Coordinated control method of microgrid flexibility resources based on multi-granularity feature matching

Abstract

The present invention discloses a method for coordinated regulation of microgrid flexibility resources based on multi-granularity feature matching. Firstly, a quantitative model of lithium mine load flexibility is established by considering the production characteristics of lithium extraction from salt lakes, and the coordinated response mechanism of multiple types of flexibility resources under granularity attributes such as response power, response time, lag time and response rate is revealed; secondly, a discrete selection model is combined with granularity attribute differences to establish the regulation priority of multiple types of flexibility resources, and a response sequence for power rebalancing of independent microgrids with heterogeneous micro-sources is formed. Finally, simulation analysis is carried out based on the actual operation data of industrial and mining microgrids in a certain area. The example results show that the proposed method effectively improves the flexible interaction ability of flexibility resources to accurately track and regulate control instructions.

Description

Technical Field

[0001] The present invention relates to the field of power regulation, and in particular to a method for coordinated regulation of microgrid flexibility resources based on multi-granularity feature matching. Background Art

[0002] Modeling and quantification of industrial load flexibility is an important part of participating in demand response and realizing the coordinated optimization of multi-type flexibility resources. At present, relevant research has been carried out at home and abroad on the participation of high-energy-consuming industrial loads in the optimization operation of power grids. Existing literature has proposed a power control strategy that meets the production safety and effective response constraints of electrolytic copper based on the industrial production process of high-energy-consuming loads to maximize the load response benefits. Existing literature has established an economic dispatch model of the power system considering the emission characteristics of typical cement plant industrial loads, and used a step-type time-of-use electricity price to enhance the enthusiasm of industrial loads to participate in demand response. Existing literature has explored the flexibility potential of different types of industrial loads based on their production characteristics, and realized the effective use of industrial load resources through a multi-time scale rolling optimization dispatch model. Existing literature has regarded industrial loads as transferable loads and included them in virtual power plants to participate in power grid demand response to optimize the operation and regulation of the power system. As a representative of typical high-energy-consuming industrial loads, electrolytic aluminum loads have constructed the demand response regulation capacity boundary by analyzing their production characteristics and regulation constraints, and enhanced the ability of large-capacity electrolytic aluminum loads to flexibly participate in the two-way interaction of the power grid. The above studies have considered the industrial production process and widely explored the flexible regulation potential of high-energy-consuming industrial loads such as electrolytic copper, cement plants, and electrolytic aluminum, effectively improving the economic benefits of industrial enterprises and the stable operation capacity of the power grid. However, compared with the power supply of industrial loads in large power grids, the electricity demand of high-energy-consuming lithium mines has brought more severe power supply pressure to clean energy independent microgrids that lack the support of large power grids. It is particularly urgent to quantify the flexibility of lithium mine loads participating in power grid demand response, but research in this area is still rarely reported. In addition, the precise response of high-energy-consuming industrial loads to regulation instructions is an important guarantee for participating in power grid demand response. Many of the above literatures focus on analyzing the characterization model of high-energy-consuming industrial load flexibility regulation capabilities, but there is still no in-depth research on the suppression of response deviations of industrial loads.

[0003] To achieve accurate response of various types of flexible resources, an independent microgrid control model considering multi-granularity control attributes is established. At present, related studies focus on the heterogeneous properties of parameters, types, and feature differences of various types of flexible resources. Generalized models such as aggregation analysis, feasible domain construction, and feature matching are often used to deal with their differentiated response characteristics, and the granularity attributes of flexible resources are rarely considered. In order to improve the precise response capability of flexible resources under multi-granularity electricity consumption attributes, existing literature has borrowed the idea of ​​granular computing, constructed multiple time granularity expression spectra to fully explore the adjustable capabilities when the load gradient changes, and realized adaptive tracking of the net load changes of the power grid. Based on this, existing literature has analyzed the control priority of power-time multi-granularity attributes for the control power deviation problem of thermal load participating in demand response, and realized the synergistic complementarity of multi-granularity thermal loads. Existing literature has decomposed the flexible load into coarse-grained, medium-grained, and fine-grained layers according to capacity scale and response accuracy, reflecting the response characteristics of flexible loads at different time scales. Existing literature has established an adaptive segmented model of the net load curve with a constant total dispatch period at 5min, 15min, 30min and 1h time granularity to avoid continuous start and stop of units and improve the economy of the system. Existing literature has coordinated industrial loads adjusted at coarse granularity and energy storage systems regulated at fine granularity to participate in grid peak load auxiliary services, effectively overcoming the limitation of power granularity. However, in the above studies, there are still problems such as single granularity attribute consideration category and insufficient complementary matching of multi-level granularity. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for coordinated regulation of microgrid flexibility resources based on multi-granularity feature matching.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A method for coordinated control of microgrid flexibility resources based on multi-granularity feature matching includes the following steps:

