Power distribution and micro-grid integrated collaborative optimization scheduling method based on multiple time scales

Abstract

The invention discloses a power distribution network and micro-grid integrated collaborative optimization scheduling method based on multiple time scales, and the method comprises the steps: constructing a power distribution network and micro-grid double-layer optimization scheduling model, and enabling an upper-layer power distribution network optimization scheduling model to mainly consider the model operation cost under the condition of the lowest network loss; a lower-layer micro-grid optimization scheduling model is combined with a feeder terminal to establish a micro-grid topology change constraint, and a multi-time-scale micro-grid optimization operation strategy based on a model prediction control algorithm and considering day-ahead optimization, intra-day rolling optimization and real-time feedback correction is researched. And finally, realizing the micro-grid integrated double-level collaborative optimization scheduling method under multiple time scales. According to the invention, upper and lower models can cooperate, combine and perform interactive feedback, and the operation reliability of the micro-grid is effectively improved; and the system scheduling difficulty and the network loss are greatly reduced, and the purpose of double-layer optimization mutual benefit and win-win of the power distribution microgrid and the power distribution network is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution microgrid optimization scheduling, and in particular to a distribution microgrid integrated collaborative optimization scheduling method based on multiple time scales. Background Art

[0002] The integration of distributed generation (DG) into the grid changes the structure and performance of the distribution network itself. Improper output can alter the direction of power flow, causing local overvoltages, undermining system power supply reliability, and increasing the difficulty of relay protection in the distribution network. Furthermore, traditional distribution networks are relatively passive in their acceptance of DG (Distributed Generation) integration, failing to fully leverage the advantages of DG integration.

[0003] At present, the accuracy of the optimal dispatch plan planned by the single-time-scale optimization control method depends on the prediction accuracy of the uncertainty factors. If the prediction accuracy is low, there will be a large difference between the actual operation of the distribution microgrid and the dispatch plan, and it will be difficult to achieve smooth access to the upstream network for the distribution microgrid and ensure the power supply reliability of the distribution microgrid.

[0004] Chinese patent, publication number: CN115409396A, publication date: November 29, 2022, discloses a multi-time scale scheduling method for an integrated energy system based on double-layer rolling optimization, which can be based on the output data of each unit equipment and the change data of various loads at different time scales, and respectively use deep reinforcement learning algorithm and model predictive control method to perform day-ahead optimization scheduling and intraday optimization scheduling, and analyze the equipment output and load forecasts at different time scales, and at the same time propose corresponding scheduling modes and operation strategies, thereby improving the operation reliability of the integrated energy system; however, this method cannot solve the problem of how to reduce the network loss of the upper distribution network and how to implement a multi-time scale optimization operation strategy based on MPC (Model Predictive Control) in the island mode of the lower microgrid, nor can it solve the problem of how to realize that the scheduling models of the upper and lower microgrids are both independent and interrelated.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the application and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to address the problem that the existing scheduling schemes consider a single factor, which leads to low operational reliability of distribution microgrids. A multi-time-scale distribution microgrid double-layer optimization scheduling model is proposed. The double-layer optimization scheduling is constructed based on the upper-layer distribution network optimization scheduling model and the lower-layer microgrid optimization scheduling model. Through the collaborative joint interactive feedback of the upper-layer distribution network optimization scheduling model and the lower-layer microgrid optimization scheduling model, the scheduling accuracy of the distribution microgrid system is effectively improved, thereby improving the operational reliability of the distribution microgrid.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: S1. Construct an upper-level distribution network optimization dispatching model based on network loss as the objective function; S2, taking into account the multi-time scale optimization operation strategy of day-ahead optimization, intra-day rolling optimization, and real-time feedback correction, to build the lower-level microgrid optimization scheduling model; S3. Based on the upper-level distribution network optimization scheduling model and the lower-level microgrid optimization scheduling model, a two-layer optimization scheduling is constructed, and the two-layer optimization scheduling is collaboratively decoupled to obtain the optimization scheduling result.

[0008] Specifically, a multi-time-scale integrated collaborative optimization scheduling model for distribution and microgrids based on model predictive control is constructed, including a lower-level microgrid optimization scheduling model of active and reactive resource models such as photovoltaics, hydropower, energy storage, adjustable loads, static VAR compensators, and capacitors. In addition, constraints on the topology changes of distribution and microgrids are established in combination with feeder terminals, and a multi-time-scale microgrid optimization operation strategy based on model predictive control algorithms, taking into account day-ahead optimization, intraday rolling optimization, and real-time feedback correction is studied.

[0009] Optionally, as a further improvement of the present invention: S11. Taking the lowest network loss in the upper distribution network system as the first objective function, calculate the operating cost of the generator set under the condition of lowest network loss, and set the constraint conditions of the distribution network optimization scheduling model.

