Distribution Network - Microgrid Electric Energy Interactive Trading Method Considering Power Market Risks

Abstract

The present invention belongs to the technical field of electricity trading, and specifically relates to a distribution network - microgrid electricity interactive trading method considering power market risks. The steps include: constructing a distribution network - microgrid collaborative architecture, including an upper layer based on the distribution network and a lower layer based on multiple microgrids; constructing the objective function and its constraint conditions of the upper layer model with the goal of maximizing the daily profit of the distribution network agent; constructing the objective function and its constraint conditions of the lower layer model with the goal of minimizing the daily comprehensive operating cost of the multi - microgrid system; transforming the lower layer model into an additional constraint condition of the upper layer model, obtaining the objective function of the transformed upper layer model and solving it to obtain an optimized distribution network - microgrid collaborative interactive electricity trading scheme. The present invention realizes the interest balance and risk sharing between the distribution network and the microgrid under the power market fluctuations through a dynamic pricing mechanism, a division of energy storage trading modes and an interruption load mechanism, can improve the new energy consumption rate, and enhance the system economy and flexibility.

Description

Technical Field

[0001] The invention belongs to the technical field of electric energy trading, and particularly relates to a distribution network - microgrid electric energy interactive trading method considering power market risks. Background Art

[0002] Currently, the coordinated scheduling and operation of the distribution network and the microgrid are mainly divided into centralized optimal operation and distributed optimal operation. However, the traditional centralized optimal operation is difficult to meet the demand of coping with the changes in external power market trading prices under the background of high penetration of renewable energy. Therefore, it is urgent to study the distributed optimal scheduling control theory and method for the distribution network - microgrid.

[0003] The distribution network can interact with the microgrid by setting up energy storage. When the power generation of distributed power sources in the microgrid exceeds the load demand, the surplus power is sold to the distribution network to avoid the cost of wind and light curtailment. At the same time, the distribution network stores the purchased electricity in its own energy storage. When the purchase price in the external power market is higher than the purchase price from the microgrid, the energy storage of the distribution network discharges and sells electricity to the power market to complete the electricity price transfer. The distribution network - microgrid collaborative interaction balances the interests of both sides.

[0004] However, existing research does not consider the impact of real - time price fluctuations in the power market on the distribution network - microgrid transaction, and lacks a dynamic pricing and external risk coupling mechanism; existing research encourages emerging market players to directly participate in power market transactions, focusing on the P2P flat trading mode. However, when there is a deviation between the contract electricity quantity and the actual electricity consumption, both trading parties are at risk of losing benefits, ignoring the buffering role of the distribution network under the hierarchical market structure, resulting in limited risk - dispersion ability; existing research ignores the impact of energy storage on system economy under external market trading price fluctuations, and does not deeply explore the potential of energy storage to avoid risks of external power market price changes and balance the interests of both trading parties; existing research implements fixed pricing or quantity - based pricing for the interruption load compensation price, ignoring the impact of changes in external market conditions and price variables other than the compensation price on the demand side, resulting in insufficient flexibility for the demand side to participate in electric energy trading and not deeply exploring the potential of user willingness and two - way interaction of electric energy resources. Summary of the Invention

[0005] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a distribution network - microgrid electric energy interactive trading method considering power market risks. Through a dynamic pricing mechanism, a division of energy storage trading modes and an interruption load mechanism, the present invention realizes the interest balance and risk sharing between the distribution network and the microgrid under power market fluctuations, can improve the new - energy consumption rate, and enhance the economy and flexibility of the system.

[0006] To achieve the above - mentioned purpose, the present invention provides a distribution network - microgrid electric energy interactive trading method considering power market risks, including the following steps:

[0007] S1. Construct a coordinated distribution network - microgrid architecture, including an upper layer based on the distribution network and a lower layer based on multiple microgrids. The upper layer architecture includes a distribution network agent, a distribution network control center, and an energy storage system composed of distribution network energy storage and shared energy storage. Each microgrid in the lower layer contains wind power, users, photovoltaic power, and a microgrid control center, and each microgrid forms a multi - microgrid system;

[0008] S2. With the goal of maximizing the daily profit of the distribution network agent, construct the objective function and its constraints of the upper - layer model;

[0009] S3. With the goal of minimizing the daily comprehensive operating cost of the multi - microgrid system, construct the objective function and its constraints of the lower - layer model;

[0010] S4. Transform the lower - layer model into an additional constraint condition of the upper - layer model, obtain the objective function of the transformed upper - layer model and solve it to obtain an optimized coordinated distribution network - microgrid interactive power trading scheme. In this scheme:

[0011] In the day - ahead stage, the microgrid reports the maximum amount of its interruptible load to the microgrid control center. The microgrid control center reports its new - energy output and load information to the distribution network control center. The distribution network formulates a day - ahead contract power purchase strategy based on the information reported by the microgrid;

[0012] In the real - time stage, the distribution network formulates distribution network pricing information for real - time power purchase and sale to the microgrid according to real - time electricity market price information, as well as a power trading strategy and a distribution network energy storage capacity division strategy, and transmits the electricity market price information and distribution network pricing information to the lower - layer microgrid. The microgrid adjusts its interruptible load pricing strategy and power consumption strategy according to the information transmitted by the distribution network and gives real - time feedback to the distribution network;

[0013] Based on the feedback from the microgrid and on the basis of the energy storage capacity division strategy, the distribution network makes a secondary adjustment to the pricing of new - energy output power purchase and sale for the microgrid.

[0014] As a preferred solution of the present invention, in S2, the upper - layer interest subject is the distribution network agent, considering the operation income of the distribution network shared energy storage 、the income from the distribution network's real - time power sale to the microgrid 、the income from the distribution network agent's real - time power sale to the electricity market 、the cost of the distribution network agent's real - time power purchase from the electricity market 、the cost of the distribution network agent's day - ahead power purchase from the electricity market 、the cost of the distribution network agent's deviation declaration for the next day's transaction with the electricity market , the objective function of the upper - layer model is:

[0015] (1);

[0016] The upper-layer model constraints include the interactive power balance constraint between the distribution network and the microgrid, the interactive power balance constraint of the shared energy storage, the charge and discharge constraints of the shared energy storage, the charge and discharge constraints of the distribution network energy storage, the maximum charge and discharge power allocation constraint of the energy storage, the state of charge constraint of the shared energy storage, the state of charge constraint of the distribution network energy storage, the energy storage capacity allocation constraint, the electricity selling price constraint from the distribution network to the microgrid, the electricity selling and buying price constraints of the shared energy storage to the microgrid, the trading constraint between the distribution network agent and the upper-level power market, and the interest constraint of the distribution network agent.

[0017] As a preferred embodiment of the present invention, The calculation method of

[0018] is: (2);

[0019] In the formula, t is the index of the scheduling period, and T is the total number of scheduling periods; i is the index of the microgrid, and N is the total number of microgrids; , are respectively the electricity selling price and the electricity buying price of the shared energy storage to the microgrid i in the t-th scheduling period; , are respectively the electricity selling power and the electricity buying power of the shared energy storage to the microgrid i in the t-th scheduling period; is the time interval;

[0020] The calculation method of

[0021] is: (3);

[0022] In the formula, is the electricity selling price from the distribution network to the microgrid i in the t-th scheduling period; is the electricity selling power from the distribution network to the microgrid i in the t-th scheduling period;

[0023] , The calculation method of

[0024] is: (4);

[0025] is: (5);

[0026] In the formula, , are respectively the real-time electricity selling price and the real-time electricity buying price of the distribution network agent to the power market in the t-th scheduling period; , are respectively the real-time electricity selling power and the real-time electricity buying power of the distribution network to the power market in the t-th scheduling period; is the real-time electricity selling power of the distribution network shared energy storage to the power market in the t-th scheduling period;

[0027] The calculation method of

[0028] (6);

[0029] Wherein, is the day-ahead contract price for the t dispatching period; is the day-ahead benchmark contract power for the t dispatching period; , are the positive and negative deviations of the day-ahead contract declaration for the t dispatching period respectively;

[0030] The power market recovers the declared deviation part according to the corresponding settlement revenue, The calculation method of is:

[0031] (7);

[0032] (8);

[0033] Wherein, , are the positive and negative deviation prices of the next-day declaration respectively; , are the positive and negative deviation settlement revenue coefficients of the declaration respectively.

