Energy optimization scheduling method and device for micro energy grid considering grid frequency characteristics

Abstract

The present application relates to a kind of micro energy network energy optimization scheduling method and device considering grid-connected frequency characteristic, method includes establishing cold and heat load constraint and intraday temperature model;Cold and heat load constraint is established based on the establishment of cold and heat electric power balance constraint;The output of wind power and photovoltaic is determined value setting, calculate power imbalance, according to power imbalance frequency characteristic model of power system is constructed;With power system total cost as target, consider power system operation and maintenance, carbon emission, frequency deviation penalty, equipment start-stop, purchase power cost and purchase gas cost establish the objective function of micro energy network optimization scheduling;Using mixed integer linear optimization theory solver is called to power system dispatching situation, power system total cost and frequency deviation are solved.The present application constructs a cold, heat, electricity multi-energy collaborative micro energy network intraday optimization scheduling model considering grid-connected frequency characteristic, improves model accuracy and multi-energy complementary collaborative effect, and suppresses the frequency deviation generated by different load coupling.

Description

Technical Field

[0001] This invention belongs to the field of micro-energy network operation optimization technology, specifically relating to a micro-energy network energy optimization scheduling method and device that takes into account grid connection frequency characteristics. Background Technology

[0002] The development and application of sustainable energy and the introduction of alternative fuels are among the main approaches to building new power systems and an inevitable choice for addressing the increasingly severe environmental threats and climate problems. However, as the proportion of renewable energy in integrated energy systems increases, the complexity of the coupling relationships between various systems and the resulting system frequency issues are also gradually intensifying. The high proportion of new energy power generation units has replaced a large number of traditional synchronous generators with rotating inertia, reducing the system's inertia level. This significantly reduces the frequency support capability of the power grid when subjected to disturbances, leading to increased frequency deviations under disturbances. Furthermore, new energy power generation units generally lack inertia and primary frequency regulation capabilities, causing the system's frequency regulation capability to decrease significantly with the increase in the proportion of new energy, and prolonging the time required for the system to recover stability after frequency deviations.

[0003] Research on the relationship between load fluctuations and frequency deviations in integrated energy networks is still in its initial stage. Existing research focuses on frequency fluctuations caused by power fluctuations or imbalances in a single energy source or electrical energy source within the system. There are relatively few system load types, and there is limited analysis of frequency fluctuation problems arising from multi-energy complementary micro-energy network scheduling optimization scenarios. Furthermore, there are few research inventions that specifically consider frequency fluctuations in system operating cost calculations. 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, existing technologies have significant shortcomings in analyzing frequency fluctuations and calculating system operating costs that take frequency fluctuations into account in the scenario of multi-energy complementary micro-energy network scheduling optimization. They are insufficient to meet the actual needs of safe and economical operation of integrated energy networks. Therefore, conducting research on these issues has important theoretical significance and application value. Summary of the Invention

[0005] Therefore, the technical problem to be solved by this invention is to overcome the technical defects existing in the prior art, and to propose a microgrid energy optimization scheduling method and device that takes into account the grid connection frequency characteristics.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a microgrid energy optimization scheduling method considering grid connection frequency characteristics, comprising the following steps:

[0007] Establish a cold and heat load constraint and intraday temperature model that considers the thermal inertia of temperature propagation;

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

[0009] Based on the power balance constraints of heating, cooling, electricity and gas, the output values ​​of wind power and photovoltaic power are set to a certain value. The power imbalance is calculated by combining the certain value with the power balance formula. The frequency characteristic model of the power system is constructed based on the power imbalance.

[0010] Based on the frequency characteristic model, and taking the total cost of the power system as the objective, an objective function for the optimal scheduling of micro energy networks is established, taking into account power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, electricity purchase costs, and gas purchase costs.

[0011] The solution is obtained by using mixed integer linear optimization theory to solve for the power system dispatch status, total power system cost and frequency deviation, and to obtain an optimized dispatch scheme for microgrids that takes into account grid connection frequency characteristics.

[0012] In one embodiment of the present invention, establishing cold and hot load constraints that take into account the thermal inertia of temperature propagation includes:

[0013] Initial outdoor temperature constraints for heating:

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

[0015] In the formula, T out,hot,1 and T out,hot,2 These represent the outdoor temperatures at times 1 and 2, respectively. A value of 0 indicates that the system is not started or is in a cooling state.

[0016] Outdoor temperature constraints for heating after normal operation:

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

[0018] In the formula, T out,hot,i and T out,hot,i-2 Let i and i-2 represent the outdoor temperatures at times i and i-2, respectively.

