Method and device for robust autonomous operation of an electrical-hydrogen-biomass oil coupled microgrid, electronic device, medium

Abstract

The application discloses a kind of electric-hydrogen-biomass oil coupling microgrid robust autonomous operation method and device, electronic equipment, medium, the method is: using robust optimization to depict source and load two sides uncertainty, establishes two-stage three-layer electric-hydrogen-biomass oil coupling microgrid robust autonomous operation optimization model, first stage is long time period source and load energy matching, second stage is short time period power real-time balancing;Using column constraint generation algorithm iteration to solve the optimization model, obtains electric-hydrogen-biomass oil coupling microgrid autonomous operation scheme.Compared with prior art, the present application will be coordinated with short time scale power real-time balancing scheme before week long time scale source and load energy matching scheme, reduce the influence of renewable energy and load long cycle prediction error on dispatching result, enhance the reliability of electric-hydrogen-biomass oil coupling microgrid long cycle autonomous operation.

Description

Technical Field

[0001] This invention belongs to the field of microgrid autonomous operation technology, and specifically relates to a robust autonomous operation method, device, electronic equipment, and medium for an electricity-hydrogen-biomass oil coupled microgrid. Background Technology

[0002] For economic and reliability reasons, islands and other remote areas are typically powered by autonomously operated microgrids, with fuel-fired generators being the most widely used. Due to high fuel and transportation costs and significant air pollution emissions, the use of renewable energy in microgrids has become increasingly prevalent. With the development of hydrogen energy and its synthetic biomass oil technology, electricity-hydrogen-biomass oil coupled microgrids have broad development prospects. They can convert surplus renewable energy output into biomass oil and utilize biomass oil for power generation when output is insufficient, thereby improving the economic efficiency and reliability of autonomous microgrid operation.

[0003] Scholars both domestically and internationally have also launched related research, including biomass hydrogenation to oil production technology, energy management problems of electro-hydrogen coupled microgrids, and economic dispatch problems of integrated electricity-hydrogen-heat-gas microgrids. Currently, the commonly used method is to optimize the operation plan for the following day based on the current day's optimization.

[0004] However, two problems still need to be addressed. First, current research has failed to synergistically optimize electricity, hydrogen, and biomass oil. Second, current optimization methods are limited to power scheduling on short timescales such as day-ahead, failing to consider energy scheduling issues for long-term microgrid autonomous operation, leading to uneconomical or unreliable operating schemes. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a robust autonomous operation method, device, electronic equipment, and medium for an electro-hydrogen-biomass oil coupled microgrid.

[0006] Therefore, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a robust autonomous operation method for an electricity-hydrogen-biomass oil coupled microgrid, comprising: employing robust optimization to characterize the uncertainties on both sides of the source and load, establishing a two-stage, three-layer robust autonomous operation optimization model for the electricity-hydrogen-biomass oil coupled microgrid, wherein the first stage is long-term (day-based) source-load energy matching, and the second stage is short-term (hour-based) real-time power balancing; and employing a column constraint generation algorithm to iteratively solve the optimization model to obtain an autonomous operation scheme for the electricity-hydrogen-biomass oil coupled microgrid.

[0008] A second aspect of the present invention provides a robust autonomous operation device for an electro-hydrogen-biomass oil coupled microgrid, comprising:

[0009] The model generation module uses robust optimization to characterize the uncertainties on both sides of the source and load, and establishes a two-stage, three-layer electric-hydrogen-biomass oil coupled microgrid robust autonomous operation optimization model. The first stage is the source and load energy matching over a long period (in days), and the second stage is the real-time power balance over a short period (in hours).

[0010] The model solving module uses a column constraint generation algorithm to iteratively solve the optimization model and obtain the autonomous operation scheme of the electricity-hydrogen-biomass oil coupled microgrid.

[0011] A third aspect of the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in the first aspect.

