Distributed energy system energy management method and system considering demand side adjustment

Abstract

The invention discloses a distributed energy system energy management method and system considering demand side adjustment. The method is based on related parameters of industrial equipment capable of adjusting operation time in a distributed energy system and the distributed energy system with photovoltaic power generation equipment. The method comprises the following steps: simulating source load data during actual operation in a mode of adding normal distribution noise, and generating a scene set of operation of a distributed energy system; constructing a distributed energy system energy management model based on two-stage stochastic optimization and considering the load flexible adjustment potential; solving the distributed energy system energy management model in the day-ahead planning stage, and determining an operation strategy of distributed energy system equipment; and in an intra-day operation stage, executing an equipment operation strategy of the distributed energy system, solving an energy management model of the distributed energy system according to actual operation scene data, and performing real-time adjustment by using standby electric heating load resources based on the obtained adjustable electric heating load response scheme and the required electric heating load standby amount.

Description

Background Art

[0002] Distributed energy systems can achieve local production and consumption of energy. They can not only improve energy utilization efficiency, but also reduce the dependence on centralized energy supply through multi-energy complementarity. Their energy distribution and equipment scheduling strategies directly affect the economy and energy efficiency of the system. In a distributed energy system, flexible load resources on the demand side such as electric vehicle charging piles and heating thermal loads can achieve a dynamic balance between energy demand and supply by flexibly adjusting the energy consumption behavior of users. When formulating the energy distribution and equipment scheduling strategies for a distributed energy system, considering the flexible load resources on the demand side of the distributed energy system and exploring the regulation potential on the demand side of the system are of great significance for improving the economy and energy utilization efficiency of the operation of the distributed energy system.

[0003] However, the randomness of new energy power generation system output, the volatility of user demand, and the uncertainty of flexible load regulation potential pose great challenges to the formulation of energy distribution and equipment scheduling strategies for distributed energy systems. Inaccurate prediction of new energy power generation and flexible load regulation potential will lead to sub-optimal energy management strategies and increase the real-time regulation cost during the intraday operation stage of the distributed energy system. Currently, there is still a lack of an energy management method for distributed energy systems under uncertain conditions of supply, demand, and load-side regulation potential. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an energy management method and system for a distributed energy system considering demand-side regulation in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problem that the uncertainty of energy supply, demand, and load-side regulation potential in the distributed energy system reduces the economy of the energy management strategy.

[0005] The present invention adopts the following technical solutions:

[0006] An energy management method for a distributed energy system considering demand-side regulation includes the following steps:

[0007] Based on the relevant parameters of the distributed energy system, by adding normally distributed noise, simulate the source-load data during actual operation to generate a scenario set for the operation of the distributed energy system;

[0008] Construct a flexible load regulation model and a distributed energy system equipment operation model under the uncertain scenario set respectively;

[0009] Based on the flexible load regulation model and the distributed energy system equipment operation model under the uncertain scenario set, construct an energy management model for the distributed energy system considering the flexible regulation potential of the load based on two-stage stochastic optimization;

[0010] Based on the scenario set of the distributed energy system operation, solve the energy management model of the distributed energy system in the day-ahead planning stage to determine the operation strategies of the distributed energy system equipment;

[0011] In the intra-day operation stage, execute the operation strategies of the distributed energy system equipment, and determine the adjustable electric and thermal load response plan and the required electric and thermal load reserve based on the energy management model of the distributed energy system solved according to the actual operation scenario data; based on the obtained adjustable electric and thermal load response plan and the required electric and thermal load reserve, use the backup electric and thermal load resources for real-time adjustment.

[0012] Preferably, the relevant parameters include equipment parameters, meteorological parameters, load parameters, and economic parameters.

[0013] Preferably, the flexible load regulation model under the uncertain scenario set specifically includes:

[0014] The shiftable load model. The shiftable load can adjust the power demand within the set time and is shifted according to the power system supply situation and system economy;

[0015] The interruptible load model. In the case of peak power demand or high grid load pressure, it is a type of load where the power supply side negotiates with the user to temporarily interrupt the power supply;

[0016] The flexible thermal load model. Under the premise of not affecting the system operation and user comfort, it adjusts the heat energy demand according to the supply and demand situation.

[0017] Preferably, the shiftable load model is as follows:

[0018]

[0019]

[0020] Wherein, represents the total shiftable load at time t; and respectively represent the actually operating load and the actually shifted load under scenario s, is positive, indicating that the original planned electrical load has shifted at this time, and vice versa indicating the number of electrical loads shifted at other times that are operating additionally; α represents the participation degree of shiftable electrical load users; represents the deviation of the prediction of the user participation degree α;

[0021] The interruptible load model is as follows:

[0022]

[0023] Wherein, is the actual load reduction amount in the t period under the actual operation scenario s; is the maximum reducible amount of interruptible load during period t; is the prediction deviation of the regulation potential of interruptible electrical load;

[0024] The flexible thermal load model is as follows:

[0025]

[0026] Among them, represents the actual power of the system's flexible thermal load at time t under operating scenario s; S is the heating area; ω is the heat dissipation coefficient of the temperature difference between the inside and outside of the building; C is the heat capacity per unit heating area; and are the indoor temperature and outdoor temperature under operating scenario s, respectively; and represent the upper and lower limits of the heating interval temperature that meets human comfort, respectively.

