Micro energy grid energy optimization scheduling method and device considering grid-connected frequency characteristics

Abstract

The invention relates to a micro-energy grid energy optimization scheduling method and device considering grid-connected frequency characteristics. The method comprises the following steps: establishing a cooling and heating load constraint and intraday temperature model; based on the cooling and heating load constraint, establishing a cooling and heating electrical power balance constraint; setting determined values of wind power and photovoltaic output force, calculating a power unbalance amount, and constructing a frequency characteristic model of the power system according to the power unbalance amount; taking the total cost of the power system as a target, and considering operation and maintenance of the power system, carbon emission, frequency deviation penalty, equipment start and stop, power purchase cost and gas purchase cost to establish a target function of micro-energy network optimization scheduling; and calling a solver by adopting a mixed integer linear optimization theory to solve the dispatching condition of the power system, the total cost of the power system and the frequency deviation. According to the method, a cold, heat and electricity multi-energy collaborative micro-energy network intra-day optimization scheduling model considering the grid-connected frequency characteristics is constructed, the model accuracy and the multi-energy complementary collaborative effect are improved, and the frequency deviation generated by coupling of different loads is stabilized.

Description

Technical Field

[0001] The present invention belongs to the technical field of micro energy network operation optimization, and in particular relates to a micro energy network energy optimization scheduling method and device taking into account grid-connected frequency characteristics. Background Art

[0002] The development and application of sustainable energy and the introduction of alternative fuel types are one of the main ways to build a new power system. They are also an inevitable choice to address the increasingly severe environmental threats and intensified climate problems. As the proportion of renewable energy in the integrated energy system increases, the complexity of the coupling relationship between the various systems and the resulting system frequency problems are also gradually increasing. The high proportion of new energy power generation units has replaced a large number of traditional synchronous generators with rotational inertia, reducing the inertia level of the system. As a result, the frequency support capacity of the power grid is significantly reduced when it is disturbed, and the frequency deviation of the system under disturbance increases. In addition, new energy power generation units generally lack inertia capacity and primary frequency regulation capability, which makes the system frequency regulation capability decrease significantly as the proportion of new energy increases, and the time required for the system to recover stability after frequency deviation is prolonged.

[0003] At present, the research on the relationship between load fluctuation and frequency deviation in integrated energy networks is still in its initial stage. Existing research inventions focus on frequency fluctuations caused by power fluctuations or imbalances generated by a single energy or electrical energy in the system. There are fewer types of system loads, and there is less analysis of frequency fluctuation problems derived from the scheduling optimization scenarios of multi-energy complementary micro-energy networks. At the same time, there are few related research inventions that specifically take frequency fluctuations into consideration in the calculation of system operating costs. A complete theoretical and methodological system has not yet been formed. Key issues such as how to accurately quantify the impact of frequency fluctuations on operating costs and their universal application in different types of integrated energy networks still need further exploration.

[0004] In summary, the existing technology has obvious deficiencies in the analysis of frequency fluctuation problems in the scheduling optimization scenario of multi-energy complementary micro-energy networks and the calculation of system operation costs considering frequency fluctuations, which makes it difficult to meet the actual needs of safe and economic operation of integrated energy networks. Therefore, conducting research on the above-mentioned problems has important theoretical significance and application value. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the technical defects existing in the prior art and propose a micro energy network energy optimization scheduling method and device taking into account the grid frequency characteristics.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a method for optimizing energy scheduling of a micro-energy grid taking into account the grid-connected frequency characteristics, comprising the following steps:

[0007] Establish cooling and heating load constraints and intraday temperature models considering thermal inertia of temperature propagation;

[0008] Establish cooling and heating electrical power balance constraints taking into account unit categories based on cooling and heating load constraints;

[0009] Based on the balance constraints of cooling, heating and electrical power, a fixed value is set for the output of wind power and photovoltaic power. The fixed value is combined with the electric power balance formula to calculate the power imbalance. The frequency characteristic model of the power system is constructed based on the power imbalance.

[0010] Combined with the frequency characteristic model, taking the total cost of the power system as the target, the objective function for optimizing the scheduling of the micro-energy network is established by considering power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, and electricity and gas purchase costs.

[0011] The mixed integer linear optimization theory is used to call the solver to solve the power system scheduling situation, the total cost of the power system and the frequency deviation, and the optimal scheduling scheme of the micro energy network taking into account the grid-connected frequency characteristics is obtained.

[0012] In one embodiment of the present invention, establishing a cooling and heating load constraint considering the thermal inertia of temperature propagation includes:

[0013] Initial heating outdoor temperature constraint:

[0014] T out,hot,1 =0,T out,hot,2 =0;

[0015] Where, T out,hot,1 and T out,hot,2 Represents the outdoor temperature at time 1 and time 2 respectively. A value of 0 indicates that the system is not started or is in cooling state.

