Micro-grid scheduling method and system considering various uncertainties

Abstract

The invention discloses a micro-grid scheduling method and system considering various uncertainties, and the method comprises the steps: constructing a micro-grid optimization scheduling model considering various uncertain factors of weather, demand response and energy price, obtaining a plurality of micro-grid typical scenes based on the various uncertainties of the operation of a micro-grid, and carrying out the optimization scheduling of the micro-grid. And in each typical micro-grid scene, solving the micro-grid optimal scheduling model to obtain a micro-grid optimal scheduling scheme in each typical micro-grid scene. According to the method, a micro-grid optimal scheduling model takes economic benefit maximization as a target, an interval linear optimization technology is applied, the working state and future operation arrangement of equipment are optimized and adjusted, uncertainty of new energy output and user load in the micro-grid is considered, corresponding interval prediction results are obtained by adopting different prediction methods, and the optimal scheduling of the micro-grid is realized. And the day-ahead scheduling plan research of the integrated energy microgrid is carried out in an auxiliary manner.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning and operation, and in particular relates to a microgrid scheduling method and system taking multiple uncertainties into consideration. Background Art

[0002] As a new type of energy system, microgrid integrates a variety of energy conversion and storage equipment, such as wind power, photovoltaic, power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boiler and battery. Each of these devices has different operating characteristics and efficiency, so it is crucial to develop a comprehensive dispatching strategy. The current research advantage is that it comprehensively considers the dispatching strategy of microgrid internal equipment, not only focusing on technical and operational factors, but also taking cost-effectiveness, environmental benefit goals and reliability goals into consideration.

[0003] However, although the current dispatch strategy has taken many aspects into consideration, there are still some shortcomings. The most obvious one is that the current strategy is not comprehensive enough and may ignore certain key factors, such as equipment maintenance costs, life cycle, and interaction with the external power grid. In addition, the strategy may be too biased in one aspect during implementation, such as over-emphasizing cost-effectiveness while ignoring environmental benefits and reliability improvements. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention proposes a microgrid scheduling method and system taking into account multiple uncertainties.

[0005] The technical solution of the present invention is as follows:

[0006] A microgrid dispatching method considering multiple uncertainties, wherein the components of the microgrid include photovoltaic units, wind turbine units, power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boilers, and energy storage equipment, and the method includes:

[0007] Based on the historical operation data of the microgrid components, an operation model of the microgrid components is constructed;

[0008] Based on the operation model of the microgrid component equipment, a microgrid optimization scheduling model is constructed. The microgrid optimization scheduling model uses the operation scheduling schemes of the power-to-gas equipment, the gas turbine equipment, the heat pump equipment, the waste heat boiler and the energy storage equipment as decision variables, and takes the lowest total operation cost of the microgrid as the goal, and takes the climbing constraint, energy balance constraint, new energy output constraint, energy storage operation constraint, tie line constraint, and power purchase and sale state constraint as constraint conditions;

[0009] Based on the various uncertainties of microgrid operation, several typical microgrid scenarios are obtained;

[0010] In each typical microgrid scenario, the microgrid optimization scheduling model is solved to obtain the microgrid optimization scheduling plan in each typical microgrid scenario.

[0011] Furthermore, the operation models of the microgrid component equipment include: photovoltaic unit operation model, wind turbine unit operation model, power-to-gas equipment operation model, gas turbine equipment operation model, heat pump equipment operation model, waste heat boiler operation model, and energy storage equipment operation model;

[0012] The expression of the photovoltaic unit operation model is:

[0013] P PV (t) = f PV P PV,rated G real [1+K r (T r -T stc )] / G stc

[0014] Where P PV (t) is the photovoltaic power at time t; f PV is the photovoltaic power generation power coefficient; P PV,rated is the rated output power of the photovoltaic system; G real is the actual light radiation intensity of the photovoltaic panel; G stc K is the light radiation intensity at the rated power of the photovoltaic unit; r is the power temperature coefficient; Tr is the actual surface temperature of the photovoltaic array; T stc It is the reference temperature under the standard test environment;

[0015] The expression of the wind turbine operation model is:

[0016]

[0017] Where v is the wind speed at the hub height of each wind turbine; h is the hub height of each wind turbine; v0 is the wind speed at a known height; n is an exponential parameter; v ci is the fan cut-in speed; v c0 is the fan cut-out speed; P wr is the rated output of the wind turbine; v r is the normal operating speed of the fan;

[0018] The expression of the power-to-gas equipment operation model is:

[0019]

[0020] In the formula, is the amount of natural gas produced in one cycle; η P2Gfor the efficiency of producing natural gas; The electric power consumed by the photovoltaic unit;

