Diversified load transaction type demand response method for tough power distribution system

Abstract

The invention relates to the technical field of power system control, and provides a ductile power distribution system-oriented diversified load transaction type demand response method, which comprises the following steps of: establishing a refined model of diversified loads in a power distribution network; based on the refined model, establishing a double-layer master-slave game optimization model between a power distribution system operator and diversified load users; an upper layer model of the double-layer master-slave game optimization model is a power distribution system operator recovery optimization model; a lower-layer model of the double-layer master-slave game optimization model is a load side optimization model, the double-layer master-slave game optimization model is solved through a KKT condition and a linearization method, and an optimal excitation strategy of a power distribution system operator and an optimal power utilization response strategy of a load side user are obtained. According to the invention, the load interruption loss of the power distribution system during the service recovery period is reduced by combining the load transfer type demand response with the power limit management of various loads, and the profit of a customer can be improved by adjusting the power distribution among different processes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system control, in particular to a diversified load transactional demand response method for a resilient power distribution system. BACKGROUND

[0002] At present, demand response as a technology to achieve better matching between supply and demand by changing the energy use mode of terminal users is increasingly concerned in improving the resilience of power distribution networks. Within a certain load variation range, demand response can flexibly adjust the power consumption mode according to the control requirements. The transferable load can change the power consumption time and power, and the interruptible load can ensure the power supply of other critical loads by cutting. The existing invention [3-7] adjusts the power consumption mode through direct load control, but since most loads are private assets, direct coordination is often difficult to achieve. Therefore, the existing invention lacks an emergency power trading mechanism between the power distribution network and private loads, and there is no research on resilience improvement.

[0003] Disadvantages of the prior art: 1. The traditional load restoration method mainly focuses on the scheduling of topological structure and power generation output, and fails to fully tap the potential of private loads through transactional demand response. 2. Lack of fine modeling of response load operation adjustment. SUMMARY

[0004] In order to solve the technical problems that the existing technology fails to fully tap the potential of private loads through transactional demand response and lacks fine modeling of response load operation adjustment, the embodiments of the present application provide a diversified load transactional demand response method for a resilient power distribution system. By coordinating the participation of industrial and commercial users, a demand response mechanism that encourages voluntary load adjustment is designed. A fine model covering the energy-material flow of industrial load is established according to the differences in user power consumption characteristics. Finally, simulation verification is carried out based on the improved IEEE33 node power distribution network, and the results show that the proposed method can effectively protect user benefits and reduce load interruption loss through demand resource scheduling in the restoration phase. The technical solution is as follows: The embodiments of the present application provide a diversified load transactional demand response method for a resilient power distribution system, which comprises: S1, a fine model of diversified loads in a power distribution network is established, the diversified loads including industrial loads, commercial loads and residential loads; S2, based on the fine model, a two-level master-slave game optimization model between a power distribution system operator and diversified load users is established; the upper model of the two-level master-slave game optimization model is a restoration optimization model of the power distribution system operator, aiming to minimize the total system restoration cost; the lower model of the two-level master-slave game optimization model is a load-side optimization model, aiming to maximize the respective benefits. S3, solving the bi-level master-slave game optimization model through KKT condition and linearization method to obtain the optimal incentive strategy of the power distribution system operator and the optimal power consumption response strategy of the load side user, which are used to guide the operation of the power distribution network during power supply restoration.

[0005] Preferably, in step S1, the refined model of the industrial load is an energy-material flow model, and establishing the model comprises: dividing the industrial load into continuous load and discrete load; establishing constraints describing the relationship between the output of the discrete production process and the required production time; establishing constraints describing the continuous power adjustment range of the continuous load; establishing material balance constraints and inventory non-negative constraints describing the relationship between material consumption and output between the continuous load and the discrete load; establishing an industrial user production revenue model based on final product calculation.

[0006] Preferably, in step S1, establishing the energy-material flow model further comprises establishing a continuous load startup model considering the cold start process, which distinguishes the rated operation, shutdown and power climbing state by introducing state indicator variables, and defines the power characteristics and startup cost of each state.

[0007] Preferably, in step S1, establishing the refined model of the commercial load comprises: dividing the commercial load into reference load and adjustable load, wherein the adjustable load includes interruptible load and transferable load; establishing capacity constraints of the interruptible load and the transferable load; establishing an economic loss model related to customer churn rate caused by reference load reduction; establishing a comprehensive revenue model of the commercial user participating in demand response.

