Multi-type energy storage park collaborative optimization scheduling model construction method and system

Abstract

The invention provides a construction method and system for a collaborative optimization scheduling model of a multi-type energy storage park, and the method comprises the steps: constructing an energy storage and utilization model of an electric hydrogen heat system according to the equipment operation parameters of the electric hydrogen heat system in the park in combination with the energy interaction relation between an air conditioner in the park and the electric hydrogen heat system; based on the incentive subsidy and the park operation total cost, determining the daily operation total cost of the energy storage and consumption model participating in the incentive demand response; constructing a target function by taking the total daily operation cost as the minimum; setting constraint conditions for the target function based on the environmental thermal comfort evaluation index, the balance relation of the electric cooling load in the electric hydrogen heat system and the demand response power reduction range; on the basis of the objective function and the constraint condition, a collaborative optimization scheduling model of the multi-type energy storage park is constructed; according to the model constructed by the invention, the air conditioner participates in the optimization scheduling of the excitation type demand response of the park system, and the method and the system for participating in the collaborative optimization of the multi-type energy storage park by taking the air conditioner as the flexible load are provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of interaction between power load supply and demand, and specifically relates to a method and system for constructing a collaborative optimization scheduling model for multiple types of energy storage parks. Background Art

[0002] With the continuous optimization of the global energy structure and the rapid advancement of science and technology, the diversification of the power system has been further improved. More and more new energy power generation resources with volatility and uncertainty are connected to the power grid. Traditional power regulation methods are difficult to maintain the stable operation of the power system. The use of demand response mechanisms to regulate power supply provides an economical and efficient way to enhance the safety and stability of power system operation. Usually, power users respond to market price signals or incentive signals and change their original power consumption plans, thereby achieving safe and stable operation of the power grid. In addition, as an emerging technology, the hydrogen energy system is gradually participating in the comprehensive regulation of the power grid system, which has the characteristics of uncertainty and intermittency.

[0003] At present, air conditioning loads account for a high proportion of the entire network during the summer peak period, and are also the main energy consumption of commercial parks. The existing technology lacks relevant research on using the electric power of summer air conditioning as a flexible load to participate in the comprehensive and precise regulation of the system. In addition, most of the existing commercial park energy optimization scheduling methods focus on the response of traditional energy loads, while hydrogen energy has many advantages such as cleanliness and efficiency. However, the existing technology lacks relevant research on the participation of hydrogen energy in the comprehensive coordinated response of power grid system resources, and when considering the economic efficiency of the system, it focuses more on electricity price-based demand response, and there are few scenarios that consider incentive-based demand response. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes a method for constructing a collaborative optimization scheduling model for a multi-type energy storage park, comprising:

[0005] According to the equipment operating parameters of the electric hydrogen heating system in the park, combined with the energy interaction between the air conditioners and the electric hydrogen heating system in the park, an energy storage and energy utilization model of the electric hydrogen heating system is constructed;

[0006] Based on the incentive subsidy and the total cost of park operation, determine the total daily operating cost of the energy storage model of the electric hydrogen heat system participating in the grid incentive demand response; construct an optimization scheduling objective function with the total daily operating cost as the minimum;

[0007] Based on the environmental thermal comfort evaluation index, the balance relationship of the electric cooling load in the electric hydrogen heat system, and the demand response power reduction range, setting constraints for the optimization scheduling objective function;

[0008] Based on the optimization scheduling objective function and the constraints, a collaborative optimization scheduling model for multiple types of energy storage parks is constructed.

[0009] Preferably, the electric hydrogen heat system comprises: an electrolytic cell, a hydrogen storage tank, a fuel cell and a cold storage tank;

[0010] The equipment operating parameters of the electric hydrogen heat system include: the working efficiency of the electrolytic cell, the hydrogen storage and gas filling and discharging efficiency of the hydrogen storage tank, the working efficiency of the fuel cell and the cold storage and release efficiency of the cold storage tank;

[0011] The energy storage and energy usage model of the electric hydrogen heat system includes one or more of the following: an electrolytic cell energy usage model, a hydrogen storage tank energy storage model, a fuel cell energy usage model, a cold storage tank air conditioning interaction model, and an air conditioning indoor temperature interaction model.

[0012] Preferably, the energy storage and energy utilization model of the electric hydrogen heat system is constructed based on the equipment operation parameters of the electric hydrogen heat system in the park and in combination with the energy interaction relationship between the air conditioner and the electric hydrogen heat system in the park, including one or more of the following:

[0013] According to the interaction between hydrogen and electricity of the electrolytic cell, based on the conversion coefficient between electricity and hydrogen of equal energy and the working efficiency of the electrolytic cell, an energy consumption model of the electrolytic cell is constructed;

[0014] Based on the hydrogen storage filling and discharging efficiency of the hydrogen storage tank, constructing an energy storage model of the hydrogen storage tank;

[0015] According to the interaction between hydrogen and electricity of the fuel cell, based on the conversion coefficient between electricity and hydrogen of equal energy and the working efficiency of the fuel cell, the energy consumption model of the fuel cell is constructed;

[0016] According to the thermoelectric interaction between the air conditioner and the cold storage tank, based on the air conditioning refrigeration power and the cold storage and release efficiency of the cold storage tank, the cold storage tank air conditioning interaction model is constructed;

[0017] According to the heat exchange relationship between the air conditioner and the room temperature, based on the air conditioner refrigeration electric power and the air conditioner refrigeration energy efficiency, the air conditioner indoor temperature interaction model is constructed.

[0018] Preferably, the cold storage tank air conditioning interaction model is as follows:

[0019]

[0020] in: is the cold storage capacity of the cold storage tank at time t+1; is the cold storage capacity of the cold storage tank at time t; η2 is the refrigeration energy efficiency ratio of the second air conditioner; P t AC is the cooling power of the air-conditioning unit at time t; η chs is the cold storage efficiency of the cold storage tank; is the cooling capacity of the cold storage tank at time t; η dhs is the cooling efficiency of the cold storage tank; Δt is the time step;

[0021] The air-conditioning indoor temperature interaction model is shown in the following formula:

[0022]

[0023] Where: T i t+1 is the indoor temperature at time t+1 when the air conditioner is turned on; is the outdoor temperature at time t+1 when the air conditioner is turned on; P t AC is the cooling power of the air-conditioning unit at time t; η is the air-conditioning cooling energy efficiency ratio; η1 is the first air-conditioning cooling energy efficiency ratio; T i t is the indoor temperature at time t when the air conditioner is turned on; C is the specific heat of gas; R is the specific heat of solid; Δt is the time step.

[0024] Preferably, the total daily operating cost of the electric hydrogen heat system energy storage model participating in the grid incentive demand response is determined based on the incentive subsidy and the total cost of park operation, including:

[0025] Determining the incentive subsidy based on the park incentive response electric power and the incentive subsidy unit price;

[0026] Determine the total operating cost of the park based on the electricity purchase cost and operating cost of the electric hydrogen heat system in the park;

[0027] Based on the difference between the incentive subsidy and the total operating cost of the park, the total daily operating cost of the park's electric hydrogen heat system energy storage model participating in the grid incentive demand response is determined.