[0027] Preferably, the operating model of the distributed energy system equipment includes:

[0028] Photovoltaic power generation system model:

[0029]

[0030] Among them, and are the predicted value and actual utilization power of the fluctuation factor of the photovoltaic power generation system at time t, respectively; is the amount of abandoned light; is the maximum output power of the photovoltaic power generation system; represents the prediction deviation of the photovoltaic power output under the actual operating scenario;

[0031] Micro gas turbine model:

[0032]

[0033] Among them, represents the amount of natural gas consumed by the combined heat and power unit; and represent the electrical power and thermal power generated by the combined heat and power unit, respectively; is the maximum output power of the micro gas turbine; η gp and η g-h are the electricity generation and heat generation efficiencies of the micro gas turbine at time t, respectively;

[0034] Electric boiler electro-thermal conversion model:

[0035]

[0036] Among them, and respectively represent the heat power generated by the electric boiler and the electric power consumed at time t; η EB represents the heat production efficiency of the electric boiler; represents the maximum input electric power of the electric boiler;

[0037] Gas boiler model:

[0038]

[0039] Among them, and respectively represent the heat power generated by the gas boiler and the natural gas consumed at time t; η GB represents the heat production efficiency of the gas boiler; represents the maximum output power of the gas boiler;

[0040] Battery model:

[0041] Battery charge and discharge limit:

[0042]

[0043] Charge and discharge power limit:

[0044]

[0045] State of charge equation and energy storage capacity limit:

[0046]

[0047] E0 = E init

[0048] E min ≤ E t ≤ E max

[0049] Among them, and are 0-1 variables representing the charge and discharge state of the battery at time t; and represent the charge and discharge power of the battery at time t, P in,max and P out,max represent the upper limit of the charge and discharge power per unit time of the battery, σ p represents the battery capacity loss coefficient, η p,in and η p,out represent the charge and discharge efficiency of the battery, E t represents the stored electricity of the battery at time t, E max and E min are the upper and lower limits of the battery capacity; E init is the initial charge of the battery;

[0050] Heat storage tank model:

[0051]

[0052] Q0 = Q init

[0053] Q min ≤Q t ≤Q max

[0054] wherein, and are 0-1 variables, representing the heat flow in and out state of the heat storage tank at time t; and represent the heat flow in and out of the heat storage tank at time t; H in,max and H out,max represent the upper limit of the heat flow in and out of the heat storage tank per unit time; σ h represents the heat loss coefficient of the heat storage tank; η h,in and η h,out represent the heat flow in and out efficiency of the heat storage tank; Q t represents the heat storage capacity of the heat storage tank at time t; Q max and Q min are the upper and lower limits of the heat storage capacity of the heat storage tank; Q init is the initial heat of the heat storage tank;

[0055] Gas storage tank model:

[0056]

[0057] V0 = V init

[0058] V min ≤V t ≤V max

[0059] wherein, and are 0-1 variables, representing the gas in and out state of the gas storage tank at time t; and represent the in and out flow rate of the gas storage tank at time t; G in,max and G out,max represent the upper limit of the gas in and out flow rate of the gas storage tank per unit time; σ g represents the gas loss coefficient of the gas storage tank; η g,in and η g,out represent the gas in and out efficiency of the gas storage tank; V t represents the gas storage capacity of the gas storage tank at time t; V max and V min are the upper and lower limits of the gas storage capacity of the gas storage tank; V init is the initial gas storage capacity of the gas storage tank.

[0060] Preferably, the energy management model of the distributed energy system considering the flexible regulation potential of the load based on two-stage stochastic optimization takes the minimization of the total operating cost of the distributed energy system as the objective function, and the total operating cost of the distributed energy system includes the total operating cost of each device and the expected cost of intra-day regulation.

[0061] Preferably, the objective function:

[0062]

[0063] Among them, Φ is the total operating cost of each device in the distributed energy system, S is the set of uncertainty scenarios, and π s is the probability of the occurrence of scenario s. and are the reserve energy regulation cost and the carbon emission cost generated by using the reserve energy regulation corresponding to scenario s, respectively; is the compensation cost given to users after the transferable load is scheduled; is the compensation cost after the load interruption is adjusted; is the comfort compensation cost given to users.

[0064] Preferably, the constraint conditions:

[0065]

[0066]

[0067] Among them, and represent the conventional electrical load and the conventional heat load at time t in the distributed energy system, respectively. and represent the upper limits of the upward and downward regulation of the adjustable electrical and heat loads, respectively.

[0068] Preferably, the adjustable electrical and heat load response plan includes the amount of electrical load interruption, the amount of electrical load transfer, the actual operating power of the flexible heat load, and the required amount of upward and downward regulation of the electrical and heat loads.

[0069] On the second aspect, the embodiment of the present invention provides an energy management system for a distributed energy system considering demand-side regulation, including:

[0070] A simulation module, based on the relevant parameters of the distributed energy system, simulates the source-load data during actual operation by adding normal distribution noise to generate a set of scenarios for the operation of the distributed energy system;

[0071] Building modules respectively build a flexible load regulation model and a distributed energy system equipment operation model under an uncertain scenario set. Based on the flexible load regulation model and the distributed energy system equipment operation model under the uncertain scenario set, a distributed energy system energy management model considering the flexible regulation potential of the load is built based on two-stage stochastic optimization;

[0072] A strategy module, based on the scenario set of the distributed energy system operation, solves the distributed energy system energy management model in the day-ahead planning stage to determine the operation strategy of the distributed energy system equipment;

[0073] A control module, in the intra-day operation stage, executes the operation strategy of the distributed energy system equipment, and determines an adjustable electric-heat load response plan and the required electric-heat load reserve according to the distributed energy system energy management model solved from the actual operation scenario data; Based on the obtained adjustable electric-heat load response plan and the required electric-heat load reserve, the standby electric-heat load resources are used for real-time adjustment.