[0010] The calculation formula of the first objective function F is as follows: Where, P loss is the total network loss of the upper distribution network system; L is the network branch; Z ij is the branch impedance; I ij is the current between node i and node j; P ij is the active power of branch ij; Q ij is the reactive power of branch ij; V i is the voltage amplitude at node i.

[0011] As a further improvement of the present invention: The operating cost of the generator set under the condition of the lowest distribution network loss is: Where, C(P Gi ) is the operating cost of the generator set; P Gi is the active power generation of the i-th generator set; N is the number of generator sets; α i , β i , γ i are the consumption characteristic curve parameters of the i-th generator respectively.

[0012] Optionally, as a further improvement of the present invention: The constraints of the distribution network optimization scheduling model include power balance constraints, which are expressed as follows: Where, P Li is the active power of the i-th group of generators; Q Li is the reactive power of the i-th group of generators; V i is the voltage amplitude of node i; θ i is the phase angle of node i, where θ ij =θ i -θ j ; G ij is the real part of the element in the i-th row and j-th column of the node admittance matrix; B ij is the imaginary part of the element in the i-th row and j-th column of the node admittance matrix.

[0013] As a further improvement of the present invention: The lower-level microgrid optimization scheduling model includes: constructing a lower-level microgrid grid-connected mode scheduling model based on minimizing operating cost as an objective function and constructing a lower-level microgrid island mode scheduling model based on minimizing operating cost as an objective function.

[0014] As a further improvement of the present invention: The construction of the lower-level microgrid grid-connected mode scheduling model based on the minimum operating cost as the objective function includes the following steps: S21. Based on the constraints of the day-ahead optimization phase, the day-ahead optimization scheduling of the lower-layer microgrid grid-connected mode scheduling model is constructed by minimizing the operating cost of the day-ahead optimization phase of the lower-layer microgrid grid-connected mode as the second objective function; S22. Taking the minimization of the operating cost of the lower-level microgrid grid-connected mode in the daily rolling optimization phase as the third objective function, constructing the daily rolling optimization of the lower-level microgrid grid-connected mode scheduling model, and generating a rolling correction plan for the grid-connected mode; S23. Taking the minimum output adjustment amount of the adjustable resources at the current moment of the real-time feedback correction of the lower-level microgrid grid-connected mode as the fourth objective function, construct the real-time feedback correction of the lower-level microgrid grid-connected mode scheduling model, and perform real-time feedback correction based on the rolling correction plan of the generated grid-connected mode.

[0015] Optionally, as a further improvement of the present invention: The construction of the lower-layer microgrid island mode scheduling model based on the minimum operating cost as the objective function includes the following steps: S25, minimizing the total economic operating cost of the lower-layer microgrid island mode in the day-ahead optimization phase as the fifth objective function, constructing the day-ahead optimization scheduling of the lower-layer microgrid island mode scheduling model; S26, taking the minimum total economic operating cost of the lower-level microgrid island mode in the daily rolling optimization stage as the sixth objective function, constructing the daily rolling optimization of the lower-level microgrid island mode scheduling model, and generating a rolling correction plan for the island mode; S27. Taking the minimum current adjustable resource output adjustment amount of the real-time feedback correction of the lower-level microgrid island mode as the seventh objective function, construct the real-time feedback correction of the lower-level microgrid island mode scheduling model, and perform real-time feedback correction based on the rolling correction plan for generating the island mode.

[0016] As a further improvement of the technical solution of the present invention, the constraints described in step S21 include: microgrid power balance constraints, MT (gas turbine) output constraints, tie line transmission power constraints, energy storage system constraints, and distributed power supply output constraints.

[0017] The microgrid power balance constraint is expressed as follows: P WT (t)+P PV (t)+P ES (t)+P HY (t)+P PCC (t) = P load (t); Where, P WT (t) is the power of the micro gas turbine device at time t; P PV (t) is the power of the photovoltaic power generation device at time t; P ES (t) is the power of the energy storage device at time t; P HY (t) is the power of the small hydropower device at time t; P PCC (t) is the power of the tie line between the microgrid and the distribution network at time t and P load (t) is the power of the load in the microgrid at time t; Microturbine operating constraints: Where, P MT，t，min is the corresponding minimum power of the gas turbine; P MT，t，max is the corresponding maximum power of the gas turbine; ΔP MT，t + is the upper limit of the gas turbine's ramp power; ΔP MT，t - is the lower limit of the gas turbine's ramp power; M onMT is the shortest start-up time of the gas turbine; T onMT，t-1 is the corresponding continuous operating time of the gas turbine; M offMT is the shortest downtime of the gas turbine; T offMT，t-1 is the corresponding continuous shutdown time of the gas turbine; Microturbine ramping constraints: Where, is the upward ramp rate of the i-th gas turbine unit; is the ramp-down rate of the i-th gas turbine unit; the tie line power constraint is: P PCC,min ≤P PCC,i (t)≤P PCC,max ; Where, P PCC，max is the maximum value of the tie line power; P PCC，min is the minimum value of the tie line power; P PCC i(t) is the power of the tie line between the upper distribution network and the microgrid at node i at time t; Energy storage equipment charge constraints: SOC min ≤SOC≤SOC max ; Energy storage equipment power constraints: P ES,min ≤P ES ≤P ES,max ; Distributed power generation output constraints: Where, is the active power dispatched by the i-th distributed generation at time t; is the reactive power dispatched by the i-th distributed generation at time t; is the limit value of the distributed power supply ramp rate; The constraints in the lower-level microgrid island mode are the same as those in the lower-level microgrid grid-connected mode, except for the tie-line power.

