Flexible distributed resource control method and system for improving photovoltaic consumption of low-voltage transformer area

Abstract

The invention discloses a flexible distributed resource control method and system for improving photovoltaic consumption of a low-voltage transformer area. The method comprises the following steps: acquiring purchase and sale electricity price, energy storage capacity and power, photovoltaic generating capacity, user side rigid and flexible load and transformer capacity data of a superior power distribution network; according to the capacity of the transformer, constructing a reverse heavy overload segmented penalty model of the transformer; according to equipment physical constraints and market pricing constraints, constructing a load aggregator operation cost minimization model and a low-voltage transformer area operator revenue maximization model; constructing a master-slave game model taking a low-voltage transformer area operator as a leader and a load aggregator as a follower, and simplifying the master-slave game model; solving the master-slave game model, outputting a flexible load control strategy, different ownership energy storage charging and discharging plans and an electricity purchasing and selling scheme to a load aggregator, and outputting an internal real-time purchasing and selling electricity price to a low-voltage transformer area operator; and adjusting the segmented penalty coefficient, repeatedly solving the master-slave game model, and outputting a control strategy meeting a convergence condition.

Description

Technical Field

[0001] The present invention relates to the field of new energy consumption technology for distribution networks, and in particular to a flexible distributed resource control method and system for improving photovoltaic consumption in low-voltage areas. Background Art

[0002] In recent years, with the construction of new power systems, there has been a strong demand for user-side rooftop photovoltaic grid connection. The bidirectional flow characteristics and strong randomness of power of large-scale renewable energy have posed a huge challenge to the safe and stable operation of the distribution network. Specifically, during the noontime, the photovoltaic output is high and the load power consumption is low, resulting in a large amount of power reverse transmission, causing the distribution transformer to trip due to heavy overload. At the same time, with the flexibility of electricity loads, the diversification of energy storage resources, and the rapid development of demand-side response technology, this will help guide flexible resources to participate in the operation and regulation of the power grid and enhance the flexibility of power grid operation and management. Therefore, how to regulate user-side flexible resources (flexible loads, energy storage, etc.), promote the level of photovoltaic consumption during the noontime, and alleviate the problem of reverse heavy overload of transformers has become one of the research hotspots in the supply and demand interaction of new power systems.

[0003] A patent has proposed a user-side load control method (Source: Qian Weijie, Yin Jijun, Duan Jun, et al. A Non-Perceptual User Load Interactive Adjustment System [P]. Zhejiang Province: CN202010633687.8, 2023-12-22). However, control rests entirely with the grid, ignoring user preferences and economic considerations. Other supply-demand interaction technologies have primarily focused on pricing and incentives, addressing peak and frequency regulation, orderly charging, and energy trading. These technologies are unsuitable for scenarios with a high proportion of photovoltaic access and struggle to address reverse overload conditions. Furthermore, the competing interests of numerous market participants further complicate the coordination and interaction between distributed resources and distribution networks. Therefore, how to establish a reasonable interaction mechanism based on the competing interests of multiple parties, propose flexible resource control strategies that balance economics and comfort to users, and propose pricing strategies that balance economics and safety to distribution networks, thereby achieving a win-win situation for both users and distribution networks, has become an urgent challenge for operators, supply chains, and demand sides. Summary of the Invention

[0004] In order to solve the above technical problems, the present application proposes a flexible distributed resource control method and system for improving the photovoltaic absorption of low-voltage substations. It comprehensively considers the interests and comfort of the user side and the economy and safety of the grid side, and can fully mobilize the enthusiasm of users to interact, improve the photovoltaic absorption level of the substation, solve the problem of reverse heavy overload of transformers, and increase the safety of the grid.

[0005] The purpose of the present invention is achieved by at least one of the following technical solutions.

[0006] A flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas includes the following steps:

[0007] S1. Obtain data on electricity purchase and sales prices, energy storage capacity and power, photovoltaic power generation, user-side rigid and flexible loads, and transformer capacity from the upper-level distribution network;

[0008] S2. Calculate the penalty cost based on the transformer capacity using the transformer reverse heavy overload segment penalty model;

[0009] S3. Based on the physical constraints of the equipment and the market pricing constraints, the load aggregator operating cost minimization model and the low-voltage substation operator revenue maximization model are used to obtain the benefit costs of the load aggregator and the low-voltage substation operator;

[0010] S4. Construct a master-slave game model with the low-voltage substation operator as the leader and the load aggregator as the follower, and simplify the master-slave game model;

[0011] S5. Solve the master-slave game model and output flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators. Output internal real-time power purchase and sales prices to low-voltage substation operators.

[0012] S6. Adjust the segmented penalty coefficient of the segmented penalty model, repeatedly solve the master-slave game model, and output a control strategy that meets the convergence conditions.

[0013] Furthermore, step S1 includes the following steps:

[0014] S1.1、Obtain the power purchase price of the upper-level distribution network from the power market and electricity prices

[0015] S1.2. Aggregate the rigid load, flexible load, and day-ahead rooftop PV power generation of user groups in each distribution station area. The specific aggregation formula is as follows:

[0016]

[0017] Where, and is the aggregate amount of rigid load, flexible load and rooftop photovoltaic power generation in the ith distribution station area during period t, and N is the rigid load, flexible load, and day-ahead rooftop photovoltaic power generation of the jth power user in the i-th distribution station area during period t. i is the total number of electricity users in the ith distribution station area;

[0018] S1.3. Select the distribution substation area that requires flexible resource control. The specific selection formula is as follows:

[0019]

[0020] Where, O i is the selection status of the ith distribution station area, 1 represents that the station area is selected, and 0 represents that the station area is not selected; S i is the transformer capacity of the i-th distribution station area;

[0021] S1.4. Based on the ownership rights, energy storage is divided into ownership energy storage and usage right energy storage, and they are aggregated separately:

[0022]

[0023] Where, and E i,t is the discharge power, charging power and device status of the aggregated energy storage under the i-th distribution station area in time period t; and E i,j,t The discharge power, charging power and device status of the energy storage owned by the jth power user in the i-th distribution station area during period t; and The discharge power, charging power and device status of the aggregated usage rights energy storage in the ith distribution station area during time period t; and The discharge power, charging power and device status of the energy storage of the jth power user's usage right under the i-th distribution station area in time period t.