[0008] Preferably, in step S1, the refined model of the residential load is a power outage sensitivity model, and establishing the model comprises: establishing a correlation function between the power outage duration of the residential load and the health loss cost thereof.

[0009] Preferably, in step S2, the objective function of the upper model is to minimize the total system restoration cost, and the total system restoration cost includes: load reduction cost, main grid power purchase cost, response load incentive cost, distributed power generation cost and residential load medical cost.

[0010] Preferably, in step S2, the objective function of the lower model is to maximize the total revenue of the industrial user and the commercial user, and the total revenue of the industrial user and the commercial user includes production profit and interactive revenue participating in demand response.

[0011] Preferably, in step S3, the double-layer master-slave game optimization model is solved by KKT condition and linearization method, specifically: the lower layer optimization problem is converted into its KKT condition, and is taken as a constraint of the upper layer model, so that the double-layer model is reconstructed into a single-layer mathematical programming problem; for the binary variable existing in the model, the big M method is used for linearization processing, and finally the optimization solver is used for iterative solving until convergence.

[0012] The technical scheme provided by the embodiment of the application has at least the following beneficial effects: The application provides a diversified load transaction type demand response method for a resilient power distribution system, which establishes a detailed model of diversified loads, including an energy-material flow model of industrial customers, an adjustable load model of commercial customers and a power outage sensitive model of residential customers. A double-layer coordination model is solved by iteration through a Stackelberg game theory method. The method combines a transfer demand response with power limit management of diversified loads to reduce load interruption loss of the power distribution system during service recovery, and adjusts power distribution between different processes to improve customer profits. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0014] Figure 1 A diversified load transaction type demand response method flow chart for a resilient power distribution system is shown according to the embodiment of the application; Figure 2 An industrial user energy-material flow process chart is shown according to the embodiment of the application; Figure 3 A continuous load cold start process curve chart is shown according to the embodiment of the application; Figure 4 An upper layer model and a lower layer model solving flow chart by a CPLEX solver is shown according to the embodiment of the application; Figure 5 A microgrid formation schematic diagram of a test system in the embodiment 1 of the application is shown; Figure 6 A power supply and demand situation chart of a substation power supply area in the embodiment 2 of the application is shown; Figure 7 A power supply and demand situation chart of MG#1 in the embodiment 2 of the application is shown; Figure 8An energy and production profile diagram according to IL#5 in Embodiment 3 of the present application is shown; Figure 9 An energy and production profile diagram according to IL#30 in Embodiment 3 of the present application is shown; Figure 10 An energy and production profile diagram according to IL#16 in Embodiment 3 of the present application is shown. DETAILED DESCRIPTION

[0015] The technical solutions in the present application will be described below with reference to the drawings.

[0016] In the embodiments of the present application, the words such as "example", "for example" and the like are used to represent an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0017] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "corresponding" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0018] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0019] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail with reference to the drawings and specific embodiments.

[0020] The embodiments of the present application provide a diversified load transaction type demand response method for a resilient power distribution system, as shown in Figure 1 The method comprises the following steps: S1, a fine model of diversified load in a power distribution network is established, and first, industrial load, commercial load and residential load in the power distribution network participating in demand response are modeled, which comprises an energy-material flow model of industrial users, a adjustable load model of commercial users and a power outage sensitivity model of residential load, as shown in the flowchart. Figure 2 Industrial users usually cover factory types such as steel plants, which internally contain a variety of devices closely related to production output.