[0021] The expression of the gas turbine equipment operation model is:

[0022] P GT =(HV)η GT m f

[0023] Where P GT is the output power of the gas turbine; η GT is the conversion efficiency of the gas turbine; HV is the calorific value of the fuel; m f is the mass flow rate of fuel;

[0024] The expression of the heat pump equipment operation model is:

[0025]

[0026] In the formula, is the output power of the heat pump; Electrical power input to the heat pump; COP HP is the heat pump performance efficiency; η HP is the operating efficiency of the heat pump;

[0027] The expression of the waste heat boiler operation model is:

[0028]

[0029] In the formula, H WH (t) is the heat power recovered in the time period t; η WT is the heat recovery efficiency; K gs It is the ratio of the circulation power between the top and bottom of the waste heat boiler;

[0030] The expression of the energy storage device operation model is:

[0031]

[0032] Where E(t) is the electrical energy generated by the storage system at time t; P ch (t) is the power that the storage system can charge at time t; P dis (t) is the power discharged by the storage system at time t; δ is the self-discharge rate of the storage system; η ch is the charging rate of the storage system; η dis is the discharge rate of the storage system.

[0033] Furthermore, the objective function of the microgrid interval optimization scheduling model is:

[0034] min[Feco ] ± =[F en ] ± +[F wd ] ± +[F mat ] ± +[F csu ] ± +[F IDR ] ±

[0035]

[0036] In the formula, [F eco ] ± is the overall cost range of the system during the scheduling period; [F en ] ± is the range of changes in the system environmental cost during the scheduling period; [F wd ] ± Penalty cost for wind curtailment; [F mat ] ± The cost range for system operation and maintenance during the dispatch period; [F csu ] ± is the energy consumption cost range of the system during the scheduling period; [F IDR ] ± is the cost interval of the system IDR during the scheduling period; α r is the pollution control cost of the rth pollutant; [P i (t)] ± is the output interval value of unit i during period t; [P wd (t)] ± is the wind power abandonment interval value in period t; [P in.e (t)]± is the interval value of the purchased power during the period t; [P in.g (t)] ± is the gas purchase power interval value in period t; [|P h,cut (t)|] ± is the absolute value range of heat load reduction during period t; [|P ell (t)-P el0 (t)|] ± is the interval value of the absolute value of the electric load transfer in period t; E r ([P i (t)] ± ) is the rth pollutant corresponding to the i-th equipment in period t; D w Penalty for unit abandonment of wind power; D i is the maintenance cost of unit i; C cut is the incentive demand response cost per unit heat load; C mov The demand response cost is the unit electricity load price.

[0037] Furthermore, the climbing constraint is as follows:

[0038] -v jmax ≤v j (t)-v j (t-1)≤v jmax

[0039] In the formula, v j (t) is the output energy of device j during the scheduling period; v jmax is the maximum climbing constraint of equipment j during the scheduling period.

[0040] Furthermore, the energy balance constraint is as follows:

[0041]

[0042] In the formula, It is the electricity, heat and gas load demand in the dispatching stage; Power output for gas turbines, wind turbines and photovoltaics; is the charging and discharging power of the electric energy storage; The power consumption of power-to-gas equipment and electric boilers; Produce heat for gas turbines, electric boilers and gas boilers; QS,ch,t, Charge and release thermal power for energy storage; The gas production of the power-to-gas equipment; Gas consumption of gas boilers and gas turbines.

[0043] Furthermore, the new energy output constraints are as follows:

[0044]

[0045] Where P wt,min (t), P wt,max (t) is the minimum and maximum output of the wind turbine in period t; P pv,min (t), P pv,max (t) is the minimum and maximum output power of the photovoltaic power generation system during period t.

[0046] Furthermore, the energy storage operation constraints are as follows:

[0047]

[0048] In the formula, ——is the minimum capacity and maximum capacity of the equipment.

[0049] Furthermore, the tie line constraints are as follows:

[0050]

[0051] Where P g,min , P g,max P is the maximum and minimum value of the purchased electricity; c,min , P c,max The maximum and minimum values ​​of gas purchase volume.

[0052] Furthermore, the power purchase and sale status constraints are as follows:

[0053]

[0054] In the formula, Power purchased from the distribution system for the energy hub; The power sold by the energy hub to the distribution system; P net_max is the maximum power allowed for interconnection of the tie lines; U buy (t) = 1 means that electricity is purchased at time t; U sell (t)=1 means that electricity is sold at time t.