[0028] Preferably, the optimization scheduling objective function is as follows:

[0029] min F=C buy +C run -C dr

[0030]

[0031] C run =C base +C st +C om

[0032]

[0033] Among them: F is the total operating cost of the park response day; C buy is the electricity purchase cost; C run is the operating cost; C dr To incentivize response subsidies; is the power load of the park at time t after optimization;t P P is the electricity price implemented by the power grid at time t; t DR is the park excitation response power at time t; t DR is the incentive subsidy unit price at time t; C base is the basic cost of the electric hydrogen heat system; C st The start-up and shutdown costs of the units in the electric hydrogen heat system; is the start-stop cost coefficient of the air conditioning system; is the start-stop cost coefficient of the energy storage device in the electric hydrogen heat system; is the operating status of the air conditioning device at time t; is the operating status of the air conditioning device at time t+1; is the operating state of the energy storage device in the electric hydrogen heat system at time t; is the operating state of the energy storage device in the electric hydrogen heat system at time t+1; C om is the operation and maintenance cost of the energy storage device in the electric hydrogen heat system; P t es is the energy storage capacity of the energy storage device in the electric hydrogen heat system at time t; It is the operation and maintenance cost coefficient per unit energy of the energy storage device in the electric hydrogen heat system.

[0034] Preferably, the constraints include one or more of the following: environmental thermal comfort evaluation index constraints, system electric load balance constraints, system cooling load balance constraints, demand response power reduction constraints, and output constraints of each equipment in the park's electric hydrogen heat system.

[0035] Preferably, the environmental thermal comfort evaluation index constraint is as shown in the following formula:

[0036] λ PMV =(0.303e -0.036M +0.028){MW-3.05×10 -3 ×[5733-6.99(MW)-P a ]-0.42(MW-58.15)-1.7×10 -5 M·(5867-P a )-1.4×10 -3 M(34-T i )-3.96×10 -8 f cl ·[(t cl +273) 4 -(t r +273) 4 ]-f cl h c (t cl -T i)}

[0037] Where: PMV is the environmental thermal comfort evaluation index; M is the human energy metabolism rate; W is the mechanical power of human energy metabolism; f cl h is the ratio of the body area covered by clothing to the exposed area; c is the surface heat transfer coefficient; P a is the water vapor partial pressure of the air around the human body; r is the average radiation temperature around the human body; t cl is the outer surface temperature of human clothing; T i is the indoor temperature.

[0038] Based on the same inventive concept, the present invention application also proposes a method for collaborative optimization and scheduling of multiple types of energy storage parks, including:

[0039] Obtain the incentive subsidy unit price, incentive response power range and environmental thermal comfort evaluation index value for the reduction period of the park within a cycle;

[0040] Based on the incentive subsidy unit price, the incentive response power range and the environmental thermal comfort evaluation index value, a pre-constructed multi-type energy storage park collaborative optimization scheduling model is solved to obtain a collaborative optimization scheduling plan for the park air conditioning load;

[0041] The coordinated optimization scheduling plan for the air conditioning load in the park includes: total daily operating cost, incentive response subsidy, regulation cost, distribution plan for electric load and cooling load in each period, and output distribution plan for system energy-consuming equipment in each period;

[0042] The multi-type energy storage park collaborative optimization scheduling model is an optimization scheduling model constructed by any of the methods for constructing a multi-type energy storage park collaborative optimization scheduling model as described above.

[0043] Based on the same inventive concept, the present invention also proposes a multi-type energy storage park collaborative optimization scheduling system, including:

[0044] Data acquisition module: used to obtain the incentive subsidy unit price, incentive response power range and environmental thermal comfort evaluation index value of the park during the reduction period in a cycle;

[0045] Optimization scheduling module: used to solve the pre-built multi-type energy storage park collaborative optimization scheduling model based on the incentive subsidy unit price, the incentive response power range and the environmental thermal comfort evaluation index value, and obtain the collaborative optimization scheduling plan for the air conditioning load in the park;

[0046] The coordinated optimization scheduling plan for the air conditioning load in the park includes: total daily operating cost, incentive response subsidy, regulation cost, distribution plan for electric load and cooling load in each period, and output distribution plan for system energy-consuming equipment in each period;

[0047] The multi-type energy storage park collaborative optimization scheduling model is an optimization scheduling model constructed by any of the methods for constructing a multi-type energy storage park collaborative optimization scheduling model as described above.

[0048] Based on the same inventive concept, the present invention also proposes an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0049] The memory is used to store one or more programs;

[0050] When the one or more programs are executed by the at least one processor, a method for constructing a collaborative optimization scheduling model for multiple types of energy storage parks and an optimization scheduling method as described above are implemented.

[0051] Based on the same inventive concept, the present invention also proposes a readable storage medium on which an execution program is stored. When the execution program is executed, a method for constructing a collaborative optimization scheduling model for a multi-type energy storage park and an optimization scheduling method as described above are implemented.

[0052] Compared with the closest prior art, the present invention has the following beneficial effects:

[0053] The present invention provides a method for constructing a collaborative optimization scheduling model for a multi-type energy storage park, including: constructing an energy storage and energy use model for the electric hydrogen heat system according to the equipment operating parameters of the electric hydrogen heat system in the park, combined with the energy interaction relationship between the air conditioner and the electric hydrogen heat system in the park; determining the total daily operating cost of the energy storage and energy use model participating in the incentive-type demand response based on the incentive subsidy and the total operation cost of the park; constructing an objective function with the total daily operating cost as the minimum; setting constraints for the objective function based on an environmental thermal comfort evaluation index, a balance relationship between electric cooling loads in the electric hydrogen heat system, and a demand response power reduction range; constructing a collaborative optimization scheduling model for a multi-type energy storage park based on the objective function and the constraints; The present invention provides a new idea for the research on the related issues of fine-grained regulation of air conditioners participating in multi-type energy storage systems in summer, which is lacking in the prior art, by constructing a multi-type energy storage electric hydrogen heat system in the park to enhance the overall flexible regulation performance of the commercial park, and then constrain the call of air conditioners with environmental thermal comfort evaluation indicators. Finally, the entire commercial park system responds to the grid incentive demand response to obtain more incentive subsidies, reduce the daily operating costs of the park system, and thus improve the economy of the commercial park operation. It provides a method for the overall optimization and scheduling of commercial parks in summer, in which air conditioners are used as flexible loads to participate in low-cost collaborative optimization of multi-type energy storage parks.

[0054] The present invention provides a method for collaborative optimization scheduling of multiple types of energy storage parks, including: obtaining the incentive subsidy unit price, incentive response power range and environmental thermal comfort evaluation index value of the park during a reduction period within a cycle; based on the incentive subsidy unit price, the incentive response power range and the environmental thermal comfort evaluation index value, solving a pre-constructed collaborative optimization scheduling model for multiple types of energy storage parks to obtain a collaborative optimization scheduling plan for the park's air-conditioning load; based on the incentive subsidy and the environmental thermal comfort evaluation index, the present invention flexibly regulates the park's electric hydrogen heat system and air conditioning through an optimized scheduling model, and can dynamically adjust the power of the air-conditioning unit on the cold load side according to the indoor temperature, thereby regulating the output of each equipment in the park's electric hydrogen heat system, completing the park's multi-energy spatiotemporal redistribution, so as to reduce more electricity consumption to obtain more incentive subsidies, and improve the potential for commercial parks to participate in incentive-based demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of a method for constructing a collaborative optimization scheduling model for a multi-type energy storage park provided by the present invention;

[0056] Figure 2 A schematic diagram of the structure of an electric hydrogen heating system for a commercial park provided by the present invention;

[0057] Figure 3A schematic diagram of a process flow of a collaborative optimization scheduling method for multiple types of energy storage parks provided by the present invention;

[0058] Figure 4 A schematic diagram of a flow chart of a method for collaborative optimization scheduling of air conditioning loads in a commercial park considering multiple types of energy storage devices provided by the present invention;