[0034] As a preferred solution of the present invention, each constraint condition of the upper-layer model is specifically:

[0035] Power balance constraint for the interaction between the distribution network and the microgrid:

[0036] (9);

[0037] Wherein, , are the charging and discharging powers of the distribution network energy storage for the t dispatching period respectively;

[0038] Shared energy storage interaction power balance constraint. During the period when the microgrid purchases and sells electricity to / from the shared energy storage, the charging and discharging power of the shared energy storage is determined by the total energy demand after the energy exchange at each microgrid busbar. Its constraint is:

[0039] (10);

[0040] (11);

[0041] (12);

[0042] Wherein, , are the charging and discharging powers of the shared energy storage for the t dispatching period respectively; , is the power purchased by the shared energy storage from the microgrid i and used to sell to the power market and the power-deficient microgrid during the t dispatching period; , is the charging power of the shared energy storage for sale to the power market and power-deficient microgrids during the t dispatching period; , is the discharging power of the shared energy storage for sale to the power market and power-deficient microgrids during the t dispatching period;

[0043] Charging and discharging constraints of the shared energy storage:

[0044] (13);

[0045] In the formula, , are the charging and discharging flag bits of the shared energy storage during the t dispatching period, with values of 0 or 1. A value of 1 indicates that charging and discharging are in progress. The meanings of the other flag bits are the same; is the maximum charging and discharging power of the shared energy storage;

[0046] Charging and discharging constraints of the distribution network energy storage:

[0047] (14);

[0048] In the formula, , are the charging and discharging powers of the distribution network energy storage during the t dispatching period respectively; , are the charging and discharging flag bits of the distribution network energy storage during the t dispatching period respectively; is the maximum charging and discharging power of the distribution network energy storage;

[0049] Constraint on the distribution of the maximum charging and discharging power of the energy storage:

[0050] (15);

[0051] In the formula, is the total maximum charging and discharging power of the energy storage;

[0052] Constraint on the state of charge of the shared energy storage:

[0053] (16);

[0054] (17);

[0055] In the formula, , are the states of charge of the shared energy storage during the t and t-1 dispatching periods respectively; is the charging and discharging efficiency of the energy storage; , are the upper and lower limit coefficients of the state of charge of the energy storage respectively; is the maximum stored energy of the shared energy storage;

[0056] Distribution network energy storage state of charge constraint:

[0057] (18);

[0058] (19);

[0059] Wherein, , are the state of charge of the distribution network energy storage at the t and t-1 scheduling periods, respectively; is the maximum energy storage capacity of the distribution network energy storage;

[0060] Energy storage capacity allocation constraint:

[0061] (20);

[0062] Wherein, is the maximum total energy storage capacity;

[0063] Distribution network to microgrid power selling price constraint:

[0064] (21);

[0065] (22);

[0066] (23);

[0067] Wherein, , are the upper and lower limits of the distribution network to microgrid power selling price at the t scheduling period, respectively; is the power selling flag bit of the distribution network to microgrid i at the t scheduling period; is the maximum power selling times of the distribution network to microgrid i;

[0068] The power purchase and sale price constraint of the shared energy storage to the microgrid is as follows:

[0069] (24);

[0070] (25);

[0071] Wherein, , are the upper and lower limits of the power selling price of the shared energy storage to the microgrid at the t scheduling period, respectively; , are the upper and lower limits of the power purchase price of the shared energy storage from the microgrid at the t scheduling period, respectively;

[0072] At the same time, the power purchase and sale price of the shared energy storage is coupled with the power purchase price of the external power market to protect the interests of both the distribution network and the microgrid:

[0073] (26);

[0074] (27);

[0075] (28);

[0076] (29);

[0077] Wherein, and are respectively the selling and purchasing power flag bits of the shared energy storage to Microgrid i during the t scheduling period; is the real-time power purchase coefficient of the power market corresponding to the full consumption of new energy in the microgrid without setting up energy storage; is the current real-time power purchase coefficient of the power market; and are respectively the price protection coefficients for selling and purchasing electricity; and are respectively the maximum selling and purchasing times of the shared energy storage to Microgrid i;

[0078] Trading constraints between the distribution network agent and the superior power market:

[0079] (30);

[0080] (31);

[0081] (32);

[0082] (33);

[0083] (34);

[0084] (35);

[0085] (36);

[0086] (37);

[0087] Wherein, is the maximum power purchase of the distribution network's day-ahead contract with the power market; and are respectively the power purchase and selling power flag bits during the t scheduling period; and are respectively the real-time maximum power purchase and selling powers of the distribution network with the power market; represents or ; represent or , which are the maximum positive and negative deviation powers reported for the distribution network in the electricity market trading the day after the next day, respectively; and are the positive and negative deviation status flag bits in the t dispatching period, respectively, represent or ; represent or , which are the positive and negative deviation coefficients, respectively;

[0088] Regarding the interest constraint of the distribution network agent, when the real-time electricity purchase coefficient in the external electricity market is not equal to the reference value, there is a risk of loss of benefits for the distribution network to absorb the new energy of the microgrid. To ensure that the interests of the distribution network are not damaged and to prevent the operation cost of the microgrid from rising due to unreasonable secondary pricing of the distribution network, the following constraints are set:

[0089] (38);

[0090] In the formula, and are the upper and lower limits of the incremental revenue after secondary pricing, respectively; is the electricity purchase price of the distribution network from microgrid i in the t dispatching period.

[0091] As a preferred solution of the present invention, in S3, the lower-level interest subject is a multi-microgrid system, considering the cost of the microgrid using shared energy storage services , the electricity purchase cost of the microgrid from the distribution network , the compensation cost paid by the microgrid's interruptible load to users , and the cost of wind and light abandonment , the objective function of the lower-level model is:

[0092] (39);

[0093] The constraint conditions of the lower-level model include the microgrid power balance constraint, the power purchase power constraint of the microgrid from the distribution network, the power purchase and sale power constraint of the microgrid with the shared energy storage, the internal interruptible load constraint of the microgrid, and the wind and light abandonment constraint of the microgrid.

[0094] As a preferred solution of the present invention, The calculation method of

[0095] (40);

[0096] The calculation method of

[0097] (41);

[0098] The calculation method is as follows:

[0099] (42);

[0100] In the formula, is the compensation price per unit of electricity paid by Microgrid i to users during the t dispatching period; is the interrupted load power of Microgrid i during the t dispatching period;

[0101] The calculation method is as follows:

[0102] (43);

[0103] In the formula, is the unit cost of curtailed wind and solar power; is the curtailed wind and solar power of Microgrid i during the t dispatching period.

[0104] As a preferred solution of the present invention, the specific constraint conditions of the lower-layer model are as follows:

[0105] Microgrid power balance constraint:

[0106] (44);

[0107] In the formula, , , are the wind power, photovoltaic power, and load output of Microgrid i during the t dispatching period, respectively; is the Lagrange multiplier of the equality constraint;

[0108] In the optimization problem, when the objective function reaches an extreme value under the condition of satisfying the equality constraint, the Lagrange multiplier of the equality constraint reflects the sensitivity of the objective function to the constraint conditions, helps to incorporate the constraint conditions into the optimization process, and guides the adjustment direction of the optimization variables to find the optimal solution that satisfies the constraints.

[0109] Microgrid power purchase from the distribution network constraint:

[0110] (45);

[0111] In the formula, is the maximum power purchase of the microgrid from the distribution network; , are the Lagrange multipliers corresponding to the inequality constraints; is the power selling flag of Microgrid i to the distribution network during the t dispatching period;

[0112] The function of the colon is to indicate , are the Lagrange multipliers related to this inequality constraint, and , is non - negative.

[0113] Corresponding lower - bound constraint , when is close to or equal to 0, this constraint is active, and the corresponding Lagrange multiplier will be non - zero. If is non - zero, it means that during the optimization process, is restricted by the lower - bound constraint.