[0055] A microgrid dispatching system considering multiple uncertainties, wherein the components of the microgrid include photovoltaic units, wind turbine units, power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boilers, and energy storage equipment, and includes a component equipment operation model building module, a microgrid optimization dispatching model building module, a typical scenario acquisition module, and a solution module;

[0056] The component equipment operation model building module is used to build an operation model of the microgrid component equipment based on the historical operation data of the microgrid component equipment;

[0057] The microgrid optimization scheduling model construction module is used to construct a microgrid optimization scheduling model based on the operation model of the microgrid component equipment. The microgrid optimization scheduling model uses the operation scheduling schemes of the power-to-gas equipment, the gas turbine equipment, the heat pump equipment, the waste heat boiler and the energy storage equipment as decision variables, and takes the lowest total operation cost of the microgrid as the goal, and takes the climbing constraint, energy balance constraint, new energy output constraint, energy storage operation constraint, tie line constraint, and power purchase and sale state constraint as constraint conditions;

[0058] The typical scenario acquisition module is used to acquire several typical scenarios of the microgrid based on various uncertainties of the microgrid operation;

[0059] The solution module is used to solve the microgrid optimization scheduling model in each typical microgrid scenario to obtain the microgrid optimization scheduling plan in each typical microgrid scenario.

[0060] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0061] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention proposes a microgrid dispatching method and system that considers multiple uncertainties. The method constructs a microgrid optimization dispatching model that considers multiple uncertainties such as weather, demand response, and energy prices, and obtains several typical microgrid scenarios based on the multiple uncertainties of microgrid operation. In each typical microgrid scenario, the microgrid optimization dispatching model is solved to obtain the microgrid optimization dispatching plan in each typical microgrid scenario. The microgrid optimization dispatching model of the method of the present invention aims to maximize economic benefits, and uses interval linear optimization technology to optimize and adjust the working status of the equipment and future operation arrangements. Considering the uncertainty of new energy output and user load in the microgrid, different prediction methods are used to obtain the corresponding interval prediction results, which assists in carrying out the day-ahead dispatching plan research of the integrated energy microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flowchart of a microgrid scheduling method considering multiple uncertainties in an embodiment;

[0065] Figure 2 is a logic diagram of the overall cost of the system during the scheduling period in the embodiment;

[0066] Figure 3 It is a microgrid grid structure diagram in the application embodiment;

[0067] Figure 4 This is a graph of new energy generation and load forecasting without considering uncertainty in scenario 1 of the application embodiment;

[0068] Figure 5 It is a new energy generation and load forecast curve chart that only considers the uncertainty of new energy output on the source side of the integrated energy microgrid in scenario 2 of the application embodiment;

[0069] Figure 6 It is a load forecast curve diagram after comprehensive demand response considering only the uncertainty of the load side of the comprehensive energy microgrid in scenario three of the application embodiment;

[0070] Figure 7This is a grid dispatching operation condition diagram that considers the uncertainties on both sides of the source and load of the integrated energy microgrid in scenario 4 of the application embodiment;

[0071] Figure 8 This is a gas grid dispatching operation condition diagram that considers the uncertainties on both sides of the source and load of the integrated energy microgrid in scenario 4 of the application embodiment;

[0072] Fig. 9 This is a heat network dispatching operation condition diagram that considers the uncertainty on both sides of the source and load of the integrated energy microgrid in scenario 4 of the application embodiment;

[0073] Fig.10 This is a battery operation condition diagram that takes into account the uncertainties on both the source and load sides of the integrated energy microgrid in scenario 4 of the application embodiment. DETAILED DESCRIPTION

[0074] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0075] Embodiment 1:

[0076] The present invention provides a microgrid dispatching method that takes into account multiple uncertainties. The components of the microgrid include photovoltaic units, wind turbine units, power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boilers, and energy storage equipment. The method of the present invention includes:

[0077] Based on the historical operation data of the microgrid components, an operation model of the microgrid components is constructed;

[0078] Based on the operation model of the microgrid components, a microgrid optimization scheduling model is constructed. The microgrid optimization scheduling model uses the operation scheduling schemes of power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boiler and energy storage equipment as decision variables, and takes the lowest total operation cost of the microgrid as the goal, and takes climbing constraints, energy balance constraints, new energy output constraints, energy storage operation constraints, interconnection line constraints, and power purchase and sales status constraints as constraints.

[0079] Based on the various uncertainties of microgrid operation, several typical microgrid scenarios are obtained;

[0080] In each typical microgrid scenario, the microgrid optimization scheduling model is solved to obtain the microgrid optimization scheduling plan in each typical microgrid scenario.

[0081] Among them, typical scenarios of microgrids can be roughly divided into typical scenarios with uncertain output of new energy on the source side, typical scenarios with uncertain power and comprehensive demand response on the load side, and typical scenarios with multiple uncertainties on the source and load sides.