[0059] Figure 5 A schematic diagram of a park load forecasting method according to the present invention;

[0060] Figure 6 A schematic diagram of outdoor temperature prediction and air conditioning setting value provided by the present invention;

[0061] Figure 7 A schematic diagram of the optimization results of electric load and cooling load provided by the present invention;

[0062] Figure 8 A schematic diagram of electric load output distribution provided by the present invention;

[0063] Fig. 9 A schematic diagram of cooling load output distribution provided by the present invention;

[0064] Fig.10 A schematic diagram of the optimization results of electric load and cooling load provided by the present invention;

[0065] Fig.11 A schematic diagram of electric load output distribution provided by the present invention;

[0066] Fig.12 A schematic diagram of cold load processing distribution provided by the present invention;

[0067] Fig.13 A schematic diagram of the system structure for constructing a collaborative optimization scheduling model for a multi-type energy storage park provided by the present invention;

[0068] Fig.14 A schematic diagram of the structure of a multi-type energy storage park collaborative optimization scheduling system provided by the present invention;

[0069] Fig.15 A schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0070] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0071] Embodiment 1:

[0072] The present invention provides a method for constructing a collaborative optimization scheduling model for a multi-type energy storage park. Specifically, Figure 1As shown, it is a flow chart of a method for constructing a collaborative optimization scheduling model for multiple types of energy storage parks provided by an embodiment of the present disclosure, including the following steps:

[0073] Step 1: Based on the equipment operating parameters of the electric hydrogen heating system in the park and the energy interaction relationship between the air conditioners and the electric hydrogen heating system in the park, an energy storage and energy utilization model of the electric hydrogen heating system is constructed;

[0074] Step 2: Based on the incentive subsidy and the total cost of park operation, determine the total daily operating cost of the electric hydrogen heat system energy storage model participating in the grid incentive demand response; construct an optimization scheduling objective function with the total daily operating cost as the minimum;

[0075] Step 3: Based on the environmental thermal comfort evaluation index, the balance relationship of the electric cooling load in the electric hydrogen heat system, and the demand response power reduction range, set constraints for the optimization scheduling objective function;

[0076] Step 4: Based on the optimization scheduling objective function and the constraints, a collaborative optimization scheduling model for multiple types of energy storage parks is constructed.

[0077] During the peak period of electricity consumption in commercial parks, the air conditioning system is one of the main energy-consuming components. Its call is strongly related to the time of commercial activities and human comfort perception, so it has a strong regulation potential. In the present invention application, the air conditioning system of the commercial park during the peak period in summer is used as a flexible load, which is combined with a cold storage device and a hydrogen storage system. By analyzing the energy allocation of each device in each period when the commercial park participates in the incentive-type demand response, the optimal allocation result is finally solved, which solves the multi-energy optimization operation problem of the commercial park participating in the incentive-type demand response and improves the economy of the park operation.

[0078] Step 1, specifically, in the present invention, the electric hydrogen heat system in the park mainly includes but is not limited to: electrolytic cell, hydrogen storage tank, fuel cell and cold storage tank; Figure 2 As shown, it is a structural diagram of a commercial park electric hydrogen heat system provided by an embodiment of the present invention, which mainly includes: an electrolytic cell, a hydrogen storage tank, a fuel cell, a cold storage tank, an air-conditioning system and a photovoltaic power generation device. This system includes conventional electrical loads and cold loads, and the system is mainly powered by the grid side.

[0079] In the present invention, firstly, based on the energy interaction relationship between the electrolytic cell, hydrogen storage tank, fuel cell and cold storage tank, air conditioning system and the equipment operation parameters of the electric hydrogen heat system in the park, the energy storage model and energy consumption model of the electric hydrogen heat system in the commercial park, that is, the energy storage and energy consumption model of the electric hydrogen heat system, are established. Specifically, the equipment operation parameters of the electric hydrogen heat system in the park include but are not limited to: the working efficiency of the electrolytic cell, the hydrogen storage and gas filling and discharging efficiency of the hydrogen storage tank, the working efficiency of the fuel cell, the cold storage and release efficiency of the cold storage tank and the equipment operation parameters of the air conditioning system.

[0080] The energy storage and usage models of the electric hydrogen heat system in the commercial park include but are not limited to: electrolytic cell energy usage model, hydrogen storage tank energy storage model, fuel cell energy usage model, cold storage tank air conditioning interaction model and air conditioning indoor temperature interaction model.

[0081] Specifically, the electrolytic cell energy consumption model is constructed based on the hydrogen-electricity interaction of the electrolytic cell. The electrolytic cell can consume electrical energy to electrolyze water into hydrogen and oxygen to effectively absorb, store electrical energy and convert clean energy. In this specific embodiment, when modeling the electrolytic cell, the conversion efficiency of each part is set to a fixed value. The simplified electrolytic cell is a linear efficiency conversion model, and its mathematical expression is shown in the following formula:

[0082]

[0083] Where: P t EC is the electric power input to the electrolytic cell at time t, and its unit in the embodiment of the present invention is kW; is the hydrogen production of the electrolytic cell at time t, in kg; η EC The working efficiency of the electrolytic cell; It is the conversion factor between electrical energy and hydrogen of equal energy, and its unit is kW·h / kg.

[0084] Specifically, in the present invention, the hydrogen storage tank is used to store the hydrogen produced by the electrolytic cell and also provide fuel for the fuel cell. Its operating mechanism can improve the flexibility of energy distribution of the electric hydrogen heat system in the commercial park. The mathematical model of the hydrogen storage tank is shown as follows:

[0085]

[0086] in: is the amount of hydrogen in the hydrogen storage tank at time t+1, in kg; is the amount of hydrogen in the hydrogen storage tank at time t, in kg; η ch is the filling efficiency of the hydrogen storage tank; η dis Degassing efficiency of hydrogen storage tanks; is the hydrogen production of the electrolytic cell at time t; is the amount of hydrogen consumed by the fuel cell at time t, in kg; Δt is the time step.

[0087] Specifically, in the present invention, a fuel cell energy model is constructed based on the interaction between hydrogen energy and electric energy of the fuel cell. The fuel cell is mainly composed of an anode, a cathode, an electrolyte membrane and an external circuit. Usually, the anode is a hydrogen electrode and the cathode is an oxygen electrode. The fuel cell is a device that directly converts the chemical energy of hydrogen and oxygen into electrical energy. The output power expression of the fuel cell is as follows:

[0088]

[0089] Where: P t FC is the output power of the fuel cell at time t, in kW; η FC For fuel cell working efficiency; is the amount of hydrogen consumed by the fuel cell at time t, in kg; It is the conversion factor between electrical energy and hydrogen of equal energy, and its unit is kW·h / kg.

[0090] Usually, the cold storage tank stores cold water based on the principle of cold and hot water stratification, and the energy loss during normal operation is less than 1%. In the present invention, based on the thermoelectric energy interaction between the air conditioner and the cold storage tank, and also based on the relationship between the cold storage amount and the cold release power, a cold storage tank air conditioning interaction model is constructed, and its expression is as follows:

[0091]

[0092] in: is the cold storage capacity of the cold storage tank at time t+1; is the cold storage capacity of the cold storage tank at time t, and its unit in the embodiment of the present invention is kW; P t AC is the cooling power of the air-conditioning unit at time t, in kW; η2 is the cooling energy efficiency ratio of the second air-conditioning; is the cooling capacity of the cold storage tank at time t, in kW; η chs is the cold storage efficiency of the cold storage tank; ηdh s is the cooling efficiency of the cold storage tank; Δt is the time step.

