Improved MVO-based low-carbon optimal scheduling method and system for electric heating gas coupling comprehensive energy

Abstract

The invention is suitable for the technical field of low-carbon optimal scheduling, and provides an improved MVO-based low-carbon optimal scheduling method and system for electric heating gas coupling comprehensive energy, and the method comprises the steps: building a low-carbon scheduling model of an electric heating gas coupling comprehensive energy system with the minimum operation cost as a target, the electric heating gas coupling comprehensive energy system comprises a heat and power cogeneration unit, an air source heat pump, electric heating gas of electricity-to-gas and related stored energy. Determining an electric load balance constraint, a thermal load balance constraint, a gas load balance constraint, an equipment and climbing constraint, a charge-discharge power constraint of a storage battery and a heat storage tank, and a tie line and gas purchase constraint; and performing parameter optimization on the low-carbon scheduling model through an improved MVO optimization algorithm to obtain an optimal solution of the low-carbon scheduling scheme. Based on the influence of ASHP variable working condition operation and stepped carbon transaction in system economy and emission reduction, the problems of energy waste and cost increase are reduced through the proposed low-carbon optimization scheduling strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon optimization scheduling, and in particular to a low-carbon optimization scheduling method and system for electric, thermal and gas coupled comprehensive energy based on improved MVO. Background Art

[0002] Integrated Energy System (IES) integrates, optimizes and coordinates multiple energy types to achieve a clean, low-carbon and efficient energy system. As the installed capacity of renewable energy increases year by year, improving the absorption capacity of renewable energy has become an important research direction for IES optimization scheduling. The reliable absorption of renewable energy helps reduce dependence on fossil fuels, reduce greenhouse gas emissions, and further promote environmental sustainable development.

[0003] In the prior art, researchers have provided a solution to the complementary consumption of renewable energy through power to gas (P2G) technology and gas turbines based on the complementary characteristics of electrical coupling equipment, which can realize two-way conversion of gas and electricity. Therefore, some have proposed that P2G and energy storage constitute hybrid energy storage, optimized the scheduling of the IES day-ahead interval containing renewable energy, and verified that the model can improve the consumption rate of wind and solar power and reduce the operating cost of the system. Others, starting from improving the efficiency of wind power utilization, have established an optimized scheduling model of electric-thermal coupling containing air source heat pump (ASHP) to reduce the coal consumption of combined heat and power (CHP) system operation. In addition, a variable operating condition capacity output and energy efficiency ratio model of ASHP has been constructed, and the variable operating condition model of ASHP has been analyzed to consider the actual operation of ASHP more comprehensively. However, the above technology ignores the waste heat generated by P2G. Specifically, it can be seen from the chemical formula that the reaction heat in the methane production stage can be well recycled, thereby expanding the potential for P2G wind and solar power consumption. When the ASHP optimization scheduling operation model is included, the variable operating characteristics of ASHP are not taken into account, which makes the wind power consumption inaccurate, resulting in an imbalance between source and load supply and demand, and further causing energy waste and cost increase. Summary of the invention

[0004] The embodiments of the present application provide a low-carbon optimization scheduling method and system for electric, thermal and gas coupled integrated energy based on improved MVO, which is used to solve the problems of energy waste and cost increase.

[0005] The first aspect of the embodiment of the present application provides a low-carbon optimization scheduling method for electric, thermal and gas coupled comprehensive energy based on improved MVO, including:

[0006] A low-carbon dispatch model of an electric, heat and gas coupled integrated energy system is constructed with the goal of minimizing operating costs. The electric, heat and gas coupled integrated energy system includes a combined heat and power unit, an air source heat pump, electric heat and gas from a power-to-gas converter, and related energy storage.

[0007] Determining the constraints of the low-carbon dispatch model, the constraints including electric load balance constraints, thermal load balance constraints, gas load balance constraints, equipment and ramp constraints, battery and thermal storage tank charge and discharge power constraints, and tie line and gas purchase constraints;

[0008] The parameters of the low-carbon scheduling model are optimized through the improved MVO optimization algorithm to obtain the optimal solution of the low-carbon scheduling scheme.