[0007] S1. Establish a typical scenario of independent microgrids with heterogeneous micro-sources and establish a model for mining the potential of industrial and mining load regulation in this scenario;

[0008] S2, establish the flexibility resource objective function and constraints based on the model established in S1;

[0009] S3. Establish a microgrid control priority model based on the flexibility resource objective function and constraints and determine the response sequence of the flexibility resources;

[0010] S4. Calculate evaluation indicators sequentially based on the responses of the determined flexibility resources.

[0011] Furthermore, the objective function in S2 is expressed as:

[0012]

[0013] in, C The comprehensive operating cost of independent microgrids with heterogeneous micro-sources;

[0014] is the response cost of multiple types of flexibility resources and:

[0015]

[0016] In the formula, Resources for flexibility The unit response cost is The response power reported to the independent microgrid dispatching department for flexibility resources, For time;

[0017] C WL is the cost of the flexibility resource's willingness to respond and:

[0018]

[0019] In the formula, The unit cost of response deviation caused by the mismatch between the response power and time granularity of flexible resources; The response deviation is caused by the mismatch between response power and time granularity; is the unit cost of response deviation caused by mismatch of lag time granularity, The response deviation is caused by the mismatch of lag time granularity; is the unit cost of response deviation caused by response rate granularity mismatch, It is the response deviation caused by the mismatch of the hysteresis response rate strength;

[0020] Provides microgrid regulation benefits and:

[0021]

[0022]

[0023] In the formula, Adjusting benefit costs for units; F RD ( t ) is the system unbalanced power to be assessed; Reduced system unbalanced power for independent microgrids; To adjust the benchmark ratio for the segment; is the initial unbalanced power of the system.

[0024] Furthermore, the constraints in S2 include: power balance constraints, power generation constraints, lithium mine load regulation potential constraints, residential load flexibility regulation constraints, energy storage constraints, and multi-granularity matching constraints, where:

[0025] The power balance constraint is expressed as:

[0026]

[0027] In the formula, , , and They are the response powers of gas turbine, wind power, photovoltaic and electrochemical energy storage; Adjustable power for lithium ore load, is the operating power of the lithium ore load; for t The original planned power consumption of loads that can be transferred at any time; for t The response power of the load that can be transferred at any time; for t The original planned power consumption of the load can be reduced at any time; for t The response power of the load can be reduced at any time;

[0028] The power generation constraint is expressed as:

[0029]

[0030] In the formula, is the minimum output power of the wind turbine; is the minimum output power of the photovoltaic generator; is the predicted output power of the wind turbine; is the predicted output power of the photovoltaic generator; is the minimum output coefficient of the gas turbine; is the rated power of the gas turbine; is the ramp rate limit of the gas turbine;

[0031] The flexibility regulation constraint of residential load is expressed as:

[0032]

[0033] In the formula, and are the lower and upper potential coefficients of the transferable load

[0034] The energy storage constraint is expressed as:

[0035]

[0036] In the formula, , is the energy storage rated power and rated capacity, is the energy storage serial number, is the time granularity, Charge and discharge for energy storage 0- t Any moment in time.

[0037] Furthermore, the lithium ore load regulation potential constraints include:

[0038] The heat balance equation is expressed as:

[0039]

[0040] In the formula, is the heat input to MVR; Δ H Heat consumed in the production process; is the amount of heat loss per unit time; is the heat transfer coefficient between the outside and the heat storage tank; It is the surface area of ​​the heat storage tank in contact with the outside world;

[0041] Power-temperature coupling, expressed as:

[0042]

[0043] Where: Represents the quality of brine; represents the specific heat capacity of brine; Represents brine in and The temperature of the moment; η represents the thermal efficiency coefficient of heating, for t The temperature value at the moment, Adjustable power for lithium ore load;

[0044] Temperature adjustment range, expressed as:

[0045]

[0046] In the formula, Represents the temperature of the brine; Represents the upper limit of brine temperature; Represents the lower limit of brine temperature;

[0047] Thermal inertia load adjusts the upper and lower limits of power, expressed as:

[0048]

[0049] In the formula, , The upper and lower limits of the power can be adjusted for lithium ore load. is the unit time step.