[0019] Electricity system preheating or steady-state initial conditions for indoor heating temperature constraints:

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

[0021] In the formula, T in，hot，1 and T in，hot，2 These represent the indoor temperatures at times 1 and 2, respectively.

[0022] Constraints on the relationship between indoor, heating, and outdoor temperatures:

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

[0024] In the formula, T gong,i Let represent the heating temperature at time i, α represent the historical temperature inertia coefficient, β represent the engineering temperature influence coefficient, and χ represent the outlet temperature feedback coefficient.

[0025] Heating network water supply temperature constraints:

[0026] T hui,i-2 ≤T gong,i ≤120;

[0027] In the formula, T hui,i-2 This represents the 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] In the formula, T hui,i-2 T represents the temperature at time i-2. in,hot,k T represents the indoor temperature at time k. out,hot,k Let T be the outdoor temperature at time k. gong,i Let α1 and α2 represent the thermal inlet temperature coefficients, β1, β2, and β3 represent the engineering temperature coefficients, and δ1, δ2, and δ3 represent the thermal outlet temperature coefficients.

[0033] Lower limit constraint of heat load:

[0034] 10≤load h,i-2 ;

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

[0036] In the formula, loadh，i-2 T represents the heat load at time i-2. gong,i T represents the heating temperature at time i. hui,i-2 This represents the historical temperature at time i-2;

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

[0038]

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

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

[0041] Electrical balance constraints:

[0042]

[0043] In the formula, P WT,i P represents the predicted wind power output at time i. PV,i P represents the photovoltaic power output prediction at time i. net,i P represents the net exchange power at time i. G3,i P represents the generator output at time i. EC,i COP represents the output cooling power of the electric refrigeration unit at time i. EC P represents the energy efficiency ratio of electric refrigeration equipment. EG,i L represents the gas power output of the P2G device at time i, EG represents the energy efficiency ratio of the P2G device, and L represents the power output of the P2G device at time i. e,i This represents the load demand at time i;

[0044] Thermal equilibrium constraint:

[0045]

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

[0047] Cold balance constraint:

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

[0049] In the formula, load c,i This 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] In the formula, G grid，i G represents the gas power purchased from the natural gas network at time i. EG,i G represents the gas production power of the P2G device at time i. L,i G represents the natural gas demand of users in the microgrid at time i. G3，i G represents the gas turbine's gas production power at time i. GH,i This represents the gas consumption power of the gas boiler at time i;

[0053] Gas balance constraints:

[0054]

[0055] In the formula, G buy,i η represents the amount of gas purchased at time i. G3 GH represents the electrical efficiency of the gas turbine, and L represents the efficiency of the gas boiler. g,i This represents the gas load demand at time i.

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

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

[0058]

[0059] In the formula, ΔP i P represents the power imbalance at time i. EC,i-1 P represents the power consumption of the electric chiller at time i. EG,i-1 L represents the power consumption of the P2G device at time i-1. e,i-1 Represents the electrical load at time i-1; Δf iLet represent the frequency deviation at time i, M represent the system's inertial time constant, and D represent the system's damping coefficient.

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

[0061]

[0062] In the formula, Δf i , Δf i-1 These represent the frequency deviation values ​​at time i and time i-1, respectively;

[0063] The frequency deviation value is calculated at each discrete time step using the frequency deviation formula. Once the frequency deviation value is calculated, the generator responds automatically through the speed governor, and its output deviation is proportional to the frequency deviation.

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

[0065] In the formula, P G3,scheduled,i K represents the planned active power of generator G3 at time i. G3 It is the adjustment coefficient of generator G3.

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

[0067]

[0068] In the formula, Δt represents the value of time.

[0069] In one embodiment of the present invention, the objective function for the optimized scheduling of the micro-energy network is:

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

[0071] In the formula, C Ng C represents the cost of purchasing natural gas. buy C represents the cost of purchasing electricity. sell C represents the revenue from selling electricity. fluctuation C represents the frequency fluctuation penalty. carbon C represents the cost of carbon emissions. start_stop C represents the start-up and shutdown cost of the equipment.OM This indicates the operating and maintenance costs of the equipment;

[0072] The equipment start-up and shutdown costs are as follows:

[0073]

[0074] In the formula, C start_G3 N represents the single-start cost of generator G3. start C represents the number of times generator G3 is started within a scheduling cycle. stop_G3 N represents the single-stop cost of generator G3. stop This indicates the number of times generator G3 is shut down within a scheduling cycle;

[0075] The carbon emission cost is:

[0076]

[0077] In the formula, C carbon p represents the total carbon emission cost. carbon G represents the price per unit of carbon emissions, 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, and G buy,k P represents the purchase quantity of the k-th coal-fired power plant. buy,k This represents the purchase quantity of the k-th gas or other high-carbon energy source;

[0078] Equipment maintenance costs are:

[0079]

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

[0081] In one embodiment of the present invention, a solver is invoked using mixed-integer linear optimization theory to solve for power system dispatching conditions, total power system cost, and frequency deviation, including:

[0082] The Gurobi solver was called using Yalmip to solve for the power system dispatch, total power system cost, and frequency deviation, and the intraday dispatch of different energy types, frequency deviation and total cost results before and after optimization were obtained.