[0018] Beneficial effects of the present invention: (1) In order to solve the problem of grid connection between microgrid and upper distribution network, a multi-time scale two-layer optimization dispatching model for distribution microgrid is established. The upper layer is the distribution network optimization dispatching model, and the lower layer is the microgrid optimization dispatching model. The upper and lower layers exchange power through the interconnection line. The two-layer optimization dispatching model interacts synergistically. The upper and lower microgrid dispatching models can be dispatched independently and interrelatedly, and feedback and adjustment are timely provided, which effectively reduces the difficulty of system dispatching, economic operation cost and network loss, and achieves the goal of mutual benefit and win-win between microgrid and upper distribution network.

[0019] (2) In the optimization scheduling of the lower-level microgrid, the method proposed in the present invention includes three stages: day-ahead optimization scheduling, intraday rolling optimization, and real-time feedback correction. It constructs a closed-loop optimization control based on multiple time scales, and timely and accurately corrects the scheduling result deviation caused by the prediction error, thereby maximally eliminating the impact of the uncertainty factors of renewable energy and load power on the scheduling plan.

[0020] The above content of the invention is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Like reference characters are used throughout the drawings to represent like parts.

[0022] Figure 1 This is the dispatching flow chart of the integrated coordinated optimization dispatching method for distribution and microgrids based on multiple time scales; Figure 2 This is the specific flow chart of the lower-level microgrid optimization of the integrated coordinated optimization scheduling method for distribution and microgrids based on multiple time scales; Figure 3 This is the improved IEEE33 node network structure diagram of the integrated coordinated optimization scheduling method for distribution and microgrids based on multiple time scales; Figure 4 This is the load forecast result curve of the microgrid based on model predictive control based on the multi-time-scale integrated coordinated optimization scheduling method of distribution and microgrid; Figure 5 This is the network loss before and after the distribution network is connected to the microgrid based on the multi-time scale integrated collaborative optimization scheduling method of distribution and microgrid. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but can also have additional steps not included in the figures; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0025] Example 1: Figure 1 As shown, the integrated coordinated optimization scheduling method for distribution and microgrids based on multiple time scales is characterized by comprising the following steps: S1. Construct an upper-level distribution network optimization dispatching model based on network loss as the objective function; S2, taking into account the multi-time scale optimization operation strategy of day-ahead optimization, intra-day rolling optimization, and real-time feedback correction, to build the lower-level microgrid optimization scheduling model; S3. Based on the upper-level distribution network optimization scheduling model and the lower-level microgrid optimization scheduling model, a two-layer optimization scheduling is constructed, and the two-layer optimization scheduling is collaboratively decoupled to obtain the optimization scheduling result.

[0026] Optionally, the multi-time-scale integrated coordinated optimization scheduling method for distribution and microgrids includes: Specifically, step S3 constructs a multi-time-scale integrated collaborative optimization scheduling model for distribution microgrids based on model predictive control, including constructing a lower-level microgrid model of active and reactive resource models such as photovoltaics, hydropower, energy storage, adjustable loads, static VAR compensators, capacitors, etc., and establishing distribution microgrid topology change constraints in combination with feeder terminals, and studying multi-time-scale microgrid optimization operation strategies based on model predictive control algorithms, taking into account day-ahead optimization, intraday rolling optimization, and real-time feedback correction.

[0027] Specifically, in S1, constructing an upper-level distribution network optimization scheduling model based on network loss as the objective function includes: S11, taking the lowest network loss in the upper-level distribution network system as the first objective function, calculating the operating cost of the generator set under the condition of lowest network loss, and setting the constraint conditions of the distribution network optimization scheduling model.

[0028] Specifically, in step S11, The calculation formula of the first objective function F is as follows: Where, P loss is the total network loss of the upper distribution network system; L is the network branch; Z ij is the branch impedance; I ij is the current between node i and node j; P ij is the active power of branch ij; Q ij is the reactive power of branch ij; V i is the voltage amplitude at node i.

[0029] The operating cost of the generator set under the condition of the lowest distribution network loss is: Where, C(P Gi ) is the operating cost of the generator set; P Gi is the active power generation of the i-th generator set; N is the number of generator sets; α i , β i , γ i are the consumption characteristic curve parameters of the i-th generator respectively.