[0016] Heating outdoor temperature constraints after normal operation:

[0017] T out,hot,i =T out,i-2 ;

[0018] Where, T out,hot,i and T out,hot,i-2 represent the outdoor temperatures at time i and i-2 respectively;

[0019] Power system preheating or steady-state initial condition heating room temperature constraints:

[0020] T in,hot,1 =25,T in,hot,2 =25;

[0021] Where, T in，hot，1 and T in，hot，2 Respectively represent the indoor temperature at time 1 and time 2;

[0022] Indoor, heating and outdoor temperature relationship constraints:

[0023] T in,hot,i =αT in,hot,i-1 +βT gong,i-1 +χT out,hot,i-1 ;

[0024] Where, T gong,i represents the heating temperature at time i, α represents the historical temperature inertia coefficient, β represents the engineering temperature influence coefficient, and χ represents the outlet temperature feedback coefficient;

[0025] Heating network water supply temperature constraints:

[0026] T hui,i-2 ≤T gong,i ≤120;

[0027] Where, T hui,i-2 Indicates the recovery temperature at time i-2;

[0028] Constraints on the relationship between water supply temperature and outdoor temperature:

[0029] T hui,i-2 =α1T in,hot,k +α2T in,hot,i-2 +

[0030] β1T gong,i -β2T gong,i-1 -β3T gong,i-2 +;

[0031] δ1T out,hot,i -δ2T out,hot,i-1 +δ3T out,hot,i-2

[0032] Where, T hui,i-2 Indicates the recovery temperature at time i-2, T in,hot,k represents the indoor temperature at the kth moment, T out,hot,k is the outdoor temperature at time k, T gong,i represents the current engineering temperature at time i, α1 and α2 represent the thermal measurement inlet temperature coefficients, β1, β2, and β3 represent the engineering temperature coefficients, and δ1, δ2, and δ3 represent the thermal measurement outlet temperature coefficients;

[0033] Heat load lower limit constraint:

[0034] 10≤load h,i-2 ;

[0035] load h,i-2 =0.63·(T gong,i -T hui,i-2 );

[0036] Where, loadh，i-2 represents the heat load at time i-2, T gong,i represents the heating temperature at time i, T hui,i-2 Indicates the historical temperature recovery at time i-2;

[0037] Constraints on the dynamic balance relationship between cooling load and temperature:

[0038]

[0039] Where loαd c,i-1 represents the cooling load at time i-1, T in,cold,i represents the cold side inlet temperature at time i, T out,i-1 represents the outlet temperature at time i-1, R represents thermal resistance, c c Represents the cold side heat capacity, exponential term Represents the attenuation factor that reflects the dynamic characteristics of the power system.

[0040] In one embodiment of the present invention, establishing a cooling and heating electrical power balance constraint taking into account unit types based on cooling and heating load constraints includes:

[0041] Electric balance constraints:

[0042]

[0043] Where, P WT,i represents the wind power output forecast at time i, P PV,i represents the photovoltaic output forecast at time i, P net,i represents the net exchange power at time i, P G3,i represents the generator output at time i, P EC,i Represents the output cooling power of the electric refrigerator at time i, COP EC Represents the energy efficiency ratio of electric refrigerator equipment, P EG,i represents the gas power output by the P2G device at time i, EG represents the energy efficiency ratio of the P2G device, L e,i represents the load demand at time i;

[0044] Thermal balance constraints:

[0045]

[0046] Where, P EH,i represents the output power of the waste heat boiler at time i, P GH,i represents the output power of the gas heater at time i, P AC,i represents the input power of the air conditioner at time i, COP AC Indicates the energy efficiency ratio of air conditioning, load h,i represents the heat load demand at time i;

[0047] Cold balance constraints:

[0048] P EC,i +P AC,i =load c,i ;

[0049] Where, load c,i represents the cooling load demand at time i;

[0050] Natural gas supply and demand balance constraints:

[0051] G grid,i +G EG,i =G L,i +G G3,i +G GH,i ;

[0052] Where G grid，i represents the gas power purchased from the natural gas network at time i, G EG,i represents the gas production power of the P2G device at time i, G L,i represents the natural gas demand of users in the micro energy grid at time i, G G3，i represents the gas production power of the gas turbine at time i, G GH,i Indicates the gas consumption power of the gas boiler at time i;

[0053] Gas balance constraints:

[0054]

[0055] Where G buy,i represents the gas purchase amount at time i, η G3 represents the electrical efficiency of the gas turbine, GH represents the efficiency of the gas boiler, and L g,i It represents the gas load demand at time i.

[0056] In one embodiment of the present invention, constructing a frequency characteristic model of a power system according to a power imbalance includes:

[0057] The variation of frequency deviation is driven by the power imbalance, and its dynamic characteristics are described by the differential equation: where the power imbalance and the differential equation are:

[0058]

[0059] Where, ΔP i represents the power imbalance at time i, P EC,i-1 represents the power consumption of the electric refrigerator at time i, P EG,i-1 represents the power consumption of the P2G device at time i-1, L e,i-1 represents the power load at time i-1; Δf irepresents the frequency deviation value at time i, M represents the inertia time constant of the system, and D represents the damping coefficient of the system;

[0060] Discretize the differential equation and construct the frequency deviation relationship between adjacent time steps:

[0061]

[0062] Where Δf i , Δf i-1 Represent the frequency deviation values ​​at time i and time i-1 respectively;

[0063] The frequency deviation value is solved at each discrete time step through the frequency deviation relationship. After the frequency deviation value is calculated, the generator automatically responds through the speed regulator, and its output deviation is proportional to the frequency deviation:

[0064] P G3,i =P G3,scheduled,i -K G3 Δf i

[0065] Where, P G3,scheduled,i represents the planned active power of generator G3 at time i, K G3 is the regulation coefficient of generator G3.