[0050] Furthermore, the S3 specifically includes the following steps:

[0051] S31. Establish a multi-dimensional and multi-granular attribute model of flexibility resources and control instructions in terms of response power, response rate, lag time, and response time, and calculate the response deviation of multiple types of flexibility resources and the response deviation of control instructions through response capacity:

[0052] S32, projecting the high-dimensional feasible domain of multiple types of flexible resources onto the plane of control instructions to calculate the control priority;

[0053] S33, the combination scheme of multiple types of flexible resources is equivalent to the dependent variable in the discrete selection model, the corresponding power, response time, corresponding rate, subsequent time and random items are equivalent to the indicator variables in the discrete selection model, and the constructed discrete selection model is used to solve the optimal matching of flexible resources to obtain a multi-type flexible resource matching model;

[0054] S34. Perform multiple rounds of matching based on the condition of maximum utility, match at most one type of flexible resource at each moment in each round, and correspondingly reduce the adjustment boundary and control instruction of the flexible resource in the next round until the adjustment boundary of the flexible resource is reached or the control instruction is 0, and end the matching to obtain a flexible resource response sequence.

[0055] Furthermore, the response deviation of the multi-type flexibility resources in S31 is expressed as:

[0056]

[0057] In the formula, is the response bias of multiple types of flexibility resources, is the proportionality coefficient between the response time and its granularity, is the power granularity of the independent microgrid control command, is the time granularity, Resources for flexibility The time granularity coefficient, for t The ratio of the control instructions and granularity of the independent microgrid in the time period, Resources for flexibility The ratio of response power to flexibility resource granularity, Resources for flexibility Power granularity; is the response deviation caused by the mismatch of lag time granularity, is the proportionality factor of the response time; is the response rate deviation, The delay time of independent microgrid control instructions caused by communication and other factors, The response power reported to the independent microgrid dispatching department for flexibility resources, is the response rate of the flexibility resource, These are the delay times for stages 1, 2, and 3 respectively.

[0058] Furthermore, the flexibility resource matching model in S33 is expressed as:

[0059]

[0060] In the formula, Indicates that the control time t Time flexibility resources m Matching control instructions, for Flexibility resources for every moment The probability of is the total number of flexibility resources, Resources for flexibility The deterministic utility value.

[0061] Furthermore, the control instruction in S34 is expressed as:

[0062]

[0063] In the formula, , For microgrid , Matching rounds of regulation instructions; For the Round Optimal Flexibility Resources The response power.

[0064] The present invention has the following beneficial effects:

[0065] The proposed method effectively explores and quantifies the flexible adjustment of lithium mine load and microgrid, and improves the flexible interaction ability of multiple types of flexible resources to accurately track control instructions, effectively alleviating the risk of control instruction tracking deviation and microgrid power imbalance risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 The figure is a flow chart of the method for coordinated control of microgrid flexibility resources based on multi-granularity feature matching according to the present invention.

[0067] Figure 2 Schematic diagram of the thermal process of the MVR system according to an embodiment of the present invention.

[0068] Figure 3A schematic diagram of quantifying the flexibility resource response deviation according to an embodiment of the present invention.

[0069] Figure 4 Schematic diagram of high-dimensional polyhedron projection according to an embodiment of the present invention.

[0070] Figure 5 This is a diagram illustrating the granularity matching of flexible resources and control requirements in an embodiment of the present invention.

[0071] Figure 6 It is a schematic diagram of the satisfaction of control instructions from the full-cycle perspective of an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0073] A coordinated control method of microgrid flexibility resources based on multi-granularity feature matching, such as Figure 1 As shown, the following steps are included:

[0074] S1. Establish a typical scenario of independent microgrid with heterogeneous micro-sources and establish a model for mining the potential of industrial and mining load regulation in this scenario;

[0075] In this embodiment, the lithium extraction process of salt lake is complex and different. At present, the most commonly used lithium extraction process methods at home and abroad mainly include precipitation, solvent extraction, adsorption, calcination and electrodialysis. Among them, the widely used and relatively mature process flow is precipitation. The basic process flow includes steps such as natural evaporation and crystallization of salt lake, reaction precipitation, solid-liquid separation, heating evaporation concentration, precipitation separation, etc. Among them, the heating evaporation concentration using mechanical vapor recompression (MVR) is the main power consumption stage. Its main thermal process is as follows: Figure 2 As shown. When the salt lake liquid is evaporated (such as Figure 2 After the process 1-2), the secondary steam is generated, and then it enters the steam compressor after removing the entrained droplets through the separator. Figure 2 After the process 2-3'), the enthalpy value increases and the temperature rises, and then it enters the evaporator as heating steam to heat the original liquid, which is the condensation process (such as Figure 2 In process 3-4), the secondary steam is condensed and discharged as clean water. By repeating this cycle, the water in the salt lake raw liquid can be evaporated and removed, achieving the purpose of raw liquid concentration or crystallization.