[0083] Based on the same inventive concept, the present invention also provides a microgrid energy optimization scheduling device that takes into account grid connection frequency characteristics, comprising:

[0084] The constraint unit is used to establish cold and hot load constraints and intraday temperature models that take into account the thermal inertia of temperature propagation, and to establish cold, hot, electrical and power balance constraints that take into account the type of unit based on the cold and hot load constraints.

[0085] The modeling unit is used to set deterministic values ​​for the output of wind power and photovoltaic power based on the power balance constraints of cold, heat and electricity, combine the deterministic values ​​with the power balance formula to calculate the power imbalance, and construct the frequency characteristic model of the power system based on the power imbalance.

[0086] The target unit is used to combine the frequency characteristic model and establish the objective function for the optimal scheduling of micro energy networks with the total cost of the power system as the objective, taking into account the power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, electricity purchase cost and gas purchase cost.

[0087] The solution unit is used to call the solver using mixed integer linear optimization theory to solve for the power system dispatch status, total power system cost and frequency deviation, and obtain the microgrid optimal dispatch scheme taking into account the grid connection 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, wherein when the processor executes the computer program, it implements the method described above.

[0089] Based on the same inventive concept, the present invention also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0090] The beneficial effects of this invention are as follows: By introducing mixed integer linear optimization theory, this invention constructs an intraday optimization scheduling model for a multi-energy coordinated microgrid that considers grid-connected frequency characteristics, which can significantly improve the accuracy of integrated energy system modeling, enhance the synergistic efficiency of multi-energy complementarity, and effectively mitigate the frequency deviation caused by the coupled operation of different types of loads, thereby optimizing the efficiency of energy resource allocation while ensuring the stability of system frequency. Attached Figure Description

[0091] Figure 1 A flowchart of a microgrid energy optimization scheduling method that takes into account grid connection frequency characteristics, provided for an embodiment.

[0092] Figure 2 A schematic diagram of the structure of a microgrid energy optimization scheduling device that takes into account grid connection frequency characteristics, provided for an embodiment.

[0093] Figure 3 This is a schematic diagram of the operation process of the power system in the embodiment.

[0094] Figure 4 The graph shows the frequency deviation of the power system before and after operation optimization for one cycle in the example.

[0095] Figure 5 The indoor and outdoor temperature curves are shown in the example.

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

[0097] Figure 7 The examples show different types of load curves. Detailed Implementation

[0098] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0099] Reference Figure 1 As shown, this embodiment of the invention provides a microgrid energy optimization scheduling method that takes into account grid connection frequency characteristics, including the following steps:

[0100] Step S1: Establish cold and heat load constraints and intraday temperature model that consider the thermal inertia of temperature propagation;

[0101] Step S2: Establish cooling, heating, electrical and power balance constraints based on cooling and heating load constraints, taking into account unit type;

[0102] Step S3: Based on the power balance constraints of cold, heat, electricity and gas, set the output values ​​of wind power and photovoltaic power, combine the set values ​​with the power balance formula to calculate the power imbalance, and construct the frequency characteristic model of the power system based on the power imbalance.

[0103] Step S4: Combining the frequency characteristic model, with the total cost of the power system as the objective, and considering power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, electricity purchase cost and gas purchase cost, establish the objective function for the optimal scheduling of micro energy networks;

[0104] Step S5: Using mixed integer linear optimization theory, the solver is called to solve for the power system dispatch status, total power system cost, and frequency deviation, so as to obtain an optimized dispatch scheme for the micro-energy network that takes into account the grid connection frequency characteristics.

[0105] This invention introduces mixed-integer linear optimization theory to construct an intraday optimal scheduling model for a multi-energy coordinated microgrid that considers grid-connected frequency characteristics, including cooling, heating, and electricity. This model can significantly improve the accuracy of integrated energy system modeling, enhance the synergistic efficiency of multi-energy complementarity, and effectively mitigate the frequency deviation caused by the coupled operation of different types of loads, thereby optimizing the efficiency of energy resource allocation while ensuring system frequency stability.