[0030] The constraints of the distribution network optimization scheduling model include power balance constraints, which are expressed as follows: Where, P Li is the active power of the i-th group of generators; Q Li is the reactive power of the i-th group of generators; V i is the voltage amplitude of node i; θ i is the phase angle of node i, where θ ij =θ i -θ j ; G ij is the real part of the element in the i-th row and j-th column of the node admittance matrix; B ij is the imaginary part of the element in the i-th row and j-th column of the node admittance matrix.

[0031] Specifically, the lower-level microgrid optimization scheduling model includes: constructing a lower-level microgrid grid-connected mode scheduling model based on minimizing operating cost as an objective function and constructing a lower-level microgrid island mode scheduling model based on minimizing operating cost as an objective function.

[0032] Specifically, such as Figure 2The following is a detailed flow chart of the lower-level microgrid scheduling model. During the day-ahead optimization scheduling phase of a microgrid in grid-connected mode, based on the day-ahead predicted power values of renewable energy sources and with the goal of minimizing system operating costs, the output of each micropower source, the charge and discharge power of energy storage devices, and the exchange power of the tie lines are optimized. A basic power generation plan is formulated for each hour of the following day and issued in advance. The total cycle for day-ahead optimization scheduling is 24 hours, with a one-hour sampling period. A scheduling plan for the next 24 hours is formulated the day-ahead.

[0033] Specifically, constructing a lower-level microgrid grid-connected mode scheduling model based on the objective function of minimizing the operating cost includes the following steps: S21. Based on the constraints of the day-ahead optimization phase, the day-ahead optimization scheduling of the lower-layer microgrid grid-connected mode scheduling model is constructed by minimizing the operating cost of the day-ahead optimization phase of the lower-layer microgrid grid-connected mode as the second objective function; S22. Taking the minimization of the operating cost of the lower-level microgrid grid-connected mode in the daily rolling optimization phase as the third objective function, constructing the daily rolling optimization of the lower-level microgrid grid-connected mode scheduling model, and generating a rolling correction plan for the grid-connected mode; S23. Taking the minimum output adjustment amount of the adjustable resources at the current moment of the real-time feedback correction of the lower-level microgrid grid-connected mode as the fourth objective function, construct the real-time feedback correction of the lower-level microgrid grid-connected mode scheduling model, and perform real-time feedback correction based on the rolling correction plan of the generated grid-connected mode.

[0034] Specifically, the construction of the lower-layer microgrid island mode scheduling model based on the minimum operating cost as the objective function includes the following steps: S25, minimizing the total economic operating cost of the lower-layer microgrid island mode in the day-ahead optimization phase as the fifth objective function, constructing the day-ahead optimization scheduling of the lower-layer microgrid island mode scheduling model; S26, taking the minimum total economic operating cost of the lower-level microgrid island mode in the daily rolling optimization stage as the sixth objective function, constructing the daily rolling optimization of the lower-level microgrid island mode scheduling model, and generating a rolling correction plan for the island mode; S27. Taking the minimum current adjustable resource output adjustment amount of the real-time feedback correction of the lower-level microgrid island mode as the seventh objective function, construct the real-time feedback correction of the lower-level microgrid island mode scheduling model, and perform real-time feedback correction based on the rolling correction plan for generating the island mode.

[0035] Specifically, the second objective function includes grid interaction cost, system operation and maintenance cost, micro gas turbine power generation cost, adjustable load control cost, and compensation capacitor adjustment cost, and is expressed as follows: Among them, ψMT is the node set connected to the micro gas turbine; ψ CL The set of nodes connected to the controllable load; is the power exchange cost between the microgrid and the upper distribution network tie line at time t the day before; is the total operation and maintenance cost of the microgrid equipment at time t; is the gas purchase cost for micro gas turbine power generation at node i at time t on the previous day; is the cost of controlling the controllable load at time t the day before; To compensate for the capacitor's all-day gear adjustment cost; The grid interaction cost includes the cost of purchasing electricity and the cost of selling electricity. When the load is at its peak, the microgrid acts as a power source to supply electricity to the load, incurring a cost of selling electricity. When the load is at its off-peak, the microgrid acts as a load to purchase electricity from the grid, incurring a cost of purchasing electricity. During the dispatch process, the sum of the microgrid's cost of selling electricity and the cost of purchasing electricity is the grid interaction cost. The cost of interacting with the main grid is expressed as follows: Where c s (t) is the price of electricity sold by the microgrid to the main grid during period t; c p (t) is the price of electricity purchased by the microgrid from the main grid during period t, and p MG (t) represents the power size of the interaction between the microgrid and the main grid during the period t, that is, the power size flowing through the PCC (point of common coupling); sMG is a 0-1 integer variable. When sMG = 0, the microgrid sells electricity to the main grid. There is p MG (t)<0; On the contrary, when sMG=1, the microgrid purchases electricity from the main grid, and there is p MG (t)≥0.