[0012] A fourth aspect of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0013] The technical solution provided by this invention has the following beneficial effects:

[0014] This invention synergistically optimizes electricity, hydrogen, and biomass oil, while employing a robust optimization method to describe the uncertainties on both the source and load sides. It also synergistically considers the energy scheduling problem of long-term microgrid autonomous operation and the power scheduling problem of short-term time scale, thereby improving the economy and reliability of the electricity-hydrogen-biomass oil coupled microgrid.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] Figure 1 This is a flowchart illustrating a robust autonomous operation method for an electro-hydrogen-biomass oil coupled microgrid according to an exemplary embodiment;

[0018] Figure 2 This is a block diagram illustrating an electro-hydrogen-biomass oil coupled microgrid robust autonomous operation device according to an exemplary embodiment. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0020] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0021] Figure 1 This is a flowchart illustrating a robust autonomous operation method for an electro-hydrogen-biomass oil coupled microgrid according to an exemplary embodiment, with reference to... Figure 1 This invention provides a robust autonomous operation method for an electricity-hydrogen-biomass oil coupled microgrid, which includes the following steps:

[0022] Step S11: Robust optimization is used to characterize the uncertainties on both sides of the source and load, and a two-stage, three-layer electricity-hydrogen-biomass oil coupled microgrid robust autonomous operation optimization model is established.

[0023] Step S12: The optimization model is iteratively solved using a column constraint generation algorithm to obtain an autonomous operation scheme for the electricity-hydrogen-biomass oil coupled microgrid.

[0024] As can be seen from the above, the robust autonomous operation method of the electric-hydrogen-biomass oil coupled microgrid of the present invention optimizes electricity, hydrogen and biomass oil in a coordinated manner, and adopts a robust optimization method to describe the uncertainties on both the source and load sides. It also considers the energy scheduling problem of long-term microgrid autonomous operation and the power scheduling problem of short-term microgrid, thereby improving the economy and reliability of the electric-hydrogen-biomass oil coupled microgrid.

[0025] In the specific implementation of step S11, robust optimization is used to characterize the uncertainties on both sides of the source and load, and a two-stage three-layer electric-hydrogen-biomass oil coupled microgrid robust autonomous operation optimization model is established.

[0026] (1.1) Construction of the objective function in the first stage

[0027] The first stage of this invention is long-term (in days) source-load energy matching, the objective function of which is to minimize the total operating cost of the microgrid, as shown in equation (1).

[0028]

[0029] In the formula, C main D represents the objective function value for the first stage; C represents the time set of the long-term periodic problem under study; O This is the fuel cost coefficient; O d Fuel consumption on day d; C ESS The charging and discharging cost coefficient for electrochemical energy storage; The electrochemical energy storage charge on day d; C represents the electrochemical energy storage discharge on day d. ELoad This is the load transfer cost coefficient; C represents the electrical load transfer on day d. HLoad This is the cost coefficient for the comprehensive utilization of hydrogen energy; The hydrogen energy produced on day d.

[0030] (1.2) First stage constraint construction

[0031] The first-stage constraints of this invention include: system energy supply constraints, fuel consumption constraints for fuel generators, electrochemical energy storage constraints, and demand-side response energy constraints.

[0032] (1.2.1) System energy supply constraints

[0033]

[0034]

[0035] In the formula, η O For fuel-to-electric energy conversion efficiency; Forecast of renewable energy generation on day d; This represents the load forecast for day d. This represents the load transfer amount on day d. This represents the maximum amount of hydrogen energy produced on day d.

[0036] (1.2.2) Fuel Constraints for Fuel Generators

[0037]

[0038]

[0039] In the formula, O max To optimize the total amount of fuel available during the cycle; η H denoted as η, where η is the efficiency of converting hydrogen energy into biomass oil; D is the total number of scheduling days; and m is the fuel overdraft rate, used to constrain daily fuel consumption. The smaller the value, the more conservative the operating plan.

[0040] (1.2.3) Energy confinement of electrochemical energy storage

[0041]

[0042] (1.2.4) Demand-side response energy constraints

[0043]

[0044]

[0045] In the formula, τ is the proportion of transferable load to total load.