[0093] The regulation of air conditioning is related to indoor temperature and outdoor temperature. Therefore, in the present invention, according to the heat exchange relationship between air conditioning and indoor and outdoor temperature, based on air conditioning refrigeration power and air conditioning refrigeration energy efficiency, an air conditioning indoor temperature interaction model is constructed. Specifically, its mathematical expression is shown in the following formula:

[0094]

[0095] Where: T i t+1 is the indoor temperature at time t+1 when the air conditioner is turned on; T i t is the indoor temperature at time t when the air conditioner is turned on; is the outdoor temperature at time t+1 when the air conditioner is turned on; P t AC is the cooling power of the air-conditioning unit at time t, in kW; η is the air-conditioning refrigeration energy efficiency ratio; η1 is the first air-conditioning refrigeration energy efficiency ratio; C is the gas specific heat, in J / ℃; R is the solid specific heat, in J / ℃; Δt is the time step.

[0096] At present, demand response in the prior art is generally divided into two types. One is price-based demand response, which guides users to adjust their electricity consumption behavior through flexible electricity price policies (time-of-use electricity prices, real-time electricity prices, peak electricity prices, etc.). The second is incentive-based demand response, which uses economic compensation or electricity prices to encourage users to participate in load reduction based on the signed contract or agreement. However, incentive-based demand response has higher requirements for the energy coordination ability of the park. The present invention takes hydrogen energy into consideration in the energy planning of the park by constructing an energy storage and energy use model for the electric hydrogen heat system. Hydrogen energy, due to its inherent uncertainty and intermittent characteristics, can be used to improve the flexibility of energy coordination in the park, thereby enabling the park system to better respond to incentive-based demand response. In addition, in the energy storage and energy use model of the electric hydrogen heat system, the air-conditioning system is used as a flexible load to participate in the optimal scheduling of the comprehensive operation of the park together with the cold storage device and the hydrogen storage system, which can further improve the optimization efficiency of the economic efficiency of the park system.

[0097] Step 2: Based on the incentive subsidy and the total cost of park operation, determine the total daily operating cost of the electric hydrogen heat system energy storage model participating in the grid incentive demand response; and construct an optimization scheduling objective function with the total daily operating cost as the minimum;

[0098] Specifically, in the present invention, the optimization scheduling objective function is constructed with the goal of minimizing the total operating cost of the business park response day, which includes the power purchase cost, operating cost and incentive subsidy. The power purchase cost is determined by the power consumption and the power price implemented by the power grid, the operating cost is determined by the basic cost, the unit start-up and shutdown cost and the operation and maintenance cost, and the incentive subsidy is determined by the power reduction amount and the subsidized power unit price.

[0099] minF=C buy +C run -C dr

[0100]

[0101] C run =C base +C st +C om

[0102]

[0103] Among them: F is the total operating cost of the park response day; C buy is the electricity purchase cost; C run is the park operation cost; C dr To provide incentive subsidies; The power load of the park at time t after optimization, the unit is kW; P is the electricity price implemented by the power grid at time t, and its unit is yuan / kW·h; t DRis the park response power at time t, in kW; is the incentive subsidy unit price at time t, in units of yuan / kW·h; C base is the basic cost; C st The start-up and shutdown cost of the unit; is the start-stop cost coefficient of the air conditioning system; is the start-stop cost coefficient of the energy storage device; is the operating status of the air conditioning device at time t; is the operating status of the air conditioning device at time t+1; is the operating state of the energy storage device at time t; is the operating state of the energy storage device at time t+1; C om is the operation and maintenance cost of the energy storage device; P t es is the energy storage capacity of the energy storage device at time t, in kW; is the operation and maintenance cost coefficient per unit energy of the energy storage device.

[0104] The present invention realizes an operation strategy based on electricity price and incentive subsidy mechanism by constructing an objective function, which adopts valley energy storage and peak energy release. It can enable the park to obtain high incentive subsidies on the response day, and conducts comprehensive and coordinated optimization scheduling of the park with the goal of minimizing the total daily operating cost of the park, which can improve the overall economy of the park planning.

[0105] Step 3, specifically, according to the environmental thermal comfort evaluation index, the balance relationship of the electric and cold loads in the electric hydrogen heat system, and the demand response power reduction range, set constraints for the optimization scheduling objective function constructed in step 2. In the present invention, the environmental thermal comfort evaluation index (PMV index) is used to guide the regulation of the air-conditioning load, taking into account the parameters of each device in the electric hydrogen heat system and the performance of the equipment in the energy storage and energy consumption model of the electric hydrogen heat system, as well as the conditions required for the safe operation of each link, to establish a constraint set for the coordinated optimization operation of indoor comfort and each device in the system; specifically, the constraint set includes but is not limited to: environmental thermal comfort evaluation index constraints, system electric load balance constraints, system cold load balance constraints, demand response power reduction constraints, and output constraints of each device in the park electric hydrogen heat system.

[0106] (1) Construction of environmental thermal comfort evaluation index constraints:

[0107] The user's experience of thermal comfort is ambiguous, and the predicted mean vote (PMV) index is one of the most commonly used indoor thermal comfort evaluation indicators. The existing domestic "Heating, Ventilation and Air Conditioning Design Code" stipulates that generally -1≤λ PMV ≤1.λ PMV is the PMV index value. The mathematical equation of the PMV index value is:

[0108] λ PMV =(0.303e -0.036M +0.028){MW-3.05×10 -3 ×[5733-6.99(MW)-P a ]-0.42(MW-58.15)-1.7×10 -5 M·(5867-P a )-1.4×10 -3 M(34-t a )-3.96×10 -8 f cl ·[(t cl +273) 4 -(t r +273) 4 ]-f cl h c (t cl -t a )}

[0109] Where: PMV is the PMV index value; M is the energy metabolism rate of the human body; W is the mechanical power of the human body's energy metabolism; f cl h is the ratio of the body area covered by clothing to the exposed area; c is the surface heat transfer coefficient; P a is the water vapor partial pressure of the air around the human body; a is the air temperature around the human body, t r is the average radiation temperature around the human body; t cl It is the outer surface temperature of human clothing.

[0110] In the embodiment of the present invention, the mathematical equation of the PMV index value is a given value except for the air temperature around the human body. a , is the indoor temperature T in the energy storage and energy use model of the electric hydrogen thermal system i , and further, in the present invention, the relationship between comfort and air conditioning power consumption is established by combining the environmental thermal comfort evaluation index (PMV index) with the air conditioning indoor temperature interaction model to guide the regulation of air conditioning load. Specifically, in the embodiment of the present disclosure, it is required that the value of the environmental thermal comfort evaluation index (PMV index) does not exceed 1 during the optimization process; the mathematical expression of the environmental thermal comfort evaluation index is as follows:

[0111] λ PMV =(0.303e -0.036M +0.028){MW-3.05×10 -3 ×[5733-6.99(MW)-P a]-0.42(MW-58.15)-1.7×10 -5 M·(5867-P a )-1.4×10 -3 M(34-T i )-3.96×10 -8 f cl ·[(t cl +273) 4 -(t r +273) 4 ]-f cl h c (t cl -T i )}

[0112] Where: PMV is the environmental thermal comfort evaluation index; M is the human energy metabolism rate; W is the mechanical power of human energy metabolism; f cl h is the ratio of the body area covered by clothing to the exposed area; c is the surface heat transfer coefficient; P a is the water vapor partial pressure of the air around the human body; r is the average radiation temperature around the human body; t cl is the outer surface temperature of human clothing; T i is the indoor temperature.