[0076] The temperature adjustment flexibility of MVR is explored. It is found that there is adjustable space in the temperature change during the process from isobaric evaporation to isobaric condensation. The mathematical model of thermal inertia is established by considering factors such as the specific heat capacity of brine, the brine temperature range that maintains process production unaffected, the heat energy transfer effect between the compression process and the environment, and the load heating efficiency.

[0077] The heat balance equation is:

[0078] (1)

[0079] Where: Represents the heat input to the MVR; Heat consumed in the production process; Represents the amount of heat loss per unit time; Represents the heat transfer coefficient between the outside and the heat storage tank; Represents the surface area of ​​the thermal storage tank in contact with the outside world.

[0080] Power-Temperature Coupling

[0081] (2)

[0082] Where: Represents the quality of brine; represents the specific heat capacity of brine; Represents the temperature change of brine; η Represents the thermal efficiency coefficient of heating.

[0083] Temperature adjustment range:

[0084] (3)

[0085] Where: Represents the temperature of the brine. Represents the upper limit of brine temperature; Represents the lower limit of brine temperature.

[0086] The upper and lower limits of thermal inertia load power regulation are:

[0087] (4)

[0088] Where: Adjustable power for lithium ore load; , The upper and lower power limits can be adjusted for lithium ore load.

[0089] The overall production task of lithium ore load remains unchanged and can be characterized as:

[0090] (5)

[0091] Considering the lithium mine load production adjustment process, there are three operating states including power maintenance state, power increase state and power decrease state, and the states are unique.

[0092] (6)

[0093] Where: , , Characterize the lithium ore load in t The power maintenance state, power increase state and power reduction state at each moment.

[0094] In order to avoid frequent fluctuations in MVR temperature caused by continuous adjustment of lithium ore load and to ensure production stability to the greatest extent, continuous adjustments cannot be made at two adjacent moments, including continuous upward adjustment and continuous downward adjustment. At the same time, the load power after each adjustment must maintain the production temperature at a minimum for a period of time.

[0095] (7)

[0096] Where: To maintain production temperature.

[0097] In order to prevent excessive adjustments from causing damage to MVR related equipment, the maximum number of MVR adjustments is limited to:

[0098] (8)

[0099] Where: It is the upper limit of the number of times within the lithium mine load control cycle.

[0100] Based on the lithium mine load flexibility model established above, multi-type flexibility resources of independent microgrids are constructed, including distributed wind power, distributed photovoltaic, gas turbine, electrochemical energy storage, flexible lithium mine load, transferable residential load, and curtailable residential load. Considering the differences in discrete power changes, minimum time interval of continuous power control, minimum time interval of response instructions, and discrete ramp rate changes of heterogeneous flexibility resources, multi-granularity computing theory is introduced to establish a granular model of heterogeneous flexibility resource response power, response rate, lag time, and response rate.

[0101] The response power granularity quantifies the degree of discrete power change of heterogeneous flexibility resources. When the minimum change in the flexibility resource response power is greater than the minimum power amount of the control command power, it is considered that the power granularity of the flexibility resource is coarse and cannot accurately track the control command. On the contrary, when the power granularity of the flexibility resource is fine, the independent microgrid control command can be accurately tracked. A multi-type and multi-granularity model of independent microgrid flexibility resources is established to help achieve the coordinated and complementary response of coarse-grained flexibility resources and fine-grained flexibility resources, and the multi-granularity flexibility resources jointly complete the accurate tracking of the independent microgrid power balance control needs.

[0102] To this end, this paper sets the power granularity of the control instruction as the benchmark granularity and adopts Δ p It is assumed that the power granularity of the flexible resource is the instruction granularity. α times, the value is α Δ p The actual power of the control command should be its power granularity Δ p The actual power of the thermal load should be an integer multiple of the load granularity. ϖ m Δ p Therefore, the granulated control instructions and the flexibility resource response power are given as follows:

[0103] (9)

[0104] Where: Control instructions for independent microgrids; The power granularity of the independent microgrid control command; for t The ratio of the control instructions and granularity of the independent microgrid in the time period; The response power reported to the independent microgrid dispatch department for flexibility resources; Resources for flexibility m The ratio of response power to flexibility resource granularity; Resources for flexibility m The power granularity ranges from (0, ].