[0024] Furthermore, in step S2, a transformer reverse heavy overload segment penalty model is constructed according to the transformer capacity, including:

[0025] S2.1. Set the segment penalty coefficient as follows:

[0026]

[0027] Where, is the penalty coefficient for period t, c h is the reverse overload penalty coefficient, c o is the reverse overload penalty coefficient, is the reverse load rate of the ith distribution station area in period t:

[0028]

[0029] Where S i is the transformer capacity of the ith distribution station area; is the electricity sales of the load aggregator in the ith distribution station area during period t. The load aggregator is an aggregate of power users in a region, representing the entire user group in external power transactions and internal flexible resource control;

[0030] S2.2, in combination with step S2.1, a transformer reverse overload penalty model is constructed:

[0031]

[0032] In the formula, is the penalty cost of the reverse overload of the ith power distribution area at the t period.

[0033] Further, in step S3, the load aggregator operating cost minimization model includes:

[0034] The objective function of the load aggregator operating cost minimization model is specifically formulated as follows:

[0035]

[0036] In the formula, c om is the operating cost coefficient of the all-ownership energy storage, is the discharge power and charge power of the aggregated all-ownership energy storage at the ith power distribution area at the t period, k tr is the load transfer inappropriateness, c ch and c dis are the charge and discharge prices of the usage-right energy storage, respectively; and are the load transfer amount, the electricity selling amount, the electricity purchasing amount, the internal electricity purchasing price and the internal electricity selling price of the load aggregator at the t period in the ith power distribution area, respectively; f i LA is the cost of the load aggregator in the ith power distribution area, and T is the number of periods, is the discharge power and charge power of the aggregated usage-right energy storage at the t period in the ith power distribution area;

[0037] The constraint conditions of the load aggregator operating cost minimization model include the power balance constraint, the load transfer amount constraint, the energy storage constraint, and the electricity purchasing and selling constraint.

[0038] II. The power balance constraint indicates that the power flowing into the load aggregator should be equal to the power flowing out plus the power consumed, and the formula is:

[0039]

[0040] In the formula, is the aggregated amount of the day-ahead rooftop photovoltaic power generation at the t period in the ith power distribution area; is the optimized load amount at the t period in the ith power distribution area;

[0041] II. The load transfer amount constraint:

[0042]

[0043] in is the rigid load and flexible load of the ith distribution station area in period t, is the load transfer amount of the load aggregator in the t period under the i-th distribution station area; in the t period, the actual load transfer amount cannot exceed the original flexible load, and the sum of the load transfer amounts is 0;

[0044] III. Ownership Constraints on Energy Storage:

[0045]

[0046] Where, and P i cap are the capacity and maximum charge and discharge power of all energy storage under the i-th distribution station area; and are the charging efficiency and discharging efficiency of the energy storage under the i-th distribution station area; S i,int 、S i,min and S i,max are the initial state coefficient, minimum state coefficient and maximum state coefficient of all energy storage under the i-th distribution station area; It is the charging flag, which is a Boolean variable; and E i,t is the discharge power, charging power, and device status of the aggregated ownership energy storage in the i-th distribution station area in time period t; T is the number of time periods. The formula indicates that for ownership energy storage, the energy storage state at time t is equal to the energy storage state in time period t-1 plus the charge and discharge amount; the energy storage state cannot exceed the minimum energy storage capacity and the maximum energy storage capacity, and the initial and final energy storage states must be equal; the energy storage charge and discharge power has an upper limit, and charging and discharging are not simultaneous.

[0047] IV. Constraints on energy storage using rights:

[0048]

[0049] Where, and P i cap1 are the capacity and maximum charge and discharge power of the energy storage under the usage right of the i-th distribution station area; and are the charging efficiency and discharging efficiency of the energy storage using the right under the ith distribution station area; and are the initial state coefficient, minimum state coefficient and maximum state coefficient of the energy storage of the usage right under the i-th distribution station area; It is the charging flag, which is a Boolean variable; are the discharge power, charging power, and device state of the aggregated ownership energy storage under the i-th distribution station area during period t. The formula indicates that for the right-of-use energy storage, the energy storage state at time t is equal to the energy storage state during period t-1 plus the charge and discharge amount. The energy storage state cannot exceed the minimum and maximum energy storage capacities, requiring the initial and final energy storage states to be equal. The energy storage charge and discharge power has an upper limit, and charging and discharging cannot occur simultaneously.

[0050] V. Power purchase and sales constraints mean that simultaneous power purchase and sales are not allowed:

[0051]

[0052] Where, and are the upper limit of electricity purchase and sales of the load aggregator under the i-th distribution area; It is the electricity purchase flag, which is a Boolean variable.

[0053] Furthermore, in step S3, the low-voltage substation operator's profit maximization model includes:

[0054] The low-voltage area operator maximizes the objective function of the model. The specific formula is as follows:

[0055]

[0056] The formula represents the sum of the transaction income between the low-voltage area and the upper-level distribution network, the transaction income with the load aggregator and the penalty cost of reverse overload; Indicates the electricity purchase price and electricity sales price of the upper-level distribution network; and are the internal electricity purchase price and internal electricity selling price of the load aggregator in the t period under the i-th distribution station area; f i DSO is the total revenue of the operator of the ith distribution area;

[0057] The pricing constraints of the low-voltage area operator's revenue maximization model are as follows:

[0058]

[0059] The formula indicates that the internal average purchase price of electricity shall not exceed the external average purchase price of electricity for the user; the internal average sales price of electricity shall not be lower than the external average sales price of electricity for the user; the pricing of internal purchase and sales prices shall comply with the upper and lower limits of electricity prices allowed by the market; where r b,av and r s,av are the average electricity purchase price and average electricity selling price of the user and the external power grid respectively; r b,min and r b,max are the upper and lower limits of the internal electricity purchase price; r s,min and r s,maxThey are the upper and lower limits of the internal electricity sales price respectively; the above-mentioned internal electricity price refers to the price between the low-voltage station operator and the load aggregator.