[0021] In step S1, the fine model of industrial load is an energy-material flow model, and the establishment of the model includes: According to the continuity of electrical characteristics, the industrial load is divided into continuous load and discrete load; The running state of industrial users is simultaneously affected by power supply and material consumption. To characterize the related constraints, constraints describing the relationship between discrete production process output and required production time are established; constraints describing the continuous adjustment of continuous load power and its variation range are established; material balance constraints and inventory non-negative constraints between continuous load and discrete load are established, such as equations (1)-(8): (1); (2); (3); (4); (5); (6); (7); The production income model of industrial users based on final product calculation is established, such as equations (8)-(9): (8); (9); In equations (1)-(9), and represent binary variables of whether the discrete and continuous processes are powered; k represent the production state of discrete processes ; k k represent the time required for a single task of discrete processes RT ; k and are the scheduled power and rated power of discrete processes ; k , , , represent the scheduled power, upper power limit and lower power limit of continuous processes k ; represent the material inventory of processes k ; represent the conversion coefficients between the power and material output of each process; and represent the industrial user iproduction and interaction benefits. (1) describes the discrete production process needs to end after the required production time RT to form output; (2) represents the power characteristics of the discrete process. Due to the operating state of this type of equipment is limited to start-stop mode, its power consumption cannot be continuously adjusted; (3) shows that the energy consumption behavior of continuous load can achieve continuous controllable step change; (4) specifies the power change range of continuous load; (5)-(7) describe the material change relationship between different processes of continuous / discrete load, wherein (5-6) represent the material output and consumption of continuous process and discrete process respectively, and (7) ensures that the material inventory is always non-negative. (8) describes the benefits obtained by the industrial user based on the final product; (9) determines the actual benefits of participating in demand response. In step S1, the energy-material flow model also includes establishing a continuous load startup model considering the cold startup process, which distinguishes the rated operation, shutdown and power climbing state by introducing a state indicator variable, and defines the power characteristics and startup cost of each state.

[0022] Power shortage may cause continuous load and discrete load in the factory to encounter power interruption at the same time. For industrial users, continuous load usually involves equipment such as boilers, Figure 3 A continuous load cold start process curve is provided, and the continuous load equipment needs to gradually restore power supply and perform a cautious startup process to resume normal operation in the cold start process as shown in Figure 3 In addition, frequent start-stop cycles will result in higher maintenance and repair costs. Therefore, a continuous load startup model is established to represent the power consumption characteristics and startup cost of the continuous load when it is running.

[0023] The continuous load startup model is as follows: (10); (11); (12); (13); (14); (15); (16); (17); (18); (19); In formulas (10)-(19), u (1) , u(2) and u (3) denote the continuous load k at time t , corresponding to normal operation, outage and ramping (cold load starting) states; denotes the outage event occurrence variable; CT k denotes the duration of cold load starting interval; denotes the load demand in different states, is the rated power, is the power increment during ramping; is the total outage cost, c k denotes the single outage event cost. Introduce three state indicator variables Figure 3 as shown in equations (10) - (12). u (1) , u (2) and u (3) to describe the cold load starting process, corresponding to rated operation state, outage state and power ramping state, respectively. Equation (10) ensures that the continuous load can only be in one of the three states at the same time. Equations (11-12) reveal the logical relationship between the three states and the power-on state. The outage time point of the load is determined by equation (13). Equation (14) defines the time interval of the cold load starting. Equations (15-17) describe the power demand of the continuous load in different stages, including rated operation power, zero power during outage, and power demand during cold load starting. Equation (18) shows that the total load demand of the industrial user is equal to the sum of the power of multiple discrete loads and the continuous load. The outage cost of the industrial user is calculated by equation (19).

[0024] In step S1, the establishment of the refined model of commercial load includes: dividing the commercial load into benchmark load and adjustable load, the adjustable load including interruptible load and transferable load; establishing capacity constraints of interruptible load and transferable load; establishing an economic loss model related to customer churn rate caused by benchmark load reduction; establishing a comprehensive benefit model of commercial users participating in demand response.

[0025] The commercial load model can be divided into adjustable load and benchmark load. The adjustable load can be reduced or transferred to alleviate power shortage, while the benchmark load as the basic electricity demand of commercial users will directly affect commercial activities and cause economic losses. The commercial load model is as shown in equations (20) - (26).