[0066] In one embodiment of the present invention, after the frequency deviation value is obtained, the frequency change rate is calculated and a frequency change rate constraint check is performed. The frequency change rate constraint is:

[0067]

[0068] Where Δt represents the time value.

[0069] In one embodiment of the present invention, the objective function of the micro energy network optimization scheduling is established as follows:

[0070] F=C Ng +C buy -C sell +C carbon +C OM +C fluctuation +C start_stop ;

[0071] Where C Ng represents the natural gas purchase cost, C buy Indicates the cost of purchasing electricity, C sell represents the revenue from selling electricity, C fluctuation represents the frequency fluctuation penalty, C carbon represents the carbon emission cost, C start_stop represents the start-up and shutdown cost of the equipment, COM Indicates the operation and maintenance cost of the equipment;

[0072] Among them, the equipment start-up and shutdown costs are:

[0073]

[0074] Where C start_G3 N represents the single startup cost of generator G3. start Indicates the number of times the generator G3 is started in a scheduling cycle, C stop_G3 N represents the single downtime cost of generator G3. stop Indicates the number of shutdowns of generator G3 within a scheduling cycle;

[0075] The cost of carbon emissions is:

[0076]

[0077] Where C carbon represents the total carbon emission cost, p carbon represents the unit carbon emission price, ε1 represents the carbon emission coefficient of coal-fired power generation, ε2 represents the carbon emission coefficient of gas-fired power generation or other energy sources, G buy,k represents the purchase amount of the kth coal-fired power generation, P buy,k represents the purchase amount of the kth gas or other high-carbon energy;

[0078] Equipment operation and maintenance costs are:

[0079]

[0080] Where OM x represents the unit operation and maintenance cost coefficient of equipment x, P x,k represents the output power of device x at time k, G3 represents gas turbine, GH represents gas boiler, EH represents waste heat boiler, EC represents electric chiller, AC represents absorption chiller, and EG represents P2G device.

[0081] In one embodiment of the present invention, a mixed integer linear optimization theory is used to call a solver to solve the power system dispatch situation, the total cost of the power system and the frequency deviation, including:

[0082] Yalmip is used to call the Gurobi solver to solve the power system dispatch situation, total cost of the power system and frequency deviation, and the intraday dispatch situation of different energy types, frequency deviation before and after optimization and total cost results are obtained.

[0083] Based on the same inventive concept, the present invention also provides a micro-energy grid energy optimization scheduling device taking into account grid-connected frequency characteristics, comprising:

[0084] A constraint unit is used to establish cooling and heating load constraints and a daily temperature model that considers thermal inertia of temperature propagation, and to establish cooling and heating electrical power balance constraints that consider unit types based on the cooling and heating load constraints;

[0085] A modeling unit is used to set a fixed value for wind power and photovoltaic power output based on the cooling, heating and electrical power balance constraints, combine the fixed value with the electric power balance formula to calculate the power imbalance, and construct a frequency characteristic model of the power system based on the power imbalance;

[0086] The target unit is used to combine the frequency characteristic model, take the total cost of the power system as the target, and consider the power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start and stop, electricity purchase costs, and gas purchase costs to establish the objective function for optimizing the scheduling of the micro-energy network;

[0087] The solving unit is used to call the solver using mixed integer linear optimization theory to solve the power system scheduling situation, the total cost of the power system and the frequency deviation, and obtain the micro-energy network optimization scheduling plan taking into account the grid-connected frequency characteristics.

[0088] Based on the same inventive concept, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described above is implemented.

[0089] Based on the same inventive concept, the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0090] The beneficial effects of the present invention are as follows: by introducing mixed integer linear optimization theory, the present invention constructs a daily optimization scheduling model for a cold, hot and electric multi-energy collaborative micro-energy network taking into account the grid-connected frequency characteristics. The model can significantly improve the accuracy of integrated energy system modeling, enhance the synergistic efficiency of multi-energy complementarity, and effectively smooth out the frequency deviation caused by the coupling operation of different types of loads to the system, thereby optimizing the efficiency of energy resource allocation while ensuring the stability of the system frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A flowchart of a method for optimizing energy scheduling in a micro-energy grid taking into account grid-connected frequency characteristics is provided in an embodiment.

[0092] Figure 2 A schematic diagram of the structure of a micro-energy network energy optimization and scheduling device taking into account the grid-connected frequency characteristics provided in an embodiment.

[0093] Figure 3 Schematic diagram of the operation flow of the power system in the embodiment.

[0094] Figure 4 This is a frequency deviation curve diagram of a system operation cycle before and after the operation optimization of the power system in the embodiment.

[0095] Figure 5 Graph showing indoor and outdoor temperature in the embodiment.

[0096] Figure 6 This is a graph showing predicted output curves for photovoltaic and wind turbines in the embodiment.