[0046] (1.3) Construction of the objective function in the second stage

[0047] The second stage of this invention is real-time power balancing over a short time period (in hours), with the objective function being to minimize the power imbalance under the worst operating conditions to an acceptable range based on the scheduling plan of the first stage.

[0048]

[0049] In the formula, C sub is the objective function value for the first stage; U is the set of uncertain constraints; T is the time set of the short-time period problem under study; and These are slack variables representing the excess or deficiency of power generation at hour t on day d.

[0050] (1.4) Second-stage constraint construction

[0051] The second-stage constraints of this invention include: uncertainty set constraints, power balance constraints, fuel generator operation constraints, electrochemical energy storage operation constraints, demand-side response constraints, and convergence condition constraints.

[0052] (1.4.1) Uncertain set constraints

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] In the formula, The actual output of renewable energy unit r at time t on day d; The predicted output of renewable energy unit r at time t on day d; and These represent the maximum upward and downward deviations between the output of renewable energy unit r at time t on day d and the predicted value. and These respectively characterize the degree of upper and lower deviations; Γ r,d The output uncertainty limit value for renewable energy unit r on day d; Let l be the actual value of load l at time t on day d; Let l be the predicted value of load l at time t on day d; and These represent the maximum upper and lower deviations between the actual and predicted values ​​of load l at time t on day d. and These respectively characterize the degree of upper and lower deviations; Γ l,d Let l be the uncertainty limit value for load l on day d.

[0061] (1.4.2) Power balance constraints

[0062]

[0063]

[0064] In the formula, G, R, E, L and H represent the collections of fuel generators, renewable energy, electrochemical energy storage, electrical load and hydrogen load, respectively; Let g be the output of the fuel-powered generator set g at time t on day d; and Let be the discharge and charging power of the electrochemical energy storage e at time t on day d, respectively; and These represent the amount of load l transferred in and the amount transferred out at time t on day d, respectively. Let be the power of the hydrogen load h at time t on day d.

[0065] (1.4.3) Operating constraints of fuel generators

[0066]

[0067]

[0068]

[0069] In the formula, This represents the maximum output power of the fuel-powered generator set g. ΔT represents the maximum power ramp-up value of the fuel-powered generator set at adjacent times; ΔT is the time interval.

[0070] (1.4.4) Operational constraints of electrochemical energy storage

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] In the formula, This represents the maximum output power of the electrochemical energy storage e; Let e ​​be the energy stored in the electrochemical energy storage at time t on day d; and These represent the charging and discharging efficiencies of the electrochemical energy storage e, respectively. and These represent the minimum and maximum values ​​of the energy stored in electrochemical energy storage, e, respectively.

[0077] (1.4.5) Demand-side response constraints

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] In the formula, This represents the maximum power of the hydrogen load h.

[0084] (1.4.6) Convergence Condition Constraints

[0085]

[0086] In the formula, Ψ d This is the power imbalance limit value for day d.

[0087] In the specific implementation of step S12, the optimization model is iteratively solved using a column constraint generation algorithm to obtain an autonomous operation scheme for the electricity-hydrogen-biomass oil coupled microgrid.

[0088] The column constraint generation (C&CG) algorithm is used to solve the model through iterative solving of the principal subproblems; the model described in (1)-(31) is written in a compact form as shown in equations (33)-(36):

[0089]

[0090] st F 0 x 0 ≤f (34)

[0091]

[0092]

[0093] In the formula, x 0 x u z and s represent the first-stage variable, the second-stage variable, the auxiliary variable of uncertainty, and the slack variable, respectively; c, I, and F 0 F u f, J, j, V and v are the corresponding coefficient matrices; Equation (33) represents the objective function of the first stage, the constraints of the first stage are (34)-(35), and the objective function and constraints of the second stage are Equation (36).

[0094] The subproblem is the two-layer short-time-period real-time power balancing problem in the second stage. This invention transforms the inner-layer problem into its dual problem, constructing an equivalent single-layer bilinear optimization problem. The subproblems in the m-th iteration are shown in equations (37)-(39).