[0026] (20); (21); (22); (23); (24); (25); (26); In formula (20)-(26), denotes the commercial user i at time t , the total adjustable load, which consists of interruptible load and transferable load. Specifically, denotes the interruptible load, which can be reduced to alleviate power shortage; denotes the transferable load, which can be transferred between different time periods. and denote the upper limit of their response, respectively. Formula (20) shows that the adjustable load consists of interruptible load and transferable load; formulas (21) and (22) respectively specify the range of allowable interruptible load capacity and allowable transferable load capacity; formula (23) shows that the actual transferred load is allowed to be less than the required value. denotes the load shedding of the commercial customer baseline load; formula (24) specifies the range of allowable reduction of the commercial user baseline load; formula (25) represents the economic loss of the commercial user caused by the reduction of the baseline load, which is related to the load loss coefficient α i and customer churn rate γ i , specifically, the customer churn rate is often higher during peak customer flow periods; formula (26) calculates the comprehensive benefits of the commercial user participating in demand response by adjusting the baseline load and the interruptible load. Denotes the total adjustable load of the commercial user i at time t , which consists of interruptible load and transferable load. Specifically, PD IL i , t denotes the interruptible load, which can be reduced to alleviate power shortage; denotes the transferable load, which can be transferred between different time periods. And respectively denote the upper limit of their response. Formula (20) shows that the adjustable load consists of interruptible load and transferable load; formulas (21) and (22) respectively specify the range of allowable interruptible load capacity and allowable transferable load capacity; formula (23) shows that the actual transferred load is allowed to be less than the required value. PD shedding i , trepresents the load shedding of the commercial customer baseline load; (24) formula defines the allowable reduction range of the commercial user baseline load; (25) formula represents the economic loss of the commercial user caused by the baseline load reduction, which is related to the load loss coefficient α i and customer churn rate γ i , and in particular, the customer churn rate is often higher during the peak period of customer flow; (26) formula calculates the comprehensive income of the commercial user participating in demand response by adjusting the baseline load and interruptible load.

[0027] In step S1, the refined model of the residential load is a power outage sensitivity model, and the establishment of the model includes: establishing a correlation function between the power outage duration of the residential load and the health loss cost thereof.

[0028] For the residential load, exposure to extreme weather conditions during power outage may cause multiple hazards to the health of the population. Therefore, an interruption loss correlation function based on the power outage duration is established, and the specific expression is as formula （27）-（28） : (27); (28); In formulas (27) - (28), represents the power outage duration of the residential load, and the value thereof depends on the power supply recovery state thereof v i,t 。

[0029] S2, based on the refined model, a double-layer master-slave game optimization model between the power distribution system operator and the diversified load user is established; Specifically, for the power supply reduction of the main network after the extreme event and the line fault problem, the present application utilizes transactional demand response to establish a double-layer master-slave game optimization model including the power distribution system operator and the load side.

[0030] 1. The power distribution system operator recovery optimization model is an upper model of the double-layer master-slave game optimization model, and the target function is to minimize the total system recovery cost, including: load reduction cost, main grid power purchase cost, response load incentive cost, distributed power generation cost and residential load medical cost.

[0031] In the system model, the power distribution system operator reduces the load loss after the extreme event by coordinating the line switch, the distributed power supply, the response load and other non-dispatchable loads.

[0032] 1) Objective function The objective of the upper model is to minimize the operation cost and the loss of load shedding during restoration, which is expressed as (29) - (30): (29); (30); The parameters of the distribution system in (29) - (30) include the cost of load shedding , the cost of purchasing power from the main grid , the cost of responding to load incentives , the cost of distributed generation (DG) , and the cost of medical treatment of residential loads .

[0033] The operation constraints of the distribution system are as follows: (40); (41); (42); (43); (44); (45); (46); (47); (48); (49); (50); (51); In (40) - (51), PS i,t and QS i,t represent the active power and reactive power purchased from the substation node i , , respectively, and , represent their corresponding capacity limits. PG i,t and QG i,t are the active and reactive power output of the distributed generator, and , represent their maximum allowable output. Pi,j,t With Q i,j,t For the line i - j active and reactive power flow. U i,t The voltage amplitude of node i , and With respectively represent the lower and upper limits of its voltage. This constraint ensures that the system operating parameters are always maintained within the safety limit range. Equations (40-41) represent the constraint condition that the power purchase of the substation is limited by the power supply capacity of the main grid; equation (42) determines the total amount of load restored to power supply; equations (43-44) specify the active and reactive power output limits of the distributed generation unit; equations (45-46) describe the power flow characteristics in the energized line; and equations (47-51) show the linearized DistFlow power flow model, in which equations (47-48) are the active and reactive power balance equations, equations (49-50) are the node voltage calculation expressions, and equation (51) specifies the allowed range of voltage deviation.

[0034] 2. The load-side optimization model, which is the lower model of the bi-level master-slave game optimization model, aims to maximize the respective benefits, and the objective function is to maximize the total benefits of industrial users and commercial users, including production profits and interactive benefits from participating in demand response.