[0097] Figure 7 Figure 2 is a graph showing different types of load curves in the embodiment. DETAILED DESCRIPTION

[0098] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0099] Reference Figure 1 As shown, an embodiment of the present invention provides a method for optimizing energy scheduling of a micro energy network taking into account grid-connected frequency characteristics, comprising the following steps:

[0100] Step S1, establishing a cooling and heating load constraint and a daytime temperature model considering the thermal inertia of temperature propagation;

[0101] Step S2: establishing a cooling and heating electrical power balance constraint taking into account the unit type based on the cooling and heating load constraints;

[0102] Step S3: Based on the cooling, heating and electrical power balance constraints, set fixed values ​​for wind power and photovoltaic power output, combine the fixed values ​​with the electric power balance formula to calculate the power imbalance, and construct a frequency characteristic model of the power system based on the power imbalance;

[0103] Step S4: In combination with the frequency characteristic model, taking the total cost of the power system as the target, and considering power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, electricity purchase costs, and gas purchase costs, establish an objective function for optimizing the scheduling of the micro-energy network;

[0104] Step S5: Using mixed integer linear optimization theory to call a solver to solve the power system dispatching situation, the total cost of the power system and the frequency deviation, and obtain an optimized dispatching scheme for the micro energy network taking into account the grid-connected frequency characteristics.

[0105] The present invention introduces mixed integer linear optimization theory to construct an intraday optimization scheduling model for a micro-energy network with multi-energy coordination of cooling, heating and electricity, taking into account the grid-connected frequency characteristics. This model can significantly improve the accuracy of integrated energy system modeling, enhance the synergistic efficiency of multi-energy complementarity, and effectively smooth out the frequency deviation caused by the coupling operation of different types of loads, thereby optimizing the efficiency of energy resource allocation while ensuring the stability of the system frequency.

[0106] In step S1, establishing the cooling and heating load constraints considering the thermal inertia of temperature propagation includes:

[0107] Initial heating outdoor temperature constraint:

[0108] T out,hot,1 =0,T out,hot,2 =0;

[0109] Where, T out,hot,1 and T out,hot,2 Represent the outdoor temperature at time 1 and time 2 respectively. A value of 0 indicates that the system is not started or is in cooling state, which starts the subsequent iterative calculation;

[0110] Heating outdoor temperature constraints after normal operation:

[0111] T out,hot,i =T out,i-2 ;

[0112] Where, T out,hot,i and T out,hot,i-2 Represent the outdoor temperature at time i and i-2 respectively. i and i-2 represent the thermal inertia caused by the delay effect of temperature propagation during heat transfer, which causes the current heat output temperature to be determined by the temperature of the previous two time steps;

[0113] Power system preheating or steady-state initial condition heating room temperature constraints:

[0114] T in,hot,1 =25,T in,hot,2 =25;

[0115] Where, T in，hot，1 and T in，hot，2 Respectively represent the indoor temperature at time 1 and time 2;

[0116] Indoor, heating and outdoor temperature relationship constraints:

[0117] T in,hot,i =αT in,hot,i-1 +βT gong,i-1 +χT out,hot,i-1 ;

[0118] Where, T gong,i represents the heating temperature at time i, α represents the historical temperature inertia coefficient, reflecting the dependence of the power system on its own historical temperature, β represents the engineering temperature influence coefficient, reflecting the regulation effect of external engineering parameters on temperature, and χ represents the outlet temperature feedback coefficient, reflecting the reverse influence of the hot side outlet temperature on the inlet;

[0119] Heating network water supply temperature constraints:

[0120] Thui,i-2 ≤T gong,i ≤120;

[0121] Where, T hui,i-2 The temperature at time i-2 is the return temperature, such as the return temperature of a circulation system. To prevent overheating from damaging the equipment or triggering protection mechanisms, the equipment safety threshold is set to 120°C. To prevent a sudden temperature drop from causing system instability, the current project temperature must not be lower than the return temperature of the previous two time steps.

[0122] Constraints on the relationship between water supply temperature and outdoor temperature:

[0123] T hui,i-2 =α1T in,hot,k +α2T in,hot,i-2 +

[0124] β1T gong,i -β2T gong,i-1 -β3T gong,i-2 +;

[0125] δ1T out,hot,i -δ2T out,hot,i-1 +δ3T out,hot,i-2

[0126] Where, T hui,i-2 Indicates the recovery temperature at time i-2, T in,hot,k represents the indoor temperature at the kth moment, T out,hot,k represents the outdoor temperature at time k, α1 and α2 represent the heat measurement inlet temperature coefficients, β1, β2, and β3 represent the engineering temperature coefficients, and δ1, δ2, and δ3 represent the heat measurement outlet temperature coefficients. The equation represents the return temperature T hui,i-2 It is a linear combination of multiple historical temperature variables. The weight coefficient reflects the contribution of each temperature to the temperature recovery, which can be divided into two aspects. One is the positive impact: the current engineering temperature T gong,k and outlet temperature T out,hot,k It has large positive coefficients β1 and δ1. The second is historical dependence: the indoor temperature and outdoor temperature in the previous 1 to 2 steps jointly regulate the return temperature.