[0074] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned distributed energy system energy management method considering demand-side regulation are implemented.

[0075] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned distributed energy system energy management method considering demand-side regulation are implemented.

[0076] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned distributed energy system energy management method considering demand-side regulation are implemented.

[0077] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, and when the computer program is executed by the electronic device, the steps of the above-mentioned distributed energy system energy management method considering demand-side regulation are implemented.

[0078] Compared with the prior art, the present invention has at least the following beneficial effects:

[0079] A method for energy management of a distributed energy system considering demand-side regulation comprehensively considers three flexible load resources, namely shiftable electric load, interruptible electric load, and flexible thermal load, fully exploits the demand-side regulation potential of the existing distributed energy system, and can effectively improve the energy utilization efficiency and economy of the system. At the same time, the present invention takes into account the uncertainties of photovoltaic output and the response potential of flexible electric and thermal loads, constructs an energy management model of a distributed energy system based on two-stage stochastic optimization, and enhances the robustness of the energy management scheme of the distributed energy system. In engineering applications, relevant technicians can adopt the method of the present invention according to the configuration of demand-side flexible electric and thermal loads such as electric vehicle charging piles and heating systems in the actual distributed energy system, formulate a suitable energy management scheme for the distributed energy system, and coordinately dispatch the regulating resources of each device and the demand side of the system to reduce the system operation cost.

[0080] Furthermore, the shiftable load model optimizes the power supply and demand matching by flexibly adjusting the electricity consumption period, reducing the system operation cost; the interruptible load model actively cuts the peak load during the power grid pressure to improve the power supply reliability; the flexible thermal load model realizes the elastic adjustment of the heat energy demand, taking into account both the system economy and the user comfort. The three cooperate to enhance the resilience of the system to the uncertainty of the source and load, promote the consumption of renewable energy, reduce the reserve capacity demand, and improve the comprehensive energy efficiency.

[0081] Furthermore, multi-energy collaborative optimization integrates multiple types of devices such as electricity, heat, and storage to improve the energy utilization efficiency and the consumption capacity of renewable energy; the dynamic scheduling mechanism enhances the flexibility of the system, and adjusts the output of the devices coordinately to adapt to the source and load fluctuations; the uncertainty impact is quantified based on stochastic optimization, and the start-stop and output strategies of the devices are optimized to reduce the operation cost and reserve demand; it is linked with the flexible load model to form a "source-load-storage" collaborative optimization architecture, enhancing the robustness and economy of the system and reducing the operation risk in extreme scenarios.

[0082] Furthermore, the two-stage optimization takes into account the day-ahead economy and the intra-day robustness, quantifies the uncertainty through stochastic scenarios, and reduces the extreme risk; integrates the flexible regulation potential of the load, cuts the investment in reserve capacity, and reduces the regulation cost; collaboratively optimizes the "source-load-storage" resources at multiple time scales to improve the consumption of renewable energy and the utilization rate of devices; with the goal of minimizing the cost expectation, it balances the operation economy and the regulation flexibility, and enhances the adaptive ability of the system to the source and load fluctuations.

[0083] It can be understood that the beneficial effects of the above second aspect to the sixth aspect can refer to the relevant descriptions in the above first aspect, and will not be elaborated here.

[0084] In summary, the present invention comprehensively considers the uncertainties of new energy output and the regulation potential of the demand side, coordinates the flexible load resources of various devices and the demand side in the distributed energy system, formulates a more economical and efficient energy management strategy for the distributed energy system, reduces the operating cost of the distributed energy system, and improves the economy and energy utilization efficiency of the operation of the distributed energy system.

[0085] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0086] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments of the present application will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present application, and those of ordinary skill in the art can obtain other accompanying drawings based on these drawings without creative efforts.

[0087] Figure 1 It is a flowchart of the present invention;

[0088] Figure 2 It is a schematic diagram of the data before and after the adjustment of the electrical load in the embodiment of the present invention;

[0089] Figure 3 It is a schematic diagram of the data before and after the adjustment of the thermal load in the embodiment of the present invention;

[0090] Figure 4 It is a schematic diagram of a computer device provided by an embodiment of the present invention;

[0091] Figure 5 It is a block diagram of an electronic device provided by an embodiment of the present invention.

[0092] Among them, 60. Computer device; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access storage unit; 6202. Cache storage unit; 6203. Read-only storage unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Embodiments

[0093] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0094] In the description of the present invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0095] It should also be understood that the terms used in the specification of the present invention are for the purpose of describing particular embodiments only and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly dictates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0096] It should be further understood that the term " / and" as used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. For example, A and / or B may represent: the case where A exists alone, the case where A and B exist simultaneously, and the case where B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the contextually related objects.

[0097] It should be understood that although terms such as first, second, third, etc. may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0098] Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detected (stated condition or event)" or "in response to detecting (stated condition or event)".

[0099] Structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of the various regions and layers shown in the figures and their relative sizes and positional relationships are merely exemplary, and may actually deviate due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes and relative positions according to actual requirements.

[0100] The present invention provides an energy management method for a distributed energy system considering demand-side regulation. By constructing a scenario set considering the uncertainty of photovoltaic power output and load, establishing a flexible electric and thermal load regulation model and an equipment operation model, and adopting a two-stage stochastic optimization strategy. In the day-ahead stage, the equipment scheduling strategy is optimized based on the predicted data. In the intra-day stage, the load response plan is dynamically adjusted in combination with the real-time scenario, and the electric and thermal reserve capacity is reserved to achieve the coordinated optimization of the source and load. The core is to handle the uncertainty through stochastic optimization, utilize the regulation potential of industrial flexible loads, and coordinate the operation of equipment such as gas turbines and energy storage devices to improve the system operation economy and the renewable energy consumption capacity.