[0082] Embodiment 2:

[0083] This embodiment is further designed on the basis of the first embodiment in that the operation models of the microgrid components in this embodiment include: photovoltaic unit operation model, wind turbine unit operation model, power-to-gas equipment operation model, gas turbine equipment operation model, heat pump equipment operation model, waste heat boiler operation model, and energy storage equipment operation model;

[0084] Photovoltaic power generation is a technology that converts sunlight into electrical energy. Its power generation is mainly affected by factors such as weather, light radiation intensity, and temperature. The expression of the photovoltaic unit operation model is:

[0085] P PV (t) = f PV P PV，rated G real [1+K r (T r -T stc )] / G stc

[0086] Where P PV (t) is the photovoltaic power at time t (kW); f PV is the photovoltaic power generation power coefficient; P PV，rated is the rated output power of the photovoltaic system (kW); G real is the actual light radiation intensity of the photovoltaic panel (kW / m); G stc K is the light radiation intensity at the rated power of the photovoltaic unit; r is the power temperature coefficient; Tr is the actual surface temperature of the photovoltaic array (℃); T stc It is the reference temperature under standard test environment, usually 25℃;

[0087] Wind power generation converts wind energy into mechanical energy to drive the generator to generate electricity. Its power generation is mainly affected by factors such as wind speed and blades. The expression of the wind turbine operation model is:

[0088]

[0089] In actual operation, when the wind speed at the hub height of each wind turbine is known, the output of each wind turbine can be solved according to the following formula.

[0090] Where v is the wind speed at the hub height of each wind turbine (m / s); h is the hub height of each wind turbine (m); v0 is the wind speed at a known height (m / s); n is an exponential parameter, whose value is approximately between 0.125 and 0.5; v ci is the fan cut-in speed (m / s); v c0 is the fan cut-out speed (m / s); P wr is the rated output of the wind turbine; v r is the normal operating speed of the fan (m / s);

[0091] The technology of producing hydrogen by electrolysis of water is a process of producing hydrogen and oxygen by electrolyzing water with electric energy, which provides more options for the production of hydrogen energy. The expression of the operation model of the power-to-gas equipment is:

[0092]

[0093] In the formula, is the amount of natural gas produced in one cycle (m 3 );η P2G for the efficiency of producing natural gas; The power consumed by the photovoltaic unit (kW);

[0094] The expression of the gas turbine equipment operation model is:

[0095] P GT =(HV)η GT m f

[0096] Where P GT is the output power of the gas turbine; η GT is the conversion efficiency of the gas turbine; HV is the calorific value of the fuel; m f is the mass flow rate of fuel;

[0097] The expression of the heat pump equipment operation model is:

[0098]

[0099] In the formula, is the output power of the heat pump; Electrical power input to the heat pump; COP HP is the heat pump performance efficiency (, defining the ratio between its heat output and its electricity consumption); η HP is the operating efficiency of the heat pump, which is taken as 0.8;

[0100] The expression of the waste heat boiler operation model is:

[0101]

[0102] In the formula, H WH (t) is the heat power recovered in the time period t; η WT is the heat recovery efficiency; K gs It is the ratio of the circulation power between the top and bottom of the waste heat boiler;

[0103] The expression of the energy storage device operation model is:

[0104]

[0105] Where E(t) is the electrical energy generated by the storage system at time t; P ch (t) is the power that the storage system can charge at time t; P dis (t) is the power discharged by the storage system at time t; δ is the self-discharge rate of the storage system; η ch is the charging rate of the storage system; η dis is the discharge rate of the storage system.

[0106] Embodiment three:

[0107] This embodiment is further designed on the basis of the second embodiment in that, in actual applications, the economic operation of the microgrid is subject to certain constraints due to the existence of various uncertain factors such as weather, demand response, energy prices, etc. In order to meet these challenges, it is necessary to fully consider these uncertain factors, adjust and optimize the operation strategy of the system, and ensure that the system can achieve the optimization of operation cost under various circumstances.

[0108] The objective function of the microgrid interval optimization scheduling model is:

[0109] min[F eco ] ± =[F en ] ± +[F wd ] ± +[F mat ] ± +[F csu ] ± +[F IDR ] ±

[0110]

[0111] In the formula, [F eco ] ± is the overall cost range of the system during the scheduling period; [F en ] ± is the range of changes in the system environmental cost during the scheduling period; [F wd ] ± Penalty cost for wind curtailment; [F mat ]± The cost range for system operation and maintenance during the dispatch period; [F csu ] ± is the energy consumption cost range of the system during the scheduling period; [F IDR ] ± is the cost interval of the system IDR during the scheduling period; α r is the pollution control cost of the rth pollutant; [P i (t)] ± is the output interval value of unit i during period t; [P wd (t)] ± is the wind power abandonment interval value in period t; [P in.e (t)] ± is the interval value of purchased power in period t; [P in.g (t)] ± is the gas purchase power interval value in period t; [|P h，cut (t)|] ± is the absolute value range of heat load reduction during period t; [|P ell (t)-P el0 (t)|] ± is the interval value of the absolute value of the electric load transfer in period t; E r ([P i (t)] ± ) is the rth pollutant corresponding to the i-th equipment in period t; D w Penalty for unit abandonment of wind power; D i is the maintenance cost of unit i; C cut is the incentive demand response cost per unit heat load; C mov The demand response cost is the unit electricity load price.