[0127] Heat load lower limit constraint:

[0128] 10≤load h,i-2 ;

[0129] load h,i-2 =0.63·(T gong,i -T hui,i-2 );

[0130] Where, load h，i-2 represents the heat load at time i-2, and the heating temperature T at time i gong,iand the historical temperature T at time i-2 hui,i-2 The indoor initial temperature is set to -15℃ during cooling, and the indoor temperature range is between -15℃ and -20℃ during cooling. gong,i represents the heating temperature at time i, T hui,i-2 Indicates the historical temperature recovery at time i-2;

[0131] Constraints on the dynamic balance relationship between cooling load and temperature:

[0132]

[0133] Where loαd c,i-1 represents the cooling load at time i-1, T in,cold,i represents the cold side inlet temperature at time i, T out,i-1 represents the outlet temperature at time i-1, R represents thermal resistance, c c Represents the cold side heat capacity, exponential term Represents the attenuation factor that reflects the dynamic characteristics of the power system.

[0134] In step S2, the establishment of the cooling and heating electrical power balance constraint considering the unit type based on the cooling and heating load constraint includes:

[0135] Electric balance constraints:

[0136]

[0137] Where, P WT,i represents the wind power output forecast at time i, P PV,i represents the photovoltaic output forecast at time i, P net,i represents the net exchange power at time i, P G3,i represents the generator output at time i, P EC,i Represents the output cooling power of the electric refrigerator at time i, COP EC Indicates the energy efficiency ratio of electric refrigeration equipment, which indicates the cooling capacity generated per unit of electricity, P EG,i represents the gas power output by the P2G device at time i, EG represents the energy efficiency ratio of the P2G device, which represents the efficiency of converting unit electricity into gas, and L e,i represents the load demand at time i;

[0138] Thermal balance constraints:

[0139]

[0140] Where, P EH,i represents the output power of the waste heat boiler at time i, P GH,i represents the output power of the gas heater at time i, P AC,i represents the input power of the air conditioner at time i, COPAC Indicates the energy efficiency ratio of air conditioner, which indicates the heat power generated per unit electric power, load h,i represents the heat load demand at time i;

[0141] Cold balance constraints:

[0142] P EC,i +P AC,i =load c,i ;

[0143] Where, load c,i represents the cooling load demand at time i;

[0144] Natural gas supply and demand balance constraints:

[0145]

[0146] Where G grid，i represents the gas power purchased from the natural gas network at time i, G EG,i represents the gas production power of the P2G device at time i, G L,i represents the natural gas demand of users in the micro energy grid at time i, G G3，i represents the gas production power of the gas turbine at time i, G GH,i Indicates the gas consumption power of the gas boiler at time i;

[0147] Gas balance constraints:

[0148]

[0149] Where G buy,i represents the gas purchase amount at time i, η G3 represents the electrical efficiency of the gas turbine, GH represents the efficiency of the gas boiler, and L g,i It represents the gas load demand at time i.

[0150] In step S3, a frequency characteristic model of the power system is constructed according to the power imbalance, including:

[0151] The variation of frequency deviation is driven by the power imbalance, and its dynamic characteristics are described by the differential equation: where the power imbalance and the differential equation are:

[0152]

[0153] Where, ΔP i represents the power imbalance at time i, P EC,i-1 represents the power consumption of the electric refrigerator at time i, P EG,i-1 represents the power consumption of the P2G device at time i-1, L e,i-1 represents the power load at time i-1; Δfi represents the frequency deviation value at time i, M represents the inertia time constant of the system, and D represents the damping coefficient of the system;

[0154] Discretize the differential equation and construct the frequency deviation relationship between adjacent time steps:

[0155]

[0156] Where Δf i , Δf i-1 Represent the frequency deviation values ​​at time i and time i-1 respectively;

[0157] The frequency deviation value is solved at each discrete time step through the frequency deviation relationship. After the frequency deviation value is calculated, the generator automatically responds through the speed regulator, and its output deviation is proportional to the frequency deviation:

[0158] P G3,i =P G3,scheduled,i -K G3 Δf i

[0159] Where, P G3,scheduled,i represents the planned active power of generator G3 at time i, K G3 is the regulation coefficient of generator G3.

[0160] Furthermore, after calculating the frequency deviation at each moment through the above steps, it is necessary to perform a safety check on the "speed" of the frequency change, that is, to perform a rate of change of frequency (ROCOF) constraint check, where the frequency change rate constraint is:

[0161]

[0162] Wherein, Δt represents the time value, and the preferred value is one hour.