[0095]

[0096]

[0097] z∈[0,1](39)

[0098] In the formula, ξ is the dual variable of the inner problem. The bilinear terms contained in equation (37) can be exactly linearized using the Big M method, x 0(m) This represents the first-stage variable obtained during the m-th iteration.

[0099] After solving the subproblems, the worst-case operating scenario is identified, and the corresponding constraints are added to the main problem. In the m-th iteration, the main problem is shown in equations (40)-(42).

[0100]

[0101] st F 0 x 0 ≤f (41)

[0102]

[0103] In the formula, x u(j) z is a newly added control variable during the iteration process. *(j) These are auxiliary variables obtained during the iteration process, representing the worst-case operating scenario.

[0104] Based on the master subproblem described above, the C&CG algorithm solves the problem in the following steps:

[0105] 1) Initialization: Set the iteration count m = 1;

[0106] 2) Solve the main problem described in equations (40)-(42) to obtain the control variable x. u(m) ;

[0107] 3) Solve the sub-problems described in equations (37)-(39) based on the results of the main problem to obtain the control variables in the objective function. and and the worst operating conditions z *(m) ;

[0108] 4) Convergence judgment: If constraint (32) is satisfied, the problem converges and the iteration stops; otherwise, the worst operating condition constraint (42) is added to the main problem, the iteration continues, m = m + 1, and the process returns to step 2).

[0109] Corresponding to the aforementioned embodiments of the robust autonomous operation method of the electro-hydrogen-biomass oil coupled microgrid, the present invention also provides embodiments of the robust autonomous operation device of the electro-hydrogen-biomass oil coupled microgrid.

[0110] Figure 2 This is a block diagram illustrating a robust autonomous operation device for an electro-hydrogen-biomass oil coupled microgrid, according to an exemplary embodiment. (Refer to...) Figure 2 The device:

[0111] Model generation module 21 uses robust optimization to characterize the uncertainties on both sides of the source and load, and establishes a two-stage, three-layer electricity-hydrogen-biomass oil coupled microgrid robust autonomous operation optimization model;

[0112] The model solving module 22 uses a column constraint generation algorithm to iteratively solve the optimization model and obtain the autonomous operation scheme of the electricity-hydrogen-biomass oil coupled microgrid.

[0113] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0114] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0115] Accordingly, the present invention also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the robust autonomous operation method of the electro-hydrogen-biomass oil coupled microgrid as described above.

[0116] Accordingly, the present invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the robust autonomous operation method of the electro-hydrogen-biomass oil coupled microgrid as described above.

[0117] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0118] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A robust autonomous operation method for an electricity-hydrogen-biomass oil coupled microgrid, characterized in that, include: Robust optimization is used to characterize the uncertainties on both sides of the source and load. A two-stage, three-layer electric-hydrogen-biomass oil coupled microgrid robust autonomous operation optimization model is established. The first stage is long-term source and load energy matching, and the second stage is short-term real-time power balance. The optimization model is solved iteratively using a column constraint generation algorithm to obtain an autonomous operation scheme for the electricity-hydrogen-biomass oil coupled microgrid. The robust autonomous operation optimization model of the two-stage three-layer electro-hydrogen-biomass oil coupled microgrid includes the following four parts: first-stage objective function, first-stage constraints, second-stage objective function, and second-stage constraints. (1.1) Objective function of the first stage The first stage is long-term source-load energy matching, and its objective function is to minimize the total operating cost of the microgrid. The long-term period is measured in days. (1.2) First-stage constraints The constraints in the first stage include: system energy supply constraints, fuel consumption constraints for fuel generators, energy constraints for electrochemical energy storage, and energy constraints for demand-side response. (1.3) Second-stage objective function The second stage is short-term real-time power balancing. Its objective function is to minimize the power imbalance under the worst operating conditions to an acceptable range based on the scheduling plan of the first stage. The short-term period is measured in hours. (1.4) Second-stage constraints The constraints in the second stage include: uncertainty set constraints, power balance constraints, fuel generator operation constraints, electrochemical energy storage operation constraints, demand-side response constraints, and convergence condition constraints.