[0106] In step S1, establishing the cold and hot load constraints that take into account the thermal inertia of temperature propagation includes:

[0107] Initial outdoor temperature constraints for heating:

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

[0109] In the formula, T out,hot,1 and T out,hot,2 These represent the outdoor temperatures at times 1 and 2, respectively. A value of 0 indicates that the system is not started or is in a cooling state, in order to start subsequent iterative calculations.

[0110] Outdoor temperature constraints for heating after normal operation:

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

[0112] In the formula, T out,hot,i and T out,hot,i-2 Let i and i-2 represent the outdoor temperatures at times 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 temperatures of the previous two time steps.

[0113] Electricity system preheating or steady-state initial conditions for indoor heating temperature constraints:

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

[0115] In the formula, T in，hot，1 and T in，hot，2 These represent the indoor temperatures at times 1 and 2, respectively.

[0116] Constraints on the relationship between indoor, heating, and outdoor temperatures:

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

[0118] In the formula, T gong,i α represents the heating temperature at time i, α represents the historical temperature inertia coefficient, reflecting the power system's dependence on its own historical temperature, β represents the engineering temperature influence coefficient, reflecting the temperature regulation effect of external engineering parameters, and χ represents the outlet temperature feedback coefficient, which reflects the reverse influence of the heat-side outlet temperature on the inlet.

[0119] Heating network water supply temperature constraints:

[0120] Thui,i-2 ≤T gong,i ≤120;

[0121] In the formula, T hui,i-2 This represents the return temperature at time i-2, such as the return temperature of a circulating system. To prevent overheating from damaging the equipment or triggering the protection mechanism, the equipment safety threshold is set to 120℃. To prevent a sudden drop in temperature from causing system instability, the current engineering temperature is not 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] In the formula, T hui,i-2 T represents the temperature at time i-2. in,hot,k T represents the indoor temperature at time k. out,hot,k Let α1 and α2 represent the outdoor temperature at time k, β1, β2, and β3 represent the engineering temperature coefficients, and δ1, δ2, and δ3 represent the heat measurement outlet temperature coefficients. This equation represents the return temperature T. hui,i-2 It is a linear combination of multiple historical temperature variables. The weighting coefficients reflect the contribution of each temperature to the temperature recovery, which can be divided into two aspects: one is the positive influence: the current engineering temperature T gong,k and outlet temperature T out,hot,k It has large positive coefficients β1 and δ1. Secondly, it is historically dependent: the indoor and outdoor temperatures in the first one or two steps jointly regulate the return temperature.

[0127] Lower limit constraint of heat load:

[0128] 10≤load h,i-2 ;

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

[0130] In the formula, load h，i-2 This represents the heat load at time i-2, and its relationship with the heating temperature T at time i. gong,iCompared with the historical temperature T at time i-2 hui,i-2 The difference is related; the initial indoor temperature is set to -15℃ during cooling, and the indoor temperature range during cooling is between -15℃ and -20℃. (T) gong,i T represents the heating temperature at time i. hui,i-2 This represents the historical temperature at time i-2;

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

[0132]

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

[0134] In step S2, establishing a cooling, heating, electrical, and power balance constraint based on unit type, taking into account both cooling and heating load constraints, includes:

[0135] Electrical balance constraints:

[0136]

[0137] In the formula, P WT,i P represents the predicted wind power output at time i. PV,i P represents the photovoltaic power output prediction at time i. net,i P represents the net exchange power at time i. G3,i P represents the generator output at time i. EC,i COP represents the output cooling power of the electric refrigeration unit at time i. EC The energy efficiency ratio (P) of an electric refrigeration unit represents the cooling capacity generated per unit of electricity. EG,i Let EG represent the gas output power of the P2G device at time i, EG represent the energy efficiency ratio of the P2G device, and L represent the efficiency of converting unit electricity into gas. e,i This represents the load demand at time i;

[0138] Thermal equilibrium constraint:

[0139]

[0140] In the formula, P EH,i P represents the output power of the waste heat boiler at time i. GH,i P represents the output power of the gas heater at time i. AC,i COP represents the input power of the air conditioner at time i.AC The energy efficiency ratio (EER) of an air conditioner represents the heat output per unit of electrical power, and the load is also mentioned. h,i This represents the heat load demand at time i;

[0141] Cold balance constraint:

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

[0143] In the formula, load c,i This represents the cooling load demand at time i;

[0144] Natural gas supply and demand balance constraints:

[0145]

[0146] In the formula, G grid，i G represents the gas power purchased from the natural gas network at time i. EG,i G represents the gas production power of the P2G device at time i. L,i G represents the natural gas demand of users in the microgrid at time i. G3，i G represents the gas turbine's gas production power at time i. GH,i This represents the gas consumption power of the gas boiler at time i;

[0147] Gas balance constraints:

[0148]

[0149] In the formula, G buy,i η represents the amount of gas purchased at time i. G3 GH represents the electrical efficiency of the gas turbine, and L represents the efficiency of the gas boiler. g,i This represents the gas load demand at time i.