[0060] Furthermore, in step S4, a master-slave game model is constructed with the low-voltage substation operator as the leader and the load aggregator as the follower, and the master-slave game model is simplified as follows:

[0061] S4.1. Construct a master-slave game model with the low-voltage substation operator as the upper-layer model and the load aggregator as the lower-layer model. The specific expression is as follows:

[0062] G={D∪L;W,w;f i DSO ,f i LA};

[0063] Where G represents the master-slave game model, D and L represent the two game entities, the low-voltage area operator and the load aggregator, respectively; W and w represent the game strategies of the low-voltage area operator and the load aggregator, respectively; f i DSO and f i LA They represent the benefits of the low-voltage area operator and the costs of the load aggregator respectively;

[0064] S4.2. The lower-level model is transformed into the constraints of the upper-level model through the Carroll-Kuhn-Tucker (KKT) condition, making the master-slave game model easier to solve. The specific transformation formula is as follows:

[0065]

[0066] Where, L i (x,λ h ,μ k ) is the Lagrangian function of the lower model of the ith distribution station area, and equations ① to ④ are the stationary point condition, feasibility condition, dual feasibility condition, and complementary relaxation condition respectively; e h (x) and z k (x) are the hth inequality constraint and kth equality constraint of the lower model, λ h and μ k Respectively represent e h (x) and z k (x) dual variable, x represents the decision variable in the constraint condition; m and n represent the total number of inequality constraints and the total number of equality constraints respectively;

[0067] S4.3. The transformer reverse overload segment penalty model is simplified by function merging. The specific formula is as follows:

[0068]

[0069] Where, is the segment penalty cost of reverse heavy overload in the ith distribution station area during period t; the max function selects the larger value of the two; c ch is the charging price of energy storage for right of use; c o is the reverse overload penalty coefficient, is the reverse load rate of the ith distribution station area in period t;

[0070] S4.4. Use the Big-M method to convert equation ④ in simplification step S4.2 into a linear function. The specific formula is as follows:

[0071]

[0072] Where U h is a Boolean variable, M is a positive number;

[0073] S4.5. Through the duality theorem, the objective functions of the load aggregator operating cost minimization model and the low-voltage station operator maximization model are combined. Perform linear substitution, the specific formula is as follows:

[0074]

[0075] in and are the internal electricity purchase price and internal electricity selling price of the load aggregator in the t period under the i-th distribution station area; and is the electricity sales and purchases of the load aggregator in the ith distribution area during period t; is the flexible load of the ith distribution station area in period t, and P i cap are the capacity and maximum charge and discharge power of all energy storage under the i-th distribution station area; S i,int 、S i,min and S i,max are the initial state coefficient, minimum state coefficient and maximum state coefficient of all energy storage under the i-th distribution station area; and P i cap1 are the capacity and maximum charge and discharge power of the energy storage under the usage right of the i-th distribution station area; and are the initial state coefficient, minimum state coefficient and maximum state coefficient of the energy storage of the usage right under the i-th distribution station area; and are the upper limit of electricity purchase and sales of the load aggregator under the i-th distribution area; and is the aggregate amount of rigid load, flexible load and rooftop photovoltaic power generation in the ith distribution station area during period t, is the discharge power and charging power of the aggregated energy storage under the ith distribution station area in time period t; c om is the ownership energy storage operation and maintenance cost coefficient, k tr For inappropriate load transfer, c ch and c dis are the charging and discharging prices of energy storage using the right of use respectively; is the load transfer amount of the load aggregator in the t period under the ith distribution station area; is the discharge power and charging power of the aggregated usage rights energy storage under the ith distribution station area in time period t; T is the number of time periods; λ 1,t ,λ 2,t ,λ 5,t ,λ 7,t ,λ 8,t ,λ 9,t ,λ 11,t ,λ 13,t ,λ 15,t ,λ 17,t are the dual variables of the 1st, 2nd, 5th, 7th, 8th, 9th, 11th, 13th, 15th, and 17th inequality constraints respectively; μ 1,t , μ5, μ7 are the dual variables of the 1st, 5th, and 7th equality constraints respectively; μ 4,1 、μ 6,1 They are the dual variables of the 4th and 6th equality constraints during the period t=1.

[0076] Furthermore, in step S5, the master-slave game model is solved to output flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to the load aggregator, and output internal real-time power purchase and sales prices to the low-voltage substation operator, as follows:

[0077] S5.1. Solve the master-slave game model by calling the optimization solver;

[0078] S5.2. Output flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators; among them, the solution obtained by solving the master-slave game model is is a flexible load control strategy, Energy storage charging and discharging plans for different ownership rights, and For electricity purchase and sales plans;

[0079] S5.3. Output the internal real-time electricity purchase and sales prices to the low-voltage substation operators.

[0080] Furthermore, in step S6, the segmented penalty coefficient is adjusted, the master-slave game model is repeatedly solved, and a control strategy that meets the convergence conditions is output, specifically including:

[0081] S6.1. Set the reverse overload penalty coefficient c o The change step size is d o , the updated penalty coefficient c o The expression is:

[0082] c o ′=c o +d o ;

[0083] S6.2. Repeat step S5 until the reverse overload problem no longer occurs, that is, the convergence condition is met. Where, S is the electricity sales of the load aggregator in the ith distribution area during period t; i is the transformer capacity of the ith substation;

[0084] S6.3. Set the reverse overload penalty coefficient c h The change step size is d h , the updated penalty coefficient c′ h The expression is:

[0085] c′ h =c h +d h ;

[0086] S6.4, repeat step S5 until the reverse overload problem no longer occurs, that is, the convergence condition is met

[0087] S6.5. Send flexible load control strategies and energy storage charging and discharging plans with different ownership rights to load aggregators; send real-time pricing plans to low-voltage substation operators, including internal real-time electricity purchase and sales prices.