[0113] (2) The system electrical load balance constraint is as follows:

[0114]

[0115] Where: P t EC P is the electric power input to the electrolytic cell at time t; t FC P is the output power of the fuel cell at time t; t AC P is the cooling power of the air conditioner at time t; t DR P is the power reduction in response to demand at time t; t E is the conventional power load of the park at time t; P t PV is the photovoltaic output of the park roof at time t, in kW; The power load of the park after optimization at time t.

[0116] (3) The system cooling load balance constraint is as follows:

[0117]

[0118] Where: Pt AC is the air conditioning cooling power at time t; is the cold release amount of the cold storage tank at time t; is the cooling load of the park after optimization at time t; η2 is the refrigeration energy efficiency ratio of the second air conditioner.

[0119] (4) The demand response power reduction constraint is as follows:

[0120]

[0121] Where: P t DR Power reduction for demand response at time t; The minimum power reduction required by the park on the grid side; The grid side requires the park to reduce the maximum power.

[0122] (5) Air conditioning unit cooling power constraints

[0123]

[0124] Where: P t AC is the air conditioning cooling power at time t; is the rated power of the air-conditioning unit, in kW; η3 is the maximum adjustable power ratio of the air-conditioning unit.

[0125] (6) Electrolytic cell power constraints

[0126]

[0127] Where: P t E is the conventional power load of the park at time t; It is the minimum electrical load of the park; It is the maximum electrical load of the park.

[0128] (7) Hydrogen storage tank capacity constraints

[0129]

[0130] in: is the amount of hydrogen in the hydrogen storage tank at time t; It is the maximum amount of hydrogen in the hydrogen storage tank.

[0131] (8) Fuel cell power constraints

[0132]

[0133] Where: P t FC The output power of the fuel cell at time t; Outputting minimum electrical power for the fuel cell; Outputs maximum electrical power for the fuel cell.

[0134] (9) Constraints on the cold storage capacity of cold storage tanks

[0135]

[0136] in: is the cold storage capacity of the cold storage tank at time t; It is the maximum cold storage capacity of the cold storage tank.

[0137] (10) Cooling capacity constraints of cold storage tanks

[0138]

[0139] in: The cooling capacity of the cold storage tank at time t; Maximum cooling capacity of the cold storage tank.

[0140] The present invention constructs constraint conditions based on the environmental thermal comfort evaluation index (PMV index) to regulate the air conditioning load. Compared with the prior art, which only regulates the air conditioning based on the temperature, the present invention proposes to plan the energy consumption of the park based on the environmental thermal comfort index, which is more humane; at the same time, the regulation is more refined, and the power of the air conditioning unit on the cold load side can be adjusted dynamically according to the room temperature, thereby regulating the output of each device in the park's electric hydrogen heat system.

[0141] Step 4, specifically, using the above-mentioned optimization scheduling objective function and constraint set, a collaborative optimization scheduling model for air conditioning loads in a commercial park that takes into account multiple types of energy storage devices is constructed, which is a collaborative optimization scheduling model for a multi-type energy storage park in the present invention. A collaborative optimization scheduling model for a multi-type energy storage park constructed by the present invention achieves low-cost optimization and regulation of energy storage devices and air conditioning loads in the park, reduces the power supply pressure during peak load periods of the power grid, and realizes multi-energy spatiotemporal redistribution in the park, that is, collaboratively regulating air conditioning loads during peak power consumption periods in summer, allowing energy storage devices to flexibly output power, while reducing more power consumption, so that the park can obtain high subsidies and save electricity bills on response days, bringing significant economic benefits to the park.

[0142] Embodiment 2:

[0143] The present invention also provides a method for collaborative optimization and scheduling of multiple types of energy storage parks. Specifically, Figure 3 As shown, it is a flow chart of a collaborative optimization method provided by an embodiment of the present disclosure, comprising the following steps:

[0144] Step 1: Obtain the incentive subsidy unit price, incentive response power range and environmental thermal comfort evaluation index value of the park during the reduction period in a cycle;

[0145] Step 2: Based on the incentive subsidy unit price, the incentive response power range and the environmental thermal comfort evaluation index value, a pre-constructed multi-type energy storage park collaborative optimization scheduling model is solved to obtain a collaborative optimization scheduling plan for the park's air conditioning load;

[0146] The coordinated optimization scheduling plan for the air conditioning load in the park includes: total daily operating cost, incentive response subsidy, regulation cost, distribution plan for electric load and cooling load in each period, and output distribution plan for system energy-consuming equipment in each period;

[0147] The multi-type energy storage park collaborative optimization scheduling model is an optimization scheduling model constructed by the method for constructing a multi-type energy storage park collaborative optimization scheduling model described in Example 1.

[0148] Specifically, the data input into the collaborative optimization scheduling model of multi-type energy storage parks include: the range of power reduction required by the grid side, the PMV index value during the reduction period, the unit price of incentive subsidies, distributed photovoltaic power generation, conventional power load forecast data and outdoor temperature forecast data, equipment operating parameters and cost coefficients. In the present invention, the power reduction of air conditioning load is guided mainly by the relationship between indoor and outdoor temperature and human comfort; on the other hand, the total daily operating cost of the park is determined based on the incentive subsidy and the overall operating cost of the park system, and the economic performance improvement of the optimized system is measured by whether the total cost is the minimum.

[0149] In a specific embodiment of the present invention, the time period and size range during which the input grid side requires the user to reduce power are reflected in the demand response power reduction constraint. The value of the environmental thermal comfort evaluation index (PMV) during the reduction period is reflected in the environmental thermal comfort evaluation index constraint, which is used to establish the relationship between indoor temperature and comfort, and then guide the air conditioning load to reduce power through the indoor temperature. The incentive subsidy unit price, equipment operating parameters and cost coefficient are reflected in the optimization scheduling objective function, which is used to calculate the overall operating cost and expenses of the system. Distributed photovoltaic power generation and conventional power load forecast data are reflected in the system power load balance constraint and participate in the power load balance as an unadjustable quantity. The outdoor temperature forecast data is used in the air-conditioning indoor temperature interaction model to calculate the air-conditioning load power in combination with the indoor temperature.

[0150] Specifically, Figure 4 As shown, the present invention also provides a process of a collaborative optimization operation method for multiple types of energy storage devices based on demand response to cooperate with air conditioning loads to participate in grid interaction as a specific example, which mainly includes the following steps:

[0151] Step 1: Collect distributed photovoltaic power generation, conventional power load forecast data and outdoor temperature forecast data in the business park; collect equipment operating parameters of electrolytic cells, hydrogen storage tanks, fuel cells, cold storage tanks and air conditioning systems;

[0152] Step 2: Based on the energy interaction between the electrolytic cell, hydrogen storage tank, fuel cell, cold storage tank and air conditioning system, establish the energy storage model and energy use model of the commercial park's electric hydrogen heat system;

[0153] Step 3: Use the indoor comfort PMV index to guide the regulation of air conditioning load, consider the parameters of each device in S1 and the energy storage and energy consumption model of the S2 electric hydrogen heat system, as well as the conditions required for safe operation of each link, and establish a constraint set for the coordinated optimization operation of indoor comfort and each device in the system;

[0154] Step 4: Based on the time-of-use electricity price and incentive subsidy mechanism and the constraint set established in S3, with the minimum total daily operating cost of the commercial park as the objective function, a commercial park air conditioning load collaborative optimization scheduling model considering multiple types of energy storage devices is established;

[0155] Step 5: Input the range of power reduction required by the grid side, the PMV index value during the reduction period, the unit price of incentive subsidies, distributed photovoltaic power generation, conventional power load forecast data and outdoor temperature forecast data, equipment operating parameters and cost coefficients, and solve the optimization model established in S4. The daily operation economy of the park (including total daily operation cost, response subsidy, cost savings and regulation cost), the distribution of electric load and cooling load in each period, and the output distribution of system energy-consuming equipment in each period are used as output results to obtain the coordinated optimization scheduling plan for air-conditioning load in the commercial park considering multiple types of energy storage devices.