[0035] In the response load model, diversified loads such as industrial loads and commercial loads maximize profits by responding to market signals in a coordinated manner of demand response and production scheduling. The optimization goal of the load-side model is to comprehensively improve production benefits and interactive benefits. The load-side optimization model is as shown in equations (52)-(53): (52); (53); In equations (52)-(53), B Pro and B DR represent the production profits and interactive benefits of industrial users and commercial users, respectively. It should be particularly noted that the maximum production profit of commercial users is defined as the minimum economic loss caused by participating in demand response.

[0036] S3. The bi-level master-slave game optimization model is solved by the KKT condition and the linearization method to obtain the optimal incentive strategy of the distribution system operator and the optimal power consumption response strategy of the load-side user, which are used to guide the operation of the distribution network during power supply restoration.

[0037] In step S3, the double-layer master-slave game optimization model is solved by KKT condition and linearization method, specifically: the lower optimization problem is converted into its KKT condition and taken as a constraint of the upper model, so that the double-layer model is reconstructed into a single-layer mathematical programming problem; for the binary variables existing in the model, the large M method is used for linearization processing, and finally the optimization solver is used for iterative solving until convergence.

[0038] In the power supply recovery process, the responsive users adjust the load shedding according to the incentive signal provided by the distribution system operator to maximize their own benefits; and the distribution system operator dynamically adjusts the incentive price and power limiting strategy according to the user response to minimize the load loss, which further affects the power consumption behavior of the responsive users. As can be seen, the distribution system operator is in a dominant position (leader), and the responsive users are in a subordinate position (follower), which constitutes a typical master-slave game relationship. Therefore, the recovery model for coordinating demand response and power limiting management can be constructed as a double-layer master-slave game optimization model between the system operator and the responsive users.

[0039] The KKT condition is an effective method for solving such double-layer optimization models, which replaces the lower optimization problem with the KKT condition and reconstructs the original double-layer model into a single-layer model. However, due to the large number of binary variables of load operation involved in the lower model, it is difficult to guarantee the strong effectiveness of the converted dual. After processing by linearization methods such as the large M method, the upper model and the lower model can be directly solved by the CPLEX solver, and the iterative calculation is performed until the convergence condition is reached. The solving process is shown in Figure 4 As shown in the figure, the parameters of the distribution system and the responsive load are input to the CPLEX solver at the beginning; the upper model is solved to obtain the incentive price and power limiting decision; the lower model is solved to optimize the operation behavior and obtain the load demand; it is judged whether the balance state is reached, if yes, the optimal DR result and power distribution management are obtained, and the process is ended, if not, the responsive load demand is returned to the DSO, and the upper model is solved again to obtain the incentive price and power limiting decision step.

[0040] To prove the effectiveness of the method provided in the application and the specific implementation manner, examples 1-3 are described in detail below. Example

[0041] The effectiveness of the method is verified by the improved IEEE33 node system. It is assumed that three industrial users are located at nodes 5, 16 and 30. According to the difference in industry type, each industrial load model can be simplified as a combination of a plurality of discrete and continuous processes. In addition, three commercial users are located at nodes 20, 15 and 32, and the detailed parameters of the industrial users and the commercial users are shown in Tables 1 and 2, respectively.

[0042]

[0043]

[0044] Line outages F1 and F2 occur in time steps 3 and 6, respectively, due to the extreme weather event. Meanwhile, the main grid power supply gradually decreases, leading to an aggravated imbalance between supply and demand. The power restoration process is set to last for 6 hours, divided into 12 time steps of 30 minutes each.

[0045] To cope with the line outages caused by the extreme weather event, two microgrids are formed with the help of the dynamic-capable energy storage systems, in which two wind farms can provide continuous power supply. The responsive loads are dispatched through market signals, which can alleviate the power shortage by changing the electricity consumption behavior. The microgrids of the test system are formed as shown in FIG. 2. Figure 5

[0046] During time steps 1-3, the power supply of the substation begins to decrease because the upstream grid is also affected, resulting in limited power generation output. At the initial stage of the main grid power reduction, the loads of node 20 begin to take demand response measures to reduce power consumption to meet the electricity demand of other users. After the line outage F1 occurs at time step 3, MG#1 is formed with the support of ESS#2 and wind power #3 to restore the power supply of the loads of nodes 29 to 33. Since WT#3 has sufficient power generation capacity, the load demand of MG#1 can be met, but the demand response strength needs to be increased in the live area of the substation.