[0088] The system for implementing the flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas includes:

[0089] Data acquisition module: obtains the electricity purchase and sales price of the upper-level distribution network, energy storage capacity and power, photovoltaic power generation, user-side rigid and flexible loads, and transformer capacity data;

[0090] Segment penalty module: Constructs a transformer reverse heavy overload segment penalty model based on transformer capacity;

[0091] Cost and Benefit Module: Based on equipment physical constraints and market pricing constraints, it builds a model to minimize the operating costs of load aggregators and a model to maximize the benefits of low-voltage substation operators.

[0092] Model simplification module: Constructs a master-slave game model with the low-voltage substation operator as the leader and the load aggregator as the follower, and simplifies the master-slave game model;

[0093] Solution and output module: This module solves the master-slave game model, outputs flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators, and outputs internal real-time power purchase and sales prices to low-voltage substation operators.

[0094] Parameter adjustment module: adjusts the segmented penalty coefficient, repeatedly solves the master-slave game model, and outputs a control strategy that meets the convergence conditions.

[0095] A computer device of the present invention includes: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, it implements the flexible distributed resource control method for improving photovoltaic absorption in low-voltage areas.

[0096] Compared with the prior art, the present invention has the following beneficial effects:

[0097] In the context of a high proportion of photovoltaic access to the low-voltage distribution network, the present invention proposes a flexible distributed resource control method and system for improving the photovoltaic absorption of low-voltage substations. This method provides a flexible resource control method for improving the level of new energy absorption in low-voltage substations and alleviating the problem of reverse heavy overload of transformers. It takes into account the interests and comfort of the user side and the economy and safety of the grid side, and can fully mobilize the enthusiasm of users to interact, improve the photovoltaic absorption level of the substation, solve the problem of reverse heavy overload of transformers, and increase the safety of the grid. The present invention provides a flexible resource control method for improving the level of new energy absorption in low-voltage substations and alleviating the problem of reverse heavy overload of transformers, which is conducive to balancing the safety of the grid and the economy of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 The figure is a schematic diagram of the steps of a flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas according to an embodiment of the present invention.

[0099] Figure 2 This is an optimization model diagram for low-voltage substation operators and load aggregators in an embodiment of the present invention.

[0100] Figure 3 This is a control strategy effect diagram of flexible load and energy storage in an embodiment of the present invention.

[0101] Figure 4 This is a diagram of the pricing plan for low-voltage area operators in an embodiment of the present invention. DETAILED DESCRIPTION

[0102] The specific implementation of the present invention is further described below with reference to the accompanying drawings and examples.

[0103] A flexible distributed resource control method and system for improving photovoltaic consumption in low-voltage areas, such as Figure 1 As shown, the following steps are included:

[0104] S1. Obtain data on electricity purchase and sales prices from the upper-level distribution network, energy storage capacity and power, photovoltaic power generation, user-side rigid and flexible loads, and transformer capacity. Select the substations that require flexible resource control. Based on ownership, divide energy storage into ownership storage and usage right storage, and aggregate them separately. The details are as follows:

[0105] S1.1、Obtain the power purchase price of the upper-level distribution network from the power market and electricity prices Used in low voltage station operator maximization model.

[0106] S1.2. Aggregate the rigid load and flexible load of each user group in each distribution station area, as well as the day-ahead rooftop photovoltaic power generation. The specific aggregation formula is as follows:

[0107]

[0108] Where, and is the aggregate amount of rigid load, flexible load and rooftop photovoltaic power generation in the ith distribution station area during period t, and N is the rigid load, flexible load, and day-ahead rooftop photovoltaic power generation of the jth power user in the i-th distribution station area during period t. i is the total number of electricity users in the ith distribution station area.

[0109] S1.3. Select the substations that require flexible resource control. The specific selection formula is as follows:

[0110]

[0111] Where, O i is the selection status of the ith station, 1 represents that the station is selected, and 0 represents that the station is not selected; S i is the transformer capacity of the ith substation.

[0112] S1.4. Based on the ownership rights, energy storage is divided into ownership energy storage and usage right energy storage, and they are aggregated separately:

[0113]

[0114] Where, and E i,t is the discharge power, charging power and device status of the aggregated energy storage under the i-th distribution station area in time period t; and E i,j,tThe discharge power, charging power and device status of the energy storage owned by the jth power user in the i-th distribution station area during period t; and The discharge power, charging power and device status of the aggregated usage rights energy storage in the ith distribution station area during time period t; and The discharge power, charging power and device status of the energy storage of the jth power user's usage right under the i-th distribution station area in time period t.

[0115] S2. Based on the transformer capacity, a transformer reverse heavy overload segment penalty model is constructed, which is as follows:

[0116] S2.1. Set the segment penalty coefficient as follows:

[0117]

[0118] Where, is the penalty coefficient for period t, c h is the reverse overload penalty coefficient, c o is the reverse overload penalty coefficient. is the reverse load rate of the ith distribution station area in period t, which is calculated as follows:

[0119]

[0120] is the electricity sales amount of the load aggregator in the i-th distribution station area during period t; the load aggregator is an aggregate of power users in a region, representing the entire user group in external power transactions and internal flexible resource control, and can be referred to as LA.

[0121] S2.2. Combined with step S2.1, a transformer reverse overload segment penalty model is constructed. The penalty cost calculation formula is as follows:

[0122]

[0123] Where, is the section penalty cost of reverse heavy overload in the ith distribution station area during period t.

[0124] S3. Based on the physical constraints of the equipment and the market pricing constraints, a model for minimizing the operating costs of load aggregators and a model for maximizing the benefits of low-voltage station operators are constructed, such as Figure 2 The specific method is as follows:

[0125] S3.1. Construct the objective function of the load aggregator operating cost minimization model. The specific formula is as follows:

[0126]

[0127] Where c om is the ownership energy storage operation and maintenance cost coefficient, k tr For inappropriate load transfer, c ch and c dis are the charging and discharging prices of energy storage using right of use respectively; and are the load transfer amount, electricity sales amount, electricity purchase amount, internal electricity purchase price and internal electricity sales price of the load aggregator in the t period under the i-th distribution station area; f i LA is the cost of the load aggregator under the ith distribution station area, T is the number of time periods, is the discharge power and charging power of the energy storage with aggregated usage rights in the ith distribution station area during period t.