[0009] Furthermore, the low-carbon dispatch model of the electric, heat and gas coupled integrated energy system is constructed with the goal of minimizing the operating cost. The electric, heat and gas coupled integrated energy system includes a combined heat and power unit, an air source heat pump, electric heat and gas from power-to-gas, and related energy storage, including:

[0010] The expression of low-carbon dispatch model is:

[0011] minC=C om +C buy,e +C buy,g +w q +F co2

[0012] Where: C om Cost of maintaining equipment for the system, C buy,e and C buy,g are the cost of electricity purchase and gas purchase, w q is the penalty cost for abandoning wind and solar power, F co2 The cost of carbon emissions.

[0013] Furthermore, the determining of the constraint conditions of the low-carbon scheduling model includes:

[0014] Electric load balancing constraints:

[0015] P pv,t +P wt,t +P buy,t +P MT,t +P soc,dis,t =P load,t +P soc,char,t +P P2G,t +P ASHP,t

[0016] +P sell,t

[0017] Where: P pv,t is the photovoltaic power actually consumed at time t, P wt,tis the wind power output actually consumed at time t, P MT,t is the power generated by the gas turbine of the CHP unit at time t, P load,t is the user's electricity load at time t,

[0018] P buy,t and P sell,t They are the purchase and sale of electricity at time t, P P2G,t is the power consumed by P2G at time t, P ASHP,t is the power consumed by ASHP at time t, P soc,char,t and P soc,dis,t are the battery charging and discharging power respectively.

[0019] Furthermore, the determining of the constraint conditions of the low-carbon scheduling model includes:

[0020] Heat load balancing constraints:

[0021]

[0022] Where: Q HE,t is the heat generated by the waste heat boiler of the CHP unit at time t, is the thermal power generated by ASHP at time t, Q h,P2G,t is the heat energy generated at time t, Q dis,t and Q char,t are the heat released and stored in the heat storage tank at time t, Q load,t is the heat load at time t.

[0023] Furthermore, the determining of the constraint conditions of the low-carbon scheduling model includes:

[0024] Gas load balance constraints:

[0025] V P2G,t +V buy,t =V load,t +V MT,t

[0026] Where: V P2G,t is the gas production of P2G at time t, V buy,t is the gas purchase volume at time t, V load,t is the gas load at time t, V MT,t is the amount of CHP gas consumed at time t.

[0027] Furthermore, the determining of the constraint conditions of the low-carbon scheduling model includes:

[0028] Equipment and climbing constraints:

[0029] P n,t,min <P n,t <P n,t,max

[0030]

[0031] Where: P n,t is the output power of device n at time t, P n,t,min and P n,t,max are the boundary values ​​of the device output, and They are the upper and lower limits of climbing power respectively.

[0032] Furthermore, the determining of the constraint conditions of the low-carbon scheduling model includes:

[0033] The charging and discharging power constraints of batteries and thermal storage tanks:

[0034]

[0035] Where: P E,char,t and P E,dis,t are the charging and discharging power of the battery and the heat storage tank at time t, S E,t Represents the remaining energy, η c and η d is the charge and discharge efficiency, and are the minimum and maximum savings respectively.

[0036] Furthermore, the determining of the constraint conditions of the low-carbon scheduling model includes:

[0037] Tie lines and gas purchase constraints:

[0038]

[0039] Where: are the upper and lower limits of electricity purchase and sale, and They are the upper and lower limits for gas purchase respectively.

[0040] Furthermore, the low-carbon dispatch model is optimized by using an improved MVO optimization algorithm to obtain an optimal solution for the low-carbon dispatch scheme, including:

[0041] The MVO optimization algorithm is improved by improving the wormhole existence probability and travel distance rate, performing differential mutation operations, and introducing compression factors:

[0042]

[0043] Where: TDR is the travel distance rate, WEP is the probability of wormhole existence, is the mutation vector, and Φ is the compression factor.