[0105] The response time granularity is an attribute that describes the minimum time interval of continuous power changes of heterogeneous flexible resources. When the time interval of power changes of flexible resources is greater than the time granularity of control instructions, it is considered that the time granularity of flexible resources is coarse and cannot accurately track control instructions; otherwise, the time granularity of flexible resources is fine and can accurately track control instructions. In order to achieve the coordinated and complementary control of flexible resources with different time granularity attributes, the time granularity should be accurately mathematically modeled first. Establishing a time granularity model for flexible resources will help achieve the coordinated and complementary response of coarse time granularity flexible resources and fine time granularity flexible resources, so that flexible resources with multiple time granularities can jointly complete the accurate tracking of the platform control plan.

[0106] (10)

[0107] Where: Data points for regulating command and flexibility resources; is the time series of control instructions; The time granularity of the control instruction is 15min or 60min; Resources for flexibility m The actual running time series; Resources for flexibility m The time granularity coefficient is a positive integer; It is the ratio of the flexibility resource time granularity to the control instruction time granularity, which is a positive real number.

[0108] The lag time granularity is a property that describes the minimum time interval between the initiation of responses of heterogeneous flexible resources.

[0109] (11)

[0110] Where: The delay time of independent microgrid control instructions caused by communication and other factors; It is a flexible resource m Response time; It is a flexible resource m The command receiving time; ϑ is the proportional coefficient of the response time; Resources for flexibility m The lag time granularity coefficient; It is the ratio of the flexibility resource delay time granularity to the control time granularity.

[0111] Response rate granularity:

[0112] (12)

[0113] Where: is the control rate of the independent microgrid control command; The power granularity of the independent microgrid control command; is the ratio of independent microgrid control instructions to granularity; is the response rate of the flexibility resource; Resources for flexibility m The ratio of response rate to flexibility resource granularity; Resources for flexibility m The power granularity.

[0114] The response capacity difference is used to measure the response deviation between the supply-side flexible resources and the demand-side control instructions in terms of response power, response time, lag time, and response rate. Figure 3 shown.

[0115] Quantification of response deviation caused by mismatch between response power and time granularity:

[0116] (13)

[0117] Where: The response deviation is caused by the mismatch between response power and time granularity.

[0118] Combining equations (9) and (10), we can obtain:

[0119] (14)

[0120] Where: η is the proportionality coefficient between response time and its granularity.

[0121] The response deviation caused by the mismatch of lag time granularity is quantified as:

[0122] (15)

[0123] Where: It is the response deviation caused by the mismatch of lag time granularity.

[0124] Combining equations (9) and (11), we can obtain:

[0125] (16)

[0126] The response deviation caused by the mismatch of response rate granularity is quantified as:

[0127] (17)

[0128] Where: The response deviation is caused by the mismatch of response rate granularity; is the time interval corresponding to the response rate deviation.

[0129] Combining equations (9) and (12), we can obtain:

[0130] (18)

[0131] In summary, the response deviation caused by particle size mismatch is:

[0132] (19)

[0133] Where: δ It is the comprehensive response deviation caused by multi-dimensional granularity mismatch.

[0134] S2, establish the flexibility resource objective function and constraints based on the model established in S1;

[0135] In this embodiment, the constraints include: power balance constraint, power generation constraint, lithium mine load regulation potential constraint, residential load flexibility regulation constraint, energy storage constraint and multi-granularity matching constraint, among which:

[0136] Power balance constraints

[0137] (20)

[0138] Where: , , and They are the response powers of gas turbine, wind power, photovoltaic and electrochemical energy storage; Adjustable power for lithium ore load, is the operating power of the lithium ore load; for t The original planned power consumption of loads that can be transferred at any time; for t The response power of the load that can be transferred at any time; for t The original planned power consumption of the load can be reduced at any time; for t The response power of the load can be reduced at any time.

[0139] The power supply side mainly includes distributed wind power, distributed photovoltaic and gas turbine output constraints:

[0140] (twenty one)

[0141] In the formula, is the minimum output power of the wind turbine; is the minimum output power of the photovoltaic generator; is the predicted output power of the wind turbine; is the predicted output power of the photovoltaic generator; is the minimum output coefficient of the gas turbine; is the rated power of the gas turbine; is the ramp rate limit of the gas turbine;

[0142] The potential constraints of lithium ore load regulation are shown in equations (1)-(8).

[0143] Residential load flexibility regulation constraints

[0144] The total power consumption of the transferable load remains unchanged during the dispatch cycle, but the power consumption in each period can be flexibly adjusted within a certain range to meet the following requirements:

[0145] (twenty two)

[0146] Where: and x are the lower and upper potential coefficients of the transferable load.