[0047] After the line outage F2 occurs at time step 5, MG#2 is formed with the support of ESS#1 and wind power #1. However, since MG#2 is disconnected from the upstream grid, the power output is insufficient to meet the load demand. The industrial and commercial users of nodes 15 and 16 further adjust their electricity consumption patterns to minimize the loss of other critical loads.

[0048] Simulation verification based on the improved IEEE33 node system shows that the demand response method can effectively reduce the load interruption loss of the distribution network, and optimizing the power distribution between different processes can improve the user benefits. Embodiments

[0049] To address the problem of power shortage during power restoration, the proposed method is used to reduce the loss of critical loads. Figure 6 The supply and demand situation diagram for the power supply area of the substation is shown in FIG. 3. Figure 7 The supply and demand situation diagram for MG#1 is shown in FIG. 4.

[0050] Appendix Figure 6 ​The positive load of MG#2 represents the load supported by the main grid before the occurrence of fault F2, and the negative load of MG#1 represents the power export capability of wind farm #3 and energy storage system #2 to other areas after satisfying the local load. The stepwise decrease of substation power output reflects the decrease of power supply from the main grid under the influence of the extreme disaster.

[0051] The Figure 7 The sufficient power output of wind farm #3 and energy storage system #2 in MG#1 area can fully support the local load. Even if the output of wind farm #3 fluctuates and decreases, the supply-demand balance can be achieved through the slight demand response of commercial load at node 32 and industrial load at node 30. Embodiments

[0052] The energy-material flow model for industrial users aims to model the load demand and production output in different processes. The Figures 8 - 10 The energy and production profiles of three types of industrial loads are respectively shown, showing the operation decisions made to meet the interactive demand of the power distribution system while maximizing the production benefit.

[0053] As Figure 8 For IL#5, which is located in the area powered by the substation, the industrial user is composed of two discrete processes and two continuous processes, and the production process follows the order of “D1→C1→C2→D2”. Since the profitability of the final product produced by D2 is high, this discrete process maintains a constant load demand. The inventory of the final product is increased every two time steps, which corresponds to the time interval required by discrete process D2. Discrete process D1 operates normally and is shut down after the 5th time step to reduce power consumption, and it has already produced enough primary products during its operation. As the power shortage situation worsens, the load demand of continuous processes C1 and C2 gradually decreases. Under the condition of limited power supply, C1 and C2 allocate power between each other according to the principles of material flow and inventory. Finally, C1 and C2 operate in the lowest power mode to avoid interruption due to high shutdown costs.

[0054] As Figure 9 For IL#30, which is located in the area powered by MG#1, the industrial customer contains one discrete process and two continuous processes, and the production process follows the order of “D1→C1→C2”. After the F1 event occurs at time step 4, the operation switches to the island mode implemented by the formed MG#1. Due to the relatively sufficient output of distributed generation, the supply-demand imbalance can be solved through the demand response of IL#30. Discrete process D1 stops running after there is enough primary product available. Continuous processes C1 and C2 reduce demand to alleviate power shortage, but still continue to run and produce to obtain profits.

[0055] As Figure 10For IL#16, which is located in the area powered by MG#2, the industrial customer contains two discrete processes and one continuous process, and the production process follows the order of "D1→C1→D2". At step 6, the F2 event occurs, resulting in a serious imbalance between power generation and load demand, and the industrial user quickly reduces the power consumption, D1 stops running, and C1 runs in the minimum power mode. Due to the power drop of WT#1 and the insufficient battery state of charge of ESS#1, IL#16 is listed as a load affected by rotating power outage. Because of its low product cost and extremely low cost of starting and stopping continuous loads, it experiences power outage at steps 11 and 12.

[0056] By coordinating the participation of industrial and commercial users, a demand response mechanism is designed to encourage voluntary load adjustment. A refined model covering the energy-material flow of industrial loads is established according to the differences in user electricity characteristics. Finally, simulation verification is carried out based on the improved IEEE33 node distribution network, and the results show that the proposed method can effectively protect user benefits and reduce load interruption loss through demand resource scheduling in the recovery stage.