[0044] The second aspect of the embodiment of the present application provides a low-carbon optimization scheduling system for electric, thermal and gas coupled integrated energy based on improved MVO, including:

[0045] A low-carbon dispatch model building unit, used to build a low-carbon dispatch model of an electric, heat and gas coupled integrated energy system with the goal of minimizing operating costs, wherein the electric, heat and gas coupled integrated energy system includes a combined heat and power unit, an air source heat pump, electric heat and gas from a power-to-gas converter, and related energy storage;

[0046] A constraint determination unit, used to determine the constraint conditions of the low-carbon dispatch model, wherein the constraint conditions include electric load balance constraint, thermal load balance constraint, gas load balance constraint, equipment and ramp constraints, battery and heat storage tank charge and discharge power constraints, and tie line and gas purchase constraints;

[0047] The optimal solution determination unit is used to optimize the parameters of the low-carbon scheduling model through an improved MVO optimization algorithm to obtain the optimal solution of the low-carbon scheduling solution.

[0048] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0049] 1. The present invention constructs a low-carbon dispatch model with the goal of minimizing the system operation cost, and takes the electric load balance constraint, thermal load balance constraint, gas load balance constraint, equipment and ramp constraint, battery and heat storage tank charge and discharge power constraint, and interconnection line and gas purchase constraint as constraints. From the perspective of low carbon and economy, the variable operating condition operation of ASHP is considered, and the system structure of electric, thermal and gas coupling is constructed by combining CHP units and P2G with waste heat recovery. By introducing the step-by-step carbon trading mechanism, the role and influence of ASHP variable operating condition operation and step-by-step carbon trading in system economy and emission reduction are studied, and then a low-carbon optimization dispatch strategy for the electric, thermal and gas coupled integrated energy system is proposed.

[0050] 2. The parameters of the low-carbon dispatch model are optimized through the improved MVO optimization algorithm to obtain the optimal solution of the low-carbon dispatch scheme. The parameters of the MVO algorithm are improved by using inertia weights, introducing differential mutations, and combining compression factors to improve the algorithm's optimization ability and convergence speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic flow chart of an embodiment of low-carbon optimization scheduling of electric, thermal and gas coupled comprehensive energy based on improved MVO in the present invention;

[0052] Figure 2 It is the architecture diagram of the comprehensive energy system in the present invention;

[0053] Figure 3 It is a flowchart of the improved MVO algorithm in the present invention;

[0054] Figure 4 It is the predicted output curve diagram of the system in the present invention;

[0055] Figure 5 It is a convergence curve diagram of the improved MVO algorithm in the present invention;

[0056] Figure 6 It is a diagram of the power dispatching situation in the present invention;

[0057] Figure 7 This is a diagram of thermal scheduling results in the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In this embodiment, the low-carbon optimization scheduling method and system of the electric, thermal and gas coupled integrated energy based on the improved MVO are used to optimize the low-carbon scheduling strategy of the electric, thermal and gas coupled integrated energy system. The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, and the specific details are not clearly limited.

[0060] Embodiment 1

[0061] See also Figure 1 In the present invention, a low-carbon optimization scheduling method for electric, thermal and gas coupled comprehensive energy based on improved MVO comprises the following steps:

[0062] S11. Construct a low-carbon dispatch model for the electric-heat-gas coupled integrated energy system with the goal of minimizing operating costs. The electric-heat-gas coupled integrated energy system includes a combined heat and power unit, an air source heat pump, electric-heat-gas from power-to-gas, and related energy storage;

[0063] In this embodiment, taking the electric, heat and gas coupled integrated energy system with tiered carbon trading as an example, the integrated energy system architecture diagram is as follows: Figure 2 As shown, the integrated energy system includes coupled equipment such as cogeneration units, air source heat pumps, power-to-gas electric heat and gas, and related energy storage.

