are not affiliated with any community and do not participate in transactions is:

[0119] (twenty three);

[0120] in, for Time Node Active power on the producer-consumer side, for Time Node Reactive power on the consumer side; for Time Node The active power traded between the community and the power grid for The power grid is constantly moving to the nodes. The reactive power provided by producers and consumers in the local community.

[0121] S3. The active power flexibility boundary model and reactive power flexibility boundary model, by considering the community electricity purchase cost, low power factor operation penalty and flexibility reward, obtain the active power curve and reactive power curve of the producer and consumer in real time. The active power curve and reactive power curve of the producer and consumer side in the day-ahead 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 producer and consumer in real time.

[0122] Specifically, using the day-ahead active power curve of the producer-consumer side as a reference curve, the active power curve of the producer-consumer side based on real-time operation is determined as the upper or lower bound of flexibility. The difference between the upper or lower bound of flexibility and the reference curve is defined as the active power flexibility margin. Using the day-ahead reactive power curve of the producer-consumer side as a reference curve, the reactive power curve of the producer-consumer side based on real-time operation is determined as the upper or lower bound of flexibility. The difference between the upper or lower bound of flexibility and the reference curve is defined as the reactive power flexibility margin.

[0123] In this step, considering community electricity purchase costs, low power factor penalties, and flexibility rewards, we construct intraday active power flexibility boundary models and reactive power flexibility boundary models for the producer-consumer side, incorporating both photovoltaic and energy storage resources. (Intraday: refers to the current day)

[0124] The active power flexibility boundary model considers community electricity purchase costs, low power factor operation penalties, and flexibility rewards, and is defined as:

[0125] (twenty four);

[0126] in, This indicates the range of flexible service provision, starting at time [time missing]. The duration is ; For the community's electricity costs; For the community The collection of producers and consumers within; This represents the penalty factor, which converts non-compliant power factors into penalty amounts. For producers and consumers at all times The reactive power is non-compliant; For time step; Represents a node The reward received by the community for providing upward active power flexibility resources to the power distribution network. The reward received by the community for providing downlink active power flexibility resources to the distribution network is:

[0127] (25);

[0128] in, The unit price for active power flexibility services is determined by the power distribution system operator. , The electricity price sold by the power grid to producers and consumers; For time step; Indicates upward active flexibility margin. This represents the downward active power flexibility margin; at any given time, producers and consumers can only choose to provide either upward or downward flexibility.

[0129] ,

[0130] (26);

[0131] in, The day-ahead active power curve for the producer-consumer side is obtained by using the day-ahead optimization scheduling model to determine the active power of the producer-consumer side at each time point. This represents the active power curve of producers and consumers during real-time operation.

[0132] Assuming that the reactive power output of flexible resources at a given time is limited only by its power range and is not affected by reactive power at historical times, and since reactive power output does not affect active power output, this embodiment of the invention assumes that the impact of reactive power flexibility on active power output is limited. Furthermore, since the distribution network primarily focuses on the reactive power flexibility margin that producers and consumers can provide, low power factor penalties are not considered. That is, the reactive power flexibility boundary model considers flexibility rewards based on the community electricity purchase cost, defined as:

[0133] (27);

[0134] in, For the community's electricity costs; For the community The collection of producers and consumers within; This indicates the range of flexible service provision, starting at time [time missing]. The duration is ; Represents a node The community receives rewards for providing upward reactive power flexibility resources to the power grid. Represents a node The reward received by the community for providing reactive power flexibility resources to the power grid is:

[0135] (28);

[0136] in, Indicates the unit price of reactive power flexibility services; For time step; Indicates upward reactive power flexibility margin. The downside reactive power flexibility margin is represented as:

[0137] (29);

[0138] in, This represents the reactive power curve of producers and consumers during real-time operation. The reactive power curves on the producer-consumer side are obtained by using the day-ahead optimization scheduling model to obtain the reactive power on the producer-consumer side at each moment.

[0139] S4. The distribution network system optimization model is based on active power flexibility margin, reactive power flexibility margin, maximum branch current flow constraint and node voltage operating range constraint to determine the optimal active power flexibility and reactive power flexibility to minimize the branch current violation, node voltage violation and the cost of purchasing flexibility power in the distribution network system.

[0140] By analyzing the maximum flow constraints of distribution network branch currents and the operating range constraints of node voltages, producer-consumer flexibility resources are used to reduce the violations of distribution network branch currents and node voltages. A distribution network system optimization model is established so that the minimum current violation and the cost of purchasing flexibility power, the minimum voltage violation and the cost of purchasing flexibility power can be obtained through the distribution network system optimization model during optimization.

[0141] The steps for establishing an optimization model for a distribution network system incorporating flexible resource regulation include:

[0142] When the load current of the distribution network exceeds its allowable current carrying capacity, the line voltage drop increases, leading to low node voltage, ultimately causing a decline in power quality and a shortened equipment lifespan. Assume the... The maximum allowable current of the branch is Then the violation of the current at the input and output terminals of the branch can be expressed as:

[0143] (30);

[0144] in, Representative node exist The amount of violation of the input current at any given time. Representative node exist The amount of violation of the output current at any given time; Representative node exist The actual input current at that moment. Represents a node exist The actual output current at any given moment; For the first The maximum allowable current for each branch.