[0114] Corresponding upper - bound constraint , when is close to or equal to , this constraint is active, and the corresponding Lagrange multiplier will be non - zero. If is non - zero, it means that during the optimization process, is restricted by the upper - bound constraint.

[0115] The same applies to the remaining Lagrange multipliers in the following formula.

[0116] Power purchase and sale power constraints between the micro - grid and the shared energy storage:

[0117] (46);

[0118] (47);

[0119] (48);

[0120] (49);

[0121] In the formula, and are the maximum power purchase and sale between the micro - grid and the shared energy storage respectively; and are the power purchase and sale flags of the micro - grid i to the shared energy storage during the t - th scheduling period respectively; and , , , , are the Lagrange multipliers corresponding to the inequality constraints;

[0122] Interruptible load constraints within the micro - grid:

[0123] (50);

[0124] (51);

[0125] (52);

[0126] (53);

[0127] Wherein, is the maximum power of the interruptible load of microgrid i during the t scheduling period; , are the upper and lower limits of the compensation price for the interrupted load of the microgrid during the t scheduling period respectively; is the interrupted load flag bit of microgrid i during the t scheduling period; is the maximum number of times of the interrupted load of microgrid i; is the interrupt pricing protection coefficient; , , , , , are the Lagrange multipliers corresponding to the inequality constraints;

[0128] Wind and PV curtailment constraints of the microgrid:

[0129] (54);

[0130] Wherein, is the maximum value of wind and PV curtailment of the microgrid; , are the Lagrange multipliers corresponding to the inequality constraints.

[0131] As a preferred solution of the present invention, in S4, the lower-layer model is transformed into an additional constraint condition of the upper-layer model to obtain the objective function of the transformed upper-layer model. The process includes:

[0132] S4.1, Transformation of non-convex terms in the lower-layer model:

[0133] Since , are non-convex, the exponential method is used to transform them into convex function forms. Let , , , are the logarithmic transformation forms corresponding to , , then the lower-layer model is transformed into:

[0134] (55);

[0135] (56);

[0136] (57);

[0137] (58);

[0138] Wherein, , ; represents the lower limit; ln is the natural logarithm with base e;

[0139] Introduce an auxiliary variable , and convert the non-convex constraint to:

[0140] (59);

[0141] (60);

[0142] (61);

[0143] Wherein, , , are the Lagrange multipliers corresponding to the inequality constraints; M is a constant in the big M method (a very large positive value);

[0144] S4.2. Standardize the lower-level model:

[0145] Standardize the inequality constraints and equality constraints of the lower-level model:

[0146] (62);

[0147] (63);

[0148] Wherein, represents the inequality constraint; represents the equality constraint; , , respectively represent the power, price, and flag variables in the constraints;

[0149] Construct the Lagrangian function:

[0150] (64);

[0151] , are the Lagrange multipliers for the equality constraint and inequality constraint respectively; x and y are the numbers of the equality constraint and inequality constraint respectively; represents the constructed Lagrangian function;

[0152] S4.3. Transform the lower-level model:

[0153] Using the KKT method, the partial derivatives of the decision variables of the lower-level model are obtained based on Equation (64), and the lower-level model is transformed into an additional constraint condition of the upper-level model:

[0154] (65);

[0155] Where l represents the complete set of all decision variables in the equality and inequality constraints;

[0156] The nonlinear constraints in Equation (65) are transformed into the following linear form:

[0157] (66);

[0158] Where is a Boolean variable;

[0159] In the upper-level model, the constraints of Equations (13)-(14), Equation (22), and Equations (26)-(27) are non-convex. For Equations (13)-(14), they are transformed into the following form:

[0160] (67);

[0161] (68);

[0162] Where is a maximum value; j represents abs or relea, and the corresponding are respectively , , Similarly; k represents ch or dis, and the corresponding are respectively , , Similarly;

[0163] For Equations (22), (26)-(27), let:

[0164] (69);

[0165] Where, corresponding to is taken as , or , represents , or , respectively representing the products of , , the products of , , and the products of , ; Indicate 、 or , Similarly;

[0166] Then there is:

[0167] (70);

[0168] At this time, equations (22), (26), and (27) are transformed into the following convex constraints:

[0169] (71);

[0170] (72);

[0171] (73);

[0172] S4.4. The objective function of the transformed upper-layer model is:

[0173] (74).

[0174] As a preferred solution of the present invention, in S4, the solution process is as follows:

[0175] Step 1. Define 、 as the distribution network revenue and the multi-microgrid operating cost at respectively, C is the current revenue of the distribution network, and F is the current operating cost of the multi-microgrid; is the step size; 、 、 The initial values are taken as 0;

[0176] Step 2. Input the load and new energy data of each microgrid, generate the day-ahead contract volume of the distribution network at the real-time purchase and sale electricity benchmark price in the electricity market, and calculate the real-time electricity purchase coefficient of the distribution network corresponding to the full consumption of the new energy of the microgrid when the distribution network does not lose its own revenue and the energy storage does not participate in the transaction, that is ;

[0177] Step 3. Input the current real-time electricity purchase coefficient , and calculate 、 based on the conventional energy storage scenario (the distribution network sets up energy storage, but does not divide the energy storage transaction with the microgrid, and the transaction price adopts a dynamic pricing mechanism);

[0178] Step 4. According to the size relationship between and , on the basis of 、 , for , Make corrections as follows:

[0179] Judge the current real-time electricity purchase coefficient in the power market Is it greater than or equal to :

[0180] When , judge whether the current revenue C of the distribution network is less than . If so, update according to , until , when the update stops; if not, keep the current , , and judge whether the current operation cost F of the multi-microgrid is greater than . If so, update according to until ; if not, keep the current ; ;

[0181] When , judge whether holds. If so, update according to , until , ; if not, judge whether and and hold. If so, keep the current , . If not, update according to , until , ;

[0182] Step Five: Recalculate , , to obtain the optimized scheduling result. According to this scheduling result, execute the distribution network-microgrid collaborative interactive power trading plan. , , ;

[0183] As a preferred solution of the present invention, in the day-ahead stage of the distribution network-microgrid collaborative interactive power trading plan, the maximum amount of self-interruptible load refers to , the self-new energy output refers to , , the load information refers to , and the day-ahead contract power purchase strategy refers to ;

[0184] In the real - time stage, the electricity market price information refers to 、 , the distribution network pricing information refers to 、 、 , the electricity trading strategy refers to 、 、 、 、 、 、 、 , the distribution network energy storage capacity division strategy refers to 、 、 、 , the information transmitted by the distribution network refers to 、 、 、 、 , the self - interruption load pricing strategy refers to , the electricity consumption strategy refers to 、 、 、 , the purchase and sale pricing of new energy output in the micro - grid refers to 、 .

[0185] The algorithm involved in the present invention can be executed by an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The above - mentioned algorithm calculation is realized by the processor executing software.

[0186] The beneficial effects of the present invention are as follows:

[0187] The present invention introduces a dynamic pricing mechanism based on the principal - agent game principle, enabling the distribution network to flexibly adjust the selling and purchasing prices for the micro - grid according to the real - time price fluctuations in the external electricity market. This dynamic pricing strategy not only significantly improves the revenue capacity of the distribution network but also provides clear price signals for the micro - grid during electricity market price fluctuations, guiding it to optimize electricity consumption and power generation decisions. By coupling the purchase and sale prices of shared energy storage with the real - time external market prices, it effectively hedges the economic loss risks that the distribution network may suffer from absorbing new energy in the micro - grid, while safeguarding the interests of the micro - grid and achieving the interest balance between the two parties under risk fluctuations.

[0188] The present invention proposes a method for dividing energy storage trading modes, which subdivides the energy storage system into conventional energy storage for day-ahead arbitrage and shared energy storage for new energy consumption. This refined energy storage management method gives full play to the role of energy storage in transferring time-space value. On the one hand, the conventional energy storage purchases electricity at a low price during the valley price period and sells it at a high price during the peak price period, achieving the maximization of arbitrage profits; on the other hand, the shared energy storage purchases and stores electricity at a low price when new energy generation is excessive, and sells it at a high price when needed in the power market or microgrid. This not only avoids the phenomenon of curtailment of wind and solar power, ensures the full consumption of new energy, but also further improves the overall economic efficiency of the system.