[0147] The load that can be reduced can be partially or completely reduced according to the abundance of energy supply, and the load that does not require high reliability can meet the following requirements:

[0148] (twenty three)

[0149] Where: To reduce the response potential coefficient; A 0-1 state variable to determine whether load shedding occurs; To reduce the upper limit of the number of times.

[0150] The energy storage constraint is expressed as:

[0151] (twenty four)

[0152] Where: , is the energy storage rated power and rated capacity, is the energy storage serial number, is the time granularity, Charge and discharge for energy storage 0- t Any moment in time.

[0153] Multi-granularity matching constraints, as shown in (31) and (32)

[0154] S3. Establish a microgrid control priority model based on the flexibility resource objective function and constraints and determine the response sequence of the flexibility resources;

[0155] In this embodiment, the following steps are specifically included:

[0156] S31. Establish a multi-dimensional and multi-granular attribute model of flexibility resources and control instructions in terms of response power, response rate, lag time, and response time, and calculate the response deviation of multiple types of flexibility resources and the response deviation of control instructions through response capacity. The calculation method is as shown in formulas (13)-(19).

[0157] S32, projecting the high-dimensional feasible domain of multiple types of flexible resources onto the plane of control instructions to calculate the control priority;

[0158] Considering the above differentiated characteristics, including capacity, rate, time, potential and cost characteristics, different probability combinations of multi-type flexibility resources construct feasible regions Ƥ with different regulation capabilities, e.g. Figure 4 As shown, it can be expressed as:

[0159] (twenty three)

[0160] In the formula, The flexible resource regulation capability reflected by the above characteristics can constitute the feasible region of independent microgrids, which is mathematically represented as a high-dimensional polyhedron At the same time, the feasible domain is projected onto the control instruction plane V On the left, it is transformed into a homogeneous dimension space F , the goal is to completely overlap. In general, the probability combination of flexibility resources that meet the independent microgrid regulation needs may lead to response deviations. Obviously, the difference in feasible areas represents the characteristic space F The response deviation between flexible resources and scheduling instructions.

[0161] Introducing 0-1 variables It is used to characterize the matching of multiple types of flexibility resources and independent microgrid control instructions, specifically:

[0162] (twenty four)

[0163] Where: Representation flexibility resources exist t The control time matches the control instruction. Representation flexibility resources exist t The control time does not match the control instruction.

[0164] S33, the combination scheme of multiple types of flexible resources is equivalent to the dependent variable in the discrete selection model, the corresponding power, response time, corresponding rate, subsequent time and random items are equivalent to the indicator variables in the discrete selection model, and the constructed discrete selection model is used to solve the optimal matching of flexible resources to obtain a multi-type flexible resource matching model;

[0165] In econometrics, discrete choice models (DCMs) are often used to simulate the choice orientation of decision makers. Similarly, independent microgrid control instructions select different combinations of flexibility resources based on characteristic information in discrete and discontinuous sets. According to DCM, the selection behavior of independent microgrids on flexibility resources is reflected by dependent variables and indicator variables. In this study, we choose the Logit model, the dependent variable is the flexibility resource combination plan, and the indicator variable represents the comprehensive utility of the plan (including response power, response time, response rate, lag time and random terms).

[0166] In the discrete choice model, the indicator variables of flexibility resources and microgrid control instructions can be expressed as follows:

[0167] (25)

[0168] in, is the comprehensive utility value in DCM; is the deterministic utility value; is a random disturbance term.

[0169] The deterministic utility value is calculated from the response deviation caused by the multi-dimensional granularity mismatch between the flexibility resources and the control instructions mentioned above:

[0170] (26)

[0171] According to the utility maximization principle of DCM, the dispatch center selects the most matching flexible resource. Therefore, the problem of selecting flexible resources is transformed into a probability problem. Assuming that the flexible resource m The matching degree is higher than that of flexible resources h , then select flexibility resources m The probability is:

[0172] (27)

[0173] in, For t Always choose flexible resources m probability.

[0174] Since the Logit model satisfies: 1) Rational decision makers will choose the option that maximizes their utility. 2) The utility random items are independent of each other and follow the extreme value distribution. Its probability distribution function for

[0175] (28)

[0176] Therefore, the selection probability of the flexibility resource combination is converted into the probability distribution of the random disturbance term, and combined with (27) and (28), the matching flexibility resource can be obtained:m Probability:

[0177] (29)

[0178] Substituting (25) into (29), we can obtain For:

[0179] (30)

[0180] Finally, combined with formula (21), the flexibility resource matching model can be described as follows:

[0181] (31)

[0182] Where: Indicates that the control time t Time flexibility resources m Matching control instructions.