[0145] Assume the first The maximum allowable voltage of the busbar is The minimum allowable voltage is The voltage violation at the node where the busbar is located can be expressed as:

[0146] (31);

[0147] in, This represents the upper bound of the node voltage violation. These represent the lower bounds of the node voltage violation. This represents the actual voltage at the node.

[0148] When a distribution network system obtains flexibility services from community producers and consumers to reduce violations of branch currents and node voltages, optimization models are established according to different objectives.

[0149] Active power flexibility is used to reduce current violations in the distribution network system. The optimization model for the distribution network system that considers minimizing current violations and the cost of purchasing flexibility resources is as follows:

[0150] (32);

[0151] in, To optimize time periods; This is the penalty factor for the current violation. The sum of the branch current violations satisfies , Representative node exist The amount of violation of the input current at any given time. Representative node exist The amount of violation of the output current at any given time. Indicates all busbars; Indicates the unit price of active flexibility services; The sum of available active power flexibility, i.e.

[0152] (33);

[0153] in, and These are the optimal upward and downward active power flexibility determined by the distribution network system optimization model, respectively. , , This represents the upward active power flexibility margin obtained from the active power flexibility boundary model. This represents the downward active power flexibility margin obtained from the active power flexibility boundary model. This indicates the range of flexible service provision, starting at time [time missing]. The duration is ; and Represents a binary variable; when prosumers provide upward active power flexibility services, When prosumers provide downlink active power flexibility services, Two binary variables satisfy the following relationship, indicating that the flexibility service is only available when... A continuous time period ( ) is activated within:

[0154] (34);

[0155] in, In the time interval Inner Binary variables at each moment, In the time interval The binary variable from the previous moment.

[0156] At this time, the distribution network Active power of each node for:

[0157] (35);

[0158] in, This is the active power curve on the producer-consumer side. Indicates upward active flexibility margin. Indicates the downward active power flexibility margin; Indicates the first Connecting nodes A collection of branch roads.

[0159] Reactive power flexibility is used to reduce voltage violations at distribution system nodes. The distribution network system optimization model that considers minimizing voltage violations and the cost of purchasing flexibility is as follows:

[0160] (36);

[0161] in, To optimize time periods; The penalty factor represents the amount of voltage violation; The sum of node voltage violations satisfies , Representative node exist The amount of violation of the input voltage at any given time. Representative node exist The amount of violation of the output voltage at any given time; Indicates the unit price of reactive power flexibility services; The sum of available reactive power flexibility, i.e.:

[0162] (37);

[0163] in, For the community The collection of producers and consumers within; This indicates the range of flexible service provision, starting at time [time missing]. The duration is ; This represents the optimal upward reactive power flexibility determined by the distribution network system optimization model. This represents the optimal downward reactive power flexibility power determined by the optimization model of the distribution network system. , , This represents the upward reactive flexibility margin obtained from the reactive flexibility boundary model. This represents the downward reactive power flexibility margin obtained from the reactive power flexibility boundary model; This indicates that producers and consumers provide upward reactive power flexibility. This indicates that the producer-consumer provides downward reactive power flexibility, satisfying the following relationship to ensure that flexibility services are only available to... Activated within a continuous time period:

[0164] (38);

[0165] in, Indicates the time interval Inner Binary variables at each moment, In the time interval The binary variable from the previous moment.

[0166] At this time, the distribution network Reactive power of each node for

[0167] (39);

[0168] in, The current reactive power curve is shown on the consumer side. Indicates upward reactive power flexibility margin. This indicates the downside reactive power flexibility margin.

[0169] In one embodiment of the present invention, an apparatus is provided, the apparatus including a processor and a memory, the memory for storing instructions or computer programs, and the processor for executing the instructions or computer programs in the memory to cause the apparatus to perform the steps of the aforementioned method.

[0170] In one embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions that, when executed on a device, cause the device to perform the steps of the aforementioned method.

[0171] This invention optimizes community electricity costs and distribution network voltage and current violations through resource sharing, achieving low-cost, high-self-sufficiency energy management. Based on a power flow model and considering distribution network constraints, a day-ahead optimal scheduling model for the community is established with low electricity costs and low power factor penalties as optimization objectives. A shadow price is proposed as the transaction price, and an internal producer-consumer trading model is constructed to ensure fair internal transactions. Flexibility margins for active and reactive power are defined, and a power flexibility boundary model incorporating photovoltaics and energy storage is proposed to promote optimal resource allocation. Furthermore, based on the flexibility boundary, a distribution network system optimization model is established to reduce voltage and current violations, improving the stability of distribution network operation. The solution provided by this invention effectively reduces community electricity costs while balancing power factor and operational stability, offering a safe, efficient, and intelligent solution for community-distribution network energy management.