[0189] The interruption load mechanism introduced by the present invention on the microgrid side endows the microgrid with the ability to flexibly adjust its electricity consumption strategy according to real-time price signals. By dynamically correlating the interruption load compensation price with the real-time price of the power market, the microgrid can independently decide whether to interrupt some non-critical loads according to the economic principle, thereby reducing its own operating costs. This mechanism not only enhances the flexibility of the microgrid to cope with power market price fluctuations, but also significantly improves the willingness and flexibility of users to participate in electricity trading, transforming the microgrid from a passive price acceptor to an active participant in optimizing electricity consumption decisions, and further tapping the regulation potential on the demand side. BRIEF DESCRIPTION OF THE DRAWINGS

[0190] Figure 1 is the process schematic diagram of the present invention;

[0191] Figure 2 is the schematic diagram of the solution process of the present invention;

[0192] Figure 3 is the schematic diagram of the distribution network trading price in Scenario 3 during the verification process of the present invention;

[0193] Figure 4 is the schematic diagram of the distribution network trading electricity quantity in Scenario 3 during the verification process of the present invention;

[0194] Figure 5 is the schematic diagram of the trading situation between MG1 and the distribution network and shared energy storage during the verification process of the present invention;

[0195] Figure 6 is the schematic diagram of the trading situation between MG2 and the distribution network and shared energy storage during the verification process of the present invention;

[0196] Figure 7 is the schematic diagram of the trading situation between MG3 and the distribution network and shared energy storage during the verification process of the present invention;

[0197] Figure 8 is the schematic diagram of the trading volume and energy storage situation during the verification process of the present invention;

[0198] Figure 9 is the schematic diagram of the clearing strategy for the distribution network's trading with the power market during the verification process of the present invention;

[0199] Figure 10 It is a schematic diagram of the distribution network revenue and energy storage capacity allocation during the verification process of the present invention. Specific embodiments

[0200] The embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0201] As Figure 1 shown, the distribution network - microgrid electric energy interactive trading method considering power market risks includes the following steps:

[0202] S1. Construct a distribution network - microgrid collaborative architecture, including an upper layer based on the distribution network and a lower layer based on multiple microgrids. The upper layer architecture includes a distribution network agent, a distribution network control center, and an energy storage system composed of distribution network energy storage and shared energy storage. Each microgrid in the lower layer contains wind power, users, photovoltaic, and a microgrid control center, and each microgrid forms a multi - microgrid system;

[0203] S2. With the goal of maximizing the daily profit of the distribution network agent, construct the objective function and its constraint conditions of the upper - layer model;

[0204] S3. With the goal of minimizing the daily comprehensive operating cost of the multi - microgrid system, construct the objective function and its constraint conditions of the lower - layer model;

[0205] S4. Convert the lower - layer model into an additional constraint condition of the upper - layer model, obtain the objective function of the converted upper - layer model and solve it to obtain an optimized distribution network - microgrid collaborative interactive electric energy trading scheme.

[0206] The distribution network control center completes power quantity interaction by signing a day - ahead power purchase contract with the power market according to the load, photovoltaic, and wind power prediction information provided by the microgrid and the maximum interruptible load quantity declared by the microgrid users. When the day - ahead contract power quantity signed during the real - time trading process cannot meet the user's demand, the distribution network agent conducts real - time trading with the power market to make up for the shortage. When there is surplus power during the interaction between the distribution network, shared energy storage, and microgrid, the distribution network agent can sell the power to the power market in real - time to complete the electricity price transfer. At the same time, the upper - layer distribution network agent formulates a real - time dynamic pricing strategy according to the energy consumption situation and the real - time power purchase price in the external power market, including the distribution network power selling pricing strategy, the shared energy storage power purchase and selling pricing strategy, and the energy storage system allocation capacity strategy to ensure the maximization of its own revenue. The microgrid can interact with the distribution network and shared energy storage, and adjust its own electricity consumption ratio in real - time according to the real - time power purchase price in the external power market, the distribution network power selling price, and the shared energy storage power purchase and selling price to decide on a favorable power purchase and selling strategy and interruption strategy for itself, and minimize its own operating cost.

[0207] The revenue of the distribution network agent comes from the distribution network itself, shared energy storage, the lower-level microgrid, and arbitrage in the electricity market. Therefore, when there is surplus new energy in the microgrid, the microgrid can sell it to the distribution network, and then the distribution network energy storage device can sell it to the microgrid and the electricity market again to improve the economic efficiency of system operation. However, during the trading process, the real-time electricity purchase price in the electricity market will change due to market fluctuations. When the real-time electricity selling price of the distribution network to the electricity market is lower than the electricity purchase price of the distribution network from the microgrid, the interests of the distribution network will suffer losses. If the selling price of the distribution network is increased to make up for this loss, the secondary electricity selling price of new energy and the selling price of the distribution network's day-ahead purchased electricity resold to the microgrid will also increase simultaneously. However, the loss of the distribution network's interests is caused by the distribution network's absorption of the surplus new energy in the microgrid to avoid the microgrid bearing the costs of curtailment of wind and solar power. Increasing all selling prices will damage the interests of the microgrid. Therefore, to ensure the interests of both the distribution network and the microgrid, the distribution network allocates the capacity of the energy storage system. Part of the energy storage is used for charging and discharging the electricity purchased from the electricity market ahead of schedule, and the selling price of this part remains unchanged. Another part of the energy storage (shared energy storage) directly trades with the microgrid for charging and discharging new energy electricity, and the purchase and sale prices of this part of the electricity are coupled with the real-time electricity purchase price in the external electricity market to make up for the losses borne by the distribution network for absorbing new energy in the microgrid.

[0208] In summary, the coordinated interactive electricity trading scheme between the distribution network and the microgrid is as follows:

[0209] In the day-ahead stage, the microgrid reports the maximum amount of its interruptible load ( ) to the microgrid control center. The microgrid control center reports its new energy output ( , ) and load information ( ) to the distribution network control center. The distribution network formulates a day-ahead contract electricity purchase strategy ( ) based on the information reported by the microgrid;

[0210] In the real-time stage, since the real-time electricity purchase and sale prices in the electricity market fluctuate due to market impacts, the distribution network formulates the distribution network pricing information ( , ) for real-time electricity purchase and sale with the microgrid, as well as the electricity trading strategy ( , , ), the distribution network energy storage capacity division strategy ( , , , , , , , ), and the distribution network energy storage capacity division strategy ( , , , ), and transmit electricity market price information and distribution network pricing information to the lower-level microgrid. The microgrid adjusts its interruption load pricing strategy according to the information transmitted by the distribution network ( ), electricity consumption strategy ( , , , ), and gives real-time feedback to the distribution network;

[0211] Based on the feedback from the microgrid, the distribution network adjusts the purchase and sale pricing of new energy output of the microgrid ( , ) on the basis of the energy storage capacity allocation strategy to avoid losses of its own interests caused by absorbing the new energy of the microgrid.

[0212] Through real-time dynamic optimization of the information flow and energy flow, both sides obtain the optimal electricity price and the optimal transaction electricity volume, achieve a win-win situation for both sides under the master-slave game, and jointly cope with the problem of system economic decline caused by real-time price changes in the electricity market.

[0213] In S2, the upper-level interest subject is the distribution network agent, considering the operation income of the distribution network shared energy storage , the real-time electricity sales income from the distribution network to the microgrid , the real-time electricity sales income from the distribution network agent to the electricity market , the real-time electricity purchase cost from the distribution network agent to the electricity market , the day-ahead electricity purchase cost from the distribution network agent to the electricity market , and the deviation cost of the distribution network agent's transaction declaration with the superior electricity market on the next day . The objective function of the upper-level model is:

[0214] (1);

[0215] The upper-level model constraint conditions include the interactive power balance constraint between the distribution network and the microgrid, the interactive power balance constraint of the shared energy storage, the charge and discharge constraint of the shared energy storage, the charge and discharge constraint of the distribution network energy storage, the maximum charge and discharge power allocation constraint of the energy storage, the state of charge constraint of the shared energy storage, the state of charge constraint of the distribution network energy storage, the energy storage allocation constraint, the electricity sales price constraint from the distribution network to the microgrid, the electricity purchase and sale price constraint of the shared energy storage to the microgrid, the transaction constraint between the distribution network agent and the superior electricity market, and the interest constraint of the distribution network agent.