[0183] Based on this, the optimal flexibility resources for the initial matching round within the full cycle time period are obtained. At the same time, the microgrid control command gradually decreases due to the flexible resource response power, and this cycle repeats until the microgrid control command is 0, thus completing the optimal scheduling of the heterogeneous micro-source independent microgrid considering the granularity attribute.

[0184] (32)

[0185] Where: , For microgrid , Matching rounds of regulation instructions; For the Round Optimal Flexibility Resources m The response power.

[0186] S34. Perform multiple rounds of matching based on the condition of maximum utility, match at most one type of flexible resource at each moment in each round, and correspondingly reduce the adjustment boundary and control instruction of the flexible resource in the next round until the adjustment boundary of the flexible resource is reached or the control instruction is 0, and end the matching to obtain a flexible resource response sequence.

[0187] like Figure 5As shown in the figure, all types of flexible resources (FR1-FR7) and regulation requirements are granulated, which is to say that they are equally divided like particles. The resolution of peak-shaving requirements is 15 minutes, and the scheduling cycle is 24 hours. In matching round 1, each type of flexible resource and the regulation instructions at each scheduling moment are granularly matched based on the optimal matching criterion. The instructions at each scheduling moment in this matching round are matched with at most one type of flexible resource. After completing matching round 1, the particles of the optimally matched flexible resource will be reduced by 1, and the regulation instructions will be filled with the corresponding particles, that is, the regulation instructions will also be reduced. After entering matching round 2, the regulation boundary and regulation instruction requirements of the flexible resource are updated, and the granular matching continues to be based on the optimal matching criterion. At this point, the regulation boundary of FR reaches the upper limit or the regulation requirement = 0. Then, in matching round G, the response sequence of all flexible resources that meet the regulation instructions will eventually be obtained.

[0188] like Figure 6 As shown in the figure, we can see how the control instructions are met step by step from the perspective of the whole cycle. When the matching round g=0, it is the initial control instruction. When the matching round g=1, through the optimal granularity matching criterion, the flexibility resource particles at different times are matched with the control instruction particles, then the control instructions are partially filled, and its control instructions must be reduced. When the matching round g=2, the control instructions are still partially filled after matching through this round. Finally, when the adjustment boundary of FR reaches the upper limit or the control demand = 0, the final response sequence is formed.

[0189] S4. Calculate evaluation indicators sequentially based on the responses of the determined flexibility resources.

[0190] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0191] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0192] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0193] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0194] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for coordinated control of microgrid flexibility resources based on multi-granularity feature matching, characterized in that: The steps include: S1. Establish a typical scenario of independent microgrid with heterogeneous micro-sources and establish a model for mining the potential of industrial and mining load regulation in this scenario; S2, establish the flexibility resource objective function and constraints based on the model established in S1; S3. Determine the response sequence of the flexibility resources according to the flexibility resource objective function and constraints. The specific method is as follows: S31. Establish a multi-dimensional and multi-granular attribute model of flexibility resources and control instructions in terms of response power, response rate, lag time, and response time, and calculate the response deviation of multiple types of flexibility resources and the response deviation of control instructions through response capacity: S32, projecting the high-dimensional feasible domain of multiple types of flexible resources onto the plane of control instructions to calculate the control priority; S33, the combination scheme of multiple types of flexible resources is equivalent to the dependent variable in the discrete selection model, the corresponding power, response time, corresponding rate, subsequent time and random items are equivalent to the indicator variables in the discrete selection model, and the constructed discrete selection model is used to solve the optimal matching of flexible resources to obtain a multi-type flexible resource matching model; S34, performing multiple rounds of matching based on the maximum utility condition, matching at most one type of flexible resource at each time in each round, and correspondingly reducing the adjustment boundary and control instruction of the flexible resource in the next round until the flexible resource control boundary is reached or the control instruction is 0, and the matching is terminated to obtain a flexible resource response sequence; S4. Calculate evaluation indicators based on the determined response sequence of the flexibility resources.