[0036] The total cost of operating and maintaining microgrid equipment is expressed as follows: Where R PV Represents the unit power maintenance cost of photovoltaic, R MT represents the unit power maintenance cost of the micro gas turbine, R ES represents the unit power maintenance cost of the energy storage equipment and R HY represents the maintenance cost per unit power of small hydropower; Indicates the output power of photovoltaic Output power of micro gas turbine, The output power of the energy storage device and Output power of small hydropower; The cost function expression of natural gas purchased by micro gas turbine at node i at time t is as follows: Where C g (t) is the price of natural gas; is the gas power of the interconnecting line.

[0037] The cost function expression of controllable load regulation at time t the day before is as follows: in: is the controlled state of the load at node i at time t the day before (1 means controlled, 0 means uncontrolled); Compensation for load; is the controlled active power of the load.

[0038] The cost of adjusting the compensation capacitor's gear position throughout the day is expressed as follows: Among them, ρ C is the unit adjustment cost of the compensation capacitor; ΔU C To compensate for the number of times the capacitor switching position is adjusted throughout the day, only one position can be adjusted each time.

[0039] In the intraday rolling optimization phase of the lower-level microgrid grid-connected mode scheduling model, the control plan of the interruptible load and the compensation capacitor is determined on the day before and does not change during the day. The third objective function of the scheduling in this phase is still to minimize the operating cost, which is expressed as follows: Where, ψ MT A set of nodes connected to the micro gas turbine; The power exchange cost between the microgrid and the upper distribution network tie line at time t during the intraday rolling optimization phase; The total operation and maintenance cost of the microgrid equipment at time t during the intraday rolling optimization phase; The gas purchase cost for micro gas turbine power generation at node i at time t during the intraday rolling optimization phase; The startup cycle of the real-time feedback correction optimization of the microgrid in the grid-connected mode is 5 minutes; it mainly adjusts the output of each adjustable resource in the rolling stage. The real-time feedback correction mainly adjusts the output of each adjustable resource in the rolling stage. The objective function of the real-time feedback correction optimization model is to minimize the output adjustment of the adjustable resource at the current moment to ensure the stability of the system in responding to fluctuations in renewable energy; the real-time feedback correction adjusts the output of each adjustable resource in the optimization stage. The fourth objective function is expressed as follows: U1=[P G ,Q RE,i ,P CDG,i ,Q CDG,i,P ch,i ,P dis,i ,Q ESS,i ,Q SVC,i ]; Where U represents the controllable variable of the control resources in the upper distribution network during the real-time feedback correction phase, including the reactive output of renewable energy Q RE,i , controllable distributed power active and reactive output, energy storage active and reactive output, SVC (static var compensator, static var compensator) output, upper distribution network tie line exchange power change PG; u RT,real The actual output feedback value of the resource control at the current moment; Δu RT To regulate resource output adjustment, u max Solve the variables for the model; u DR It represents the output of various regulatory resources obtained during the intraday rolling optimization phase.

[0040] Specifically, the fifth objective function is as follows: The sixth objective function is as follows: The seventh objective function is as follows: U2=[P G ,Q RE,i ,P CDG,i ,Q CDG,i ,P ch,i ,P dis,i ,Q ESS,i ,Q SVC,i ].

[0041] Specifically, the constraints in step S21 include microgrid power balance constraints, MT output constraints, tie line transmission power constraints, energy storage system constraints, and distributed power output constraints.

[0042] The microgrid power balance constraint is expressed as follows: P WT (t)+P PV (t)+P ES (t0+P HY (t)+P PCC (t) = P load (t); Where, P WT (t) is the power of the micro gas turbine device at time t; P PV (t) is the power of the photovoltaic power generation device at time t; P ES (t) is the power of the energy storage device at time t; PHY (t) is the power of the small hydropower device at time t; P PCC (t) is the power of the tie line between the microgrid and the distribution network at time t and P load (t) is the power of the load in the microgrid at time t; Microturbine operating constraints: Where, P MT，t，min is the corresponding minimum power of the gas turbine; P MT，t，max is the corresponding maximum power of the gas turbine; Δ PMT，t + is the upper limit of the gas turbine's ramp power; Δ PMT，t - is the lower limit of the gas turbine's ramp power; M onMT is the shortest start-up time of the gas turbine; T onMT，t-1 is the corresponding continuous operating time of the gas turbine; M offMT is the shortest downtime of the gas turbine; T offMT，t-1 is the corresponding continuous shutdown time of the gas turbine; Microturbine ramping constraints: Where, is the upward ramp rate of the i-th gas turbine unit; is the ramp-down rate of the i-th gas turbine unit; the tie line power constraint is: P PCC,min ≤P PCC,i (t)≤P PCC,max ; Where, P PCC，max is the maximum value of the tie line power; P PCC，min is the minimum value of the tie line power; P PCC i(i) is the tie line power between the upper distribution network and the microgrid at node i at time t; Energy storage equipment charge constraints: SOC min ≤SOC≤SOC max ; Energy storage equipment power constraints: P ES,min ≤P ES ≤P ES,max ; Distributed power generation output constraints: Where, is the active power dispatched by the i-th distributed generation at time t; is the reactive power dispatched by the i-th distributed generation at time t; is the limit value of the distributed power supply ramp rate; The constraints in the lower-level microgrid island mode are the same as those in the lower-level microgrid grid-connected mode, except for the tie-line power.