[0112] like Figure 2 As shown in the figure, the logic of solving the system's overall cost is simplified. The lower limit of each type of system cost can be calculated at different times. The cost at each time is added up to get the system's overall cost result set. The minimum number in the result set is the lowest cost of the system.

[0113] Embodiment 4:

[0114] This embodiment is further designed on the basis of the third embodiment in that, during the operation of the integrated energy microgrid, the ramp constraint is an important operating restriction condition. It means that during the power generation process of each relevant device in the system, the change in power within a specific time must be controlled within a preset maximum value. This constraint is usually considered when the output of the equipment needs to be adjusted, in order to ensure the appropriateness of the speed of change of the equipment output, thereby reducing the risk and uncertainty of system operation. By imposing a ramp constraint, we can effectively control the rate of change of the equipment, reduce equipment losses, reduce maintenance costs and maintenance frequency, and thus improve the economy of the system. The ramp constraints are as follows:

[0115] -v jmax ≤v j (t)-v j (t-1)≤v jmax

[0116] In the formula, v j (t) is the output energy of device j during the scheduling period; v jmax is the maximum climbing constraint of equipment j during the scheduling period.

[0117] Embodiment five:

[0118] This embodiment is further designed on the basis of the fourth embodiment in that the energy balance constraint of the integrated energy microgrid refers to achieving a balance between the energy generation and use of the microgrid and maintaining a stable operating state. Specifically, this means that the microgrid must ensure that the total energy input in the system is equal to the total energy output. When there is insufficient electrical energy, thermal energy or other forms of energy in the system, it is necessary to adjust the energy supply strategy, such as increasing the output power of the generator, starting energy storage equipment, or optimizing energy distribution to meet energy demand. Conversely, in the case of excess electricity or other energy, it is necessary to adjust the output power of the equipment, reduce the output power of the generator or store excess energy to prevent waste and ensure the stable operation of the system. The energy balance constraints are as follows:

[0119]

[0120] In the formula, It is the electricity, heat and gas load demand in the dispatching stage; Power output for gas turbines, wind turbines and photovoltaics; is the charging and discharging power of the electric energy storage; The power consumption of power-to-gas equipment and electric boilers; Produce heat for gas turbines, electric boilers and gas boilers; QS,ch,t, Charge and release thermal power for energy storage; The gas output of the power-to-gas equipment; Gas consumption of gas boilers and gas turbines.

[0121] Embodiment six:

[0122] This embodiment is further designed on the basis of the fifth embodiment in that the new energy output constraint refers to the fact that during the process of new energy power generation, the output of new energy is unpredictable and volatile, and its output is subject to certain restrictions and constraints. In the dispatching operation of the power system, the output of new energy needs to be controlled according to the day-ahead forecast value, and is usually limited to the forecast value or below to ensure the stable operation of the system. The new energy output constraints are as follows:

[0123]

[0124] Where P wt,min (t), P wt,max (t) is the minimum and maximum output of the wind turbine in period t; P pv,min (t), P pv,max (t) is the minimum and maximum output power of the photovoltaic power generation system during period t.

[0125] Embodiment seven:

[0126] This embodiment is further designed on the basis of the sixth embodiment in that the energy storage operation constraint is an important measure to impose restrictions and regulate the charging and discharging behavior of the energy storage device, aiming to ensure the stability and reliability of the integrated energy system. Energy storage technology, with its excellent feedback mechanism, can effectively store energy when the load is low, and release its internal energy when the load increases, and transmit excess electricity to the power grid to meet user needs. In particular, when the load reaches the critical point, the energy storage device will further play its role and release internal energy to ensure the normal operation of the entire system. At the same time, the capacity of the energy storage device must be strictly controlled within the allowable range, neither exceeding nor falling below the set limit, to prevent the battery from being damaged due to excessive discharge or charging, affecting its performance. The energy storage operation constraints are as follows:

[0127]

[0128] In the formula, ——is the minimum capacity and maximum capacity of the equipment.