2. The method according to claim 1, characterized in that, The formula for the objective function in the first stage is as follows: In the formula, This represents the objective function value for the first stage. D For the time set of the long-term periodic problem being studied; This is the fuel cost coefficient; For the first d Daily fuel consumption; The charging and discharging cost coefficient for electrochemical energy storage; For the first d TianDian Chemical Energy Storage Charging Capacity; For the first d Electrochemical energy storage discharge capacity; This is the load transfer cost coefficient; For the first d Incoming load from natural gas; This is the cost coefficient for the comprehensive utilization of hydrogen energy; For the first d Hydrogen energy produced daily.

3. The method according to claim 2, characterized in that, The formula for the objective function in the second stage is as follows: In the formula, This represents the objective function value for the first stage. U For a set of uncertain constraints; T For the time set of the short-time period problem under study; and They represent the first d Heavenly t Slack variables for hourly power generation surplus and shortage.

4. The method according to claim 1, characterized in that, A column constraint generation algorithm is used to solve the robust autonomous operation model of the electricity-hydrogen-biomass oil coupled microgrid through iterative master-subproblem solving, including: (2.1) Initialization: Set the number of iterations m =1; (2.2) Solve the main problem to obtain the control variables of the main problem; (2.3) Solve the subproblems based on the results of the main problem to obtain the control variables and worst-case operating condition constraints in the objective function; (2.4) Convergence Judgment: If the control variables in the objective function of the subproblem satisfy the convergence constraints, then the problem converges and iteration stops; otherwise, the worst-case operating condition constraint is added to the main problem, and iteration continues. m = m +1, return (2.2).

5. A robust autonomous operation device for an electricity-hydrogen-biomass oil coupled microgrid, characterized in that, include: The model generation module uses robust optimization to characterize the uncertainties on both sides of the source and load, and establishes a two-stage, three-layer electric-hydrogen-biomass oil coupled microgrid robust autonomous operation optimization model. The first stage is long-term source and load energy matching, and the second stage is short-term real-time power balance. The model solving module uses a column constraint generation algorithm to iteratively solve the optimization model and obtain the autonomous operation scheme of the electricity-hydrogen-biomass oil coupled microgrid. The robust autonomous operation optimization model of the two-stage three-layer electro-hydrogen-biomass oil coupled microgrid includes the following four parts: first-stage objective function, first-stage constraints, second-stage objective function, and second-stage constraints. (3.1) Objective function of the first stage The first stage is long-term source-load energy matching, and its objective function is to minimize the total operating cost of the microgrid. The long-term period is measured in days. (3.2) First-stage constraints The constraints in the first stage include: system energy supply constraints, fuel consumption constraints for fuel generators, energy constraints for electrochemical energy storage, and energy constraints for demand-side response. (3.3) Objective function of the second stage The second stage is short-term real-time power balancing. Its objective function is to minimize the power imbalance under the worst operating conditions to an acceptable range based on the scheduling plan of the first stage. The short-term period is measured in hours. (3.4) Second-stage constraints The constraints in the second stage include: uncertainty set constraints, power balance constraints, fuel generator operation constraints, electrochemical energy storage operation constraints, demand-side response constraints, and convergence condition constraints.

6. The apparatus according to claim 5, characterized in that, A column constraint generation algorithm is used to solve the robust autonomous operation model of the electricity-hydrogen-biomass oil coupled microgrid through iterative master-subproblem solving, including: (4.1) Initialization: Set the number of iterations m =1; (4.2) Solve the main problem to obtain the control variables of the main problem; (4.3) Solve the subproblems based on the results of the main problem to obtain the control variables and worst-case operating condition constraints in the objective function; (4.4) Convergence Judgment: If the control variables in the objective function of the subproblem satisfy the convergence constraints, then the problem converges and iteration stops; otherwise, the worst-case operating condition constraint is added to the main problem, and iteration continues. m = m +1, return (4.2).

7. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-4.

8. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-4.

Images