Hybrid micro-grid optimal configuration method based on price type demand side response

Abstract

The invention discloses a hybrid micro-grid optimal configuration method based on price-type demand side response, and the method comprises the steps: S1, building a simulation hybrid micro-grid model; s2, inputting environmental parameters in the real hybrid micro-grid system, the mathematical model of the micro-source, the operation strategy and the initial load data into the simulation hybrid micro-grid model; S3, initializing environmental parameters of the simulation hybrid micro-grid model and the capacity of the micro-source; s4, inputting the set peak-time electricity price value and the valley-time electricity price value, and judging the time period of the electricity price at the current moment; s5, performing demand side response processing based on the initial load data and the time period of the electricity price to obtain a load after demand side response; s6, establishing a commutation loss target between the buses; and S7, optimizing a demand side response optimization target and a commutation loss target between the buses, repeating the S7 and performing optimization, and outputting a minimum target value obtained by optimization and a corresponding optimization configuration result. According to the invention, hybrid micro-grid optimal configuration based on price-type demand side response can be realized.

Description

Technical Field

[0001] The present invention relates to the field of capacity optimization configuration, and particularly to an optimization configuration method for a hybrid microgrid based on price-based demand-side response. Background Art

[0002] With the continuous development of the economy, the demand for energy is also increasing day by day. The shortage of traditional energy and the resulting environmental problems have made the development of new energy a consensus among countries and an inevitable trend of development. As a new energy production and utilization system, the development of microgrids will play an important role in realizing the development of new energy and building a new power system. In the optimization of the microgrid system capacity, making full use of the relationship between the power generation side, energy storage side, power transmission side and power consumption side can further improve the economy and stability of the microgrid and promote the consumption of renewable energy. Summary of the Invention

[0003] The purpose of the present invention is to provide an optimization configuration method for a hybrid microgrid based on price-based demand-side response, aiming to solve the problem of the optimization configuration of a hybrid microgrid based on price-based demand-side response.

[0004] The present invention provides an optimization configuration method for a hybrid microgrid based on price-based demand-side response, including:

[0005] S1. Establish a simulation hybrid microgrid model;

[0006] S2. Input the environmental parameters, mathematical models of micro-sources, operation strategies and initial load data in the actual hybrid microgrid system into the simulation hybrid microgrid model. The micro-sources include: wind power AC generator sets, photovoltaic DC generator sets, storage batteries and hydrogen energy storage systems;

[0007] S3. Initialize the environmental parameters and capacities of the micro-sources in the simulation hybrid microgrid model, and input the relevant constraint conditions of the simulation hybrid microgrid model to obtain the simulation hybrid microgrid model to be optimized;

[0008] S4. Input the set peak-time electricity price and valley-time electricity price values, and judge the time period in which the current electricity price is located;

[0009] S5. Perform demand-side response processing based on the initial load data and the time period in which the electricity price is located to obtain the load after demand-side response;

[0010] S6. Calculate the demand-side response optimization target based on the load after demand-side response, and establish the commutation loss target between buses. The commutation loss target between buses is positively correlated with the demand-side response optimization target;

[0011] S7. Optimize the optimization objective of the demand-side response and the commutation loss target between the buses based on the optimization requirements of the to-be-optimized simulation hybrid microgrid model. Repeat S7 and perform optimization, and output the minimum objective value obtained through optimization and the corresponding optimized configuration results.

[0012] By adopting the embodiment of the present invention, the present invention solves the problem of optimizing the capacity of the microgrid system based on price-based demand-side response. Implement a real-time price-based demand response mechanism on the user side. According to the difference between the electricity consumption on the user side and the power output on the power generation side, the electricity price is divided into peak-time electricity price and valley-time electricity price. Introduce a real-time electricity price substitution elasticity coefficient to characterize the relationship between the load on the user side and the peak-valley electricity price. Introducing real-time price-based demand-side response can more effectively reduce the configuration capacity of wind turbines and photovoltaic cells, and reduce the charge-discharge frequency and discharge depth of energy storage devices, improve the overall economy of the microgrid system, make the electricity consumption trends of users on the AC side and DC side more consistent with the output curves of each distributed power generation subsystem, improve energy utilization efficiency, reduce the commutation value between the AC and DC buses, and further reduce the commutation loss value between the buses in the AC-DC hybrid microgrid.