[0129] Embodiment eight:

[0130] This embodiment is further designed on the basis of Embodiment 7 in that, in the integrated energy system, the interconnection line plays a vital role as a key grid line connecting different energy systems. A sound energy system covers multiple components such as electricity, natural gas, and heat, which realize the effective integration and coordinated utilization of resources through the link of the interconnection line. The transmission capacity limit of the interconnection line is an important condition to ensure the safe operation of the system. Since the interconnection line has certain physical limitations when transmitting electric energy, its maximum transmission capacity needs to be strictly controlled. This involves factors such as the cross-sectional area, material, and line length of the interconnection line, which together determine the maximum transmission capacity of the interconnection line. During the scheduling process, it is necessary to ensure that the actual transmission power of the interconnection line does not exceed its maximum capacity to prevent line overload and damage. The interconnection line constraints are as follows:

[0131]

[0132] Where P g,min , P g,max P is the maximum and minimum value of the purchased electricity; c,min , P c,max The maximum and minimum values ​​of gas purchase volume.

[0133] Embodiment nine:

[0134] This embodiment is further designed on the basis of the eighth embodiment in that the microgrid needs to reasonably determine the power range of electricity purchase and sale according to its own energy supply and demand. This involves comprehensive consideration of factors such as the output of power generation equipment within the microgrid, the capacity of energy storage equipment, and user demand. By setting power constraints, it can be ensured that the microgrid will not exceed its processing capacity during the power purchase and sale process, thereby maintaining the stable operation of the system. The power purchase and sale status constraints are as follows:

[0135]

[0136] In the formula, Power purchased from the distribution system for the energy hub; The power sold by the energy hub to the distribution system; P net_max is the maximum power allowed for interconnection of the tie lines; U buy (t) = 1 means that electricity is purchased at time t; U sell (t)=1 means that electricity is sold at time t.

[0137] Embodiment ten:

[0138] A microgrid dispatching system considering multiple uncertainties of the present invention, wherein the components of the microgrid include photovoltaic units, wind turbine units, power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boilers, and energy storage equipment, and is characterized in that it includes a component equipment operation model construction module, a microgrid optimization dispatching model construction module, a typical scenario acquisition module, and a solution module;

[0139] A component equipment operation model building module is used to build an operation model of a microgrid component equipment based on historical operation data of the microgrid component equipment;

[0140] The microgrid optimization dispatch model construction module is used to construct a microgrid optimization dispatch model based on the operation model of the microgrid components. The microgrid optimization dispatch model uses the operation dispatch schemes of power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boiler and energy storage equipment as decision variables, and takes the lowest total operation cost of the microgrid as the goal, and takes climbing constraints, energy balance constraints, new energy output constraints, energy storage operation constraints, interconnection line constraints, and power purchase and sales status constraints as constraints;

[0141] Typical scenario acquisition module, used to obtain several typical scenarios of microgrids based on various uncertainties in microgrid operation;

[0142] The solution module is used to solve the microgrid optimization scheduling model in each typical microgrid scenario and obtain the microgrid optimization scheduling plan in each typical microgrid scenario.

[0143] Embodiment eleven:

[0144] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0145] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0146] Application examples:

[0147] This example simulates the implementation of the scheduling method of the present invention on a microgrid. The microgrid structure is as follows: Figure 3 As shown, the equipment operating parameters are shown in Table 1, and the comprehensive energy microgrid and main grid electricity price data are shown in Table 2:

[0148] Table 1 Equipment operating parameters

[0149]

[0150] Table 2 Comprehensive energy microgrid and main grid electricity price data

[0151] Time Electricity sales price (yuan / kWh) Electricity purchase price (yuan / kWh) 01:00-07:00, 23:00-24:00 0.36 0.42 17:00-23:00 0.75 0.55 07:00-17:00 1.16 1.35

[0152] In order to study the impact of multiple uncertain variables on the operation and dispatch of integrated energy microgrids, this example will consider the operation results of various uncertainties from three typical scenarios and define them as follows:

[0153] Scenario 1: Optimization run results without considering uncertainty.

[0154] Scenario 2: Optimized operation results considering the uncertainty of renewable energy output on the source side.

[0155] Scenario 3: Optimization operation results considering the uncertainty of load-side power and comprehensive demand response.

[0156] Scenario 4: Optimization operation results considering multiple uncertainties on the source and load sides.

[0157] The optimization results are as follows: Figures 4 to 10 shown.

[0158] Figure 4 This is the forecast curve of renewable energy power generation and load in scenario 1 without considering the uncertainty. Since the uncertainty of the integrated energy microgrid operation is not considered, the renewable energy power generation and load power are both determined.