[0101] Embodiment 1

[0102] Please refer to Figure 1 , an energy management method for a distributed energy system considering demand-side regulation of the present invention includes the following steps:

[0103] S1. Obtain the relevant parameters of flexible electric loads such as industrial equipment with adjustable operating time in the distributed energy system, flexible thermal loads, and the distributed energy system with photovoltaic power generation equipment;

[0104] The relevant parameters specifically include:

[0105] (1) Equipment parameters, such as the operating parameters and operating costs of equipment such as photovoltaic power generation equipment, storage batteries in the distributed energy system, electric boilers, and micro gas turbines.

[0106] (2) Meteorological parameters, such as the power generation prediction data of the photovoltaic equipment within the next 24 hours, the outside temperature prediction data, and the prediction data is mainly obtained by using machine learning methods based on the collected historical data.

[0107] (3) Load parameters, such as the prediction data of the conventional load and adjustable load within the next 24 hours in the area where the distributed energy system is located, and the maximum cuttable amount data of the interruptible load.

[0108] (4) Economic parameters, such as the electricity price, heat price, and natural gas purchase cost in the area where the distributed energy system is located.

[0109] S2. Based on the day-ahead prediction data of photovoltaic power generation, outside temperature, and adjustable load obtained in step S1, by adding normal distribution noise, simulate the source and load data during actual operation, and generate a scenario set S for the operation of the distributed energy system;

[0110] S3. According to the operation characteristics and regulation potential of the flexible electric and thermal load resources in the distributed energy system, construct a flexible load regulation model under the uncertain scenario set;

[0111] 1) Modeling of shiftable loads

[0112] Transferable loads can flexibly adjust power demand within a certain time range without affecting their service quality or main functions. Such loads are adjustable in time and can be shifted according to the power system supply situation and system economy. The common transferable load models are as follows:

[0113]

[0114] Among them, represents the total number of transferable loads at time t; and represent the number of loads actually operating and the number of loads actually transferred under scenario s respectively, is positive, indicating that the original planned electrical load has been transferred at this time, and vice versa, indicating the number of electrical loads transferred at other times that are operating additionally; α represents the participation degree of transferable electrical load users; represents the deviation in the prediction of the user participation degree α.

[0115] 2) Interruptible load modeling

[0116] Interruptible loads refer to a type of load that the power supply side can negotiate with users to temporarily interrupt power supply under peak power demand or high grid load pressure, and are usually used for large load users such as industry and commerce. The interruptible load model is as follows:

[0117]

[0118] Among them, is the actual load curtailment at time t under the actual operation scenario s; is the maximum load curtailment that can be achieved by interruptible loads at time t; is the prediction deviation of the regulation potential of interruptible electrical loads.

[0119] 3) Flexible thermal load modeling

[0120] Flexible thermal loads can flexibly adjust the heat energy demand according to the supply and demand situation without affecting system operation and user comfort. Since the human body has a certain tolerance range for temperature changes, the space thermal load can be adjusted within this range. The flexible thermal load model is as follows:

[0121]

[0122] Among them, represents the actual power of the system's flexible thermal load at time t under the operation scenario s; S is the heating area; w is the heat dissipation coefficient of the temperature difference between the inside and outside of the building; C is the heat capacity per unit heating area; and are the indoor temperature and outdoor temperature under the operation scenario s respectively; and respectively represent the upper and lower limits of the heating zone temperature that meets human comfort.

[0123] S4. According to the operating characteristics of various devices in the distributed energy system, construct an operating model for the distributed energy system devices;

[0124] 1) Photovoltaic power generation system

[0125] Limited by the existing technology, the day-ahead prediction accuracy of weather conditions is relatively low, and there is a deviation between the photovoltaic power generation prediction result and the actual output. When formulating the system energy management strategy day-ahead, the uncertainty of photovoltaic power generation needs to be taken into account. The photovoltaic power generation system model is expressed as:

[0126]

[0127] Among them, and are respectively the predicted value of the fluctuation factor and the actual utilization power of the photovoltaic power generation system at time t; is the amount of abandoned light; is the maximum output power of the photovoltaic power generation system; represents the prediction deviation of the photovoltaic output under the actual operating scenario.

[0128] 2) Micro gas turbine

[0129] The micro gas turbine uses natural gas as fuel and converts natural gas into electric energy and heat energy. The waste heat generated can be recovered through a recovery device and supplied to subsequent equipment. The micro gas turbine model is as follows:

[0130]

[0131] Among them, represents the amount of natural gas consumed by the combined heat and power unit; and respectively represent the electric power and heat power generated by the combined heat and power unit; is the maximum output power of the micro gas turbine; η gp and η g-h are respectively the power generation and heat generation efficiencies of the micro gas turbine at time t.

[0132] 3) Electric boiler

[0133] An electric boiler is a device that directly converts electric energy into heat energy through a resistance heating element, and is suitable for various application scenarios such as industrial production, heating, and hot water supply. The electric boiler electro-thermal conversion model is as follows:

[0134]

[0135] Among them, and respectively represent the thermal power generated by the electric boiler and the electric power consumed at time t; η EB represents the heat production efficiency of the electric boiler; represents the maximum input electric power of the electric boiler.