[0216] In S3, the lower-level interest subject is the multi-microgrid system, considering the cost of the microgrid using the shared energy storage service , the electricity purchase cost from the microgrid to the distribution network , the compensation cost paid by the microgrid interruption load to users , and the cost of curtailment of wind and solar . The objective function of the lower-level model is:

[0217] (39);

[0218] The constraint conditions of the lower-layer model include microgrid power balance constraint, power purchase constraint of the microgrid from the distribution network, power purchase and sale constraint of the microgrid with the shared energy storage, interruptible load constraint within the microgrid, and wind and light abandonment constraint of the microgrid.

[0219] After obtaining the objective function of the transformed upper-layer model, Gurobi is used for solution, and the solution process is as Figure 2 shown.

[0220] The verification process is as follows: taking a certain actual distribution network as an example, the effectiveness of the proposed method is analyzed and verified. The distribution network includes a total of one energy storage system and three microgrids (denoted as MG1, MG2, and MG3 respectively).

[0221] Each microgrid is directly connected to the upper-layer distribution network, and the microgrids are not connected to each other. Take as , as , is 0.4 yuan / kW, with a 24-hour scheduling period per day and a 1-hour scheduling period.

[0222] Take and set five case scenarios for comparative analysis. The five case scenarios are set as follows:

[0223] Scenario 1, the trading price adopts the time-of-use electricity price mechanism.

[0224] Scenario 2, the trading price adopts the dynamic pricing mechanism.

[0225] Scenario 3, the distribution network is equipped with energy storage, but the energy storage is not divided for trading with the microgrid, and the trading price adopts the dynamic pricing mechanism.

[0226] Scenario 4, the distribution network divides the energy storage for trading with the microgrid, and the trading price adopts the dynamic pricing mechanism.

[0227] Scenario 5, the distribution network divides the energy storage for trading with the microgrid, the microgrid sets an interruptible load compensation mechanism, and the trading price adopts the dynamic pricing mechanism.

[0228] The comparison of the system operation results under the 5 scenarios is shown in Table 1. , , , are the distribution network revenue, the operation cost of the multi-microgrid system, the new energy consumption rate, the ratio of the cost saved by the interruptible load to the distribution network loss revenue respectively.

[0229] Table 1 Comparison of System Operation Results under 5 Scenarios

[0230]

[0231] By comparing Scenario 2 with Scenario 1, it can be obtained that the total distribution network revenue increases by 102.78%, the comprehensive operating cost of the multi-microgrid system increases by 1.52%, and the new energy consumption rate increases by 6.48%. The dynamic pricing strategy formulated by the distribution network can significantly improve the distribution network revenue and the new energy consumption rate of the microgrid, and the increase in the operating cost of the multi-microgrid system is much smaller than the increase in the distribution network profit. Therefore, the dynamic pricing strategy has certain advantages in the distribution network and microgrid trading models.

[0232] Compared with Scenario 1 and Scenario 2, in Scenario 3, due to the existence of energy storage, the distribution network purchases a large amount of electricity for storage during the low-price period of the day-ahead price and discharges it to the microgrid and the power market during the high-price period of selling electricity, which improves the distribution network profit; in Scenario 1 and Scenario 2, the purchase price of electricity from the microgrid by the distribution network is higher than the real-time selling price to the power market in some periods, resulting in the inability to consume the excess new energy of the microgrid, thus generating wind and light curtailment. In Scenario 3, the distribution network can store the purchased new energy during this period and sell it in the high-price area of real-time selling electricity in the power market and the microgrid, avoiding the loss of benefits caused by the distribution network consuming new energy, increasing the new energy consumption rate of the microgrid, and reducing the operating cost of the microgrid.

[0233] As Figure 3 and Figure 4 shown, limited by the energy storage capacity, some of the surplus new energy output cannot be fully stored in the energy storage, and there are still some periods when the purchase price of electricity by the distribution network is higher than the real-time selling price to the power market, such as 11:00 - 13:00 and 16:00. At this time, to help the microgrid save costs, the distribution network still sells electricity to the power market in real time to reduce the microgrid cost. The distribution network loses some revenue, and as decreases, the revenue loss of the distribution network gradually increases. The total distribution network revenue in Scenario 4 is 7.55% higher than that in Scenario 3, the multi-microgrid operating cost is 3.52% higher than that in Scenario 3, but compared with the costs brought by power curtailment in Scenario 1 and Scenario 2, its costs have decreased by 5.96% and 7.37%, and the new energy consumption rate is still 100%.

[0234] Compared with Scenario 4, in Scenario 5 (the method of this embodiment), the distribution network revenue decreases by 312.5 yuan under the interruptible load mechanism, the multi-microgrid operating cost decreases by 638.2 yuan, and the cost reduction of the multi-microgrid system is 2.04 times the profit given up by the distribution network. Compared with Scenario 3, the distribution network revenue increases by 5.47% and the operating cost increases by 1.56%. The interruptible load compensation mechanism can reduce the cost of the lower-layer multi-microgrid system by multiple times the profit given up by the distribution network and further increase the flexibility of microgrid users' electricity consumption.

[0235] The system trading and energy storage situation are as Figures 5 - 8 shown. By Figures 5 - 7It can be seen that, according to the principle that the periods with more electricity sales formulated under the condition of conventional energy storage (Scenario 3) are placed at the pricing ceiling or close to the pricing ceiling, and the periods with less electricity sales are placed at the pricing floor or close to the pricing floor, the distribution network re - prices the new - energy trading price through shared energy storage.

[0236] Compared with the electricity - selling price of conventional energy storage (Scenario 3), when the electricity - selling volume of shared energy storage to the micro - grid is small, it reduces the electricity - selling price, thereby increasing the electricity - selling price during the periods that are not at the price ceiling and have the highest trading volume, and reducing the electricity - purchasing price at the moment that is not at the electricity - purchasing floor and has the largest electricity - purchasing volume to protect its own interests. For example, during the transaction process between shared energy storage and MG1, it reduces the electricity - selling prices at 20:00 and 22:00, increases the electricity - selling prices at 01:00, 09:00, and 17:00, and reduces the electricity - purchasing price at 13:00. In addition, the distribution network does not completely reduce the electricity - purchasing price when the electricity - purchasing volume is large. When the number of electricity - purchasing times is large, in order to protect the interests of the micro - grid, it will allocate part of the price reduction when the electricity - purchasing volume is small, such as at 05:00 in MG3.

[0237] The allocation results of the energy - storage capacity and the maximum charge - and - discharge power are shown in Table 2. Combining Figure 8 It can be seen that, on the one hand, the distribution network purchases a large amount of electricity during the period with the lowest day - ahead electricity - selling price, from 03:00 to 04:00. At the same time, it avoids the discharge period of shared energy storage and stores the electricity in the distribution - network energy storage, and discharges during the period with a higher electricity - selling price, from 17:00 to 19:00, to sell electricity to the micro - grid to obtain benefits. On the other hand, on the premise of avoiding damage to its own interests, the distribution network uses shared energy storage to directly trade with the micro - grid to help the micro - grid absorb the new - energy output. Under the protection of the number of transactions and the average price, it realizes the balance of the interests of itself and the micro - grid. From 02:00 to 04:00, 10:00, and 14:00 to 15:00, shared energy storage purchases electricity and sells it to the power - short micro - grid and the power market, stores the remaining electricity, and discharges during the periods with higher electricity - selling prices, from 08:00 to 09:00, 13:00, and 16:00 to 18:00. Thus, the distribution network realizes the transfer of new energy in terms of time and space through shared energy storage, achieves the full absorption of new energy, realizes the secondary pricing of the purchase and sale of new energy for the micro - grid by allocating the energy - storage capacity, avoids the secondary pricing of all electric energy, protects the interests of the micro - grid, and at the same time alleviates the interest losses caused by the distribution network's absorption of the micro - grid's new energy due to price changes in the external power market.