[0156] Specifically, a commercial park in Shanghai, China, participating in the day-ahead incentive demand response is used as an example for specific explanation: the predicted load curve of the park is shown in Figure 5 , including electrical load and photovoltaic power generation. Outdoor temperature prediction and air conditioning set value curve are shown in Figure 6 The incentive demand response period is from 13:00 to 15:00 the next day. The grid side requires the park to reduce power by 800kW to 1200kW. The incentive subsidy is 6 yuan / kW·h. The time-of-use electricity price in Shanghai is shown in Table 1. The park only purchases electricity and does not sell electricity to the grid. The typical daily photovoltaic output and load characteristic curve in the park is shown in Figure 7 The park is equipped with a hydrogen storage system and a cold storage tank. The air conditioning system is turned on from 8:00 to 20:00, the indoor set temperature is 26°C, and the outdoor temperature forecast and cooling load are shown in Figure 8 The basic parameters of each component in the park are shown in Table 2, where the solid specific heat R and gas specific heat C are the average values ​​of a single room. The cooling load of the park is calculated by adding up 1000 rooms. During the air conditioning load regulation period, λ PMV No more than 1. Indicates the maximum value of the input power of the electrolytic cell. It is the maximum value of the cold release capacity of the cold storage tank, and the control step is 1h.

[0157]

[0158]

[0159] Table 1

[0160]

[0161] Table 2

[0162] In this embodiment, the overall optimization and scheduling of the electric hydrogen heat system in the park is carried out under different scenarios to further illustrate the technical effect achieved by the technical solution in the present invention. Specifically, scenario 1: the park energy storage device and air conditioning load do not participate in the incentive demand response, which is manifested as load characteristics. This mode is the reference mode; scenario 2: the park allocates and optimizes the energy storage device to participate in the incentive demand response, and the air conditioning load does not participate in the incentive demand response; scenario 3: the energy storage device and air conditioning load of the park are coordinated and optimized to participate in the incentive demand response.

[0163] The above optimization results are as follows: the optimized economic efficiency under three scenarios, as shown in Table 3. It can be seen from the table that the economy of scenario 3 is the best, that is, the energy storage device and air-conditioning load of the park are coordinated and optimized to participate in the comprehensive coordinated optimization method of incentive-based demand response, which improves the overall economy of the park system.

[0164] The optimization results of electric load and cooling load of scenario 2 are shown in Figure 7 , the distribution of electric load output is shown in Figure 8 , cooling load output distribution see Fig. 9 ; The optimization results of electric load and cooling load of scenario 3 are shown in Fig.10 ; Electric load output distribution see Fig.11 ; Cooling load output distribution see Fig.12 ; From the above optimization results, it can be seen that in scenario 3, the incentive-type demand response of the air-conditioning load corresponding system is more precise for the comprehensive regulation of the system, which improves the overall economy of the park system.

[0165]

[0166]

[0167] Table 3

[0168] At present, the existing technology lacks research on the comprehensive coordinated response of power grid system resources with air conditioning load and hydrogen energy in summer, and the existing optimization model rarely considers incentive-based demand response when considering the overall economic benefits of the system. The present invention provides a method for coordinated optimization of air conditioning loads in commercial parks that considers multiple types of energy storage devices. It can adopt an operation strategy of valley energy storage and peak energy release based on time-of-use electricity prices and incentive subsidy mechanisms, and dynamically adjust the power of air conditioning units on the cold load side, which not only realizes the optimal configuration of the output of energy storage devices, but also meets the requirements of power reduction in demand response on the grid side, and improves the daily operation economy of commercial park users participating in demand response.

[0169] Example 3

[0170] Based on the same inventive concept, the present invention also proposes a system for constructing a collaborative optimization scheduling model for multiple types of energy storage parks. The system structure is as follows: Fig.13 As shown, including:

[0171] The module for constructing the energy storage and energy use model of the electric hydrogen heat system is used to construct the energy storage and energy use model of the electric hydrogen heat system according to the equipment operation parameters of the electric hydrogen heat system in the park and the energy interaction relationship between the air conditioners and the electric hydrogen heat system in the park;

[0172] Optimization scheduling objective function construction module: used to determine the total daily operating cost of the electric hydrogen heat system energy storage model constructed in the electric hydrogen heat system energy storage model construction module participating in the grid incentive demand response based on the incentive subsidy and the total cost of park operation; and to construct the optimization scheduling objective function with the total daily operating cost as the minimum;

[0173] Constraint condition construction module: used to set constraint conditions for the optimization scheduling objective function constructed in the optimization scheduling objective function construction module based on the environmental thermal comfort evaluation index, the balance relationship of the electric cooling load in the electric hydrogen heat system, and the demand response power reduction range;

[0174] A collaborative optimization scheduling model construction module for multiple types of energy storage parks: a collaborative optimization scheduling model for multiple types of energy storage parks is constructed based on the optimization scheduling objective function constructed by the optimization scheduling objective function construction module and the constraints constructed in the optimization scheduling objective function construction module.

[0175] Preferably, the electric hydrogen heat system in the electric hydrogen heat system energy storage and energy use model construction module includes: an electrolytic cell, a hydrogen storage tank, a fuel cell and a cold storage tank;

[0176] The equipment operating parameters of the electric hydrogen heat system include: the working efficiency of the electrolytic cell, the hydrogen storage and gas filling and discharging efficiency of the hydrogen storage tank, the working efficiency of the fuel cell and the cold storage and release efficiency of the cold storage tank;

[0177] The energy storage and energy usage model of the electric hydrogen heat system includes one or more of the following: an electrolytic cell energy usage model, a hydrogen storage tank energy storage model, a fuel cell energy usage model, a cold storage tank air conditioning interaction model, and an air conditioning indoor temperature interaction model.

[0178] Preferably, the electric hydrogen heat system energy storage energy model construction module is specifically used to: construct the electrolytic cell energy model according to the hydrogen-electricity interaction relationship of the electrolytic cell, based on the conversion coefficient between electric energy and hydrogen of equal energy and the working efficiency of the electrolytic cell;

[0179] Based on the hydrogen storage filling and discharging efficiency of the hydrogen storage tank, constructing an energy storage model of the hydrogen storage tank;

[0180] According to the interaction between hydrogen and electricity of the fuel cell, based on the conversion coefficient between electricity and hydrogen of equal energy and the working efficiency of the fuel cell, the energy consumption model of the fuel cell is constructed;

[0181] According to the thermoelectric interaction between the air conditioner and the cold storage tank, based on the air conditioning refrigeration power and the cold storage and release efficiency of the cold storage tank, the cold storage tank air conditioning interaction model is constructed;

[0182] According to the heat exchange relationship between the air conditioner and the room temperature, based on the air conditioner refrigeration electric power and the air conditioner refrigeration energy efficiency, the air conditioner indoor temperature interaction model is constructed.