[0013] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, implement it in accordance with the content of the specification, and in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically describes the specific embodiments of the present invention. Description of the Drawings

[0014] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a flowchart of the optimization configuration method for a hybrid microgrid based on price-based demand-side response according to an embodiment of the present invention.

[0016] Figure 2 It is a schematic diagram of an AC-DC hybrid microgrid system in the prior art. Detailed Embodiments

[0017] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0018] Method Embodiment

[0019] According to an embodiment of the present invention, an optimization configuration method for a hybrid microgrid based on price-based demand-side response is provided. Figure 1 It is a flowchart of the optimization configuration method for a hybrid microgrid based on price-based demand-side response according to an embodiment of the present invention, as Figure 1 shown, and specifically includes:

[0020] S1. Establish a simulation hybrid microgrid model;

[0021] In the embodiment of the present invention, a Figure 2 microgrid system is used to establish a simulation hybrid microgrid model;

[0022] As Figure 2 shown, it specifically includes:

[0023] AC bus, DC bus, AC load, wind power AC generator set, DC subnetwork, and photovoltaic DC generator set;

[0024] The DC subnetwork includes: DC load and energy storage system;

[0025] The energy storage system includes: hydrogen energy storage system and battery energy storage system;

[0026] The hydrogen energy storage system includes: electrolyzer, hydrogen storage tank, and fuel cell. The electrolyzer is connected to the hydrogen storage tank, the hydrogen storage tank is connected to the fuel cell, and the electrolyzer and the fuel cell are respectively connected to the DC bus;

[0027] The photovoltaic DC generator set, transformer, and battery energy storage system are connected to the DC bus, and the wind power AC generator set is connected to the AC bus.

[0028] The total load of the hybrid microgrid system includes: energy storage load and AC-DC load on the user side;

[0029] The system further includes: a bidirectional AC-DC converter, which is used to input the excess power on the AC side into the DC subnetwork when the output power of the wind power AC generator set is greater than the power demand of the AC load, and input the power of the DC subnetwork into the AC subnetwork to supplement the power difference on the AC side when the output power of the wind power AC generator set is less than the power demand of the AC load on the user side.

[0030] S2. Input the environmental parameters, mathematical models of micro-sources, operation strategies, and initial load data in the hybrid microgrid system into the simulation hybrid microgrid model. The micro-sources include: wind power AC generator set, photovoltaic DC generator set, battery, and hydrogen energy storage system;

[0031] The environmental parameters include: meteorological data.

[0032] The mathematical models of the micro-sources include: photovoltaic DC generator set model W pv ,

[0033] W pv = P pv × C pv × Δt;

[0034] In the formula: W pv is the sum of the electricity generated by the photovoltaic units per hour, kW·h; C pv is the capacity of the photovoltaic units, kW;

[0035]

[0036] P pv : represents the output power of the photovoltaic cell, usually in watts (W), which reflects the actual electrical energy output by the photovoltaic cell under specific conditions.

[0037] P ref = I ref V ref ;

[0038] K0: is the temperature coefficient, which reflects the characteristic of the output power of the photovoltaic cell changing with temperature, usually in per degree Celsius.

[0039] T: represents the actual working temperature of the photovoltaic cell, in degrees Celsius.

[0040] The actual working of the battery panel can be obtained from the ambient temperature of the battery. The specific expression is:

[0041] T = T E,t + 0.0138(1 + 0.031T E,t )(1 - 0.042v w,t )G t ;

[0042] In the formula: T E,t is the current ambient temperature, °C; V w,t is the ambient wind speed, m / s; G t is the light intensity at time t, W / m 2 ;

[0043] In the formula: P ref is the rated output power of the photovoltaic cell under standard test conditions; V ref is the output voltage of the photovoltaic cell under standard test conditions, I ref is the output current of the photovoltaic cell under standard test conditions, T ref is the standard working temperature of the photovoltaic cell, 25 °C; G is the actual light intensity, W / m 2 ; G ref is the reference light intensity;

[0044] The model formula of the wind power AC generator set is as follows:

[0045] W W = P W × C W × Δt;

[0046] Where: W w is the sum of the electricity generated by the wind turbine per hour, kW·h; C w is the capacity of the wind turbine in kW, P W is the output power of a single wind turbine at this wind speed;

[0047]