[0150] In step S3, the frequency characteristic model of the power system is constructed based on the power imbalance, including:

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

[0152]

[0153] In the formula, ΔP i P represents the power imbalance at time i. EC,i-1 P represents the power consumption of the electric chiller at time i. EG,i-1 L represents the power consumption of the P2G device at time i-1. e,i-1 Represents the electrical load at time i-1; Δfi Let represent the frequency deviation at time i, M represent the system's inertial time constant, and D represent the system's damping coefficient.

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

[0155]

[0156] In the formula, Δf i , Δf i-1 These represent the frequency deviation values ​​at time i and time i-1, respectively;

[0157] The frequency deviation value is calculated at each discrete time step using the frequency deviation formula. Once the frequency deviation value is calculated, the generator responds automatically through the speed governor, and its output deviation is proportional to the frequency deviation.

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

[0159] In the formula, P G3,scheduled,i K represents the planned active power of generator G3 at time i. G3 It is the adjustment coefficient of generator G3.

[0160] Furthermore, after calculating the frequency deviation at each moment through the aforementioned steps, a safety check needs to be performed on the "rate" of frequency change, i.e., a rate of change of frequency (ROCOF) constraint check is performed, where the rate of change of frequency constraint is:

[0161]

[0162] In the formula, Δt represents the time value, preferably one hour.

[0163] In step S4, the objective function for the optimized scheduling of the micro-energy network is:

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

[0165] In the formula, C Ng C represents the cost of purchasing natural gas. buy C represents the cost of purchasing electricity. sell C represents the revenue from selling electricity. fluctuation C represents the frequency fluctuation penalty. carbon C represents the cost of carbon emissions.start_stop C represents the start-up and shutdown cost of the equipment. OM This indicates the operating and maintenance costs of the equipment;

[0166] The equipment start-up and shutdown costs are as follows:

[0167]

[0168] In the formula, C start_G3 N represents the single-start cost of generator G3. start C represents the number of times generator G3 is started within a scheduling cycle. stop_G3 N represents the single-stop cost of generator G3. stop This indicates the number of times generator G3 is shut down within a scheduling cycle;

[0169] The carbon emission cost is:

[0170]

[0171] In the formula, C carbon p represents the total carbon emission cost. carbon G represents the price per unit of carbon emissions, 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, and G buy,k P represents the purchase quantity of the k-th coal-fired power plant. buy,k This represents the purchase quantity of the k-th gas or other high-carbon energy source;

[0172] Equipment maintenance costs are:

[0173]

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

[0175] In step S5, Yalmip can be used to call the Gurobi solver to solve for the power system dispatch status, total power system cost, and frequency deviation, thereby obtaining the intraday dispatch status of different energy types, frequency deviation before and after optimization, and total cost results.

[0176] The following specific implementation case will be used to elaborate on the energy optimization scheduling method for microgrids that takes into account grid connection frequency characteristics proposed in this invention.

[0177] The relevant parameter values ​​for the microgrid 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 power purchase price adopts the time-of-use electricity price for ordinary industrial users with voltage below 1kV in Province A, and the power sales price refers to the benchmark on-grid electricity price for coal-fired power generation in Province A, as shown in Table 2.

[0182]

[0183] A mixed-integer linear programming model of a microgrid, considering grid-connected frequency characteristics, was solved using the Gurobi solver via Yalmip. The results yielded intraday scheduling for different energy types, frequency deviations before and after optimization, and total cost. See the detailed solution results below. Figure 4 — Figure 7 As shown in Table 3, the results before and after the objective function optimization are compared.

[0184] Table 3

[0185]

[0186] This implementation case fully considers the impact of wind and solar power output on system frequency deviation, takes into account the coupled operation of cooling, heating, and electrical loads in the micro-energy network, and considers the thermal inertia of the energy supply system and the functional relationship between indoor, heating, and outdoor temperatures. A micro-energy network scheduling model with multi-energy complementarity of cooling, heating, and electrical loads is constructed. A mixed-integer linear programming model for the optimal configuration of the micro-energy network, taking into account grid-connected frequency characteristics, is constructed with objective function and constraints to obtain an optimal configuration method, thereby achieving the mitigation of system frequency deviation and cost optimization.