[0238] Table 2 Allocation of Energy - Storage Capacity and Maximum Charge - and - Discharge Power

[0239]

[0240] Figure 9 Indicates the clearing strategy for the distribution network's transaction with the power market. Figure 10 Indicates the distribution of the distribution network's revenue and energy - storage amount. Since this embodiment is based on As the benchmark purchase price in the real-time electricity market, the day-ahead contract volume under this coefficient is the day-ahead benchmark contract volume, and the day-ahead contract deviation rate is 0. As decreases, the way for the distribution network to absorb the new energy of the microgrid gradually changes from selling to the electricity market to selling to the microgrid secondary. And the electricity originally sold to the microgrid comes from the day-ahead contract volume of the electricity market. Therefore, the distribution network reduces the day-ahead contract volume, that is, the day-ahead contract deviation rate gradually decreases from 0, and the allocated capacity of the shared energy storage gradually increases while the allocated capacity of the distribution network energy storage gradually decreases until , at this time, the real-time electricity purchase price in the electricity market is too low, and the surplus new energy of the microgrid stored in the shared energy storage no longer sells electricity to the electricity market and all sells electricity to the microgrid secondary, corresponding to the unchanged allocated capacity of the two energy storages; when rises, at this time, there is no loss of interest for the distribution network to sell electricity to the electricity market. Therefore, the day-ahead contract deviation rate rises from 0, and the electricity sold by the shared energy storage and the distribution network to the electricity market increases. To avoid excessive profit concession and loss of the distribution network's own interests, the allocated capacity of the shared energy storage increases slightly so that most of the electricity is sold during the high-price period in the electricity market.

[0241] Under the conventional energy storage (Scenario 3), the revenue of the distribution network is greatly affected by the real-time pricing in the electricity market. As decreases, the revenue of the distribution network gradually decreases, and the peak-valley difference is large; in Scenario 5, under the condition that the distribution network perceives , the capacities of the distribution network energy storage and the shared energy storage are allocated. After the allocation, the revenue of the distribution network is less affected by the fluctuations of the electricity market pricing, the peak-valley difference of the distribution network revenue decreases, the fluctuation is more stable, and as continually decreases, the difference in revenue between the two gradually increases, which means that the distribution network has stronger risk resistance ability after allocating the energy storage capacity.

[0242] To reduce the operating cost of the microgrid, the microgrid formulates an interruption load strategy under the price decision made by the distribution network, and provides compensation to microgrid users at an interruption load pricing lower than the electricity selling price of the distribution network and the shared energy storage, reducing the operating cost of the microgrid.

[0243] To minimize its own operating cost, each microgrid sets the interruption pricing at the lower limit during the periods with more interruption loads, and at the same time, to improve the enthusiasm of microgrid users to participate in electricity use decision-making and increase the user compensation income, the microgrid sets the interruption pricing at the upper limit during the periods with less interruption loads. For example, MG1 sets the pricing at the lower limit at 01:00, 08:00 - 12:00, 14:00, 18:00 - 21:00, 23:00, and sets the pricing at the upper limit at 02:00 - 07:00, 13:00, 17:00, 22:00, 24:00.

[0244] When the electricity purchase price of shared energy storage is lower than the interruption price, the microgrid cannot obtain benefits. Considering its own interests, the microgrid encourages users to consume electricity, and the interrupted load is 0, such as 16:00 in MG2, 01:00 - 03:00, 19:00, and 23:00 in MG3.

[0245] For the interruption strategies of each microgrid under different When At this time, the real-time electricity purchase price in the power market is relatively low. To avoid losses caused by helping the microgrid absorb excess new energy, the distribution network increases the electricity selling price and reduces the electricity purchase price for this part of new energy. Similarly, the microgrid makes a feedback to the above actions of the distribution network, reduces the average value of the interruption load compensation pricing to reduce the price increment loss generated by the surplus new energy transaction, and the proportion of each microgrid's interruption pricing placed at the pricing lower limit increases, reducing the microgrid's interest loss. In addition, due to the decrease in the average electricity purchase of shared energy storage and the increase in the proportion of the period when the interruption load pricing is higher than the microgrid's electricity selling price, the microgrid cannot make a profit, so the interruption load ratio decreases.

[0246] Same Compared with When the real-time electricity purchase price in the power market is high, the distribution network can also make a profit by directly selling electricity to the power market in real time. Therefore, the shared energy storage reduces the electricity selling price and increases the electricity purchase price for this part of new energy to give up part of the benefits and reduce the microgrid's cost. Under this strategy, the microgrid increases the interruption pricing to improve the interests of microgrid users, and the proportion of each microgrid's interruption pricing placed at the pricing upper limit increases, improving the microgrid user's income on the premise of ensuring its own interests. In addition, due to the increase in the average electricity purchase of shared energy storage and the decrease in the proportion of the period when the interruption load pricing is higher than the microgrid's electricity selling price, the interruption load ratio increases.

[0247] In addition, under the two When the interruption pricing is equal to the microgrid's electricity selling price, the distribution network and the microgrid make different feedbacks to this behavior. When At 11:00 - 14:00 in MG1, 13:00 - 16:00 in MG2, and at 10:00 in MG3, the two pricing are equal. At this time, the microgrid does not choose to interrupt the load. When When it is large, the selling price of shared energy storage is lower than that of distribution network power sales at this time. The distribution network can also make a profit by directly selling electricity to the power market, and the interests of the microgrid will not be damaged. Therefore, the microgrid interrupts the load to increase the revenue of the distribution network and reduce its own operating costs.

[0248] As becomes smaller and smaller, the purchase price of shared energy storage becomes lower and lower, that is, the proportion of the purchase price of shared energy storage being less than the interruption pricing during the entire trading period is getting higher and higher, and the selling price of shared energy storage is also getting higher and higher. For its own interests, the proportion of the interrupted load of the microgrid shows a downward trend as a whole. At the same time, in order to cope with the increase in the operating costs of the microgrid caused by the re - pricing of shared energy storage, the microgrid reduces the interruption pricing to protect its own interests. When is less than 0.8, the interruption cost after coupling is always less than the interruption cost without coupling . When is greater than 0.8, the distribution network can also make a profit by directly selling electricity to the power market. At this time, the distribution network will reduce the selling price of shared energy storage and increase the purchase price of shared energy storage. The transaction cost between the microgrid and the shared energy storage decreases. Therefore, the microgrid will give up some interests to the microgrid users. At this time, the interruption cost after coupling is greater than the interruption cost without coupling .

[0249] From the above behaviors, it can be seen that the microgrid is no longer a simple passive leader who accepts pricing, but can make strategies beneficial to itself according to external market changes and the reaction of the distribution network to market changes, and respond to external market risks more flexibly.

[0250] When fluctuates, within the entire fluctuation range, the dynamic pricing strategy in Scenario 2 effectively increases the revenue of the distribution network and improves the new - energy consumption rate of the microgrid compared with the day - ahead time - of - use pricing strategy in Scenario 1, but at the same time, it will increase the operating costs of the microgrid; for the capacity allocation of distribution network energy storage and shared energy storage by the distribution network under the dynamic pricing strategy in Scenario 4 compared with Scenario 3, as decreases, the improvement of the distribution network revenue is more obvious; the operating costs of the microgrid in Scenario 5 are further reduced compared with Scenario 4, higher than Scenario 3, but much less than Scenario 2 and Scenario 1. This is because in Scenario 3, the distribution network helps the microgrid avoid the cost of curtailment of wind and light at the expense of its own interests, which does not conform to the actual situation; compared with Scenario 4 in Scenario 5, the ratio of the reduced operating cost of the microgrid to the reduced interests of the distribution network is always greater than 1.95 times. When is the case, the distribution network not only increases the revenue but also reduces the operating costs of the microgrid.