[0048] Where: v and v0 are the wind speeds at heights H and H0 respectively, with the units being m / s; q is related to the ground correction index and the ground roughness, and the value is 0.2;

[0049] Due to the randomness of the wind speed, the study of the wind speed mainly focuses on the two-parameter Weibull distribution. The Weibull distribution describes the probability density distribution of the wind speed, and its mathematical expression is:

[0050]

[0051] Where: k is the shape parameter; c is the scale parameter, and the calculation formulas for each parameter are:

[0052]

[0053] Where: σ is the standard deviation; N is the number of observed wind speed times; v i is the wind speed at the i-th moment, m / s; г is the gamma function;

[0054] The specific expression of the output power function of the wind turbine is:

[0055]

[0056] Among them,

[0057] Where: P W is the output power of a single wind turbine at this wind speed, kW; P r is the rated output power of the wind turbine, with a value of 10 kW; v ci is the cut-in wind speed of the wind turbine, with a value of 3 m / s; v r is the rated wind speed of the wind turbine, with a value of 6.5 m / s; v co is the cut-out wind speed, with a value of 20 m / s.

[0058] Battery and hydrogen energy storage system;

[0059] The battery parameters of a storage battery mainly include state of charge, rated capacity, depth of discharge, and number of cycles, etc. When the storage battery is operating, the discharging effect is mainly affected by temperature, and the main change formula is:

[0060] E bat (t) = E bat,STC [1 + δ B (T bat (t) - T bat,STC )];

[0061] In the formula: E bat,STC is the rated capacity under standard test conditions, kW·h; T bat (t) is the actual temperature of the battery at time t, °C; δ B is the capacity temperature coefficient; T bat,STC is the standard condition temperature, 25°C.

[0062] According to the law of conservation of energy, the energy during the charge and discharge of the storage battery can be expressed by the following two formulas. When the storage battery is charging:

[0063] E bat (t) = E bat (t - 1)(1 - δ) + P ch (t - 1)η ch η bat_dc-dc Δt;

[0064] When the storage battery is discharging:

[0065]

[0066] In the formula: E bat (t) is the energy stored in the storage battery at time t, with the unit of kW·h; δ is the self-discharge rate of the storage battery, taking 0.04 / day; P dh is the charging power of the storage battery; P ch is the discharging power of the storage battery; η ch is the charging efficiency of the storage battery; η dh is the discharging efficiency of the storage battery; η bat_dc-dc is the converter efficiency of the storage battery, with a value of 0.9; Δt is the time step, which is taken as 1h in this article.

[0067] The maximum charge and discharge power model of the storage battery is:

[0068] P chmax (t) = max{0, SOC max E bat - E bat (t)}μ ch ;

[0069]

[0070] Where: P chmax is the maximum charging power of the battery, in kW; P dhmax is the maximum discharging power of the battery.

[0071] The state of charge (SOC) of the battery is one of the important parameters of the battery, which represents the ratio of the remaining power to the rated capacity after the battery has been working or left unused for a long time. The value range is 0.0 to 1.0, and its mathematical model is as follows:

[0072]

[0073] Where: E STC is the rated capacity of the battery under standard test conditions.

[0074] The selected mathematical model of the hydrogen storage tank energy storage can be expressed as:

[0075]

[0076] Where: E Hs (t) is the energy stored in the hydrogen storage tank at time t, in kW·h; η Eb_dc-dc , η Fc_dc-dc are the working efficiencies of the electrolyzer and fuel cell converters respectively, taking 0.9.

[0077] The maximum output powers of the electrolyzer and fuel cell can be expressed by the following two formulas respectively:

[0078]

[0079] Where: C Eb is the capacity of the electrolyzer, in kW; C Fc is the capacity of the fuel cell in kW; E Hsmax is the maximum energy storage capacity of the hydrogen storage tank; E Hsmin is the minimum energy storage capacity of the hydrogen storage tank.

[0080] The equivalent state of hydrogen charge (SOHC) of the hydrogen storage tank, whose parameter meaning is similar to that of the battery, is used to characterize the ratio of the remaining capacity to the initial rated capacity when the hydrogen storage tank is in operation or at rest. The value range is 0.0 to 1.0, and its mathematical model is:

[0081]

[0082] The operation strategy of the hybrid microgrid is as follows:

[0083] S1C. Obtain one-year meteorological data and calculate the hourly average output powers of the wind power AC generator set and the photovoltaic DC generator set based on the one-year meteorological data;

[0084] The total power generation of the wind power system can be expressed as:

[0085] W W = P W × C W × Δt

[0086] Where: W w is the sum of the power generated by the wind turbines per hour, kW·h; C w is the capacity of the wind turbines, kW.