[0187] Based on the same inventive concept, and referring to Figure 2 As shown, this embodiment of the invention also provides a microgrid energy optimization scheduling device that takes into account grid connection frequency characteristics, including:

[0188] The constraint unit is used to establish cold and hot load constraints and intraday temperature models that take into account the thermal inertia of temperature propagation, and to establish cold, hot, electrical and power balance constraints that take into account the type of unit based on the cold and hot load constraints.

[0189] The modeling unit is used to set deterministic values ​​for the output of wind power and photovoltaic power based on the power balance constraints of cold, heat and electricity, combine the deterministic values ​​with the power balance formula to calculate the power imbalance, and construct the frequency characteristic model of the power system based on the power imbalance.

[0190] The target unit is used to combine the frequency characteristic model and establish the objective function for the optimal scheduling of micro energy networks with the total cost of the power system as the objective, taking into account power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, electricity purchase cost and gas purchase cost.

[0191] The solution unit is used to call the solver using mixed integer linear optimization theory to solve for the power system dispatch status, total power system cost and frequency deviation, and obtain the microgrid optimal dispatch scheme taking into account the grid connection frequency characteristics.

[0192] This invention introduces mixed-integer linear optimization theory to construct an intraday optimal scheduling model for a multi-energy coordinated microgrid that considers grid-connected frequency characteristics, including cooling, heating, and electricity. This model can significantly improve the accuracy of integrated energy system modeling, enhance the synergistic efficiency of multi-energy complementarity, and effectively mitigate the frequency deviation caused by the coupled operation of different types of loads, thereby optimizing the efficiency of energy resource allocation while ensuring system frequency stability.

[0193] The apparatus described above is used to implement the corresponding microgrid energy optimization scheduling method that takes into account the grid connection frequency characteristics in the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0194] It should be noted that the accompanying drawings of the embodiments of this disclosure only involve structures related to the embodiments of this disclosure, and other structures can be referred to in general design.

[0195] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0196] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0197] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the microgrid energy optimization scheduling method considering grid connection frequency characteristics as described in any of the above embodiments.

[0198] In this embodiment of the 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 can call programs stored in the memory. Specifically, the processor can execute the operations in the above-described embodiment of the microgrid energy optimization scheduling method that takes into account grid connection frequency characteristics.

[0200] The memory is used to store one or more programs, which may include program code, including computer operation 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 embodiments, this invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described microgrid energy optimization scheduling method taking into account grid connection frequency characteristics.

[0203] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0204] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0205] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0206] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0207] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A microgrid energy optimization scheduling method considering grid connection frequency characteristics, characterized in that, Includes the following steps: Establish a cold and heat load constraint and intraday temperature model that considers the thermal inertia of temperature propagation; Establish cooling, heating, electrical, and power balance constraints based on cooling and heating load constraints, taking into account unit type; Based on the power balance constraints of heating, cooling, electricity and gas, the output values ​​of wind power and photovoltaic power are set to a certain value. The power imbalance is calculated by combining the certain value with the power balance formula. The frequency characteristic model of the power system is constructed based on the power imbalance. Based on the frequency characteristic model, and taking the total cost of the power system as the objective, an objective function for the optimal scheduling of micro energy networks is established, taking into account power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, electricity purchase costs, and gas purchase costs. The solution is obtained by using mixed integer linear optimization theory to solve for the power system dispatch status, total power system cost and frequency deviation, and to obtain an optimized dispatch scheme for microgrids that takes into account grid connection frequency characteristics. Establishing cold and heat load constraints that take into account thermal inertia and temperature propagation includes: Initial outdoor temperature constraints for heating: ； In the formula, T out,hot,1 and T out,hot,2 These represent the outdoor temperatures at times 1 and 2, respectively. A value of 0 indicates that the system is not started or is in a cooling state. Outdoor temperature constraints for heating after normal operation: ； In the formula, T out,hot,i and T out,hot,i-2 They represent i and i-2 The outdoor temperature at any given time; Electricity system preheating or steady-state initial conditions for indoor heating temperature constraints: ； In the formula, T in，hot，1 and T in，hot，2 These represent the indoor temperatures at times 1 and 2, respectively. Constraints on the relationship between indoor, heating, and outdoor temperatures: ； In the formula, T gong,i express i The heating temperature at any given time Indicates the historical temperature inertia coefficient. Indicates the temperature influence coefficient of the project. Indicates the outlet temperature feedback coefficient; Heating network water supply temperature constraints: ； In the formula, T hui,i-2 express i- The temperature at time 2; Constraints on the relationship between water supply temperature and outdoor temperature: ； In the formula, T hui,i-2 express i- The temperature at time 2. Indicates the first k Indoor temperature at any given time for k outdoor temperature at any time express i The current temperature of the project at any given moment. , Indicates the thermal inlet temperature coefficient. , , Indicates the engineering temperature coefficient. , , Indicates the thermal outlet temperature coefficient; Lower limit constraint of heat load: ； ； In the formula, load h，i-2 express i- Heat load at time 2 T gong,i express i The heating temperature at any given time T hui,i-2 express i- Historical temperature at time 2; Constraints on the dynamic balance between cooling load and temperature: ； In the formula, express i- Cooling load at moment 1 express i The cold side inlet temperature at any given time, express i- The outlet temperature at time 1 Indicates thermal resistance. Indicates the cold-side heat capacity, exponential term This represents the attenuation factor, which reflects the dynamic characteristics of the power system.