[0043] During the day-ahead optimization scheduling and intraday rolling optimization scheduling stages, the control resource variables at each moment are required to meet the above constraints, while in the real-time feedback correction stage, the corrected control resource output values are required to meet the above constraints.

[0044] The intraday rolling optimization phase will manage the charging and discharging status of each unit and energy storage device in the distribution microgrid according to the day-ahead optimization scheduling; Specifically, the intraday rolling optimization performs rolling corrections with a period of MΔt. Each rolling correction considers relevant environmental information within a time window after the current time section. Without changing the status of the relevant components of the distribution microgrid and satisfying the network security operation constraints, the rolling correction plan for all units within the time window is obtained based on MPC optimization. Only the rolling correction plan for the next time period is issued at this time section. Each unit and energy storage device in the distribution microgrid performs real-time feedback correction based on the rolling correction plan and the real-time sampled unit operating status. The above process is repeated in the next scheduling period.

[0045] The upper distribution network optimization model takes network loss as the objective function, and substitutes the PCC tie line power obtained by the lower microgrid dispatch model into the upper distribution network optimization dispatch model to solve the distribution network layer DG output that meets the constraint conditions; the power balance constraint leads to the non-convex nonlinearity of the upper model. Substitute the PCC power value into the formula. If the result value is negative, the node is regarded as a load node connected to the distribution network, and substitute P Li If the result value is positive, the node is regarded as a power source and substituted into P Gi Let e i e j +f i f j =|V i ||V j |cosθ ij , e i f j -e j f i =|V i ||V j |sinθ ij , where e i and e j are the real and imaginary parts of the voltage vector at node i, e j and f jare the real and imaginary parts of the voltage vector at node j, respectively.

[0046] Using the second-order cone technique, define the variable c ij 、c ii and s ij , and the variables satisfy the relationship: The power balance constraint is thus transformed into the following formula: Substitute the PCC power obtained from the lower-level microgrid dispatch model into the upper-level distribution network line parameters and start reading data; use the second-order cone technology to transform the upper-level distribution network optimization model; call the solver to solve the upper-level distribution network optimization model and output the DG output and objective function.

[0047] To demonstrate the effectiveness of this patented invention, a simulation analysis was conducted on a regional microgrid system consisting of distributed generation units (DGs) such as MTs, WTs, PVs, and SBs. Based on the multi-timescale model predictive control-based microgrid scheduling model established in this patented invention, the scheduling processes for grid-connected and islanded modes were compared, yielding corresponding results.

[0048] The parameters are as follows: the natural gas heat production value is 9.7 (kW / m3), the natural gas selling price is 3.2 (yuan / m3), and the conversion efficiency of the micro gas turbine is η MT The wind speed is 4 m / s. The initial capacity of the battery is 0 kWh, the charging and discharging efficiency is 0.95, and its maximum capacity is 50 kWh. The upper limit of the microgrid-grid interconnection line power is 300 kW, and the upper limit of the gas turbine power is 300 kW. Table 1 Comparison of economic operation costs between microgrid grid-connected and island modes Island Mode Grid-connected mode Economic operating cost / yuan 2561 1700

[0049] As shown in Table 1, the economic operating cost of the microgrid in island mode is 2561 yuan, and the economic operating cost in grid-connected mode is 1700 yuan. This shows that the microgrid significantly reduces the economic operating cost of the system in grid-connected mode.

[0050] In order to verify the accuracy and effectiveness of the double-layer optimization scheduling of the upper distribution network under multiple time scales, this paper selects the standard example IEEE33 node, improves it on its basis, and conducts case analysis on the upper distribution network and microgrid; the improved node diagram is shown in Figure 3 shown.

[0051] The node system has a voltage of 12.66 kV and a base power of 10 MW. In the microgrid, MG1 corresponds to IEEE33 node 15. The integrated MG1 primarily consists of a wind turbine, a photovoltaic generator, and a micro gas turbine. The SB charging and discharging efficiencies are both 95%. The SB's initial state of charge is 50%, with an upper limit of 90% and a lower limit of 20%. The unit cost of natural gas is 2.08 yuan / m³.