[0136] 4) Gas boiler

[0137] The gas boiler converts chemical energy into heat energy by burning natural gas or other gaseous fuels and can provide a stable heat supply for the system. The gas boiler model is as follows:

[0138]

[0139] Among them, and respectively represent the thermal power generated by the gas boiler and the natural gas consumed at time t; η GB represents the heat production efficiency of the gas boiler; represents the maximum output power of the gas boiler.

[0140] 5) Battery

[0141] The battery model is as follows:

[0142] Battery charge and discharge limit:

[0143]

[0144] Charge and discharge power limit:

[0145]

[0146] State of charge equation and energy storage capacity limit:

[0147]

[0148] E0 = E init (19)

[0149] E min ≤ E t ≤ E max (20)

[0150] Among them, and are 0-1 variables representing the charge and discharge state of the battery at time t; and represent the charge and discharge power of the battery at time t, P in,max and P out,max represent the upper limit of the charge and discharge power per unit time of the battery, σ p represents the battery capacity loss coefficient, η p,in and η p,out represent the charge and discharge efficiency of the battery, Et Represents the stored electricity of the battery at time t, E max and E min are the upper and lower limits of the battery capacity; E init is the initial electricity of the battery.

[0151] 6) Heat storage tank

[0152] The model structure of the heat storage tank is similar to that of the battery, and the specific form is as follows:

[0153]

[0154] Q0 = Q init (25)

[0155] Q min ≤Q t ≤Q max (26)

[0156] Among them, and are 0-1 variables, representing the heat flow in and out state of the heat storage tank at time t; and represent the heat flow in and out of the heat storage tank at time t; H in,max and H out,max represent the upper limit of the heat flow in and out of the heat storage tank per unit time; σ h represents the heat loss coefficient of the heat storage tank; η h,in and η h,out represent the heat flow in and out efficiency of the heat storage tank; Q t represents the stored heat of the heat storage tank at time t; Q max and Q min are the upper and lower limits of the heat capacity of the heat storage tank; Q init is the initial heat of the heat storage tank.

[0157] 7) Gas storage tank

[0158] The model structure of the gas storage tank is similar to that of the battery and the heat storage tank, and the specific form is as follows:

[0159]

[0160] V0 = V init (31)

[0161] V min ≤V t ≤V max (32)

[0162] Among them, and are 0-1 variables, representing the gas in and out state of the gas storage tank at time t; and Denote the inlet and outlet flow rates of the gas storage tank at time t; G in,max and G out,max Denote the upper limit of the inlet and outlet gas flow rates of the gas storage tank per unit time; σ g Denote the gas loss coefficient of the gas storage tank; η g,in and η g,out Denote the inlet and outlet gas efficiency of the gas storage tank; V t Denote the gas storage volume of the gas storage tank at time t; V max and V min Are the upper and lower limits of the gas storage tank capacity; V init Is the initial gas storage volume of the gas storage tank.

[0163] S5. Based on the flexible electric heating load model and the distributed energy system equipment operation model established in steps S3 and S4, construct a distributed energy system energy management model considering the flexible regulation potential of the load based on two-stage stochastic optimization;

[0164] Objective function

[0165] With minimizing the total operating cost of the distributed energy system as the objective function, the total operating cost of the distributed energy system consists of the total operating costs of each device and the expected cost of intra-day regulation:

[0166]

[0167] Φ = Φ B + Φ M + Φ C (34)

[0168]

[0169] Among them, Φ is the total operating cost of each device (photovoltaic system, gas turbine, electric boiler, gas boiler, and energy storage device) in the distributed energy system, mainly including the energy procurement cost Φ B , the equipment maintenance cost Φ M , the carbon emission cost Φ C ; T is the scheduling period; and Are the electricity purchase volume, gas purchase volume, and heat purchase volume at time t, respectively; Are the purchase prices of unit electric energy, natural gas, and heat, respectively. The equipment maintenance cost is mainly calculated through the operating power of all devices. M is the set of distributed energy system devices, Is the maintenance cost generated by unit operating power, Is the operating power of device m. In particular, for the energy storage device, Is the sum of the input and output powers, λ C Represents the treatment cost per unit of CO2; Represents the equivalent carbon emission coefficient for purchasing unit electric energy, heat, and natural gas, represents the equivalent carbon emission coefficient of the combined heat and power unit operation, S is the set of uncertainty scenarios, and π s is the probability of the occurrence of scenario s. and represent the regulation cost of the backup energy corresponding to scenario s and the carbon emission cost generated by using the backup energy regulation respectively; and represent the positive and negative reserve of the electrical load called respectively; and represent the positive and negative reserve of the heat load called respectively; represent the positive and negative reserve call costs per unit of electrical and heat load respectively; represents the compensation cost given to users after the transferable load scheduling; λ PR represents the compensation price per unit power of the transferable load; is the compensation cost after the load interruption adjustment; λ L represents the compensation price per unit power of the interruptible load; is the comfort compensation cost given to users; γ is the subsidy cost coefficient for adjusting the indoor temperature per unit area, and T0 is the initial set temperature of the user.

[0170] Constraint conditions:

[0171]

[0172] (1)-(32)(53)

[0173] Among them, (43 - 45) represent the electrical balance constraint, heat balance constraint and gas balance constraint under the actual operation scenario respectively, where and represent the conventional electrical load and conventional heat load at time t in the distributed energy system respectively. Formulas (46 - 48) represent the power purchase limit from the distribution network, heat purchase limit from the heat network and gas purchase limit from the gas network respectively. Formulas (49 - 52) represent the upper and lower reserve limits of the electrical and heat loads that can be called; and represent the upper limits of the upward and downward reserve of the electrical and heat loads that can be called respectively. Formula (53) represents the flexible electrical and heat load regulation model and the distributed energy system equipment operation model established in steps S3 and S4.