2. The microgrid energy optimization scheduling method considering grid connection frequency characteristics according to claim 1, characterized in that, The establishment of cooling, heating, electrical, and power balance constraints based on cooling and heating load constraints, taking into account unit type, includes: Electrical balance constraints: ； In the formula, express i Real-time wind power output forecast express i Real-time photovoltaic power output forecast express i Net exchange power at time, express i The generator output at any given time Indicates that the electric refrigeration equipment is in i Output cooling power at all times Indicates the energy efficiency ratio of electric refrigeration equipment. Indicates P2G devices in i The air power output at all times, Indicates the energy efficiency ratio of P2G devices. express i The load demand at any given moment; Thermal equilibrium constraint: ； In the formula, P EH,i Indicates waste heat boiler i Output power at any moment P GH,i Indicates gas heater i Output power at any moment P AC,i Indicates that the air conditioner is i Input power at time t, COP AC Indicates the air conditioner's energy efficiency ratio. load h,i express i The heat load demand at any given time; Cold balance constraint: ； In the formula, load c,i express The cooling load requirement at any given time; Natural gas supply and demand balance constraints: ； In the formula, G grid，i express The amount of gas power constantly purchased from the natural gas network, express The gas production capacity of the P2G unit at any given time. express Natural gas demand of users in the Moment Micro Energy Network G G3，i express The gas production capacity of the gas turbine at any given time. express The gas consumption power of the gas boiler at all times; Gas balance constraints: ； In the formula, express i Gas purchase amount at any given time GH represents the electrical efficiency of the gas turbine, while GH represents the efficiency of the gas boiler. It means i The gas load demand at any given time.

3. The microgrid energy optimization scheduling method considering grid connection frequency characteristics according to claim 2, characterized in that, Based on the power imbalance, a frequency characteristic model of the power system is constructed, including: The frequency deviation is driven by the power imbalance, and its dynamic characteristics are described by a differential equation: where the power imbalance and the differential equation are respectively: ； ； In the formula, express i The power imbalance at any given moment. express i The power consumption of the electric chiller at any given time. express i- Power consumption of the P2G device at time 1. express i- Electrical load at moment 1; express i Frequency deviation value at time, M Represents the system's inertial time constant. D Indicates the damping coefficient of the system; Discretize the differential equation to construct the frequency deviation relationship between adjacent time steps: ； In the formula, , They represent in i Time and i- Frequency deviation at time 1; The frequency deviation value is calculated at each discrete time step using the frequency deviation formula. Once the frequency deviation value is calculated, the generator responds automatically through the speed governor, and its output deviation is proportional to the frequency deviation. ； In the formula, P G3,scheduled,i Indicates in The planned active power of generator G3 at time [time]. K G3 It is the adjustment coefficient of generator G3.

4. The microgrid energy optimization scheduling method considering grid connection frequency characteristics according to claim 3, characterized in that, After obtaining the frequency deviation value, the frequency change rate is calculated, and a frequency change rate constraint check is performed. The frequency change rate constraint is as follows: ； In the formula, Indicates the time value.