[0163] In step S4, the objective function of the micro-energy network optimization scheduling is established as:

[0164] F=C Ng +C buy -C sell +C carbon +C OM +C fluctuation +C start_stop ;

[0165] Where C Ng represents the natural gas purchase cost, C buy Indicates the cost of purchasing electricity, C sell represents the revenue from selling electricity, C fluctuation represents the frequency fluctuation penalty, C carbon represents the carbon emission cost, Cstart_stop represents the start-up and shutdown cost of the equipment, C OM Indicates the operation and maintenance cost of the equipment;

[0166] Among them, the equipment start-up and shutdown costs are:

[0167]

[0168] Where C start_G3 N represents the single startup cost of generator G3. start Indicates the number of times the generator G3 is started in a scheduling cycle, C stop_G3 N represents the single downtime cost of generator G3. stop Indicates the number of shutdowns of generator G3 within a scheduling cycle;

[0169] The cost of carbon emissions is:

[0170]

[0171] Where C carbon represents the total carbon emission cost, p carbon represents the unit carbon emission price, ε1 represents the carbon emission coefficient of coal-fired power generation, ε2 represents the carbon emission coefficient of gas-fired power generation or other energy sources, G buy,k represents the purchase amount of the kth coal-fired power generation, P buy,k represents the purchase amount of the kth gas or other high-carbon energy;

[0172] Equipment operation and maintenance costs are:

[0173]

[0174] Where OM x represents the unit operation and maintenance cost coefficient of equipment x, P x,k represents the output power of device x at time k, G3 represents gas turbine, GH represents gas boiler, EH represents waste heat boiler, EC represents electric chiller, AC represents absorption chiller, and EG represents P2G device.

[0175] Among them, in step S5, Yalmip can be used to call the Gurobi solver to solve the power system scheduling, power system total cost and frequency deviation, and obtain the intraday scheduling of different energy types, frequency deviation before and after optimization, and total cost results.

[0176] The following describes in detail a micro-energy grid energy optimization scheduling method taking into account grid-connected frequency characteristics proposed by the present invention through specific implementation cases.

[0177] The values ​​of the relevant parameters of the micro energy network in this case are shown in Table 1.

[0178] Table 1

[0179]

[0180]

[0181] The load data is based on a park-level power generation demonstration project in Province A. The price of electricity purchased from the grid adopts the time-of-use electricity price of ordinary industrial users with a voltage less than 1 kV in Province A, and the electricity sales price refers to the benchmark on-grid electricity price of coal-fired power generation in Province A, as shown in Table 2.

[0182]

[0183] Yalmip is used to call the Gurobi solver to solve the mixed integer linear programming model of the micro-energy grid taking into account the grid frequency characteristics, and the intraday scheduling of different energy types, the frequency deviation before and after optimization, and the total cost results are obtained. The solution results are detailed in Figure 4 — Figure 7 The comparison of the results before and after the objective function optimization is shown in Table 3.

[0184] Table 3

[0185]

[0186] This implementation case can fully consider the impact of wind power and photovoltaic output on system frequency deviation, take into account the coupled operation of cold and hot electrical loads in the micro-energy network, consider the thermal inertia of the energy supply system and the functional relationship between indoor, heating and outdoor temperatures, and construct a micro-energy network scheduling model with multi-energy complementarity of cold, hot and electrical loads; based on the objective function and constraints, a mixed integer linear programming model for the optimal configuration of the micro-energy network taking into account the grid-connected frequency characteristics is constructed, and an optimal configuration method is obtained to achieve the stabilization of system frequency deviation and cost optimization.

[0187] Based on the same inventive concept, Figure 2 As shown, an embodiment of the present invention further provides a micro-energy network energy optimization scheduling device taking into account grid-connected frequency characteristics, comprising:

[0188] A constraint unit is used to establish cooling and heating load constraints and a daily temperature model that considers thermal inertia of temperature propagation, and to establish cooling and heating electrical power balance constraints that consider unit types based on the cooling and heating load constraints;

[0189] A modeling unit is used to set a fixed value for wind power and photovoltaic power output based on the cooling, heating and electrical power balance constraints, combine the fixed value with the electric power balance formula to calculate the power imbalance, and construct a frequency characteristic model of the power system based on the power imbalance;

[0190] The target unit is used to combine the frequency characteristic model, take the total cost of the power system as the target, and consider the power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start and stop, electricity purchase costs, and gas purchase costs to establish the objective function for optimizing the scheduling of the micro-energy network;

[0191] The solving unit is used to call the solver using mixed integer linear optimization theory to solve the power system scheduling situation, the total cost of the power system and the frequency deviation, and obtain the micro-energy network optimization scheduling plan taking into account the grid-connected frequency characteristics.

[0192] The present invention introduces mixed integer linear optimization theory to construct an intraday optimization scheduling model for a micro-energy network with multi-energy coordination of cooling, heating and electricity, taking into account the grid-connected frequency characteristics. This model can significantly improve the accuracy of integrated energy system modeling, enhance the synergistic efficiency of multi-energy complementarity, and effectively smooth out the frequency deviation caused by the coupling operation of different types of loads, thereby optimizing the efficiency of energy resource allocation while ensuring the stability of the system frequency.

[0193] The device of the above embodiment is used to implement the corresponding micro-energy network energy optimization scheduling method taking into account the grid-connected frequency characteristics in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0194] It should be noted that the drawings of the embodiments of the present disclosure only involve structures related to the embodiments of the present disclosure, and other structures can refer to general designs.

[0195] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0196] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0197] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the micro-energy network energy optimization scheduling method taking into account the grid-connected frequency characteristics as described in any of the above embodiments.

[0198] In the embodiment of the present invention, the processor may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices.

[0199] The processor may call a program stored in the memory. Specifically, the processor may execute the operations in the embodiment of the above-mentioned method for optimizing energy scheduling of a micro-energy grid taking into account grid-connected frequency characteristics.