[0128] S3.2. Construct the constraints of the load aggregator operating cost minimization model.

[0129] As a preferred embodiment, the constraints include power balance constraints, load transfer constraints, energy storage constraints, and power purchase and sales constraints.

[0130] I. Power Balance Constraints

[0131]

[0132] Where, is the optimized load in the t period of the i-th distribution station area. The formula indicates that the electric power flowing into the load aggregator should be equal to the electric power flowing out plus the electric power consumed.

[0133] II. Load transfer constraints

[0134]

[0135] The formula indicates that the load consists of the rigid load, the original flexible load, and the actual load transfer; in time period t, the actual load transfer cannot exceed the original flexible load, and the sum of the load transfers is 0.

[0136] III. Ownership Constraints on Energy Storage

[0137]

[0138] Where, and P i cap are the capacity and maximum charge and discharge power of all energy storage under the i-th distribution station area; and are the charging efficiency and discharging efficiency of the energy storage under the i-th distribution station area; S i,int 、S i,min and Si,max are the initial state coefficient, minimum state coefficient and maximum state coefficient of all energy storage under the i-th distribution station area; is the charging flag, which is a Boolean variable. The above formula indicates that for all energy storage, the energy storage state at time t is equal to the energy storage state at time t-1 plus the charge and discharge amount. The energy storage state cannot exceed the minimum energy storage capacity and the maximum energy storage capacity. Generally, the initial and final state quantities of the energy storage are required to be equal. The energy storage charge and discharge power has an upper limit, and generally, charging and discharging are not performed at the same time.

[0139] IV. Constraints on energy storage using rights

[0140]

[0141] Where, are the discharge power, charging power and device status of the aggregated energy storage under the ith distribution station area in time period t; and P i cap1 are the capacity and maximum charge and discharge power of the energy storage under the usage right of the i-th distribution station area; and are the charging efficiency and discharging efficiency of the energy storage using the right under the ith distribution station area; and are the initial state coefficient, minimum state coefficient and maximum state coefficient of the energy storage of the usage right under the i-th distribution station area; is the charging flag, which is a Boolean variable. The formula indicates that for the right-of-use energy storage, the energy storage state quantity at time t is equal to the energy storage state quantity during period t-1 plus the charge and discharge quantity. The energy storage state cannot exceed the minimum energy storage capacity and the maximum energy storage capacity. Generally, the initial and final state quantities of the energy storage are required to be equal. The energy storage charge and discharge power has an upper limit, and generally, charging and discharging are not performed simultaneously.

[0142] V. Constraints on Power Purchase and Sales

[0143]

[0144] Where, and are the upper limit of electricity purchase and sales of the load aggregator under the i-th distribution area; It is the electricity purchase flag, which is a Boolean variable. The formula indicates that simultaneous purchase and sale of electricity is not allowed.

[0145] S3.3. Construct the objective function of the low-voltage area operator profit maximization model. The specific formula is as follows:

[0146]

[0147] f i DSOis the total revenue of the ith distribution area operator; the formula represents the sum of the transaction revenue between the low-voltage area operator and the upper-level distribution network, the transaction revenue with the LA, and the reverse heavy overload penalty cost.

[0148] S3.4. Construct pricing constraints for the low-voltage area operator revenue maximization model. The specific formula is as follows:

[0149]

[0150] r b,av and r s,av are the average electricity purchase price and average electricity selling price traded between users and upper-level distribution networks; r b,min and r b,max are the upper and lower limits of the internal electricity purchase price; r s,min and r s,max are the upper and lower limits of the internal electricity sales price respectively; the formula indicates that the internal average electricity purchase price shall not exceed the user's external average electricity purchase price; the internal average electricity sales price shall not be lower than the user's external average electricity sales price; the pricing of the internal electricity purchase and sales prices must comply with the upper and lower limits of electricity prices allowed by the market.

[0151] S4. Construct a master-slave game model with the low-voltage substation operator as the leader and the load aggregator as the follower, and simplify the master-slave game model.

[0152] S4.1. Construct a master-slave game model with the low-voltage substation operator as the upper-layer model and the load aggregator as the lower-layer model. The specific expression is as follows:

[0153] G={D∪L;W,w;f i DSO ,f i LA};

[0154] Where G represents the master-slave game model, D and L represent the two game entities, the low-voltage area operator and the load aggregator, respectively; W and w represent the game strategies of the low-voltage area operator and the load aggregator, respectively; f i DSO and f i LA They represent the benefits of low-voltage area operators and the costs of load aggregators respectively.

[0155] S4.2. Use KKT conditions to transform the lower-level model into the constraints of the upper-level model. The specific transformation formula is as follows:

[0156]

[0157] Where, L i (x,λ h ,μ k) is the Lagrangian function of the lower model of the ith distribution area, and equations ①, ②, ③ and ④ are the stationary point condition, the feasibility condition, the dual feasibility condition and the complementary slackness condition respectively; e h (x) and z k (x) are the hth inequality constraint and the kth equality constraint of the lower model respectively, and λ h and μ k are the dual variables of e h (x) and z k (x) respectively, and x represents the decision variable in the constraint condition; m and n represent the total number of inequality constraints and the total number of equality constraints respectively.

[0158] S4.3, simplifying the piecewise function in step S2.2 by function merging The specific formula is as follows:

[0159]

[0160] In the formula, the max function selects the larger value of the two.

[0161] S4.4, converting equation ④ in step S4.2 into a linear function by the Big-M method, and the specific formula is as follows:

[0162]

[0163] In the formula, U h is a Boolean variable, and M is a very large positive number, and as an embodiment, M is 100000000.

[0164] S4.5, linearly replacing in the objective functions of the load aggregator operating cost minimization model and the low-voltage distribution area operator maximization model by the duality theorem, and the specific formula is as follows:

[0165]

[0166] T is the number of time periods; λ 1,t , λ 2,t , λ 5,t , λ 7,t , λ 8,t , λ 9,t , λ 11,t , λ 13,t , λ 15,t , λ 17,t are the dual variables of the 1st, 2nd, 5th, 7th, 8th, 9th, 11th, 13th, 15th and 17th inequality constraints respectively; μ 1,t , μ5 and μ7 are the dual variables of the 1st, 5th and 7th equality constraints respectively; μ 4,1 , μ 6,1They are the dual variables of the 4th and 6th equality constraints during the period t=1.