[0064] Model the coupled device as follows:

[0065] 1. Air source heat pump model:

[0066] Air source heat pump is a device that uses heat in the air to convert energy, and has the advantages of high efficiency and environmental protection. The electric heat ratio of air source heat pump is closely related to the external ambient temperature, so in actual operation, it is necessary to fully consider the dynamic variable working condition characteristics it faces. The ASHP variable working condition mathematical model is as follows:

[0067] η ASHP,t =5×10 ―6 (T out,t ) 4 +0.0001(T out,t ) 3 +0.001(T out,t ) 2 +0.0596T out,t

[0068] +3.1984

[0069]

[0070] Where: η ASHP,t is the energy efficiency ratio, T out,t is the temperature at time t, and are heating power and power consumption respectively.

[0071] The additional energy purchase cost model that needs to be fully considered due to the dynamic variable operating conditions is as follows:

[0072]

[0073] Where: C extra For the additional energy purchase cost, is the heat energy generated by ASHP at fixed working condition, μ fix is the thermal efficiency coefficient, and S is the energy purchase price.

[0074] 2. Recovery model taking into account P2G and waste heat of reaction:

[0075] P2G equipment can effectively absorb the excess electricity generated by fluctuating renewable energy, and convert the excess electricity into methane through the methane reaction pool, providing part of the natural gas for the CHP unit. The conversion equation of the P2G methane generation process is:

[0076]

[0077] Where: P P2G,t is the electric energy consumed at time t, is the volume of methane produced at time t, and is the gas density of hydrogen and methane, is the calorific value of methane, and are the molar masses of methane and hydrogen, respectively.

[0078] The waste heat recovery technology is used to recover the generated heat energy. The P2G waste heat recovery mathematical model is as follows:

[0079]

[0080] Where: η h Indicates the waste heat recovery efficiency, Q h,P2G,t is the heat energy generated at time t.

[0081] 3. CHP equipment model:

[0082] CHP is an important part of the electricity-heat-gas coupling, which includes gas turbines and waste heat boilers. Through gas turbines and waste heat boilers, electricity-heat-gas coupled units can be realized. While the gas turbine is generating electricity, the waste heat generated is output through the waste heat boiler. The electrical and thermal mathematical models are:

[0083]

[0084] Where: V MT,t , P MT,t and Q HE,t are the natural gas power consumed by the gas turbine, the output electrical power and the waste heat power recovered by the waste heat boiler, η MT is the power generation efficiency, λ MT is the power-to-heat ratio of the gas turbine, η HE is the heat recovery efficiency, V g It is the lower calorific value of natural gas, and the constant is 9.7kW·h / m3.

[0085] 4. Step-by-step carbon trading mechanism model:

[0086] The energy coupling equipment mentioned in this article are P2G, ASHP and CHP. ASHP is a highly efficient and pollution-free equipment. Its carbon emissions are not considered. The carbon emissions of P2G and CHP are a part that cannot be ignored. It is assumed that all electricity purchased from the power grid comes from thermal power. The relationship between its carbon emissions and the unit is:

[0087]

[0088] Where: W0 is the total carbon emissions of the units in the system, W CHP , W buy and W P2G is the carbon emission of CHP units, power purchase from the grid and P2G, a1, b1 and c1 are the carbon emission coefficients of thermal power units, δ CHP and δ P2G are the unit carbon emission coefficients respectively.

[0089] When carbon emissions exceed the quota, a tiered price is applied to the purchase of excess carbon emission rights. The mathematical model is as follows:

[0090]

[0091] Where: C ctc is the carbon trading cost, W a is the regional carbon emission quota, C c and C f are the carbon emission rights trading prices when they are less than and exceed the carbon emission quota, respectively; δ is the carbon emission rights trading price multiplier for the part exceeding the carbon emission quota.

[0092] Specifically, based on the above-mentioned model, with economy as the objective function, economic dispatch takes the minimum operating cost of the system as the objective function, and the expression is as follows:

[0093]

[0094] Where: C om Cost of maintaining equipment for the system, C buy,e and C buy,g are the cost of electricity purchase and gas purchase, w q The penalty costs for abandoning wind and solar power. The cost of carbon emissions.