[0200] The memory is used to store one or more programs, which may include program codes, and the program codes include computer operating instructions.

[0201] In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0202] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned micro-energy network energy optimization scheduling method taking into account the grid-connected frequency characteristics are implemented.

[0203] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0204] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 steps in the process. 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.

[0205] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0206] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0207] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for optimizing energy scheduling of a micro-energy grid taking into account grid-connected frequency characteristics, characterized in that: The following steps are involved: Establish cooling and heating load constraints and intraday temperature models considering thermal inertia of temperature propagation; Establish cooling and heating electrical power balance constraints taking into account unit categories based on cooling and heating load constraints; Based on the balance constraints of cooling, heating and electrical power, a fixed value is set for the output of wind power and photovoltaic power. The fixed value is combined with the electric power balance formula to calculate the power imbalance. The frequency characteristic model of the power system is constructed based on the power imbalance. Combined with the frequency characteristic model, taking the total cost of the power system as the target, the objective function for optimizing the scheduling of the micro-energy network is established by considering power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, and electricity and gas purchase costs. The mixed integer linear optimization theory is used to call the solver to solve the power system scheduling situation, the total cost of the power system and the frequency deviation, and the optimal scheduling scheme of the micro energy network taking into account the grid-connected frequency characteristics is obtained.

2. The method for optimizing energy scheduling of a micro-energy grid taking into account grid-connected frequency characteristics according to claim 1, characterized in that: Establishing cooling and heating load constraints that consider the thermal inertia of temperature propagation includes: Initial heating outdoor temperature constraint: T out,hot,1 =0,T out,hot,2 =0; Where, T out,hot,1 and T out,hot,2 Represents the outdoor temperature at time 1 and time 2 respectively. A value of 0 indicates that the system is not started or is in cooling state. Heating outdoor temperature constraints after normal operation: T out,hot,i =T out,i-2 ; Where, T out,hot,i and T out,hot,i-2 represent the outdoor temperatures at time i and i-2 respectively; Power system preheating or steady-state initial condition heating room temperature constraints: T in,hot,1 =25,T in,hot,2 =25; Where, T in，hot，1 and T in，hot，2 Respectively represent the indoor temperature at time 1 and time 2; Indoor, heating and outdoor temperature relationship constraints: T in,hot,i =αT in,hot,i-1 +βT gong,i-1 +χT out,hot,i-1 ; Where, T gong,i represents the heating temperature at time i, α represents the historical temperature inertia coefficient, β represents the engineering temperature influence coefficient, and χ represents the outlet temperature feedback coefficient; Heating network water supply temperature constraints: T hui,i-2 ≤T gong,i ≤120; Where, T hui,i-2 Indicates the recovery temperature at time i-2; Constraints on the relationship between water supply temperature and outdoor temperature: Where, T hui,i-2 Indicates the recovery temperature at time i-2, T in,hot,k represents the indoor temperature at the kth moment, T out,hot,k is the outdoor temperature at time k, T gong,i represents the current engineering temperature at time i, α1 and α2 represent the thermal measurement inlet temperature coefficients, β1, β2, and β3 represent the engineering temperature coefficients, and δ1, δ2, and δ3 represent the thermal measurement outlet temperature coefficients; Heat load lower limit constraint: 10≤load h,i-2 ; load h,i-2 =0.63·(T gong,i -T hui,i-2 ); Where, load h，i-2 represents the heat load at time i-2, T gong,i represents the heating temperature at time i, T hui,i-2 Indicates the historical temperature recovery at time i-2; Constraints on the dynamic balance relationship between cooling load and temperature: Where loαd c,i-1 represents the cooling load at time i-1, T in,cold,i represents the cold side inlet temperature at time i, T out,i-1 represents the outlet temperature at time i-1, R represents thermal resistance, c c Represents the cold side heat capacity, exponential term Represents the attenuation factor that reflects the dynamic characteristics of the power system.

3. The micro-energy grid optimization scheduling method taking into account the grid-connected frequency characteristics according to claim 2 is characterized in that: The cooling and heating electrical power balance constraints based on the cooling and heating load constraints and considering the unit type include: Electric balance constraints: Where, P WT,i represents the wind power output forecast at time i, P PV,i represents the photovoltaic output forecast at time i, P net,i represents the net exchange power at time i, P G3,i represents the generator output at time i, P EC,i Represents the output cooling power of the electric refrigerator at time i, COP EC Represents the energy efficiency ratio of electric refrigerator equipment, P EG,i represents the gas power output by the P2G device at time i, EG represents the energy efficiency ratio of the P2G device, L e,i represents the load demand at time i; Thermal balance constraints: Where, P EH,i represents the output power of the waste heat boiler at time i, P GH,i represents the output power of the gas heater at time i, P AC,i represents the input power of the air conditioner at time i, COP AC Indicates the energy efficiency ratio of air conditioning, load h,i represents the heat load demand at time i; Cold balance constraints: P EC,i +P AC,i =load c,i ; Where, load c,i represents the cooling load demand at time i; Natural gas supply and demand balance constraints: G grid,i +G EG,i =G L,i +G G3,i +G GH,i ; Where G grid，i represents the gas power purchased from the natural gas network at time i, G EG,i represents the gas production power of the P2G device at time i, G L,i represents the natural gas demand of users in the micro energy grid at time i, G G3，i represents the gas production power of the gas turbine at time i, G GH,i Indicates the gas consumption power of the gas boiler at time i; Gas balance constraints: Where G buy,i represents the gas purchase amount at time i, η G3 represents the electrical efficiency of the gas turbine, GH represents the efficiency of the gas boiler, and L g,i It represents the gas load demand at time i.