[0167] S5. Solve the master-slave game model and output flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators. Output internal real-time power purchase and sales prices to low-voltage substation operators. Details are as follows:

[0168] S5.1. Solve the master-slave game model by calling the optimization solver.

[0169] S5.2. Output flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators; among them, the solution obtained by solving the master-slave game model is is a flexible load control strategy, Energy storage charging and discharging plans for different ownership rights, and For the purchase and sale of electricity plan.

[0170] S5.3. Output the internal real-time electricity purchase and sales prices to the low-voltage station operator.

[0171] S6. Adjust the segment penalty coefficient, repeatedly solve the master-slave game model, and output a control strategy that meets the convergence conditions, specifically including:

[0172] S6.1. Set the reverse overload penalty coefficient c o The change step size is d o , the updated penalty coefficient c o The expression is:

[0173] c o ′=c o +d o ;

[0174] S6.2. Repeat step S5 until the reverse overload problem no longer occurs, that is, the convergence condition is met.

[0175] S6.3. Set the reverse overload penalty coefficient c h The change step size is d h , the updated penalty coefficient c′ h The expression is:

[0176] c′ h =c h +d h ;

[0177] S6.4, repeat step S5 until the reverse overload problem no longer occurs, that is, the convergence condition is met

[0178] S6.5, send flexible load control strategy and energy storage charging and discharging plan to load aggregators, such as Figure 3 As shown; send the pricing plan including internal real-time purchase price and sales price to the low-voltage area operator as shown Figure 4 As shown.

[0179] This embodiment also provides a flexible distributed resource control method and system for improving photovoltaic consumption in low-voltage areas, including:

[0180] Data acquisition module: obtains the electricity purchase and sales price of the upper-level distribution network, energy storage capacity and power, photovoltaic power generation, user-side rigid and flexible loads, and transformer capacity data;

[0181] Segment penalty module: Constructs a transformer reverse heavy overload segment penalty model based on transformer capacity;

[0182] Cost and Benefit Module: Based on equipment physical constraints and market pricing constraints, it builds a model to minimize the operating costs of load aggregators and a model to maximize the benefits of low-voltage substation operators.

[0183] Model simplification module: Constructs a master-slave game model with the low-voltage substation operator as the leader and the load aggregator as the follower, and converts it into a mixed integer linear programming model;

[0184] Solution and output module: This module solves the master-slave game model, outputs flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators, and outputs internal real-time power purchase and sales prices to low-voltage substation operators.

[0185] Parameter adjustment module: adjusts the segmented penalty coefficient, repeatedly solves the master-slave game model, and outputs a control strategy that meets the convergence conditions.

[0186] Existing flexible resource control technologies in the power sector are generally implemented by the power grid and do not adequately consider the economic benefits of users. This invention considers the interests of both users and the power grid separately, which is conducive to achieving a win-win situation for both user economic benefits and power grid security.

[0187] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other modifications, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and should be included in the scope of protection of the present invention.

Claims

1. A flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas, characterized by: The following steps are involved: S1. Obtain data on electricity purchase and sales prices, energy storage capacity and power, photovoltaic power generation, user-side rigid and flexible loads, and transformer capacity from the upper-level distribution network; S2. Calculate the penalty cost based on the transformer capacity using the transformer reverse heavy overload segment penalty model; S3. Based on the physical constraints of the equipment and the market pricing constraints, the load aggregator operating cost minimization model and the low-voltage substation operator revenue maximization model are used to obtain the benefit costs of the load aggregator and the low-voltage substation operator; S4. Construct a master-slave game model with the low-voltage substation operator as the leader and the load aggregator as the follower, and simplify the master-slave game model; S5. Solve the master-slave game model and output flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators. Output internal real-time power purchase and sales prices to low-voltage substation operators. S6. Adjust the segmented penalty coefficient of the segmented penalty model, repeatedly solve the master-slave game model, and output a control strategy that meets the convergence conditions.

2. The flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas according to claim 1 is characterized in that: Step S1 includes the following steps: S1.1、Obtain the power purchase price of the upper-level distribution network from the power market and electricity prices S1.

2. Aggregate the rigid load, flexible load, and day-ahead rooftop PV power generation of user groups in each distribution station area. The specific aggregation formula is as follows: Where, and is the aggregate amount of rigid load, flexible load and rooftop photovoltaic power generation in the ith distribution station area during period t, and N is the rigid load, flexible load, and day-ahead rooftop photovoltaic power generation of the jth power user in the i-th distribution station area during period t. i is the total number of electricity users in the ith distribution station area; S1.

3. Select the distribution substation area that requires flexible resource control. The specific selection formula is as follows: Where, O i is the selection status of the ith distribution station area, 1 represents that the station area is selected, and 0 represents that the station area is not selected; S i is the transformer capacity of the i-th distribution station area; S1.

4. Based on the ownership rights, energy storage is divided into ownership energy storage and usage right energy storage, and they are aggregated separately: Where, and E i,t is the discharge power, charging power and device status of the aggregated energy storage under the ith distribution station area in time period t; and E i,j,t The discharge power, charging power and device status of the energy storage owned by the jth power user in the i-th distribution station area during time period t; and The discharge power, charging power and device status of the aggregated usage rights energy storage in the ith distribution station area during time period t; and The discharge power, charging power and device status of the energy storage of the jth power user's usage right under the i-th distribution station area in time period t.

3. The flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas according to claim 1 is characterized in that: In step S2, a transformer reverse heavy overload segment penalty model is constructed based on the transformer capacity, including: S2.