[0095]

[0096] Where: f om,i is the maintenance cost per unit of equipment i, P out,i,t is the output of device i at time t, C b,e,t and C s,e,t are the electricity purchase and sale prices at time t, P buy,t and P sell,t are the power purchased and sold at time t, f g,t is the gas price at time t, V b,g,t and V s,g,t are the volumes of natural gas purchased at time t respectively.

[0097] w q =w pv +w wt

[0098]

[0099] Where: w pv and w wt They represent the penalty costs for wind and solar abandonment in a dispatch cycle, and are the predicted powers of wind turbines and photovoltaic generators at time t respectively.

[0100]

[0101] Where: k c is the unit carbon emission cost coefficient.

[0102] S12. Determine the constraints of the low-carbon dispatch model, including electric load balance constraints, thermal load balance constraints, gas load balance constraints, equipment and ramp constraints, battery and heat storage tank charge and discharge power constraints, and tie line and gas purchase constraints;

[0103] 1. Electric load balance constraints:

[0104] P pv,t +P wt,t +P buy,t +P MT,t +P soc,dis,t =P load,t +P soc,char,t +P P2G,t +P ASHP,t

[0105] +P sell,t

[0106] Where: P pv,t is the photovoltaic power actually consumed at time t, P wt,t is the wind power output actually consumed at time t, P MT,t is the power generated by the gas turbine of the CHP unit at time t, P load,t is the user's electrical load at time t, P buy,t and P sell,t They are the purchase and sale of electricity at time t, P P2G,t is the power consumed by P2G at time t, P ASHP,t is the power consumed by ASHP at time t, P soc,char,t and P soc,dis,t are the battery charging and discharging power respectively.

[0107] 2. Heat load balance constraints:

[0108]

[0109] Where: Q HE,t is the heat generated by the waste heat boiler of the CHP unit at time t, is the thermal power generated by ASHP at time t, Q h,P2G,t is the heat energy generated at time t, Q dis,t and Q char,t are the heat released and stored in the heat storage tank at time t, Q load,t is the heat load at time t.

[0110] 3. Gas load balance constraints:

[0111] V P2G,t +V buy,t =V load,t +V MT,t

[0112] Where: V P2G,t is the gas production of P2G at time t, V buy,t is the gas purchase volume at time t, V load,t is the gas load at time t, V MT,t is the amount of CHP gas consumed at time t.

[0113] 4. Equipment and climbing constraints:

[0114] P n,t,min <P n,t <P n,t,max

[0115]

[0116] Where: P n,t is the output power of device n at time t, P n,t,min and P n,t,max are the boundary values ​​of the device output, and They are the upper and lower limits of climbing power respectively.

[0117] 5. Charge and discharge power constraints of batteries and heat storage tanks:

[0118]

[0119] Where: P E,char,t and P E,dis,t are the charging and discharging power of the battery and the heat storage tank at time t, S E,t Represents the remaining energy, η c and η d is the charge and discharge efficiency, and are the minimum and maximum savings respectively.

[0120] 6. Tie lines and gas purchase constraints:

[0121]

[0122] Where: are the upper and lower limits of electricity purchase and sale, and They are the upper and lower limits for gas purchase respectively.

[0123] S13. The parameters of the low-carbon scheduling model are optimized through the improved MVO optimization algorithm to obtain the optimal solution of the low-carbon scheduling scheme.

[0124] The IES optimization scheduling problem needs to comprehensively consider the mutual influence of multiple energy sources such as electricity, heat energy and gas energy and the timing decision of energy storage equipment, and also consider the diversity of conversion and storage methods between multiple energy sources. Therefore, the problem to be solved is a high-dimensional, complex and nonlinear model. The present invention will improve the MVO algorithm in three aspects to improve its overall efficiency in solving the proposed complex nonlinear problem.