[0057] The present application establishes a transaction market between the distribution system operator and the responsive user in order to change the electricity consumption behavior of the responsive load. The goal of the distribution system operator is to alleviate the imbalance between supply and demand, while the responsive load aims to maximize profits. In the upper model, based on the imbalance between load demand and the power provided by the main grid and distributed generation, the distribution service operator formulates a demand response strategy. According to the incentive signal, each responsive user will make an operating decision. Industrial users will allocate limited power to different production processes, while the load of commercial users is divided into adjustable demand and benchmark demand. Users will also update their power demand information to the distribution service operator to maximize profits.

[0058] The above-described embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0059] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the associated objects before and after it are in an "or" relationship, but it can also represent an "and / or" relationship, which can be understood according to the context before and after it.

[0060] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0061] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0062] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0063] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the devices, apparatuses and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0064] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0065] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0066] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0067] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0068] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A diversified load-transaction-based demand response method for resilient power distribution systems, characterized in that, The method includes: S1. Establish a refined model of diversified loads in the distribution network, including industrial loads, commercial loads, and residential loads; S2. Based on the refined model, establish a two-layer master-slave game optimization model between the power distribution system operator and diversified load users; the upper-layer model of this two-layer master-slave game optimization model is the power distribution system operator recovery optimization model, with the goal of minimizing the total system recovery cost; the lower-layer model of this two-layer master-slave game optimization model is the load-side optimization model, with the goal of maximizing their respective benefits. S3. Solve the two-layer master-slave game optimization model using KKT conditions and linearization methods to obtain the optimal incentive strategy for the distribution system operator and the optimal power consumption response strategy for load-side users, which can be used to guide the operation of the distribution network during power supply restoration.

2. The diversified load trading demand response method for resilient distribution systems according to claim 1, characterized in that, In step S1, the refined model of industrial load is an energy-material flow model. Establishing this model includes: Industrial loads are categorized into continuous loads and discrete loads. Establish constraints that describe the relationship between output and required production time in a discrete production process; Establish constraints describing the continuous adjustability of continuous load power and its range of variation; Establish material balance constraints and inventory non-negativity constraints to describe the relationship between material consumption and output between continuous and discrete loads; Establish an industrial user production revenue model based on final product calculations.

3. The diversified load trading demand response method for resilient distribution systems according to claim 2, characterized in that, In step S1, establishing the energy-material flow model also includes establishing a continuous load start-up model that considers the cold start process. This model distinguishes between rated operation, shutdown and power ramp-up states by introducing state indicator variables, and defines the power characteristics and start-up costs of each state.

4. The diversified load trading demand response method for resilient distribution systems according to claim 1, characterized in that, In step S1, establishing a refined model of the business load includes: Commercial load is divided into baseline load and adjustable load, wherein the adjustable load includes interruptible load and transferable load; Establish capacity constraints for interruptible and transferable loads; Establish a model of economic losses related to customer churn caused by baseline load reduction; Establish a comprehensive benefit model for business users to participate in demand response.

5. The diversified load trading demand response method for resilient distribution systems according to claim 1, characterized in that, In step S1, the refined model of residential load is the power outage sensitivity model. Establishing this model includes: establishing a correlation function between the duration of power outage of residential load and its health loss cost.

6. The diversified load trading demand response method for resilient distribution systems according to claim 1, characterized in that, In step S2, the objective function of the upper-level model is to minimize the total system recovery cost, which includes: load reduction cost, main grid power purchase cost, response load incentive cost, distributed generation cost, and residential load medical cost.

7. The diversified load trading demand response method for resilient distribution systems according to claim 1 or 6, characterized in that, In step S2, the objective function of the lower-level model is to maximize the total revenue of industrial users and commercial users, which includes production profits and interactive revenue from participating in demand response.

8. The diversified load trading demand response method for resilient distribution systems according to claim 1, characterized in that, In step S3, the two-layer master-slave game optimization model is solved using KKT conditions and linearization methods. Specifically, the lower-layer optimization problem is transformed into its KKT conditions and used as constraints for the upper-layer model, thereby reconstructing the two-layer model into a single-layer mathematical programming problem. For the binary variables in the model, the Big M method is used for linearization. Finally, the optimization solver is used for iterative solution until convergence.

Images