5. The microgrid energy optimization scheduling method considering grid connection frequency characteristics according to claim 1, characterized in that, The objective function for the optimized scheduling of the micro-energy network is: ； In the formula, C Ng This indicates the cost of purchasing natural gas. C buy Indicates the cost of purchasing electricity. C sell This indicates the revenue from selling electricity. C fluctuation Indicates the penalty for frequency fluctuations. C carbon Indicates carbon emission costs, C start_stop This indicates the start-up and shutdown costs of the equipment. C OM This indicates the operating and maintenance costs of the equipment; The equipment start-up and shutdown costs are as follows: ； In the formula, C start_G3 This represents the cost of starting generator G3 on a single attempt. N start This indicates the number of times generator G3 is started within a scheduling cycle. C stop_G3 This represents the cost of a single shutdown of generator G3. N stop This indicates the number of times generator G3 is shut down within a scheduling cycle; The carbon emission cost is: ； In the formula, C carbon Indicates the total cost of carbon emissions. p carbon Indicates the price per unit of carbon emissions. This represents the carbon emission coefficient of coal-fired power generation. Indicates the carbon emission coefficient of gas-fired power generation or other energy sources. G buy,k Indicates the first Purchase volume of coal-fired power generation, P buy,k Indicates the first Purchase volume of natural gas or other high-carbon energy sources; Equipment maintenance costs are: ； In the formula, OM x Indicates device The unit operation and maintenance cost coefficient, P x,k Indicates device exist k The output power at any given time, 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 equipment.

6. The microgrid energy optimization scheduling method considering grid connection frequency characteristics according to claim 1, characterized in that, The solution is obtained by using mixed-integer linear optimization theory to solve for power system dispatch, total power system cost, and frequency deviation, including: The Gurobi solver was called using Yalmip to solve for the power system dispatch, total power system cost, and frequency deviation, and the intraday dispatch of different energy types, frequency deviation and total cost results before and after optimization were obtained.

7. A microgrid energy optimization and scheduling device considering grid connection frequency characteristics, characterized in that, include: The constraint unit is used to establish cold and hot load constraints and intraday temperature models that take into account the thermal inertia of temperature propagation, and to establish cold, hot, electrical and power balance constraints that take into account the type of unit based on the cold and hot load constraints. The modeling unit is used to set deterministic values ​​for the output of wind power and photovoltaic power based on the power balance constraints of cold, heat and electricity, combine the deterministic values ​​with the power balance formula to calculate the power imbalance, and construct the frequency characteristic model of the power system based on the power imbalance. The target unit is used to combine the frequency characteristic model and establish the objective function for the optimal scheduling of micro energy networks with the total cost of the power system as the objective, taking into account power system operation and maintenance, carbon emissions, frequency deviation penalties, equipment start-up and shutdown, electricity purchase cost and gas purchase cost. The solution unit is used to call the solver using mixed integer linear optimization theory to solve for the power system dispatch status, total power system cost and frequency deviation, and obtain the microgrid optimal dispatch scheme taking into account the grid connection frequency characteristics; Establishing cold and heat load constraints that take into account thermal inertia and temperature propagation includes: Initial outdoor temperature constraints for heating: ； In the formula, T out,hot,1 and T out,hot,2 These represent the outdoor temperatures at times 1 and 2, respectively. A value of 0 indicates that the system is not started or is in a cooling state. Outdoor temperature constraints for heating after normal operation: ； In the formula, T out,hot,i and T out,hot,i-2 They represent i and i-2 The outdoor temperature at any given time; Electricity system preheating or steady-state initial conditions for indoor heating temperature constraints: ； In the formula, T in，hot，1 and T in，hot，2 These represent the indoor temperatures at times 1 and 2, respectively. Constraints on the relationship between indoor, heating, and outdoor temperatures: ； In the formula, T gong,i express i The heating temperature at any given time Indicates the historical temperature inertia coefficient. Indicates the temperature influence coefficient of the project. Indicates the outlet temperature feedback coefficient; Heating network water supply temperature constraints: ； In the formula, T hui,i-2 express i- The temperature at time 2; Constraints on the relationship between water supply temperature and outdoor temperature: ； In the formula, T hui,i-2 express i- The temperature at time 2. Indicates the first k Indoor temperature at any given time for k outdoor temperature at any time express i The current temperature of the project at any given moment. , Indicates the thermal inlet temperature coefficient. , , Indicates the engineering temperature coefficient. , , Indicates the thermal outlet temperature coefficient; Lower limit constraint of heat load: ； ； In the formula, load h，i-2 express i- Heat load at time 2 T gong,i express i The heating temperature at any given time T hui,i-2 express i- Historical temperature at time 2; Constraints on the dynamic balance between cooling load and temperature: ； In the formula, express i- Cooling load at moment 1 express i The cold side inlet temperature at any given time, express i- The outlet temperature at time 1 Indicates thermal resistance. Indicates the cold-side heat capacity, exponential term This represents the attenuation factor, which reflects the dynamic characteristics of the power system.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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