[0125] See also Figure 3 , the MVO optimization algorithm is improved by improving the wormhole existence probability WEP and the trip distance rate TDR, performing differential mutation operations, and introducing compression factors, as follows:

[0126] 1. Improve the parameters WEP and TDR:

[0127] WEP increases linearly, but the slow increase of WEP value is not conducive to the individual update of the universe, thus limiting the communication of position information between individuals. Therefore, WEP is changed to LWEP based on logarithmic growth. The expression of LWEP is as follows:

[0128]

[0129] TDR is improved to increase its early iteration speed in order to find the global optimal solution, and slow down its later iteration speed in order to find the local optimal solution. The nonlinear convergence factor of TDR is introduced into NTDR. The expression of NTDR is shown as follows:

[0130]

[0131] In the above formula: L is the number of iterations;

[0132] 2. Combined with differential mutation:

[0133] Perform differential mutation operation. When the population moves towards the optimal solution, the differential mutation step is performed. The idea is to add mutation to the multiverse population and generate another new population to ensure the retention of the dominant population, thereby improving the solution efficiency. The generation rules are as follows:

[0134]

[0135] Where: is the mutation vector, F is the scaling factor, is the difference vector;

[0136] 3. Introduce compression factor:

[0137] The boundary limit on speed can be removed, and the speed of the universe can be effectively controlled to achieve a coordinated balance between global search and local search. The compression factor Φ is introduced, and the compression factor formula is as follows:

[0138]

[0139] Where: λ is a positive integer.

[0140] In summary, we can conclude that:

[0141]

[0142] The following is a specific experiment to verify the above method, as follows:

[0143] 1. Basic information of the example:

[0144] The data is selected from a typical winter day, and the electricity, heat, gas load curves and wind and solar power forecast output curves are as follows: Figure 4 As shown, the system contains various energy storage devices such as WT, PV, ASHP, CHP and P2G. The equipment-related parameters are shown in Table 1, the grid transaction price is shown in Table 2, the energy storage parameters are shown in Table 3, and the natural gas price is 3.5 yuan / m3. The base price of carbon trading is 267.6 yuan / t, the growth coefficient is 0.25, the interval length is 2000kg, the unit carbon emission cost coefficient is 250 yuan / t, and the wind abandonment penalty coefficient is 350 yuan / MW. The example uses the improved MVO algorithm for solution, the scheduling period is 24h, and the unit time is 1h.

[0145] Table 1 Equipment related parameters

[0146]

[0147] Table 2 Grid transaction prices

[0148]

[0149]

[0150] Table 3 Energy storage parameters

[0151]

[0152] 2. Calculation steps:

[0153] For the optimization scheduling problem of the integrated energy system, the power output of each scheduling device is used as a variable, and the optimal solution, i.e., the minimum objective function, is iteratively generated under the condition of satisfying relevant constraints. In order to obtain reliable statistical results, each algorithm was independently run 10 times, and the initial population size of the algorithm was 3000 (with the same initial conditions), and the results are summarized in Table 4 and Figure 5 In the curve diagram shown.

[0154] Table 4 Algorithm iteration comparison

[0155]

[0156] In Table 4, in terms of the number of convergences, the proposed improved MVO algorithm has 133 iterations. The slope of the curve of the improved MVO is larger in the early stage, indicating that the optimization speed is slightly faster, which is conducive to finding the range of the global optimal solution; in the later stage, the improved MVO curve is relatively flatter, which is helpful to find the local optimal solution, so that a better solution is obtained with the same number of iterations. Therefore, the improved MVO can find the optimal solution of the problem more quickly and complete the solution process more efficiently, thereby saving computing time and resources.

[0157] 3. Example display:

[0158] like Figure 6 As shown in the figure, by using P2G and ASHP to supplement the gas and heat loads at different times, the excess electricity of the wind and solar units can be consumed, while reducing the demand for gas purchases and the use of heat storage tanks to improve economic efficiency. The battery is charged during the low electricity price period from 01:00 to 05:00, and the gap in the electricity load is supplemented during the peak period from 19:00 to 24:00. At this time, there is no photovoltaic output, and the output of the wind turbine and CHP unit alone cannot meet the load. Therefore, the battery is required to supplement the remaining insufficient load to ensure power balance, promote the economic operation of the system, reduce the purchase of electricity during peak hours, and play a role in shifting peaks and filling valleys.