[0052] The patent of the present invention assumes that the wind power and photovoltaic absorption rate is 100%. By adjusting the output of controllable power sources such as micro gas turbines and batteries, the internal power balance of the microgrid is maintained, and the optimal economic dispatch of the microgrid in the lower model is achieved. Example 1 Simulation The upper layer optimizes the dispatching with the upper distribution network as the object. First, the wind turbine and photovoltaic output within 24 hours a day are predicted, and 24 hours a day is a dispatching cycle. The load forecast value is the load of each node of the IEEE33 node. In the microgrid, 15 minutes is used as a dispatching level, and the wind power, photovoltaic and load forecast data within the next 96 dispatching moments are used as the forecast value. The upper limit of the load forecast value of the lower model MG1 is 180kW, the upper limit of the forecast value of the wind turbine in the microgrid is 39kW, and the upper limit of the maximum output forecast value of photovoltaic is 42kW.

[0053] In the lower model, based on the model predictive control model under multiple time scales, the load forecasting situation in the microgrid is as follows: Figure 4 As shown in the figure, it can be seen that a feedback correction link is added to the open-loop rolling optimization scheduling. Based on the actual measurement value of the system at the current moment and the forecast error feedback, the output of each adjustable resource is corrected. The tracking effect of the system load is good, and it can effectively deal with the impact of forecast errors and renewable energy fluctuations on the operation of the upper distribution network.

[0054] Network loss is an important indicator for the safe, reliable and economical operation of distribution networks. In upper-level distribution networks and microgrids, the proper distribution of active and reactive power is determined by power flow calculations, and network loss is closely related to power flow calculations. Figure 5 This figure reflects the network losses before and after the microgrid is integrated into the upper-level distribution network. It can be seen that after 6:00 AM, the network losses show a significant downward trend. This indicates that the microgrid's integration effectively reduces network losses. At this time, the microgrid sells electricity to the upper-level distribution network. The PCC tie line acts as a power source within the distribution network system, providing power to the distribution network. This reduces the output of other distributed generation sources in the distribution network, ultimately effectively reducing the distribution network's power generation costs and minimizing network losses.

[0055] The specific implementation method described above is a preferred implementation method of the integrated collaborative optimization scheduling method of distribution microgrids based on multiple time scales of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation method. All equivalent changes made in accordance with the shape and structure of the present invention are within the scope of protection of the present invention.

Claims

1. A multi-time-scale integrated coordinated optimization scheduling method for distribution and microgrids, characterized in that: The following steps are involved: S1. Construct an upper-level distribution network optimization dispatching model based on network loss as the objective function; S2, taking into account the multi-time scale optimization operation strategy of day-ahead optimization, intra-day rolling optimization, and real-time feedback correction, to build the lower-level microgrid optimization scheduling model; S3. Based on the upper-level distribution network optimization scheduling model and the lower-level microgrid optimization scheduling model, a two-layer optimization scheduling is constructed, and the two-layer optimization scheduling is collaboratively decoupled to obtain the optimization scheduling result.

2. The method for integrated coordinated optimization scheduling of distribution and microgrids based on multiple time scales according to claim 1 is characterized in that: In S1, constructing an upper-layer distribution network optimization scheduling model based on network loss as the objective function includes: S11. Taking the lowest network loss in the upper distribution network system as the first objective function, calculate the operating cost of the generator set under the condition of lowest network loss, and set the constraint conditions of the distribution network optimization scheduling model.

3. The method for integrated coordinated optimization scheduling of distribution and microgrids based on multiple time scales according to claim 2 is characterized in that: The calculation formula of the first objective function F is as follows: Where, P loss is the total network loss of the upper distribution network system; L is the network branch; Z ij is the branch impedance; I ij is the current between node i and node j; P ij is the active power of branch ij; Q ij is the reactive power of branch ij; V i is the voltage amplitude at node i.

4. The method for integrated coordinated optimization scheduling of distribution and microgrids based on multiple time scales according to claim 2 is characterized in that: The operating cost of the generator set under the condition of the lowest distribution network loss is: Where, C(P Gi ) is the operating cost of the generator set; P Gi is the active power generation of the i-th generator set; N is the number of generator sets; α i , β i , γ i are the consumption characteristic curve parameters of the i-th generator respectively.

5. The method for integrated coordinated optimization scheduling of distribution and microgrids based on multiple time scales according to claim 2 is characterized in that: The constraints of the distribution network optimization scheduling model include power balance constraints, which are expressed as follows: Where, P Li is the active power of the i-th group of generators; Q Li is the reactive power of the i-th group of generators; V i is the voltage amplitude of node i; θ i is the phase angle of node i, where θ ij =θ i -θ j ; G ij is the real part of the element in the i-th row and j-th column of the node admittance matrix; B ij is the imaginary part of the element in the i-th row and j-th column of the node admittance matrix.