[0087] The overall output power of the photovoltaic power generation system can be expressed as:

[0088] W pv = P pv × C pv × Δt

[0089] Where: W pv is the sum of the power generated by the photovoltaic units per hour, kW·h; C pv is the capacity of the photovoltaic units, kW.

[0090] Combined with the annual meteorological data, calculate the total power generated by the wind power system and the photovoltaic power generation system, and divide the total power by 8760 hours to obtain the hourly average output power of the wind power AC generator set and the photovoltaic DC generator set.

[0091] S2C. Calculate the hourly source-load power difference based on the hourly average output power of the wind power AC generator set and the photovoltaic DC generator set;

[0092] The specific S2C includes: subtracting the load from the hourly average output power of the wind power AC generator set and the photovoltaic DC generator set to obtain the hourly source-load power difference.

[0093] S3C. Perform energy control based on the hourly source-load power difference.

[0094] S3C. Initialize the environmental parameters and the capacity of the micro-sources in the microgrid system, and input the relevant constraints of the simulation hybrid microgrid model to obtain the simulation hybrid microgrid model to be optimized;

[0095] The relevant constraints of the simulation hybrid microgrid model specifically include: the constraints of the hybrid microgrid mathematical model and the demand-side response constraints.

[0096] Distributed power generation output and power balance constraints

[0097] In the AC-DC hybrid microgrid, the output power of each micro-source has upper and lower limits. Therefore, the output constraint expression for each power source is:

[0098]

[0099] Where: P pv (t) represents the output power of the photovoltaic, P w (t) represents the output power of the wind turbine, and respectively represent the rated powers of the fixed-capacity photovoltaic and wind turbine, in kW.

[0100] In the AC-DC hybrid microgrid, since the AC-side power source directly supplies power to the AC load and the DC-side power source directly supplies power to the DC load, there needs to be a power state balance constraint between each micro-source and the load in the microgrid system. Among them, energy transfer is carried out between the AC and DC buses to maintain the power balance of the AC sub-network and the DC sub-network. The specific constraint conditions are as follows:

[0101]

[0102] Where: P ac 、P dc respectively are the balanced powers on the AC side and the DC side; P ac load 、P dc load respectively are the AC and DC load powers; P ac / dc is the commutation power between the AC and DC buses; P ch 、P dh respectively are the charge and discharge powers of the HESS (hybrid microgrid system); P sp 、Pt ep respectively are the power purchase and power selling powers to the large power grid, in kW.

[0103] Capacity constraints of each micro-source in the system

[0104] In this paper, the configured capacities of each device such as the fan, photovoltaic, battery, and hydrogen energy storage system are regarded as the production to be optimized in the system. Considering the limitations of various conditions in actual projects, the capacity needs to be constrained. Considering the influence of the discharge depth of the battery and the hydrogen storage tank on the equipment, a certain reserve capacity is left when configuring the capacity of the energy storage device. The installed capacity range constraints of each distributed power source and energy storage device in the microgrid are as follows:

[0105]

[0106] Where: respectively are the maximum capacities of each device, unit, kW·h.

[0107] Charge and discharge constraints of the multi-level hybrid energy storage system;

[0108] (1) Battery charge and discharge constraints

[0109] The service life of the battery is strongly related to two factors: charge-discharge power and charge-discharge depth. Therefore, in order to increase the service life of the battery, reduce the system investment and operation and maintenance costs, it is necessary to constrain the discharge depth and the magnitude of the charge-discharge power of the battery as follows:

[0110]

[0111] In the formula: SOC(t) represents the remaining power of the battery at time t; SOC max represents the maximum value of the battery's SOC; SOC min represents the minimum value of the battery's SOC; P bat,c(t) represents the charging power at time t; P max bat,c represents the maximum charging power; P min bat,c represents the minimum charging power; P bat,d(t) represents the discharge power at time t, kW; P max bat,d represents the maximum discharge power, kW; P min bat,d represents the minimum discharge power.