Claims

1. A power distribution - microgrid electric energy interactive trading method considering power market risks, characterized in that It includes the following steps: S1. Construct a distribution network - microgrid collaborative architecture, including an upper layer based on the distribution network and a lower layer based on multiple microgrids. The upper layer architecture includes a distribution network agent, a distribution network control center, and an energy storage system composed of distribution network energy storage and shared energy storage. Each microgrid in the lower layer contains wind power, users, photovoltaic power, and a microgrid control center, and each microgrid forms a multi - microgrid system; S2. With the goal of maximizing the daily profit of the distribution network agent, construct the objective function and its constraint conditions of the upper - layer model; S3. With the goal of minimizing the daily comprehensive operating cost of the multi - microgrid system, construct the objective function and its constraint conditions of the lower - layer model; S4. Transform the lower - layer model into an additional constraint condition of the upper - layer model, obtain the objective function of the transformed upper - layer model and solve it to obtain an optimized distribution network - microgrid collaborative interaction power trading scheme. In this scheme: In the day - ahead stage, the microgrid reports the maximum amount of its interruptible load to the microgrid control center, and the microgrid control center reports its new - energy output and load information to the distribution network control center. The distribution network formulates a day - ahead contract power purchase strategy based on the information reported by the microgrid; In the real - time stage, the distribution network formulates distribution network pricing information for real - time power purchase and sale to the microgrid based on real - time electricity market price information, as well as a power trading strategy and a distribution network energy storage capacity division strategy, and transmits the electricity market price information and distribution network pricing information to the lower - layer microgrid. The microgrid adjusts its interruptible load pricing strategy and electricity - using strategy according to the information transmitted by the distribution network and feeds back to the distribution network in real - time; Based on the microgrid feedback and the energy storage capacity division strategy, the distribution network makes a secondary adjustment to the power purchase and sale pricing of the microgrid's new - energy output; In S2, the upper - layer interest subject is the distribution network agent, considering the operation revenue of the shared energy storage for distribution network , the real - time power sales revenue from the distribution network to the micro - grid , the real - time power sales revenue from the distribution network agent to the power market , the real - time power purchase cost of the distribution network agent from the power market , the day - ahead power purchase cost of the distribution network agent from the power market , the deviation cost of the distribution network agent's transaction with the power market for the next - day declaration , the objective function of the upper - layer model is: （1）； The constraint conditions of the upper - layer model include the distribution network - microgrid interactive power balance constraint, the shared energy storage interactive power balance constraint, the shared energy storage charge - discharge constraint, the distribution network energy storage charge - discharge constraint, the maximum charge - discharge power allocation constraint of the energy storage, the shared energy storage state - of - charge constraint, the distribution network energy storage state - of - charge constraint, the energy storage capacity allocation constraint, the distribution network's power sale price constraint to the microgrid, the shared energy storage's power purchase and sale price constraint to the microgrid, the distribution network agent's transaction constraint with the upper - level power market, and the distribution network agent's interest constraint; In S3, the lower-level interest subject is the multi-microgrid system, considering the cost of using shared energy storage services by the microgrid , the cost of purchasing electricity from the distribution network by the microgrid , the compensation cost paid by the microgrid's interrupted load to users , the cost of curtailed wind and solar power , and the objective function of the lower-level model is: （39）； The constraint conditions of the lower - layer model include the microgrid power balance constraint, the microgrid's power purchase power constraint from the distribution network, the microgrid's power purchase and sale power constraint with the shared energy storage, the microgrid's internal interruptible load constraint, and the microgrid's wind - power and photovoltaic - power curtailment constraint.

2. The method for interactive power trading between distribution network and microgrid considering power market risks according to claim 1, characterized in that, The calculation method is as follows: （2）； Where \(t\) is the index of the scheduling period and \(T\) is the total number of scheduling periods; \(i\) is the index of the microgrid and \(N\) is the total number of microgrids. and are the selling and purchasing prices of the shared energy storage to the microgrid \(i\) during the \(t\) scheduling period respectively. and are the selling and purchasing powers of the shared energy storage to the microgrid \(i\) during the \(t\) scheduling period respectively. is the time interval. The calculation method is as follows: （3）； wherein, is the electricity selling price from the distribution network to microgrid i during the t dispatching period; is the electricity selling power from the distribution network to microgrid i during the t dispatching period; , The calculation method is as follows: （4）； （5）； Wherein, and are respectively the real-time electricity selling and purchasing prices of the distribution network agent to the electricity market during the t dispatching period; and are respectively the real-time electricity selling and purchasing powers of the distribution network to the electricity market during the t dispatching period; is the real-time electricity selling power of the distribution network shared energy storage to the electricity market during the t dispatching period; The calculation method is as follows: （6）； Wherein, is the day-ahead contract price for the t dispatching period; is the day-ahead benchmark contract power for the t dispatching period; , are the positive and negative deviations of the day-ahead contract declaration for the t dispatching period, respectively; The electricity market recovers the declared deviation part according to the corresponding settlement income, and the calculation method is as follows: （7）； （8）； In the formula, and are the positive and negative deviation prices declared on the next day, respectively; and are the positive and negative deviation settlement income coefficients declared, respectively.

3. The power distribution - microgrid electric energy interactive trading method considering power market risks according to claim 2, characterized in that, The specific constraint conditions of the upper - layer model are as follows: Distribution network - microgrid interactive power balance constraint: （9）； Wherein, and are the charging and discharging powers of the distribution network energy storage in the t scheduling period, respectively; Shared energy storage interactive power balance constraint. During the period when the microgrid purchases and sells electricity to the shared energy storage, the charge - discharge power of the shared energy storage is determined by the total energy demand after energy exchange at each microgrid bus. Its constraint is: （10）； （11）； （12）； Wherein, and are the charging and discharging powers of the shared energy storage during the t dispatching period, respectively; and are the powers that the shared energy storage purchases from microgrid i during the t dispatching period and uses to sell to the power market and the power-deficient microgrid; and are the charging powers that the shared energy storage uses to sell to the power market and the power-deficient microgrid during the t dispatching period; and are the discharging powers that the shared energy storage uses to sell to the power market and the power-deficient microgrid during the t dispatching period; Shared energy storage charge - discharge constraint: （13）； In the formula, and are the charge and discharge flag bits of the shared energy storage during the t scheduling period, respectively. The values are 0 or 1. A value of 1 indicates that charging or discharging is in progress. The same applies to the other flag bits; is the maximum charge and discharge power of the shared energy storage; Distribution network energy storage charge - discharge constraint: （14）； Wherein, and are the charging and discharging powers of the distribution network energy storage in the t scheduling period, respectively; and are the charging and discharging flag bits of the distribution network energy storage in the t scheduling period, respectively; is the maximum charging and discharging power of the distribution network energy storage; Maximum charge - discharge power allocation constraint of the energy storage: （15）； In the formula, is the total maximum charge-discharge power of the energy storage; Shared energy storage state - of - charge constraint: （16）； （17）； In the formula, , are the state of charge of the shared energy storage during the t and t-1 scheduling periods, respectively; is the charge-discharge efficiency of the energy storage; , are the upper and lower limit coefficients of the state of charge of the energy storage, respectively; is the maximum energy storage capacity of the shared energy storage; Distribution network energy storage state - of - charge constraint: （18）； （19）； Wherein, and are the state of charge of the distribution network energy storage in the t-th and (t - 1)-th scheduling periods, respectively; is the maximum energy storage capacity of the distribution network energy storage; Energy storage capacity allocation constraint: （20）； In the formula, is the maximum value of the total energy storage; Distribution network's power sale price constraint to the microgrid: （21）； （22）； （23）； In the formula, , are respectively the upper and lower limits of the electricity selling price from the distribution network to the microgrid during the t dispatching period; is the electricity selling flag bit from the distribution network to the microgrid i during the t dispatching period; is the maximum electricity selling times from the distribution network to the microgrid i; The shared energy storage's power purchase and sale price constraint to the microgrid is as follows: （24）； （25）； Wherein, and are respectively the upper and lower limits of the selling price of the shared energy storage to the microgrid during the t scheduling period; and are respectively the upper and lower limits of the purchase price of the shared energy storage from the microgrid during the t scheduling period; At the same time, couple the shared energy storage's power purchase and sale price with the external power market's power purchase price to protect the interests of both the distribution network and the microgrid: （26）； （27）； （28）； （29）； Wherein, and are respectively the selling and purchasing power flag bits of the shared energy storage to microgrid i during the t scheduling period; is the real-time power market purchase coefficient corresponding to the full consumption of new energy in the microgrid without setting energy storage; is the current real-time power market purchase coefficient; and are respectively the price protection coefficients for selling and purchasing electricity; and are respectively the maximum selling and purchasing times of the shared energy storage to microgrid i; Distribution network agent's transaction constraint with the upper - level power market: （30）； （31）； （32）； （33）； （34）； （35）； （36）； （37）； Wherein, is the maximum power purchase of the distribution network in the day-ahead contract with the power market; and are the purchase and sale power flag bits in the t dispatching period respectively; and are the real-time maximum purchase and sale powers of the distribution network in the power market respectively; represents or ; represents or , which are the maximum positive and negative deviation powers declared by the distribution network in the power market for the next day respectively; and are the positive and negative deviation status flag bits in the t dispatching period respectively, represents or ; represents or , which are the positive and negative deviation coefficients respectively; When the real-time power purchase coefficient of the external power market is not equal to the benchmark value, there is a risk of loss of benefits for the distribution network to absorb and consume the new energy of the microgrid. To ensure that the interests of the distribution network are not damaged and to prevent the operation cost of the microgrid from rising due to unreasonable secondary pricing of the distribution network, the following constraints are set: （38）； In the formula, , are respectively the upper and lower limits of the incremental revenue after the secondary pricing; is the electricity purchase price of the distribution network from the microgrid i during the t scheduling period.