6. The method for integrated coordinated optimization and dispatching of distribution and microgrids based on multiple time scales according to claim 1 is characterized in that: The lower-level microgrid optimization scheduling model includes: constructing a lower-level microgrid grid-connected mode scheduling model based on minimizing operating cost as an objective function and constructing a lower-level microgrid island mode scheduling model based on minimizing operating cost as an objective function.

7. The method for integrated coordinated optimization scheduling of distribution and microgrids based on multiple time scales according to claim 6 is characterized in that: The construction of the lower-level microgrid grid-connected mode scheduling model based on the minimum operating cost as the objective function includes the following steps: S21. Based on the constraints of the day-ahead optimization phase, the day-ahead optimization scheduling of the lower-layer microgrid grid-connected mode scheduling model is constructed by minimizing the operating cost of the day-ahead optimization phase of the lower-layer microgrid grid-connected mode as the second objective function; S22. Taking the minimization of the operating cost of the lower-level microgrid grid-connected mode in the daily rolling optimization phase as the third objective function, constructing the daily rolling optimization of the lower-level microgrid grid-connected mode scheduling model, and generating a rolling correction plan for the grid-connected mode; S23. Taking the minimum output adjustment amount of the adjustable resources at the current moment of the real-time feedback correction of the lower-level microgrid grid-connected mode as the fourth objective function, construct the real-time feedback correction of the lower-level microgrid grid-connected mode scheduling model, and perform real-time feedback correction based on the rolling correction plan of the generated grid-connected mode.

8. The method for integrated coordinated optimization and dispatching of distribution and microgrids based on multiple time scales according to claim 6 is characterized in that: The construction of the lower-layer microgrid island mode scheduling model based on the minimum operating cost as the objective function includes the following steps: S25, minimizing the total economic operating cost of the lower-layer microgrid island mode in the day-ahead optimization phase as the fifth objective function, constructing the day-ahead optimization scheduling of the lower-layer microgrid island mode scheduling model; S26, taking the minimum total economic operating cost of the lower-level microgrid island mode in the daily rolling optimization stage as the sixth objective function, constructing the daily rolling optimization of the lower-level microgrid island mode scheduling model, and generating a rolling correction plan for the island mode; S27. Taking the minimum current adjustable resource output adjustment amount of the real-time feedback correction of the lower-level microgrid island mode as the seventh objective function, construct the real-time feedback correction of the lower-level microgrid island mode scheduling model, and perform real-time feedback correction based on the rolling correction plan for generating the island mode.

9. The method for integrated coordinated optimization and dispatching of distribution and microgrids based on multiple time scales according to claim 7 is characterized in that: The constraints in the day-ahead optimization phase include: microgrid power balance constraints, gas turbine output constraints, tie line transmission power constraints, energy storage system constraints, and distributed generation output constraints; The microgrid power balance constraint is expressed as follows: P WT (t)+P PV (t)+P ES (t)+P HY (t)+P PCC (t)=P load (t); Where, P WT (t) is the power of the micro gas turbine device at time t; P PV (t) is the power of the photovoltaic power generation device at time t; P ES (t) is the power of the energy storage device at time t; P HY (t) is the power of the small hydropower device at time t; P PCC (t) is the power of the tie line between the microgrid and the distribution network at time t and P load (t) is the power of the load in the microgrid at time t; Microturbine operating constraints: Where, P MT,t，min is the corresponding minimum power of the gas turbine; P MT,t，max is the corresponding maximum power of the gas turbine; ΔP MT,t + is the upper limit of the gas turbine's ramp power; ΔP MT,t - is the lower limit of the gas turbine's ramp power; M onMT is the shortest start-up time of the gas turbine; T onMT,t-1 is the corresponding continuous operating time of the gas turbine; M offMT is the shortest downtime of the gas turbine; T offMT,t-1 is the corresponding continuous shutdown time of the gas turbine; Microturbine ramping constraints: Where, is the upward ramp rate of the i-th gas turbine unit; is the ramp-down rate of the i-th gas turbine unit; the tie line power constraint is: P PCC,min ≤P PCC,i (t)≤P PCC,max ; Where, P PCC，max is the maximum value of the tie line power; P PCC，min is the minimum value of the tie line power; P PCC i(t) is the power of the tie line between the upper distribution network and the microgrid at node i at time t; Energy storage equipment charge constraints: SOC min ≤SOC≤SOC max ; Energy storage equipment power constraints: P ES,min ≤P ES ≤P ES,max ; Distributed power generation output constraints: Where, is the active power dispatched by the i-th distributed generation at time t; is the reactive power dispatched by the i-th distributed generation at time t; is the limit value of the distributed power supply ramp rate; The microgrid power balance constraint, gas turbine output constraint, energy storage system constraint and distributed generation output constraint in the constraint conditions of the lower-level microgrid island mode are the same as those in the lower-level microgrid grid-connected mode.

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