[0112] (2) Constraints of the hydrogen energy storage system

[0113] To ensure the safety and stability of the hydrogen storage system, it is necessary to constrain the maximum charge equivalent state of the hydrogen storage tank. If the internal pressure of the hydrogen storage tank is too high, there will be potential safety hazards. The relevant constraint conditions of the hydrogen energy storage system are as follows:

[0114]

[0115] In the formula: SOHC min 、SOHC max are the maximum and minimum values of the equivalent charge state of the hydrogen storage tank respectively; are the maximum charging power of the electrolyzer and the minimum discharge power of the fuel cell respectively, with the unit

[0116] kW·h; are the maximum charging power of the electrolyzer and the maximum discharge power of the fuel cell respectively, kW·h.

[0117] Reliability constraint for the independent power supply of the microgrid system:

[0118] The ultimate goal of the microgrid system is to achieve self-consumption and self-governance. Therefore, a high requirement for the power supply reliability of the entire system is needed. In this paper, power supply reliability is taken as one of the main constraints for microgrid capacity configuration. The ratio of the difference between the total load demand and the distributed power generation output power during the annual operation of the microgrid to the distributed power generation output power is defined as the Load Power Shortage Rate (LPSR). The specific expression and constraint conditions are as follows:

[0119]

[0120] In the formula: P load is the annual total load in the microgrid system, kW·h; PDG is the total annual distributed power generation output power in the microgrid system, kW·h. To ensure a high power supply reliability of the microgrid, LPSR max is taken as 10%.

[0121] Constraint conditions of the demand-side response model:

[0122] Total load transfer constraint

[0123] The total load transferred out and the total load transferred in during the demand response of the microgrid system in one year are equal and unchanged. The mathematical model is:

[0124]

[0125] In the formula: L(t) is the annual load before the demand-side response; L DR (t) is the annual load after the demand-side response, kW·h.

[0126] According to the above formula, there will be a certain difference between the load after the demand response and the load before the demand response. In this paper, the last 24 hours of the microgrid operation are used to balance the load difference so that the total load of the response remains unchanged. It is assumed in this paper that the total load increment after the demand response in the first 8737 hours compared with that before the demand response is L add , then the demand response load in the last 24 hours is not calculated using the above formula. Its mathematical model is:

[0127]

[0128] User electricity purchase cost constraint

[0129] The purpose of the demand response is to stimulate users to participate in the response. The ultimate goal is to enable both users and the power grid to obtain certain benefits. Therefore, the electricity purchase cost of users after participating in the demand response is lower than that before the demand response. The mathematical model is:

[0130] M in(after) < (1 - α)M in(before) ;

[0131] Min(after) is the total electricity cost of the user after participating in demand response within one year, with the unit of 10,000 yuan; Min(before) is the electricity cost of the user before participating in demand response within one year; α is the participation coefficient of the user.

[0132] Electricity price constraint

[0133] The electricity price during peak hours should be higher than that during off-peak hours, that is:

[0134] p p ≥ p ≥ p v ;

[0135] In the formula: p p is the electricity price during peak hours; p v is the electricity price during off-peak hours; p is the electricity price before implementing demand-side response, with the unit of yuan / kW·h.

[0136] Load transfer amount constraint

[0137] The load amount when the user participates in demand-side response should not be greater than the maximum transferable amount of the current period load, that is:

[0138]

[0139] In the formula: is the maximum transferable load amount of the current period; T load is the load transfer amount of the current period, kW·h.

[0140] S4. Input the set peak-time electricity price and valley-time electricity price values, and judge the time period where the current electricity price is located;

[0141] The specific method for judging the time period where the current electricity price is located includes: subtracting the load from the hourly average output power of the wind power AC generator set and the photovoltaic DC generator set to obtain the hourly source-load power difference. When the hourly source-load power difference is greater than or equal to 0, it is the valley-time electricity price; when the source-load power difference is less than 0, it is the peak-time electricity price.