2. According to the method of claim 1, the method is characterized in that: The objective function in S2 is expressed as: in, C The comprehensive operating cost of independent microgrids with heterogeneous micro-sources; is the response cost of multiple types of flexibility resources and: In the formula, Resources for flexibility The unit response cost is The response power reported to the independent microgrid dispatching department for flexibility resources, For time; C WL is the cost of the flexibility resource's willingness to respond and: In the formula, The unit cost of response deviation caused by the mismatch between the response power and time granularity of flexible resources; The response deviation is caused by the mismatch between response power and time granularity; is the unit cost of response deviation caused by mismatch of lag time granularity, The response deviation is caused by the mismatch of lag time granularity; is the unit cost of response deviation caused by response rate granularity mismatch, It is the response deviation caused by the mismatch of the hysteresis response rate strength; Provides microgrid regulation benefits and: In the formula, Adjusting benefit costs for units; F RD ( t ) is the system unbalanced power to be assessed; Reduced system unbalanced power for independent microgrids; To adjust the benchmark ratio for the segment; is the initial unbalanced power of the system.

3. According to the method of claim 1, the method is characterized in that: The constraints in S2 include: power balance constraints, power generation constraints, lithium mine load regulation potential constraints, residential load flexibility regulation constraints, energy storage constraints, and multi-granularity matching constraints, among which: The power balance constraint is expressed as: In the formula, , , and They are the response powers of gas turbine, wind power, photovoltaic and electrochemical energy storage; Adjustable power for lithium ore load, is the operating power of the lithium ore load; for t The original planned power consumption of loads that can be transferred at any time; for t The response power of the load that can be transferred at any time; for t The original planned power consumption of the load can be reduced at any time; for t The response power of load can be reduced at any time; The power generation constraint is expressed as: In the formula, is the minimum output power of the wind turbine; is the minimum output power of the photovoltaic generator; is the predicted output power of the wind turbine; is the predicted output power of the photovoltaic generator; is the minimum output coefficient of the gas turbine; is the rated power of the gas turbine; is the ramp rate limit of the gas turbine; The flexibility regulation constraint of residential load is expressed as: In the formula, and are the lower and upper potential coefficients of the transferable load The energy storage constraint is expressed as: In the formula, , is the energy storage rated power and rated capacity, is the energy storage serial number, is the time granularity, Charge and discharge for energy storage 0- t Any moment in time.

4. According to claim 3, a method for coordinated control of microgrid flexibility resources based on multi-granularity feature matching is characterized in that: The lithium ore load regulation potential constraints include: The heat balance equation is expressed as: In the formula, is the heat input to MVR; Δ H Heat consumed in the production process; is the amount of heat loss per unit time; is the heat transfer coefficient between the outside and the heat storage tank; It is the surface area of ​​the heat storage tank in contact with the outside world; Power-temperature coupling, expressed as: Where: Represents the quality of brine; represents the specific heat capacity of brine; Represents brine in and The temperature of the moment; η represents the thermal efficiency coefficient of heating, for t The temperature value at the moment, Adjustable power for lithium ore load; Temperature adjustment range, expressed as: In the formula, T lu,t Represents the temperature of the brine; T max Represents the upper limit of brine temperature; T min Represents the lower limit of brine temperature; Thermal inertia load adjusts the upper and lower limits of power, expressed as: In the formula, , The upper and lower limits of the power can be adjusted for lithium ore load. is the unit time step.

5. According to claim 1, a method for coordinated control of microgrid flexibility resources based on multi-granularity feature matching is characterized in that: The response deviation of the multi-type flexibility resources in S31 is expressed as: In the formula, is the response bias of multiple types of flexibility resources, is the proportionality coefficient between the response time and its granularity, is the power granularity of the independent microgrid control command, is the time granularity, Resources for flexibility The time granularity coefficient, for t The ratio of the control instructions and granularity of the independent microgrid in the time period, Resources for flexibility The ratio of response power to flexibility resource granularity, Resources for flexibility Power granularity; is the response deviation caused by the mismatch of lag time granularity, is the proportionality factor of the response time; is the response rate deviation, is the delay time of independent microgrid control instructions caused by communication factors, The response power reported to the independent microgrid dispatching department for flexibility resources, is the response rate of the flexibility resource, These are the delay times for stages 1, 2, and 3 respectively.

6. According to claim 1, a method for coordinated control of microgrid flexibility resources based on multi-granularity feature matching is characterized in that: The flexibility resource matching model in S33 is expressed as: In the formula, Indicates that the control time t Time flexibility resources m Matching control instructions, for Flexibility resources for moment-to-moment selection The probability of is the total number of flexibility resources, Resources for flexibility The deterministic utility value.

7. According to claim 1, a method for coordinated control of microgrid flexibility resources based on multi-granularity feature matching is characterized in that: The control instruction in S34 is expressed as: In the formula, , For microgrid , Matching rounds of regulation instructions; For the Round Optimal Flexibility Resources The response power.

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