4. The distribution network - microgrid electric energy interactive trading method considering power market risks according to claim 3, characterized in that The calculation method is as follows: （40）； The calculation method is as follows: （41）； The calculation method is as follows: （42）； wherein, is the compensation price per unit of electricity paid by Microgrid i to users during the t dispatching period; is the interrupted load power of Microgrid i during the t dispatching period; The calculation method is as follows: （43）； In the formula, is the unit cost of wind and solar power curtailment; is the wind and solar power curtailment of microgrid i during the t dispatching period.

5. The distribution network - microgrid power energy interactive trading method considering power market risks according to claim 4, characterized in that The specific constraints of the lower-layer model are as follows: Microgrid power balance constraint: （44）； Wherein, , , are the wind power, photovoltaic, and load outputs of microgrid i during the t scheduling period, respectively; is the Lagrange multiplier of the equality constraint; Power purchase power constraint of the microgrid from the distribution network: （45）； Wherein, is the maximum power purchase from the distribution network by the microgrid; , are the Lagrange multipliers corresponding to the inequality constraints; is the power selling flag of microgrid i to the distribution network during the t scheduling period; Power purchase and sale power constraint of the microgrid with the shared energy storage: （46）； （47）； （48）； （49）； Wherein, and are respectively the maximum power purchase and sale electric powers of the microgrid in the transaction with the shared energy storage; and are respectively the power purchase and sale flag bits of the microgrid i from / to the shared energy storage during the t dispatching period; 、 、 、 、 、 are the Lagrange multipliers corresponding to the inequality constraints; Interruptible load constraint within the microgrid: （50）； （51）； （52）； （53）； In the formula, is the maximum power of the interruptible load of microgrid i during the t dispatching period; , are respectively the upper and lower limits of the compensation price for the interruptible load of the microgrid during the t dispatching period; is the interruptible load flag of microgrid i during the t dispatching period; is the maximum number of times of the interruptible load of microgrid i; is the interrupt pricing protection coefficient; , , , , , are the Lagrange multipliers corresponding to the inequality constraints; Wind and light abandonment constraint of the microgrid: （54）； In the formula, is the maximum value of wind and PV curtailment in the microgrid; , are the Lagrange multipliers corresponding to the inequality constraints.

6. The method for interactive power trading between distribution network and microgrid considering power market risks according to claim 5, characterized in that In S4 described above, the process of converting the lower-layer model into the additional constraint conditions of the upper-layer model to obtain the objective function of the converted upper-layer model includes: S4.1: Transformation of non-convex terms in the lower-layer model: Due to and being non-convex, the exponential method is used to transform it into a convex function form. Let , , and be the logarithmic transformation forms corresponding to and respectively. Then the lower-level model is transformed into: （55）； （56）； （57）； （58）； Wherein, , ; represents the lower limit; Introduce auxiliary variables , and convert the non-convex constraints into: （59）； （60）； （61）； wherein , , are Lagrange multipliers corresponding to the inequality constraints; M is a constant in the big M method; S4.2: Standardization of the lower-layer model: Standardize the inequality constraints and equality constraints of the lower-layer model: （62）； （63）； In the formula, represents an inequality constraint; represents an equality constraint; , , respectively represent the power, price, and flag bit variables in the constraint; Construct the Lagrangian function: （64）； , are the Lagrange multipliers for equality constraints and inequality constraints respectively; x and y are the numbers of equality constraints and inequality constraints respectively; represents the constructed Lagrangian function; S4.3: Transformation of the lower-layer model: Using the KKT method, take the partial derivative of the decision variables of the lower-layer model based on Equation (64), and convert the lower-layer model into the additional constraint conditions of the upper-layer model: （65）； In the formula, l represents the complete set of all decision variables in the equality and inequality constraints; The formula (65) contains non-linear constraints and is converted into the following linear form: （66）； In the formula, is a Boolean variable; In the upper-layer model, the constraints of formulas (13)-(14), formula (22), and formulas (26)-(27) are non-convex. For formulas (13)-(14), they are converted into the following form: （67）； （68）； In the formula, is a maximum value; j represents abs or relea, and the corresponding are respectively and , similarly; k represents ch or dis, and the corresponding are respectively and , similarly; For formulas (22), (26)-(27), let: （69）； In the formula, corresponding to is taken as , or , represents , or , representing respectively , 's product, , 's product, and , 's product; represents , or , similarly; Then there is: （70）； At this time, formulas (22), (26), and (27) are converted into the following convex constraints: （71）； （72）； （73）； The objective function of the converted upper-layer model in S4.4 is: （74）。 7. The method for interactive power trading between distribution network and microgrid considering power market risks according to claim 6, wherein In S4 described above, the solution process is: Step 1. Define and as the network connection revenue and multi - microgrid operation cost at respectively, C is the current revenue of the distribution network, and F is the current operation cost of the multi - microgrid; is the step size; and and are initially set to 0; Step 2: Input the load and new energy data of each microgrid, generate the day-ahead contract volume of the distribution network at the real-time benchmark price of electricity purchase and sale in the electricity market, and calculate the real-time electricity purchase coefficient of the distribution network corresponding to the full consumption of the new energy of the microgrid when the distribution network does not lose its own revenue and the energy storage does not participate in the transaction, that is ; Step 3: Input the real-time electricity purchase coefficient of the current power market and calculate based on the conventional energy storage scenario , ; Step 4. According to and 's magnitude relationship, on the basis of , , correct , as follows: Judge the real-time electricity purchase coefficient of the current power market whether it is greater than or equal to : When it is determined whether the current revenue C of the distribution network is less than , if so, then according to , update , until when the update stops; if not, retain the current , , and determine whether the current operating cost F of the multi - microgrid is greater than , if so, then according to update until ; if not, retain the current ; When judge whether it holds. If so, according to and update and until ; If not, judge and whether it holds. If so, retain the current and ; If not, according to and update and ; Step 5. According to the , , recalculate , , to obtain the optimized scheduling result, and execute the distribution network - microgrid collaborative interaction electric energy trading plan according to this scheduling result.

8. The power distribution - microgrid electric energy interactive trading method considering power market risks according to claim 7, characterized in that, In the day-ahead stage of the distribution network - microgrid collaborative interactive power trading scheme, the maximum amount of its own interruptible load refers to , its own new energy output refers to , , the load information refers to , the day-ahead contract power purchase strategy refers to ; In the real-time stage, the electricity market price information refers to and , the distribution network pricing information refers to and and , the electricity trading strategy refers to and and and and and and and , the distribution network energy storage capacity division strategy refers to and and and , the information transmitted by the distribution network refers to and and and and , the self-interruptible load pricing strategy refers to , the electricity consumption strategy refers to and and and , the purchase and sale pricing of new energy output in the microgrid refers to and .

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