[0159] The thermal dispatch results are as follows: Figure 7 As shown. By recovering the waste heat of P2G, the system can adjust the output of the CHP unit during the peak period of wind and solar power, thereby improving the system's ability to absorb wind and solar energy. On the one hand, by utilizing the "load" characteristics of P2G, P2G is used to meet part of the gas load and heat load, and part of the electricity generated by wind and solar power is absorbed by increasing the electricity load; on the other hand, the "source" attribute of P2G is fully utilized to optimize the thermal energy coupling link to improve the energy supply flexibility of the CHP unit. By sharing part of the heating demand of the CHP unit, the space for compressed wind power to be integrated into the power grid due to the thermal and electric coupling operation mode of the gas turbine is released. Therefore, the waste heat recovery of P2G not only effectively improves the utilization efficiency of energy, but also enhances the flexibility of the system.

[0160] Embodiment 2

[0161] An embodiment of a low-carbon optimization scheduling system for electric, thermal and gas coupled integrated energy based on improved MVO in the present invention includes the following steps:

[0162] A low-carbon dispatch model building unit is used to build a low-carbon dispatch model for an electric, heat and gas coupled integrated energy system with the goal of minimizing operating costs. The electric, heat and gas coupled integrated energy system includes a combined heat and power unit, an air source heat pump, electric heat and gas from power-to-gas, and related energy storage;

[0163] A constraint determination unit is used to determine the constraint conditions of the low-carbon dispatch model, which include electric load balance constraint, thermal load balance constraint, gas load balance constraint, equipment and ramp constraints, battery and heat storage tank charge and discharge power constraints, and tie line and gas purchase constraints;

[0164] The optimal solution determination unit is used to optimize the parameters of the low-carbon scheduling model through the improved MVO optimization algorithm to obtain the optimal solution of the low-carbon scheduling scheme. An embodiment includes the following steps:

[0165] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0166] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0167] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0168] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0169] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A low-carbon optimization scheduling method for electric, thermal and gas coupled comprehensive energy based on improved MVO, characterized in that: include: A low-carbon dispatch model of an electric, heat and gas coupled integrated energy system is constructed with the goal of minimizing operating costs. The electric, heat and gas coupled integrated energy system includes a combined heat and power unit, an air source heat pump, electric heat and gas from a power-to-gas converter, and related energy storage. Determining the constraints of the low-carbon dispatch model, the constraints including electric load balance constraints, thermal load balance constraints, gas load balance constraints, equipment and ramp constraints, battery and thermal storage tank charge and discharge power constraints, and tie line and gas purchase constraints; The parameters of the low-carbon scheduling model are optimized through the improved MVO optimization algorithm to obtain the optimal solution of the low-carbon scheduling scheme.

2. The low-carbon optimization scheduling method of electric, heat and gas coupled comprehensive energy based on improved MVO according to claim 1 is characterized in that: The low-carbon dispatch model of the electric, heat and gas coupled integrated energy system is constructed with the goal of minimizing the operating cost. The electric, heat and gas coupled integrated energy system includes a combined heat and power unit, an air source heat pump, electric heat and gas from power-to-gas, and related energy storage, including: The expression of low-carbon dispatch model is: minC=C om +C buy,e +C buy,g +w q +F co2 Where: C om Cost of maintaining equipment for the system, C buy,e and C buy,g are the cost of electricity purchase and gas purchase, w q is the penalty cost for abandoning wind and solar power, F co2 The cost of carbon emissions.

3. The low-carbon optimization scheduling method of electric, heat and gas coupled comprehensive energy based on improved MVO according to claim 1 is characterized in that: Determining the constraint conditions of the low-carbon scheduling model includes: Electric load balancing constraints: P pv,t +P wt,t +P buy,t +P MT,t +P soc,dis,t =P load,t +P soc,char,t +P P2G,t +P ASHP,t +P sell,t Where: P pv,t is the photovoltaic power actually consumed at time t, P wt,t is the wind power output actually consumed at time t, P MT,t is the power generated by the gas turbine of the CHP unit at time t, P load,t is the user's electrical load at time t, P buy,t and P sell,t They are the purchase and sale of electricity at time t, P P2G,t is the power consumed by P2G at time t, P ASHP,t is the power consumed by ASHP at time t, P soc,char,t and P soc,dis,t are the battery charging and discharging power respectively.