[0142] S5. Perform demand-side response processing based on the initial load data and the time period where the electricity price is located to obtain the load after demand-side response;

[0143] The specific content of S5 includes:

[0144] Use CFSE to represent the ratio of the electricity consumption of the user during peak and valley hours to the corresponding electricity price. The CFSE calculation formula is as shown in the formula:

[0145]

[0146] In the formula: p a 、p bThey are the electricity prices corresponding to time periods a and b respectively, in yuan; P load,a and P load,b are the demand powers on the load side corresponding to time periods a and b respectively, in kW·h, and d represents differentiation;

[0147] When performing capacity allocation, CFSE is calculated according to the load quantity and the set peak-valley electricity prices; when a given CFSE is provided, the reduction in peak-hour load and the increase in valley-hour load under the implementation of real-time electricity prices are inversely calculated, that is, the magnitude of the elastic load value. The specific formula is as follows:

[0148]

[0149] In the formula: ΔEL p and ΔEL v are the loads transferred under peak-valley electricity prices respectively, and R V and R P are the proportions of the electricity consumption costs during peak-hour and valley-hour electricity price periods in the total daily electricity consumption cost respectively, and p i is the electricity price before the implementation of real-time electricity price response, and P load,v and P load,p are the demand powers on the load side corresponding to peak-hour and valley-hour respectively, in kW·h;

[0150] The load quantity after demand-side response within any time period is expressed as:

[0151]

[0152] In the formula: P load (k) is the initial load data at time k.

[0153] S6. Calculate the demand-side response optimization target based on the load after demand-side response, and establish the commutation loss target between buses. The commutation loss target between buses is positively correlated with the demand-side response optimization target;

[0154] S6 specifically includes:

[0155] Establish the demand-side response optimization target D, and D represents the difference between the power generated by distributed power sources in the system and the demand power on the load side. The specific expression is:

[0156]

[0157] In the formula: P pv (t), P w (t) and P load,DR (t) are the output powers of wind turbines, photovoltaic panels and the load quantity after demand-side response within time period t respectively, and Z is the number of hours;

[0158] Establish the commutation loss target between buses, and the calculation formula is:

[0159] P t-loss = (1 - η con )P ac / dc ;

[0160] Where: P ac / dc is the commutation power between the busbars, in kW; η con is the commutation efficiency of the converter.

[0161] Energy transfer is carried out between the AC and DC busbars to maintain the power balance between the AC sub-network and the DC sub-network. The specific constraint conditions are as shown in the formula:

[0162]

[0163] Where: P ac , P dc are the balance powers on the AC side and the DC side respectively; are the AC and DC load powers respectively; P ac / dc is the commutation power between the AC and DC busbars; P ch , P dh are the charging and discharging powers of the hybrid microgrid system respectively; P sp , P tep are the power purchase and power selling powers to the large power grid respectively, in kW.

[0164] S7. Based on the optimization requirements of the demand-side response optimization objective and the commutation loss objective between the busbars of the hybrid microgrid model to be optimized, repeat S7 and perform optimization, and output the minimum objective value obtained by optimization and the corresponding optimized configuration result.

[0165] Specifically, S7 includes: improving the particle swarm optimization algorithm based on the game and chaotic mapping mechanism, repeating S7 and performing optimization, outputting the minimum demand-side response optimization objective and the minimum commutation loss objective between the busbars obtained by optimization, obtaining the maximum energy storage capacity and the maximum converter capacity respectively based on the minimum demand-side response optimization objective and the minimum commutation loss objective between the busbars. The minimum demand-side response optimization objective represents the minimum required capacity of the energy storage, and the optimal energy storage capacity configuration is obtained. The minimum commutation loss objective between the busbars represents the minimum commutation loss, so the maximum commutation capacity is obtained;

[0166] In the embodiment of the present invention, the algorithm implementation of S7 is specifically as follows:

[0167] (1) According to the hybrid microgrid model to be optimized for simulation, initialize the variables to be optimized in the demand-side response optimization objective and the commutation loss objective, and randomly generate the positions and velocities of the particles, where each particle represents a set of solutions; input the particles into the demand-side response optimization objective and the commutation loss objective;

[0168] (2) Obtain the fitness values of each objective, and update the individual optimal solution and the global optimal solution.

[0169] (3) Perform particle non-dominant sorting and crowding distance stratification selection. Select two elite particles a and b to play a game with the non-elite particle x.

[0170] (4) Replace the non-elite particles in the original population with the particles that succeed in the game and the particles that fail in the game and perform re-learning, and complete the reconstruction of the new elite particle set.

[0171] (5) Through updating the chaotic mapping formula, perform chaotic search on the global optimal solution, compare the dominance relationship between the chaotic search particles and the original particles, and determine and update the global optimal solution of the system.