[0183] Preferably, the cold storage tank air conditioning interaction model in the electric hydrogen thermal system energy storage model construction module is as follows:

[0184]

[0185] in: is the cold storage capacity of the cold storage tank at time t+1; is the cold storage capacity of the cold storage tank at time t; η2 is the refrigeration energy efficiency ratio of the second air conditioner; P t AC is the cooling power of the air-conditioning unit at time t; η chs is the cold storage efficiency of the cold storage tank; is the cooling capacity of the cold storage tank at time t; η dhs is the cooling efficiency of the cold storage tank; Δt is the time step;

[0186] The air-conditioning indoor temperature interaction model is shown in the following formula:

[0187]

[0188] Where: T i t+1 is the indoor temperature at time t+1 when the air conditioner is turned on; is the outdoor temperature at time t+1 when the air conditioner is turned on; P t ACis the cooling power of the air-conditioning unit at time t; η is the air-conditioning cooling energy efficiency ratio; η1 is the first air-conditioning cooling energy efficiency ratio; T i t is the indoor temperature at time t when the air conditioner is turned on; C is the specific heat of gas; R is the specific heat of solid; Δt is the time step.

[0189] Preferably, the optimization scheduling objective function construction module is specifically used to: determine the incentive subsidy based on the park incentive response electric power and the incentive subsidy unit price;

[0190] Determine the total operating cost of the park based on the electricity purchase cost and operating cost of the electric hydrogen heat system in the park;

[0191] Based on the difference between the incentive subsidy and the total operating cost of the park, the total daily operating cost of the park's electric hydrogen heat system energy storage model participating in the grid incentive demand response is determined.

[0192] Preferably, the optimization scheduling objective function in the optimization scheduling objective function construction module is as follows:

[0193] minF=C buy +C run -C dr

[0194]

[0195] C run =C base +C st +C om

[0196]

[0197]

[0198] Among them: F is the total operating cost of the park response day; C buy is the electricity purchase cost; C run is the operating cost; C dr To incentivize response subsidies; To optimize the park's electrical load at time t; P is the electricity price implemented by the power grid at time t; t DR is the park excitation response power at time t; is the incentive subsidy unit price at time t; C base is the basic cost of the electric hydrogen heat system; C st The start-up and shutdown costs of the units in the electric hydrogen heat system; is the start-stop cost coefficient of the air conditioning system; is the start-stop cost coefficient of the energy storage device in the electric hydrogen heat system; is the operating status of the air conditioning device at time t; is the operating status of the air conditioning device at time t+1; is the operating state of the energy storage device in the electric hydrogen heat system at time t; is the operating state of the energy storage device in the electric hydrogen heat system at time t+1; C om is the operation and maintenance cost of the energy storage device in the electric hydrogen heat system; P t es is the energy storage capacity of the energy storage device in the electric hydrogen heat system at time t; It is the operation and maintenance cost coefficient per unit energy of the energy storage device in the electric hydrogen heat system.

[0199] Preferably, the constraints in the constraint construction module include one or more of the following: environmental thermal comfort evaluation index constraints, system electric load balance constraints, system cooling load balance constraints, demand response power reduction constraints and output constraints of each equipment in the park's electric hydrogen heat system.

[0200] Preferably, the environmental thermal comfort evaluation index constraint in the constraint condition building module is as follows:

[0201] λ PMV =(0.303e -0.036M +0.028){MW-3.05×10 -3 ×[5733-6.99(MW)-P a ]-0.42(MW-58.15)-1.7×10 -5 M·(5867-P a )-1.4×10 -3 M(34-T i )-3.96×10 -8 f cl ·[(t cl +273) 4 -(t r +273) 4 ]-f cl h c (t cl -T i )}

[0202] Where: PMV is the environmental thermal comfort evaluation index; M is the human energy metabolism rate; W is the mechanical power of human energy metabolism; f cl h is the ratio of the body area covered by clothing to the exposed area; c is the surface heat transfer coefficient; P a is the water vapor partial pressure of the air around the human body; r is the average radiation temperature around the human body; t cl is the outer surface temperature of human clothing; Ti is the indoor temperature.

[0203] The present invention realizes the optimal scheduling of air conditioners participating in the incentive-based demand response of multiple types of energy storage park systems through the electric hydrogen heat system energy storage energy consumption model construction module, the optimization scheduling objective function construction module, the constraint condition construction module and the multi-type energy storage park collaborative optimization scheduling model construction module, and provides a model construction system for using air conditioners as flexible loads to participate in the collaborative optimization of multiple types of energy storage parks.

[0204] Embodiment 4:

[0205] Based on the same inventive concept, the present invention also proposes a multi-type energy storage park collaborative optimization scheduling system, the system structure is as follows: Fig.14 As shown, including:

[0206] Data acquisition module: used to obtain the incentive subsidy unit price, incentive response power range and environmental thermal comfort evaluation index value of the park during the reduction period in a cycle;

[0207] Optimization scheduling module: used to solve the pre-built multi-type energy storage park collaborative optimization scheduling model based on the incentive subsidy unit price, the incentive response power range and the environmental thermal comfort evaluation index value, and obtain the collaborative optimization scheduling plan for the air conditioning load in the park;

[0208] The coordinated optimization scheduling plan for the air conditioning load in the park includes: total daily operating cost, incentive response subsidy, regulation cost, distribution plan for electric load and cooling load in each period, and output distribution plan for system energy-consuming equipment in each period;

[0209] The multi-type energy storage park collaborative optimization scheduling model is an optimization scheduling model constructed by any of the methods for constructing a multi-type energy storage park collaborative optimization scheduling model as described above.

[0210] The present invention solves the collaborative optimization scheduling scheme based on incentive subsidies and environmental thermal comfort evaluation indicators through the collaborative optimization scheduling model of multiple types of energy storage parks in the optimization scheduling module, and provides a system that can use air conditioners as flexible loads to participate in the low-cost collaborative optimization of multiple types of energy storage parks.

[0211] Example 5

[0212] like Fig.15As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0213] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to realize the steps of a construction method and an optimization method of a collaborative optimization scheduling model of a multi-type energy storage park in the above-mentioned embodiment.

[0214] Example 6

[0215] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can realize the steps of the construction method and optimization method of a collaborative optimization scheduling model of a multi-type energy storage park in the above embodiment.

[0216] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0217] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0218] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A method for constructing a collaborative optimization scheduling model for multiple types of energy storage parks, characterized in that: include: According to the equipment operating parameters of the electric hydrogen heating system in the park, combined with the energy interaction between the air conditioners and the electric hydrogen heating system in the park, an energy storage and energy utilization model of the electric hydrogen heating system is constructed; Based on the incentive subsidy and the total cost of park operation, determine the total daily operating cost of the energy storage model of the electric hydrogen heat system participating in the grid incentive demand response; construct an optimization scheduling objective function with the total daily operating cost as the minimum; Based on the environmental thermal comfort evaluation index, the balance relationship of the electric cooling load in the electric hydrogen heat system, and the demand response power reduction range, setting constraints for the optimization scheduling objective function; Based on the optimization scheduling objective function and the constraints, a collaborative optimization scheduling model for multiple types of energy storage parks is constructed.

2. The method according to claim 1, characterized in that The electric hydrogen heat system comprises: an electrolytic cell, a hydrogen storage tank, a fuel cell and a cold storage tank; The equipment operating parameters of the electric hydrogen heat system include: the working efficiency of the electrolytic cell, the hydrogen storage and gas filling and discharging efficiency of the hydrogen storage tank, the working efficiency of the fuel cell and the cold storage and release efficiency of the cold storage tank; The energy storage and energy usage model of the electric hydrogen heat system includes one or more of the following: an electrolytic cell energy usage model, a hydrogen storage tank energy storage model, a fuel cell energy usage model, a cold storage tank air conditioning interaction model, and an air conditioning indoor temperature interaction model.