[0159] Figure 5 This is the new energy power generation and load forecast curve chart that only considers the uncertainty of new energy output on the source side of the integrated energy microgrid in scenario 2. Because only the uncertainty of new energy output on the source side of the integrated energy microgrid is considered, only the electric load is predicted, the new energy power generation power takes the interval prediction value, the electric load power takes the point prediction result, and the gas load and thermal load are taken as definite values.

[0160] Figure 6 It is the load forecast curve after comprehensive demand response considering only the uncertainty on the load side of the integrated energy microgrid in scenario three. Because only the uncertainty on the load side of the integrated energy microgrid is considered, the new energy output and load power are measured at point values, and the load forecast value considers the comprehensive demand response to obtain the load curve after the final response.

[0161] Figure 7 , Figure 8 , Fig. 9 , Fig.10 They are the grid dispatching, gas network dispatching, heat network dispatching and battery operation condition diagrams in scenario 4 that simultaneously consider the uncertainties on both the source and load sides of the integrated energy microgrid. Because the uncertainties on both the source and load sides of the integrated energy microgrid are considered at the same time, the new energy power generation and load power take interval prediction values, and the load prediction value considers the comprehensive demand response to obtain the load curve after the final response.

[0162] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A microgrid dispatching method considering multiple uncertainties, wherein the components of the microgrid include photovoltaic units, wind turbine units, power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boilers, and energy storage equipment, characterized in that: The method comprises: Based on the historical operation data of the microgrid components, an operation model of the microgrid components is constructed; Based on the operation model of the microgrid component equipment, a microgrid optimization scheduling model is constructed. The microgrid optimization scheduling model uses the operation scheduling schemes of the power-to-gas equipment, the gas turbine equipment, the heat pump equipment, the waste heat boiler and the energy storage equipment as decision variables, and takes the lowest total operation cost of the microgrid as the goal, and takes the climbing constraint, energy balance constraint, new energy output constraint, energy storage operation constraint, tie line constraint, and power purchase and sale state constraint as constraint conditions; Based on the various uncertainties of microgrid operation, several typical microgrid scenarios are obtained; In each typical microgrid scenario, the microgrid optimization scheduling model is solved to obtain the microgrid optimization scheduling plan in each typical microgrid scenario.

2. A microgrid scheduling method considering multiple uncertainties according to claim 1, characterized in that: The operation models of the microgrid components include: photovoltaic unit operation model, wind turbine unit operation model, power-to-gas equipment operation model, gas turbine equipment operation model, heat pump equipment operation model, waste heat boiler operation model, and energy storage equipment operation model; The expression of the photovoltaic unit operation model is: P PV (t)=f PV P PV，rated G real [1+K r (T r -T stc )] / G stc Where P PV (t) is the photovoltaic power at time t; f PV is the photovoltaic power generation power coefficient; P PV，rated is the rated output power of the photovoltaic system; G real is the actual light radiation intensity of the photovoltaic panel; G stc K is the light radiation intensity at the rated power of the photovoltaic unit; r is the power temperature coefficient; Tr is the actual surface temperature of the photovoltaic array; T stc It is the reference temperature under the standard test environment; The expression of the wind turbine operation model is: Where v is the wind speed at the hub height of each wind turbine; h is the hub height of each wind turbine; v0 is the wind speed at a known height; n is an exponential parameter; v ci is the fan cut-in speed; v c0 is the fan cut-out speed; P wr is the rated output of the wind turbine; v r is the normal operating speed of the fan; The expression of the power-to-gas equipment operation model is: In the formula, is the amount of natural gas produced in one cycle; η P2G for the efficiency of producing natural gas; The electric power consumed by the photovoltaic unit; The expression of the gas turbine equipment operation model is: P GT =(HV)η GT m f Where P GT is the output power of the gas turbine; η GT is the conversion efficiency of the gas turbine; HV is the calorific value of the fuel; m f is the mass flow rate of fuel; The expression of the heat pump equipment operation model is: In the formula, is the output power of the heat pump; Electrical power input to the heat pump; COP HP is the heat pump performance efficiency; η HP is the operating efficiency of the heat pump; The expression of the waste heat boiler operation model is: In the formula, H WH (t) is the heat power recovered in the time period t; η WT is the heat recovery efficiency; K gs It is the ratio of the circulation power between the top and bottom of the waste heat boiler; The expression of the energy storage device operation model is: Where E(t) is the electrical energy generated by the storage system at time t; P ch (t) is the power that the storage system can charge at time t; P dis (t) is the power discharged by the storage system at time t; δ is the self-discharge rate of the storage system; η ch is the charging rate of the storage system; η dis is the discharge rate of the storage system.