[0172] The chaotic mapping formula adopted is:

[0173]

[0174] In the formula: X j is the chaotic number; j is the iteration number.

[0175] (6) Update the velocity and position of the particles using the velocity and position update formula, and determine whether the termination condition is reached. If it is satisfied, output the objective function and the values of the variables to be optimized. Otherwise, return to step (4).

[0176] The velocity and position update formulas after introducing the chaotic mechanism are as follows:

[0177] v' j = c6(X s - X j ) + c7v j

[0178] X' j = X j + v' j ;

[0179] In the formula: c6 and c7 are randomly generated vectors between [0, 1]; X s is the position of the particle that succeeded in the previous game; X j is the position of the non-elite particle; v j is the velocity of the non-elite particle, v' j and X' j are the updated velocity and position;

[0180] (7) The algorithm ends, output the optimized objectives of the minimum demand-side response and the minimum commutation loss between the buses. Based on the optimized objectives of the minimum demand-side response and the minimum commutation loss between the buses, obtain the optimal solutions of the maximum energy storage capacity and the converter capacity respectively.

[0181] The beneficial effects are as follows:

[0182] The present invention solves the problem of capacity optimization of a microgrid system based on price-based demand response. A real-time price-based demand response mechanism is implemented on the user side. According to the difference between the electricity consumption on the user side and the output of the power generation side, the electricity price is divided into peak-time electricity price and valley-time electricity price. A real-time electricity price substitution elasticity coefficient is introduced to characterize the relationship between the load on the user side and the peak-valley electricity price. Introducing real-time price-based demand response can more effectively reduce the configuration capacity of wind turbines and photovoltaic cells, and reduce the charge-discharge frequency and discharge depth of energy storage devices, improve the overall economy of the microgrid system, make the electricity consumption trends of AC-side and DC-side users more consistent with the output curves of each distributed power generation subsystem, improve energy utilization efficiency, reduce the commutation value between the AC and DC buses, and further reduce the commutation loss value between the buses in the AC-DC hybrid microgrid.

[0183] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the technical solutions of the various embodiments of the present invention deviate from the scope of this solution.

Claims

1. A hybrid microgrid optimal configuration method based on price-based demand-side response, characterized in that including, S1. Establish a simulation hybrid microgrid model; S2. Input the environmental parameters, mathematical models of micro-sources, operation strategies, and initial load data in the actual hybrid microgrid system into the simulation hybrid microgrid model. The micro-sources include: wind power AC generator sets, photovoltaic DC generator sets, storage batteries, and hydrogen energy storage systems; S3. Initialize the environmental parameters and capacities of the micro-sources in the simulation hybrid microgrid model, and input the relevant constraint conditions of the simulation hybrid microgrid model to obtain the simulation hybrid microgrid model to be optimized; S4. Input the set peak-time electricity price and valley-time electricity price values, and judge the time period in which the current electricity price is located; S5. Perform demand-side response processing based on the initial load data and the time period in which the electricity price is located to obtain the load after demand-side response; S6. Calculate the demand-side response optimization target based on the load after demand-side response, and establish the commutation loss target between buses. The commutation loss target between buses is positively correlated with the demand-side response optimization target; S7. Optimize the demand-side response optimization target and the commutation loss target between buses based on the simulation hybrid microgrid model to be optimized, repeat S7 and perform optimization, and output the minimum target value obtained by optimization and the corresponding optimized configuration result.

2. The method according to claim 1, wherein The relevant constraint conditions of the simulation hybrid microgrid model specifically include: hybrid microgrid mathematical model constraint conditions and demand-side response constraint conditions.

3. The method according to claim 1, characterized in that, The judgment of the time period in which the current electricity price is located specifically includes: subtracting the load from the hourly average output power of the wind power AC generator set and the photovoltaic DC generator set to obtain the hourly source-load power difference. When the hourly source-load power difference is greater than or equal to 0, it is the valley-time electricity price, and when the source-load power difference is less than 0, it is the peak-time electricity price.