3. The method according to claim 2, characterized in that The energy storage and energy utilization model of the electric hydrogen heat system is constructed based on the equipment operation parameters of the electric hydrogen heat system in the park and the energy interaction relationship between the air conditioners in the park and the electric hydrogen heat system, including one or more of the following: According to the interaction between hydrogen and electricity of the electrolytic cell, based on the conversion coefficient between electricity and hydrogen of equal energy and the working efficiency of the electrolytic cell, an energy consumption model of the electrolytic cell is constructed; Based on the hydrogen storage filling and discharging efficiency of the hydrogen storage tank, constructing an energy storage model of the hydrogen storage tank; According to the interaction between hydrogen and electricity of the fuel cell, based on the conversion coefficient between electricity and hydrogen of equal energy and the working efficiency of the fuel cell, the energy consumption model of the fuel cell is constructed; According to the thermoelectric interaction between the air conditioner and the cold storage tank, based on the air conditioning refrigeration power and the cold storage and release efficiency of the cold storage tank, the cold storage tank air conditioning interaction model is constructed; According to the heat exchange relationship between the air conditioner and the room temperature, based on the air conditioner refrigeration electric power and the air conditioner refrigeration energy efficiency, the air conditioner indoor temperature interaction model is constructed.

4. The method according to claim 3, characterized in that The cold storage tank air conditioning interaction model is shown in the following formula: in: is the cold storage capacity of the cold storage tank at time t+1; is the cold storage capacity of the cold storage tank at time t; η2 is the refrigeration energy efficiency ratio of the second air conditioner; is the cooling power of the air-conditioning unit at time t; η chs is the cold storage efficiency of the cold storage tank; is the cooling capacity of the cold storage tank at time t; η dhs is the cooling efficiency of the cold storage tank; Δt is the time step; The air-conditioning indoor temperature interaction model is shown in the following formula: in: is the indoor temperature at time t+1 when the air conditioner is turned on; is the outdoor temperature at time t+1 when the air conditioner is turned on; is the cooling power of the air-conditioning unit at time t; η is the air-conditioning cooling energy efficiency ratio; η1 is the first air-conditioning cooling energy efficiency ratio; is the indoor temperature at time t when the air conditioner is turned on; C is the specific heat of gas; R is the specific heat of solid; Δt is the time step.

5. The method according to claim 1, characterized in that The total daily operating cost of the electric hydrogen heat system energy storage model participating in the grid incentive demand response is determined based on the incentive subsidy and the total cost of park operation, including: Determining the incentive subsidy based on the park incentive response electric power and the incentive subsidy unit price; Determine the total operating cost of the park based on the electricity purchase cost and operating cost of the electric hydrogen heat system in the park; Based on the difference between the incentive subsidy and the total operating cost of the park, the total daily operating cost of the park's electric hydrogen heat system energy storage model participating in the grid incentive demand response is determined.

6. The method according to claim 5, characterized in that The optimization scheduling objective function is as follows: minF=C buy +C run -C dr C run =C base +C st +C om Among them: F is the total operating cost of the park response day; C buy is the electricity purchase cost; C run is the operating cost; C dr To provide incentive response subsidies; To optimize the park's electrical load at time t; The electricity price implemented by the power grid at time t; is the park excitation response power at time t; is the incentive subsidy unit price at time t; C base is the basic cost of the electric hydrogen heat system; C st The start-up and shutdown costs of the units in the electric hydrogen heat system; is the start-stop cost coefficient of the air conditioning system; is the start-stop cost coefficient of the energy storage device in the electric hydrogen heat system; is the operating status of the air conditioning device at time t; is the operating status of the air conditioning device at time t+1; is the operating state of the energy storage device in the electric hydrogen heat system at time t; is the operating state of the energy storage device in the electric hydrogen heat system at time t+1; C om The operation and maintenance costs of energy storage devices in the electric hydrogen heat system; is the energy storage capacity of the energy storage device in the electric hydrogen heat system at time t; It is the operation and maintenance cost coefficient per unit energy of the energy storage device in the electric hydrogen heat system.

7. The method according to claim 1, characterized in that The constraints include one or more of the following: environmental thermal comfort evaluation index constraints, system electric load balance constraints, system cooling load balance constraints, demand response power reduction constraints, and output constraints of each device in the park's electric hydrogen heat system.

8. The method according to claim 7, characterized in that The environmental thermal comfort evaluation index constraint is as follows: λ PMV =(0.303e -0.036M +0.028){M-W-3.05×10 -3 ×[5733-6.99(M-W)-P a ] - 0.42(M-W-58.15)-1.7×10 -5 M·(5867-P a )-1.4×10 -3 M(34-T i )- 3.96×10 -8 f cl ·[(t cl +273) 4 -(t r +273) 4 ]-f cl h c (t cl -T i )} Where: PMV is the environmental thermal comfort evaluation index; M is the human energy metabolism rate; W is the mechanical power of human energy metabolism; f cl h is the ratio of the body area covered by clothing to the exposed area; c is the surface heat transfer coefficient; P a is the water vapor partial pressure of the air around the human body; r is the average radiation temperature around the human body; t cl is the outer surface temperature of human clothing; T i is the indoor temperature.

9. A method for collaborative optimization and scheduling of multiple types of energy storage parks, characterized in that: include: Obtain the incentive subsidy unit price, incentive response power range and environmental thermal comfort evaluation index value for the reduction period of the park within a cycle; Based on the incentive subsidy unit price, the incentive response power range and the environmental thermal comfort evaluation index value, a pre-constructed multi-type energy storage park collaborative optimization scheduling model is solved to obtain a collaborative optimization scheduling plan for the park air conditioning load; The coordinated optimization scheduling plan for the air conditioning load in the park includes: total daily operating cost, incentive response subsidy, regulation cost, distribution plan for electric load and cooling load in each period, and output distribution plan for system energy-consuming equipment in each period; The multi-type energy storage park collaborative optimization scheduling model is an optimization scheduling model constructed by a method for constructing a multi-type energy storage park collaborative optimization scheduling model according to any one of claims 1 to 8.

10. A multi-type energy storage park collaborative optimization scheduling system, characterized in that: include: Data acquisition module: used to obtain the incentive subsidy unit price, incentive response power range and environmental thermal comfort evaluation index value of the park during the reduction period in a cycle; Optimization scheduling module: used to solve the pre-built multi-type energy storage park collaborative optimization scheduling model based on the incentive subsidy unit price, the incentive response power range and the environmental thermal comfort evaluation index value, and obtain the collaborative optimization scheduling plan for the air conditioning load in the park; The coordinated optimization scheduling plan for the air conditioning load in the park includes: total daily operating cost, incentive response subsidy, regulation cost, distribution plan for electric load and cooling load in each period, and output distribution plan for system energy-consuming equipment in each period; The multi-type energy storage park collaborative optimization scheduling model is an optimization scheduling model constructed by a method for constructing a multi-type energy storage park collaborative optimization scheduling model according to any one of claims 1 to 8.

11. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for constructing a collaborative optimization scheduling model for multiple types of energy storage parks as described in any one of claims 1 to 8 is implemented.

12. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a method for constructing a collaborative optimization scheduling model for multiple types of energy storage parks as described in any one of claims 1 to 8 is implemented.

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