3. A microgrid dispatching method considering multiple uncertainties according to claim 2, characterized in that: The objective function of the microgrid interval optimization scheduling model is: min[F eco ] ± =[F en ] ± +[F wd ] ± +[F mat ] ± +[F csu ] ± +[F IDR ] ± In the formula, [F eco ] ± is the overall cost range of the system during the scheduling period; [F en ] ± is the range of changes in the system environmental cost during the scheduling period; [F wd ] ± Penalty cost for wind curtailment; [F mat ] ± The cost range for system operation and maintenance during the dispatch period; [F csu ] ± is the energy consumption cost range of the system during the scheduling period; [F IDR ] ± is the cost interval of the system IDR during the scheduling period; α r is the pollution control cost of the rth pollutant; [P i (t)] ± is the output interval value of unit i during period t; [P wd (t)] ± is the wind power abandonment interval value in period t; [P in.e (t)] ± is the interval value of purchased power in period t; [P in.g (t)] ± is the gas purchase power interval value in period t; [|P h,cut (t)|] ± is the absolute value range of heat load reduction during period t; [|P ell (t)-P el0 (t)|] ± is the interval value of the absolute value of the electric load transfer in period t; E r ([P i (t)] ± ) is the rth pollutant corresponding to the i-th equipment in period t; D w Penalty for unit abandonment of wind power; D i is the maintenance cost of unit i; C cut is the incentive demand response cost per unit heat load; C mov The demand response cost is the unit electricity load price.

4. A microgrid dispatching method considering multiple uncertainties according to claim 3, characterized in that: The climbing constraints are as follows: -v jmax ≤v j (t)-v j (t-1)≤v jmax In the formula, v j (t) is the output energy of device j during the scheduling period; v jmax is the maximum climbing constraint of equipment j during the scheduling period.

5. A microgrid dispatching method considering multiple uncertainties according to claim 4, characterized in that: The energy balance constraints are as follows: In the formula, It is the electricity, heat and gas load demand in the dispatching stage; Power output for gas turbines, wind turbines and photovoltaics; is the charging and discharging power of the electric energy storage; The power consumption of power-to-gas equipment and electric boilers; Heat production for gas turbines, electric boilers and gas boilers; QS, ch, t, Charge and release thermal power for energy storage; The gas production of the power-to-gas equipment; Gas consumption of gas boilers and gas turbines.

6. A microgrid dispatching method considering multiple uncertainties according to claim 5, characterized in that: The new energy output constraints are as follows: Where P wt，min (t), P wt，max (t) is the minimum and maximum output of the wind turbine in period t; P pv，min (t), P pv，max (t) is the minimum and maximum output power of the photovoltaic power generation system during period t.

7. A microgrid dispatching method considering multiple uncertainties according to claim 6, characterized in that: The energy storage operation constraints are as follows: In the formula, ——is the minimum capacity and maximum capacity of the equipment.

8. A microgrid dispatching method considering multiple uncertainties according to claim 7, characterized in that: The tie line constraints are as follows: Where P g，min , P g，max is the maximum and minimum value of the electricity purchased; P c，min , P c，max The maximum and minimum values ​​of gas purchase volume.

9. A microgrid dispatching method considering multiple uncertainties according to claim 8, characterized in that: The power purchase and sale status constraints are as follows: In the formula, Power purchased from the distribution system for the energy hub; The power sold by the energy hub to the distribution system; P net_max is the maximum power allowed for interconnection of the tie lines; U buy (t) = 1 means that electricity is purchased at time t; U sell (t)=1 means that electricity is sold at time t.

10. A microgrid dispatching system considering multiple uncertainties, wherein the components of the microgrid include photovoltaic units, wind turbine units, power-to-gas equipment, gas turbine equipment, heat pump equipment, waste heat boilers, and energy storage equipment, characterized in that: It includes a component equipment operation model building module, a microgrid optimization dispatch model building module, a typical scenario acquisition module and a solution module; The component equipment operation model building module is used to build an operation model of the microgrid component equipment based on the historical operation data of the microgrid component equipment; The microgrid optimization scheduling model construction module is used to construct a microgrid optimization scheduling model based on the operation model of the microgrid component equipment. The microgrid optimization scheduling model uses the operation scheduling schemes of the power-to-gas equipment, the gas turbine equipment, the heat pump equipment, the waste heat boiler and the energy storage equipment as decision variables, and takes the lowest total operation cost of the microgrid as the goal, and takes the climbing constraint, energy balance constraint, new energy output constraint, energy storage operation constraint, tie line constraint, and power purchase and sale state constraint as constraint conditions; The typical scenario acquisition module is used to acquire several typical scenarios of the microgrid based on various uncertainties of the microgrid operation; The solution module is used to solve the microgrid optimization scheduling model in each typical microgrid scenario to obtain the microgrid optimization scheduling plan in each typical microgrid scenario.

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