[0174] S6. Based on the source-load change scenario set constructed in step S2, solve the distributed energy system energy management model in step S5 during the day-ahead planning stage to determine the operation strategies of equipment such as micro gas turbines, electric boilers, gas turbines, batteries, heat storage tanks, gas storage tanks, etc. in the system;

[0175] S7. During the daily operation stage, execute the operation strategy of the distributed energy system equipment determined in step S6, and solve the distributed energy system capacity management model in step S5 according to the actual operation scenario data to determine the adjustable electric and thermal load response plan and the required electric and thermal load reserve;

[0176] It includes the interruption amount of electric load, the transfer amount of electric load, the actual operating power of flexible thermal load, and the required upward and downward reserve of electric and thermal load.

[0177] S8. Execute the adjustable load response plan determined in step S7 and use the standby electric and thermal load resources for real-time adjustment.

[0178] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0179] Embodiment 2

[0180] The present invention provides a distributed energy system energy management system considering demand-side regulation, which can be used to implement the distributed energy system energy management method considering demand-side regulation. Specifically, the distributed energy system energy management system considering demand-side regulation includes an analog module, a construction module, a strategy module, and a control module.

[0181] Among them, the analog module, based on the relevant parameters of the distributed energy system, simulates the source-load data during actual operation by adding normal distribution noise to generate a scenario set for the operation of the distributed energy system;

[0182] The construction module respectively constructs a flexible load regulation model and a distributed energy system equipment operation model under the uncertain scenario set, and constructs a distributed energy system energy management model considering the flexible regulation potential of the load based on the flexible load regulation model and the distributed energy system equipment operation model under the uncertain scenario set;

[0183] The strategy module, based on the scenario set of the distributed energy system operation, solves the distributed energy system energy management model in the day-ahead planning stage to determine the operation strategy of the distributed energy system equipment;

[0184] The control module, during the intraday operation phase, executes the operation strategy of the distributed energy system equipment, and determines the adjustable electric-heat load response plan and the required electric-heat load reserve based on the distributed energy system energy management model solved from the actual operation scenario data; based on the obtained adjustable electric-heat load response plan and the required electric-heat load reserve, it uses the standby electric-heat load resources for real-time adjustment.

[0185] Embodiment 3

[0186] The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the distributed energy system energy management method considering demand-side regulation, including:

[0187] Obtain relevant parameters of flexible electrical loads such as industrial equipment with adjustable operating time, flexible thermal loads, and distributed energy systems with photovoltaic power generation equipment in the distributed energy system; based on the relevant parameters, simulate the source-load data during actual operation by adding normally distributed noise to generate a set of scenarios for the operation of the distributed energy system; construct a flexible load regulation model under the uncertain scenario set according to the operating characteristics and regulation potential of the flexible electric and thermal load resources in the distributed energy system; construct an operating model of the distributed energy system equipment according to the operating characteristics of various equipment in the distributed energy system; based on the flexible load regulation model and the distributed energy system equipment operating model under the uncertain scenario set, construct a distributed energy system energy management model considering the flexible regulation potential of the load based on two-stage stochastic optimization; based on the set of scenarios for the operation of the distributed energy system, solve the distributed energy system energy management model in the day-ahead planning stage to determine the operating strategies of the distributed energy system equipment; in the intraday operation stage, execute the operating strategies of the distributed energy system equipment, and solve the distributed energy system energy management model according to the actual operation scenario data to determine the adjustable electric and thermal load response plan and the required electric and thermal load reserve; based on the obtained adjustable electric and thermal load response plan and the required electric and thermal load reserve, use the standby electric and thermal load resources for real-time adjustment.

[0188] Please refer to Figure 4 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the method for energy management of the distributed energy system considering demand-side regulation in the embodiment. To avoid repetition, it will not be elaborated here one by one. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the distributed energy system energy management system considering demand-side regulation in the embodiment. To avoid repetition, it will not be elaborated here one by one.

[0189] The computer device 60 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 4 merely examples of the computer device 60, which do not constitute a limitation on the computer device 60, may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0190] The so-called processor 61 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0191] The memory 62 may be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0192] Furthermore, the memory 62 may also include both the internal storage unit of the computer device 60 and the external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0193] Please refer to Figure 5 , the terminal device is the electronic device 600, and the electronic device 600 is presented in the form of a general computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0194] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the above method part of this specification. For example, the processing unit 610 may execute the steps as shown in Figure 1 .

[0195] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0196] The storage unit 620 may also include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0197] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0198] The electronic device 600 may also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or may communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem). Such communication may be carried out through the input / output interface 650. Also, the electronic device 600 may communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0199] Example 4

[0200] The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. The computer-readable storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0201] The computer-readable storage medium also includes data signals propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0202] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0203] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the distributed energy system energy management method considering demand-side regulation in the above embodiments; the one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0204] Obtain relevant parameters of flexible electrical loads such as industrial equipment with adjustable operating time, flexible thermal loads, and distributed energy systems with photovoltaic power generation equipment in the distributed energy system; based on the relevant parameters, simulate the source-load data during actual operation by adding normally distributed noise to generate a scenario set for the operation of the distributed energy system; construct a flexible load regulation model under the uncertain scenario set according to the operating characteristics and regulation potential of the flexible electro-thermal load resources in the distributed energy system; construct a distributed energy system equipment operation model according to the operating characteristics of various devices in the distributed energy system; based on the flexible load regulation model under the uncertain scenario set and the distributed energy system equipment operation model, construct a distributed energy system energy management model considering the flexible regulation potential of the load based on two-stage stochastic optimization; based on the scenario set of the operation of the distributed energy system, solve the distributed energy system energy management model in the day-ahead planning stage to determine the operation strategy of the distributed energy system equipment; in the intra-day operation stage, execute the distributed energy system equipment operation strategy, and solve the distributed energy system energy management model according to the actual operation scenario data to determine the adjustable electro-thermal load response plan and the required electro-thermal load reserve; based on the obtained adjustable electro-thermal load response plan and the required electro-thermal load reserve, use the standby electro-thermal load resources for real-time adjustment.