4. The system according to claim 1, wherein The S5 specifically includes: Use CFSE to represent the ratio of the electricity consumption of users during peak and valley periods to the corresponding electricity prices. The CFSE calculation formula is as shown in the formula: Where: p a , p b are the electricity prices corresponding to time periods a and b respectively, in yuan; P load,a , P load,b are the demand powers on the load side corresponding to time periods a and b respectively, in kW·h, and d represents the differential symbol; When performing capacity configuration, calculate CFSE according to the load and the set peak-valley electricity prices; when CFSE is given, inversely calculate the reduction of the peak-period load and the increase of the valley-period load under the implementation of real-time electricity prices, that is, the magnitude of the elastic load value. The specific formula is as shown: Where: ΔEL p , ΔEL v are the transferred loads under peak-valley electricity prices respectively, R V , R P are the proportions of electricity consumption costs during peak and valley electricity price periods respectively in the total daily electricity consumption cost, p i is the electricity price before implementing real-time electricity price response, P load,v , P load,p are the demand powers on the load side corresponding to peak and valley times respectively, kW·h; The load after demand-side response in any time period is expressed as: Where: P load (k) is the initial load data at time k.

5. The method according to claim 1, wherein S6 specifically includes: Establish a demand-side response optimization target D, where D represents the difference between the electricity generated by distributed power sources in the system and the demand power on the load side. The specific expression is: Where: P pv (t), P w (t) and P load，DR (t) are the output power of the wind turbine, the photovoltaic output power, and the load after demand-side response during the time period t, respectively; Z is the number of hours. Establish the commutation loss target between buses, and the calculation formula is: P t-loss = (1 - η con )P ac / dc ; Where: P ac / dc is the commutation power between busbars, in kW; η con is the commutation efficiency of the converter. Energy transmission is carried out between the AC and DC buses to maintain the power balance between the AC sub-network and the DC sub-network. The specific constraint conditions are as shown in the formula: Where: P ac , P dc are the balanced powers on the AC side and the DC side respectively; are the AC and DC load powers respectively; P ac / dc is the commutation power between the AC and DC buses; P ch , P dh are the charging and discharging powers of the hybrid microgrid respectively; P sp , P tep are the power purchase and power selling powers from / to the large power grid respectively, in kW.

6. The method according to claim 1, wherein S7 specifically includes: S71. According to the simulation hybrid microgrid model to be optimized, initialize the variables to be optimized in the demand-side response optimization target and the commutation loss target, and randomly generate the positions and velocities of the particles. Each particle represents a set of solutions; input the particles into the demand-side response optimization target and the commutation loss target; S72. Obtain the fitness values of the demand-side response optimization target and the commutation loss target, and update the individual optimal solution and the global optimal solution; S73. Perform non-dominated sorting of the particles and select two elite particles a and b and a non-elite particle x for game through the crowding distance stratification; S74. Replace the non-elite particles in the original population with the particles that have achieved successful games and the particles that need to re-learn after failed games to complete the reconstruction of the new elite particle set; S75. Through the chaotic update of the chaotic mapping formula, perform chaotic search on the global optimal solution, compare the dominance relationship between the chaotic search particles and the original particles, and determine and update the global optimal solution of the system; The chaotic mapping formula adopted is: Where: X j is a chaotic number; j is the number of iterations; S76. Update the velocity and position of the particles using the velocity and position update formula, and determine whether the termination condition is met. If it is met, output the values of the variables to be optimized in the objective function, the demand-side response optimization objective, and the commutation loss objective; Otherwise, return to step S74; The velocity and position update formulas after introducing the chaotic mechanism are as follows: v' j = c6(X s - X j ) + c7v j ; X' j = X j + v' j ; Where: c6 and c7 are randomly generated vectors between [0,1]; X s is the position of the particle with successful game in the previous time; X j is the position of non-elite particles; v j is the velocity of non-elite particles, v' j and X' j are the updated velocity and position; S77. The algorithm ends, output the optimized minimum demand-side response optimization objective and the commutation loss objective between the minimum busbars, and obtain the optimal solutions of the maximum energy storage capacity and the maximum converter capacity based on the minimum demand-side response optimization objective and the commutation loss objective between the minimum busbars respectively.

7. The method according to claim 1, wherein The operation strategy specifically includes: obtaining one-year meteorological data, and calculating the hourly average output power of the wind power AC generator set and the PV DC generator set based on the one-year meteorological data; Subtract the hourly AC and DC load demands on the user side from the hourly average output power of the wind power AC generator set and the PV DC generator set to obtain the hourly source-load power difference; Perform energy control based on the hourly source-load power difference.

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