1. Set the segment penalty coefficient as follows: Where, is the penalty coefficient for period t, c h is the reverse overload penalty coefficient, c o is the reverse overload penalty coefficient, is the reverse load rate of the ith distribution station area in period t: Where S i is the transformer capacity of the ith distribution station area; is the electricity sales of the load aggregator in the ith distribution station area during period t. The load aggregator is an aggregate of power users in a region, representing the entire user group in external power transactions and internal flexible resource control; S2.

2. Combined with step S2.1, a transformer reverse heavy overload segment penalty model is constructed: Where, is the section penalty cost of reverse heavy overload in the ith distribution station area during period t.

4. The flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas according to claim 1 is characterized in that: In step S3, the load aggregator operating cost minimization model includes: The objective function of the load aggregator operating cost minimization model is as follows: Where c om is the ownership energy storage operation and maintenance cost coefficient, is the discharge power and charging power of the aggregated energy storage in the ith distribution station area during period t, k tr For inappropriate load transfer, c ch and c dis are the charging and discharging prices of the right-of-use energy storage; and are the load transfer amount, electricity sales amount, electricity purchase amount, internal electricity purchase price and internal electricity sales price of the load aggregator in the t period under the i-th distribution station area; f i LA is the cost of the load aggregator under the ith distribution station area, T is the number of time periods, is the discharge power and charging power of the energy storage under the aggregated usage rights in the ith distribution station area during period t; The constraints of the load aggregator's operating cost minimization model include power balance constraints, load transfer constraints, energy storage constraints, and power purchase and sales constraints; I. Power balance constraint means that the power flowing into the load aggregator should be equal to the power flowing out plus the power consumed. The formula is: Where, is the aggregated amount of rooftop photovoltaic power generation under the i-th distribution station area during period t; is the optimized load of the ith distribution station area in the t period; II. Load transfer constraints: in is the rigid load and flexible load of the ith distribution station area in period t, is the load transfer amount of the load aggregator in the t period under the i-th distribution station area; in the t period, the actual load transfer amount cannot exceed the original flexible load, and the sum of the load transfer amounts is 0; III. Ownership Constraints on Energy Storage: Where, and P i cap are the capacity and maximum charge and discharge power of all energy storage under the i-th distribution station area; and are the charging efficiency and discharging efficiency of the energy storage under the i-th distribution station area; S i,int 、S i,min and S i,max are the initial state coefficient, minimum state coefficient and maximum state coefficient of all energy storage under the i-th distribution station area; It is the charging flag, which is a Boolean variable; and E i,t is the discharge power, charging power, and device status of the aggregated ownership energy storage in the i-th distribution station area in time period t; T is the number of time periods. The formula indicates that for ownership energy storage, the energy storage state at time t is equal to the energy storage state in time period t-1 plus the charge and discharge amount; the energy storage state cannot exceed the minimum energy storage capacity and the maximum energy storage capacity, and the initial and final energy storage states must be equal; the energy storage charge and discharge power has an upper limit, and charging and discharging are not simultaneous. IV. Constraints on energy storage using rights: Where, and P i cap1 are the capacity and maximum charge and discharge power of the energy storage under the usage right of the i-th distribution station area; and are the charging efficiency and discharging efficiency of the energy storage using the right under the ith distribution station area; and are the initial state coefficient, minimum state coefficient and maximum state coefficient of the energy storage of the usage right under the i-th distribution station area; It is the charging flag, which is a Boolean variable; are the discharge power, charging power, and device state of the aggregated ownership energy storage under the i-th distribution station area during period t. The formula indicates that for the right-of-use energy storage, the energy storage state at time t is equal to the energy storage state during period t-1 plus the charge and discharge amount. The energy storage state cannot exceed the minimum and maximum energy storage capacities, requiring the initial and final energy storage states to be equal. The energy storage charge and discharge power has an upper limit, and charging and discharging cannot occur simultaneously. V. Power purchase and sales constraints mean that simultaneous power purchase and sales are not allowed: Where, and are the upper limit of electricity purchase and sales of the load aggregator under the i-th distribution area; It is the electricity purchase flag, which is a Boolean variable.

5. The flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas according to claim 4 is characterized in that: In step S3, the low-voltage area operator's profit maximization model includes: The low-voltage area operator maximizes the objective function of the model. The specific formula is as follows: The formula represents the sum of the transaction income between the low-voltage area and the upper-level distribution network, the transaction income with the load aggregator and the penalty cost of reverse overload; Indicates the electricity purchase price and electricity sales price of the upper-level distribution network; and are the internal electricity purchase price and internal electricity selling price of the load aggregator in the t period under the i-th distribution station area; f i DSO is the total revenue of the operator of the ith distribution area; The pricing constraints of the low-voltage area operator's revenue maximization model are as follows: The formula indicates that the internal average purchase price of electricity shall not exceed the external average purchase price of electricity for the user; the internal average sales price of electricity shall not be lower than the external average sales price of electricity for the user; the pricing of internal purchase and sales prices shall comply with the upper and lower limits of electricity prices allowed by the market; where r b,av and r s,av are the average electricity purchase price and average electricity selling price of the user and the external power grid respectively; r b,min and r b,max are the upper and lower limits of the internal electricity purchase price; r s,min and r s,max They are the upper and lower limits of the internal electricity sales price respectively; the above-mentioned internal electricity price refers to the price between the low-voltage station operator and the load aggregator.

6. The flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas according to claim 5 is characterized in that: In step S4, a master-slave game model is constructed with the low-voltage substation operator as the leader and the load aggregator as the follower, and the master-slave game model is simplified as follows: S4.

1. Construct a master-slave game model with the low-voltage substation operator as the upper-layer model and the load aggregator as the lower-layer model. The specific expression is as follows: G={D∪L;W,w;f i DSO ,f i LA }; Where G represents the master-slave game model, D and L represent the two game entities, the low-voltage area operator and the load aggregator, respectively; W and w represent the game strategies of the low-voltage area operator and the load aggregator, respectively; f i DSO and f i LA They represent the benefits of the low-voltage area operator and the costs of the load aggregator respectively; S4.