[0205] The databases involved in the embodiments provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. The processors involved in the embodiments provided in this application may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0206] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0207] In this example, an energy management strategy is formulated for a distributed energy system with interruptible and transferable electrical loads and flexible thermal loads. The parameters of energy conversion devices such as gas turbines in the system are obtained as shown in Table 1.

[0208] Table 1 Main equipment parameters of the distributed energy system

[0209]

[0210] By solving the models (36)-(53), the optimal energy management strategy of this embodiment is obtained. The situation before and after the adjustment of the electrical load is as Figure 2 shown, and the situation before and after the adjustment of the thermal load is as Figure 3 shown. The cost comparison between the scheme without considering the load adjustment potential and the scheme considering the load adjustment potential is shown in Table 2:

[0211] Table 2 Cost composition of different schemes

[0212]

[0213] As can be seen from Table 2, in this embodiment, the energy management method of the present invention is adopted, which reduces the total operating cost of the distributed energy system by 18%. Those skilled in the art can determine the operating schemes of various devices in the distributed energy system according to the optimization results to ensure the stable operation of the distributed energy system and effectively save the system operating cost.

[0214] In summary, an energy management method and system for a distributed energy system considering demand-side regulation according to the present invention takes into account the existing flexible electric and thermal load resources in the system, constructs an effective and economical energy strategy for the distributed energy system, and improves the economic benefits of the system operation through the coordinated scheduling of various devices and flexible loads in the system, providing a decision-making basis for the formulation of the operating scheme of the distributed energy system.

[0215] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0216] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0217] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0218] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are only illustrative. For example, the division of the module or unit is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the device or unit can be in electrical, mechanical or other forms.

[0219] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0220] In addition, in each embodiment of the present invention, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0221] If the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present invention, it may also be completed by instructing relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0222] This application is described with reference to the flowcharts and / or block diagrams of methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0223] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the process in Figure 1one or more processes and / or blocks Figure 1 the functions specified in one or more blocks.

[0224] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one or more processes and / or blocks Figure 1 or more processes and / or blocks.

[0225] The above is only to illustrate the technical idea of the present invention and should not be used to limit the protection scope of the present invention. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A distributed energy system energy management method considering demand-side regulation, characterized in that: The following steps are involved: Based on the relevant parameters of the distributed energy system, the source and load data during actual operation are simulated by adding normal distribution noise to generate a set of scenarios for the operation of the distributed energy system. The flexible load regulation model and distributed energy system equipment operation model under uncertain scenario sets are constructed respectively; Based on the flexible load regulation model and distributed energy system equipment operation model under uncertain scenario sets, a distributed energy system energy management model considering the potential of load flexible regulation based on two-stage stochastic optimization is constructed; Based on the scenario set of distributed energy system operation, the distributed energy system energy management model is solved in the day-ahead planning stage to determine the operation strategy of distributed energy system equipment; During the intraday operation stage, the distributed energy system equipment operation strategy is executed, and the distributed energy system energy management model obtained by solving the actual operation scenario data is used to determine the adjustable electric heating load response plan and the required electric heating load reserve capacity; based on the obtained adjustable electric heating load response plan and the required electric heating load reserve capacity, the reserve electric heating load resources are used for real-time adjustment.

2. The distributed energy system energy management method considering demand-side regulation according to claim 1 is characterized in that: Relevant parameters include equipment parameters, meteorological parameters, load parameters and economic parameters.

3. The distributed energy system energy management method considering demand-side regulation according to claim 1 is characterized in that: The flexible load regulation model under the uncertain scenario set specifically includes: Transferable load model: transferable load can adjust power demand within a set time and transfer according to power system supply and system economics; Interruptible load model: in the case of peak power demand or heavy grid load pressure, the power supplier negotiates with the user to temporarily interrupt the power supply; The flexible heat load model adjusts the heat demand according to the supply and demand conditions without affecting the system operation and user comfort.

4. The distributed energy system energy management method considering demand-side regulation according to claim 3 is characterized in that: The transferable load model is as follows: in, represents the total load that can be transferred at time t; and They represent the actual number of loads in operation and the actual number of loads transferred under scenario s, If it is positive, it means that the originally planned electric load has been transferred at this time, otherwise it means that the number of electric loads transferred at other times has been additionally operated; α represents the participation of users of transferable electric loads; represents the deviation of the prediction of user engagement α; The interruptible load model is as follows: in, is the actual load reduction during period t under the actual operation scenario s; is the maximum amount of interruptible load that can be reduced in period t; Forecast deviation of interruptible load regulation potential; The flexible heat load model is as follows: in, It represents the actual power of the system's flexible heat load in the operation scenario s during the period t; S is the heating area; ω is the heat dissipation coefficient of the temperature difference between the inside and outside of the building; C is the heat capacity per unit heating area; and are the indoor temperature and outdoor temperature under the operation scenario s respectively; and They respectively represent the upper and lower limits of the heating temperature range that meets human comfort.