[0172] The foregoing embodiments of the present invention, by considering distribution network constraints in community optimization, formulating internal energy trading strategies, and defining flexibility boundaries, realize internal energy flow and flexible resource utilization at the community level, and achieve dual optimization of economy and minimizing voltage and current violations at the distribution network level, providing an innovative path for the intelligent development of community-distribution network structures.

[0173] 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 implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A community-based energy optimization method for distribution networks considering flexible resources and internal transactions, characterized in that: The community-distribution network energy optimization model is used to optimize the energy of the community-distribution network. In this model: The intra-community producer-consumer transaction model considers the electricity transactions of members within the same community and uses the shadow price, which reflects the marginal supply and demand balance, as the intra-community transaction electricity price to obtain the intra-community transaction power. The community-based day-ahead optimization scheduling model, based on power transactions within the community members, distribution network constraints, and considering producer-consumer power balance, obtains the active and reactive power on the producer-consumer side at each time step by minimizing the total cost of community-grid transactions and penalties for non-compliant power factors. This yields the day-ahead active and reactive power curves on the producer-consumer side. The decision variables of the objective function of the community-based day-ahead optimization scheduling model are the active power of photovoltaic (PV) and energy storage systems, the reactive power output of PV, energy storage systems, and capacitors, the power transactions within the community members, and the active power transactions between the community and the grid. The objective function is used to obtain the node... At any time, the consumer is in the production and consumption area Non-compliant reactive power Based on nodes At any time, the consumer is in the production and consumption area Non-compliant reactive power The boundary between active and reactive power on the producer-consumer side is determined by the sum of decision variables including reactive power from photovoltaic and energy storage systems, power transactions within the community, reactive power provided by capacitors, and reactive power transactions between the community and the grid. The boundary of reactive power is determined by the active power on the producer-consumer side, the reactive power of the producer-consumer load, and the minimum power factor. The community-internal producer-consumer transaction model includes the producer-consumer balance power equation, the community-internal transaction power balance equation, and the producer-consumer power equation for non-community participants. In the producer-consumer power equation for non-community participants, producers only exchange power with the grid. The active power flexibility boundary model and the reactive power flexibility boundary model take into account the community electricity purchase cost, low power factor operation penalty and flexibility reward to obtain the active power curve and reactive power curve of the producer and consumer in real time. The active power curve and reactive power curve of the producer and consumer side in day-ahead 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 producer and consumer in real time. Using the day-ahead active power curve of the producer-consumer side as a reference curve, the active power curve of the producer-consumer based on real-time operation is determined as the upper or lower bound of flexibility. The difference between the upper or lower bound of flexibility and the reference curve is defined as the active power flexibility margin. Using the reactive power curve of the producer-consumer side as a reference curve, the reactive power curve of the producer-consumer side based on real-time operation is determined as the upper limit or lower limit of flexibility. The difference between the upper limit or lower limit of flexibility and the reference curve is defined as the reactive power flexibility margin. The distribution network system optimization model determines the optimal active and reactive power flexibility based on active power flexibility margin, reactive power flexibility margin, maximum branch current flow constraint, and node voltage operating range constraint, in order to minimize the branch current violation, node voltage violation, and cost of purchasing flexibility power in the distribution network system.

2. The community-distribution network energy optimization method considering flexible resources and internal transactions according to claim 1, 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 flexible resources and internal transactions according to claim 1, characterized in that, The active power flexibility boundary model considers the community electricity purchase cost, low power factor operation penalty, and flexibility reward, while the reactive power flexibility boundary model considers the flexibility reward on the basis of the community electricity purchase cost, but does not consider the low power factor operation penalty.

4. The community-distribution network energy optimization method considering flexible resources and internal transactions according to any one of claims 1-3, characterized in that, Active power flexibility is used to reduce current violations in the distribution network system. The optimization model for the distribution network system that considers minimizing current violations and the cost of purchasing flexibility resources is as follows: ； in, For a moment, To optimize time periods; This is the penalty factor for the current violation. The sum of the branch current violations; Indicates the unit price of active flexibility services; The sum of available active power flexibility, For time step, For nodes, Representing the community A collection of producers and consumers within the region.

5. The community-distribution network energy optimization method considering flexible resources and internal transactions according to any one of claims 1-3, characterized in that, Reactive power flexibility is used to reduce voltage violations at distribution system nodes. The optimization model for the distribution network system, considering minimizing voltage violations and the cost of purchasing flexibility, is as follows: ； in, For a moment, To optimize time slots, The penalty factor represents the amount of voltage violation; The sum of node voltage violations; Indicates the unit price of reactive power flexibility services; The sum of available reactive power flexibility, For time step.

6. A device, characterized in that, The device includes a processor and a memory for storing instructions or computer programs, and the processor for executing the instructions or computer programs in the memory to cause the device to perform the steps of the method according to any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on the device, cause the device to perform the steps of the method according to any one of claims 1-5.

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