4. The micro-energy grid optimization scheduling method taking into account the grid-connected frequency characteristics according to claim 3 is characterized in that: The frequency characteristic model of the power system is constructed based on the power imbalance, including: The variation of frequency deviation is driven by the power imbalance, and its dynamic characteristics are described by the differential equation: where the power imbalance and the differential equation are: Where, ΔP i represents the power imbalance at time i, P EC,i-1 represents the power consumption of the electric refrigerator at time i, P EG,i-1 represents the power consumption of the P2G device at time i-1, L e,i-1 represents the power load at time i-1; Δf i represents the frequency deviation value at time i, M represents the inertia time constant of the system, and D represents the damping coefficient of the system; Discretize the differential equation and construct the frequency deviation relationship between adjacent time steps: Where Δf i , Δf i-1 Represent the frequency deviation values ​​at time i and time i-1 respectively; The frequency deviation value is solved at each discrete time step through the frequency deviation relationship. After the frequency deviation value is calculated, the generator automatically responds through the speed regulator, and its output deviation is proportional to the frequency deviation: P G3,i =P G3,scheduled,i -K G3 ·Δf i Where, P G3,scheduled,i represents the planned active power of generator G3 at time i, K G3 is the regulation coefficient of generator G3.

5. The micro-energy grid optimization scheduling method taking into account the grid-connected frequency characteristics according to claim 4 is characterized in that: After solving for the frequency deviation value, calculate the frequency change rate and perform a frequency change rate constraint check. The frequency change rate constraint is: Where Δt represents the time value.

6. The micro-energy grid optimization scheduling method taking into account grid-connected frequency characteristics according to claim 1 is characterized in that: The objective function of the established micro-energy network optimal scheduling is: F=C Ng +C buy -C sell +C carbon +C OM +C fluctuation +C start_stop ; Where C Ng represents the natural gas purchase cost, C buy Indicates the cost of purchasing electricity, C sell represents the revenue from selling electricity, C fluctuation represents the frequency fluctuation penalty, C carbon represents the carbon emission cost, C start_stop represents the start-up and shutdown cost of the equipment, C OM Indicates the operation and maintenance cost of the equipment; Among them, the equipment start-up and shutdown costs are: Where C start_G3 N represents the single startup cost of generator G3. start Indicates the number of times the generator G3 is started in a scheduling cycle, C stop_G3 N represents the single downtime cost of generator G3. stop Indicates the number of shutdowns of generator G3 within a scheduling cycle; The cost of carbon emissions is: Where C carbon represents the total carbon emission cost, p carbon represents the unit carbon emission price, ε1 represents the carbon emission coefficient of coal-fired power generation, ε2 represents the carbon emission coefficient of gas-fired power generation or other energy sources, G buy,k represents the purchase amount of the kth coal-fired power generation, P buy,k represents the purchase amount of the kth gas or other high-carbon energy; Equipment operation and maintenance costs are: Where OM x represents the unit operation and maintenance cost coefficient of equipment x, P x,k represents the output power of device x at time k, G3 represents gas turbine, GH represents gas boiler, EH represents waste heat boiler, EC represents electric chiller, AC represents absorption chiller, and EG represents P2G device.

7. The micro-energy grid optimization scheduling method taking into account grid-connected frequency characteristics according to claim 1 is characterized in that: The mixed integer linear optimization theory is used to call the solver to solve the power system dispatch situation, total power system cost and frequency deviation, including: Yalmip is used to call the Gurobi solver to solve the power system dispatch situation, total cost of the power system and frequency deviation, and the intraday dispatch situation of different energy types, frequency deviation before and after optimization and total cost results are obtained.

8. A micro-energy network energy optimization and scheduling device taking into account the grid-connected frequency characteristics, characterized in that: include: A constraint unit is used to establish cooling and heating load constraints and a daily temperature model that considers thermal inertia of temperature propagation, and to establish cooling and heating electrical power balance constraints that consider unit types based on the cooling and heating load constraints; A modeling unit is used to set a fixed value for wind power and photovoltaic power output based on the cooling, heating and electrical power balance constraints, combine the fixed value with the electric power balance formula to calculate the power imbalance, and construct a frequency characteristic model of the power system based on the power imbalance; The target unit is used to combine the frequency characteristic model, take the total cost of the power system as the target, and consider the power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start and stop, electricity purchase costs, and gas purchase costs to establish the objective function for optimizing the scheduling of the micro-energy network; The solving unit is used to call the solver using mixed integer linear optimization theory to solve the power system scheduling situation, the total cost of the power system and the frequency deviation, and obtain the micro-energy network optimization scheduling plan taking into account the grid-connected frequency characteristics.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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