4. The low-carbon optimization scheduling method of electric, heat and gas coupled comprehensive energy based on improved MVO according to claim 3 is characterized in that: Determining the constraint conditions of the low-carbon scheduling model includes: Heat load balancing constraints: Where: Q HE,t is the heat generated by the waste heat boiler of the CHP unit at time t, Q h ASHP,t is the thermal power generated by ASHP at time t, Q h,P2G,t is the heat energy generated at time t, Q dis,t and Q char,t are the heat released and stored in the heat storage tank at time t, Q load,t is the heat load at time t.

5. The low-carbon optimization scheduling method of electric, heat and gas coupled comprehensive energy based on improved MVO according to claim 3 is characterized in that: Determining the constraint conditions of the low-carbon scheduling model includes: Gas load balance constraints: V P2G,t +V buy,t =V load,t +V MT,t Where: V P2G,t is the gas production of P2G at time t, V buy,t is the gas purchase volume at time t, V load,t is the gas load at time t, V MT,t is the amount of CHP gas consumed at time t.

6. The low-carbon optimization scheduling method of electric, heat and gas coupled comprehensive energy based on improved MVO according to claim 3 is characterized in that: Determining the constraint conditions of the low-carbon scheduling model includes: Equipment and climbing constraints: P n,t,min <P n,t <P n,t,max Where: P n,t is the output power of device n at time t, P n,t,min and P n,t,max are the boundary values ​​of the device output, and They are the upper and lower limits of climbing power respectively.

7. The low-carbon optimization scheduling method of electric, heat and gas coupled comprehensive energy based on improved MVO according to claim 3 is characterized in that: Determining the constraint conditions of the low-carbon scheduling model includes: The charging and discharging power constraints of batteries and thermal storage tanks: Where: P E,char,t and P E,dis,t are the charging and discharging power of the battery and the heat storage tank at time t, S E,t Represents the remaining energy, η c and η d is the charge and discharge efficiency, and are the minimum and maximum savings respectively.

8. The low-carbon optimization scheduling method of electric, heat and gas coupled comprehensive energy based on improved MVO according to claim 3 is characterized in that: Determining the constraint conditions of the low-carbon scheduling model includes: Tie lines and gas purchase constraints: Where: are the upper and lower limits of electricity purchase and sale, and They are the upper and lower limits for gas purchase respectively.

9. The low-carbon optimization scheduling method of electric, heat and gas coupled comprehensive energy based on improved MVO according to claim 1 is characterized in that: The method of optimizing the parameters of the low-carbon dispatch model by using the improved MVO optimization algorithm to obtain the optimal solution of the low-carbon dispatch scheme includes: The MVO optimization algorithm is improved by improving the wormhole existence probability and the travel distance rate, performing differential mutation operations, and introducing compression factors: Where: TDR is the travel distance rate, WEP is the probability of wormhole existence, is the mutation vector, and Φ is the compression factor.

10. A low-carbon optimization scheduling system for electric, thermal and gas coupled comprehensive energy based on improved MVO, characterized in that: include: A low-carbon dispatch model building unit, used to build a low-carbon dispatch model of an electric, heat and gas coupled integrated energy system with the goal of minimizing operating costs, wherein the electric, heat and gas coupled integrated energy system includes a combined heat and power unit, an air source heat pump, electric heat and gas from a power-to-gas converter, and related energy storage; A constraint determination unit, used to determine the constraint conditions of the low-carbon dispatch model, wherein the constraint conditions include electric load balance constraint, thermal load balance constraint, gas load balance constraint, equipment and ramp constraints, battery and heat storage tank charge and discharge power constraints, and tie line and gas purchase constraints; The optimal solution determination unit is used to optimize the parameters of the low-carbon scheduling model through an improved MVO optimization algorithm to obtain the optimal solution of the low-carbon scheduling solution.

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