2. The lower-level model is transformed into the constraints of the upper-level model through the Carroll-Kuhn-Tucker (KKT) condition, making the master-slave game model easier to solve. The specific transformation formula is as follows: Where, L i (x,λ h ,μ k ) is the Lagrangian function of the lower model of the ith distribution station area, and equations ① to ④ are the stationary point condition, feasibility condition, dual feasibility condition, and complementary relaxation condition respectively; e h (x) and z k (x) are the hth inequality constraint and kth equality constraint of the lower model, λ h and μ k Respectively represent e h (x) and z k (x) dual variable, x represents the decision variable in the constraint condition; m and n represent the total number of inequality constraints and the total number of equality constraints respectively; S4.

3. The transformer reverse overload segment penalty model is simplified by function merging. The specific formula is as follows: Where, is the segment penalty cost of reverse heavy overload in the ith distribution station area during period t; the max function selects the larger value of the two; c ch is the charging price of energy storage for right of use; c o is the reverse overload penalty coefficient, is the reverse load rate of the ith distribution station area in period t; S4.

4. Use the Big-M method to convert equation ④ in simplification step S4.2 into a linear function. The specific formula is as follows: Where U h is a Boolean variable, M is a positive number; S4.

5. Through the duality theorem, the objective functions of the load aggregator operating cost minimization model and the low-voltage station operator maximization model are combined. Perform linear substitution, the specific formula is as follows: in and are the internal electricity purchase price and internal electricity selling price of the load aggregator in the t period under the i-th distribution station area; and is the electricity sales and purchases of the load aggregator in the ith distribution area during period t; is the flexible load of the ith distribution station area in period t, and P i cap are the capacity and maximum charge and discharge power of all energy storage under the i-th distribution station area; S i,int 、S i,min and S i,max are the initial state coefficient, minimum state coefficient and maximum state coefficient of all energy storage under the i-th distribution station area; and P i cap1 are the capacity and maximum charge and discharge power of the energy storage under the usage right of the i-th distribution station area; and are the initial state coefficient, minimum state coefficient and maximum state coefficient of the energy storage of the usage right under the i-th distribution station area; and are the upper limit of electricity purchase and sales of the load aggregator under the i-th distribution area; and is the aggregate amount of rigid load, flexible load and rooftop photovoltaic power generation in the ith distribution station area during period t, is the discharge power and charging power of the aggregated energy storage in the ith distribution station area during period t; c om is the ownership energy storage operation and maintenance cost coefficient, k tr For inappropriate load transfer, c ch and c dis are the charging and discharging prices of energy storage using the right of use respectively; is the load transfer amount of the load aggregator in the t period under the ith distribution station area; is the discharge power and charging power of the aggregated usage rights energy storage under the ith distribution station area in time period t; T is the number of time periods; λ 1,t ,λ 2,t ,λ 5,t ,λ 7,t ,λ 8,t ,λ 9,t ,λ 11,t ,λ 13,t ,λ 15,t ,λ 17,t are the dual variables of the 1st, 2nd, 5th, 7th, 8th, 9th, 11th, 13th, 15th, and 17th inequality constraints respectively; μ 1,t , μ5, μ7 are the dual variables of the 1st, 5th, and 7th equality constraints respectively; μ 4,1 、μ 6,1 They are the dual variables of the 4th and 6th equality constraints during the period t=1.

7. The flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas according to claim 1 is characterized in that: In step S5, the master-slave game model is solved to output the flexible load control strategy, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to the load aggregator, and output the internal real-time power purchase and sales price to the low-voltage substation operator. The details are as follows: S5.

1. Solve the master-slave game model by calling the optimization solver; S5.

2. Output flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators; among them, the solution obtained by solving the master-slave game model is is a flexible load control strategy, Energy storage charging and discharging plans for different ownership rights, and For electricity purchase and sales plans; S5.

3. Output the internal real-time electricity purchase and sales prices to the low-voltage substation operators.

8. The flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas according to claim 1 is characterized in that: In step S6, the segmented penalty coefficient is adjusted, the master-slave game model is repeatedly solved, and a control strategy that meets the convergence conditions is output, specifically including: S6.

1. Set the reverse overload penalty coefficient c o The change step size is d o , the updated penalty coefficient c o The expression is: c o ′=c o +d o ; S6.

2. Repeat step S5 until the reverse overload problem no longer occurs, that is, the convergence condition is met. Where, S is the electricity sales of the load aggregator in the ith distribution area during period t; i is the transformer capacity of the ith substation; S6.

3. Set the reverse overload penalty coefficient c h The change step size is d h , the updated penalty coefficient c′ h The expression is: c′ h =c h +d h ; S6.4, repeat step S5 until the reverse overload problem no longer occurs, that is, the convergence condition is met S6.

5. Send flexible load control strategies and energy storage charging and discharging plans with different ownership rights to load aggregators; send real-time pricing plans to low-voltage substation operators, including internal real-time electricity purchase and sales prices.

9. A system for implementing the flexible distributed resource control method for improving photovoltaic consumption in low-voltage areas as described in claim 1, characterized in that: include: Data acquisition module: obtains the electricity purchase and sales price of the upper-level distribution network, energy storage capacity and power, photovoltaic power generation, user-side rigid and flexible loads, and transformer capacity data; Segment penalty module: Constructs a transformer reverse heavy overload segment penalty model based on transformer capacity; Cost and Benefit Module: Based on equipment physical constraints and market pricing constraints, it builds a model to minimize the operating costs of load aggregators and a model to maximize the benefits of low-voltage substation operators. Model simplification module: Constructs a master-slave game model with the low-voltage substation operator as the leader and the load aggregator as the follower, and simplifies the master-slave game model; Solution and output module: This module solves the master-slave game model, outputs flexible load control strategies, energy storage charging and discharging plans with different ownership rights, and power purchase and sales plans to load aggregators, and outputs internal real-time power purchase and sales prices to low-voltage substation operators. Parameter adjustment module: adjusts the segmented penalty coefficient, repeatedly solves the master-slave game model, and outputs a control strategy that meets the convergence conditions.

10. A computer device, characterized in that: It comprises: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, it implements a flexible distributed resource control method for improving photovoltaic absorption in low-voltage areas as described in any one of claims 1 to 8.

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