5. The distributed energy system energy management method considering demand-side regulation according to claim 1 is characterized in that: The distributed energy system equipment operation model includes: Photovoltaic power generation system model: in, and They are respectively the predicted value of the fluctuation factor and the actual utilized power of the photovoltaic power generation system at time t; is the amount of abandoned light; is the maximum output power of the photovoltaic power generation system; Indicates the prediction deviation of PV output under actual operation scenario; Microturbine Model: in, Indicates the amount of natural gas consumed by the cogeneration unit; and They represent the electrical power and thermal power generated by the cogeneration unit, respectively; is the maximum output power of the micro gas turbine; η gp and η g-h are the electricity and heat production efficiencies of the micro gas turbine at time t respectively; Electric boiler electric heat conversion model: in, and They represent the thermal power generated and the electrical power consumed by the electric boiler at time t respectively; η EB Indicates the heat generation efficiency of electric boiler; Indicates the maximum input power of the electric boiler; Gas boiler model: in, and They represent the thermal power generated by the gas boiler and the natural gas consumed at time t respectively; GB Indicates the heat generation efficiency of gas boiler; Indicates the maximum output power of the gas boiler; Battery model: Restrictions on simultaneous charging and discharging of batteries: Charging and discharging power limit: State of charge equation and energy storage capacity limitations: E0=E init AND min ≤E t ≤E max in, and is a 0-1 variable, indicating the charge and discharge status of the battery at time t; and Indicates the charging and discharging power of the battery at time t, P in,max and P out,max Indicates the upper limit of battery charging and discharging power per unit time, σ p Represents the battery capacity loss coefficient, η p,in and η p,out Indicates the battery charging and discharging efficiency, E t Indicates the storage capacity of the battery at time t, E max and E min E is the upper and lower limits of battery capacity; init is the initial charge of the battery; Thermal storage tank model: Q0=Q init Q min ≤Q t ≤Q max in, and is a 0-1 variable, indicating the heat flow in and out state of the heat storage tank at time t; and represents the heat flow in and out of the heat storage tank at time t; H in,max and H out,max Represents the upper limit of the heat flow rate in and out of the heat storage tank per unit time; σ h Represents the heat loss coefficient of the heat storage tank; η h,in and η h,out Indicates the heat flow efficiency in and out of the heat storage tank; Q t represents the heat storage capacity of the heat storage tank at time t; Q max and Q min is the upper and lower limits of the heat storage tank’s thermal capacity; Q init is the initial heat of the heat storage tank; Gas tank model: V0=V init In min ≤V t ≤V max in, and is a 0-1 variable, indicating the gas inlet and outlet status of the gas tank at time t; and G represents the inflow and outflow flow of the gas tank at time t; in,max and G out,max Indicates the upper limit of the gas flow rate per unit time of the gas storage tank; σ g Indicates the gas loss coefficient of the gas tank; η g,in and η g,out Indicates the efficiency of gas in and out of the gas tank; V t Indicates the gas storage volume of the gas tank at time t; V max and V min V is the upper and lower limits of the gas tank capacity; init is the initial gas storage capacity of the gas tank.

6. The distributed energy system energy management method considering demand-side regulation according to claim 1, characterized in that: The energy management model of distributed energy system considering the potential of flexible load regulation based on two-stage stochastic optimization takes minimizing the total operating cost of the distributed energy system as the objective function. The total operating cost of the distributed energy system includes the total operating cost of each equipment and the expected intraday regulation cost.

7. The distributed energy system energy management method considering demand-side regulation according to claim 6 is characterized in that: Objective function: Among them, Φ is the total operating cost of each device in the distributed energy system, S is the uncertainty scenario set, and π s is the probability of scene s appearing, and are the backup energy regulation cost corresponding to scenario s and the carbon emission cost generated by using backup energy regulation; It is the compensation given to users after the transferable load is dispatched; Compensation costs adjusted for load interruptions; Compensation for the comfort afforded to the user.

8. The distributed energy system energy management method considering demand-side regulation according to claim 7 is characterized in that: Constraints: in, and They represent the conventional electric load and conventional thermal load at time t in the distributed energy system, and They respectively represent the upper and lower reserve limits of the adjustable electric and heating loads.

9. The distributed energy system energy management method considering demand-side regulation according to claim 1, characterized in that: The adjustable electric and thermal load response plan includes the amount of electric load interruption, the amount of electric load transfer, the actual operating power of the flexible thermal load, and the required electric and thermal load increase and decrease reserve amount.

10. A distributed energy system energy management system considering demand-side regulation, characterized in that: include: The simulation module simulates the source and load data during actual operation based on the relevant parameters of the distributed energy system by adding normal distribution noise, and generates a set of scenarios for the operation of the distributed energy system; The construction module constructs the flexible load regulation model and the distributed energy system equipment operation model under the uncertain scenario set respectively. Based on the flexible load regulation model and the distributed energy system equipment operation model under the uncertain scenario set, the distributed energy system energy management model considering the load flexible regulation potential based on two-stage stochastic optimization is constructed; The strategy module solves the distributed energy system energy management model in the day-ahead planning stage based on the distributed energy system operation scenario set and determines the operation strategy of the distributed energy system equipment; The control module executes the distributed energy system equipment operation strategy during the daily operation stage, and determines the adjustable electric heating load response plan and the required electric heating load reserve according to the distributed energy system energy management model solved by the actual operation scenario data; based on the obtained adjustable electric heating load response plan and the required electric heating load reserve, the reserve electric heating load resources are used for real-time adjustment.

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