Comprehensive energy system real-time scheduling method considering price fluctuation under multiple subjects

Abstract

The invention discloses an integrated energy system real-time scheduling method considering price fluctuation under multiple subjects, and the method comprises the following steps: S1, determining all subjects of an integrated energy system and an energy consumption mode of the subjects, and introducing a reward and punishment stepped carbon transaction mechanism to restrain a model of system carbon emission; s2, taking the comprehensive energy service provider as a leader and the other subjects as followers, and establishing a multi-subject transaction model; s3, based on the optimization target, the constraint condition and the price fluctuation, constructing an economic optimization scheduling model of the integrated energy system; and S4, calling a CPLEX toolbox in MATLAB software to solve the economic optimization scheduling model, and obtaining a real-time scheduling optimal strategy of the integrated energy system. According to the method, the minimum total operation cost of the system is taken as a target, the effectiveness of the proposed method is verified by setting an example, flexible demand response resources under real-time scheduling can be fully excavated, and balance of benefits of all subjects and economic and low-carbon operation of the system are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system scheduling, and in particular to a real-time scheduling method for an integrated energy system taking price fluctuations into account under multiple subjects. Background Art

[0002] The current integrated energy system uses various advanced technologies to coordinate the coupling and complementation of multiple energy sources in the system, thereby integrating a large-scale energy management system with different energy forms such as electricity, heat, cold, and gas. Unlike traditional power systems, integrated energy systems can collect various types of energy forms on the energy supply side, gathering energy of various different natures including solar energy, natural gas energy, thermal energy, and various biomass energy. On the demand side, in addition to the energy users dominated by users, there are also multi-source energy storage devices with energy consumption properties. The integrated energy system can provide more flexible deployment characteristics for its own production, conversion, consumption, and storage through diversified allocation and mutual coordination of both supply and demand sides. As the scale of the system continues to grow, its internal structure and division are becoming more and more complex, and a good operation and management strategy is urgently needed to reasonably distribute its internal interests. In the future integrated energy system, with the influx of a large number of emerging market players, distributed interactions of multiple entities such as integrated energy service providers, energy suppliers, energy storage operators, and user aggregators are gradually emerging. The system is no longer a simple conventional energy dispatch, but also contains multi-layer operation management such as energy trading and energy price setting. In the energy market, each investment entity in the integrated energy system will pursue its own maximum interests, so reasonable energy management and trading operation strategies are crucial to the future development of energy. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a real-time scheduling method for an integrated energy system under multiple subjects taking into account price fluctuations, with the goal of minimizing the total operating cost. By fully tapping the flexible demand response resources under real-time scheduling, the interests of all subjects can be balanced and the system can be operated economically and low-carbon.

[0004] Technical solution: A real-time dispatching method for an integrated energy system taking into account price fluctuations under multiple subjects, comprising the following steps:

[0005] S1, establish the various entities of the integrated energy system and their energy consumption patterns, and introduce a model of reward and punishment tiered carbon trading mechanism to constrain system carbon emissions;

[0006] S2, with the integrated energy service provider as the leader and the other entities as followers, a multi-agent transaction model is established;

[0007] S3, constructs an economic optimization dispatch model for the integrated energy system based on optimization objectives, constraints and price fluctuations;

[0008] S4, calling the CPLEX toolbox in MATLAB software to solve the economic optimization scheduling model and obtain the optimal real-time scheduling strategy for the integrated energy system.

[0009] Furthermore, the entities include the integrated energy service provider EHO, the energy supplier EGO, the energy storage operator ESO and the multi-energy user USER. The energy-using equipment includes photovoltaic units, wind turbines, gas turbines GT and gas boilers GB; the energy coupling equipment includes electric refrigerators, absorption refrigerators and waste heat boilers; the energy storage equipment includes batteries and heat storage tanks;

[0010] The main sources of carbon emissions in the aforementioned reward-and-penalty tiered carbon trading mechanism are: electricity purchases from higher authorities, gas boilers and gas turbines;

[0011] The carbon emission function actually participated by each subject is as follows:

[0012]

[0013] Where: x∈{EHO,EGO}, E cx The carbon emissions of each entity actually participating in the market; is the actual total carbon emission for a cycle; is the total carbon emission quota obtained by each entity; its integrated energy service provider bears the carbon emission expenditure generated by purchasing electricity from the power grid, and the remaining carbon emission expenditure is borne by the energy supplier; T is the total operation cycle.

[0014] Furthermore, the carbon emission quota function of each entity is as follows:

[0015]

[0016] Where: and They are free carbon emission quotas for regional integrated energy system service providers, energy suppliers and energy storage operators; e , g They are the carbon emission quotas per unit electricity consumption of coal-fired units and per unit natural gas consumption of natural gas-fired units; They are the amount of electricity purchased from the upper power grid and the amount of power supplied by the gas turbine by the integrated energy service provider at time t; are the heating power of the gas boiler and the waste heat boiler at time t; η h The conversion factor for converting power generation into heat supply;

[0017] The actual carbon emission function of the integrated energy system is:

[0018]

[0019] Where: and are the actual carbon emissions of regional integrated energy system service providers, energy suppliers and energy storage operators; H total (t) is the equivalent output power of GB and GT at time t; a1, b1, c1 are the actual carbon emission model calculation parameters of coal-fired units; a2, b2, c2 are the actual carbon emission model calculation parameters of natural gas-consuming energy supply equipment;

[0020] When the actual carbon emissions of each entity are lower than the carbon quota, subsidies will be given. Otherwise, the entity will need to purchase the excess carbon emission quota. This will establish a reward-penalty ladder-type carbon trading cost. The objective function is as follows:

[0021]

[0022] Where: x∈{EHO,EGO}, is the carbon emissions of each stakeholder actually participating in the market at time t; χ is the carbon trading base price; L is the length of the carbon emission interval; δ and h are the reward and penalty coefficients of carbon trading.

[0023] Furthermore, in step S2, the integrated energy service provider sets the purchase and sale prices based on the time-of-use electricity price, takes the sum of the energy sale income, energy purchase cost, subsidy cost and carbon trading cost as the optimization target, and obtains the maximum benefit through game interaction with other entities. The objective function of the integrated energy service provider is as follows:

[0024]

[0025] Where: It represents the revenue from energy sales by EHO to ESO and users; They represent the energy purchase costs from EHO to EGO and external power grid respectively; They represent the demand response subsidy and carbon subsidy given by EHO to users respectively; represents the carbon trading cost of EHO; the superscript t represents the time, and T is the total operation cycle;

[0026] and They represent the electricity, heat and cooling energy loads on the user side at time t respectively; and They represent the electricity, heat and cooling that EHO obtains from EGO. When the electricity consumption is greater than the power supply, it is necessary to purchase electricity from the upper power grid; They represent the electricity and heat sold by EHO to ESO respectively; and They represent the energy selling prices announced by EHO to ESO and users respectively; and They represent the energy purchase price of EHO from EGO respectively; are the grid connection and electricity sales prices at time t respectively; To provide users with compensation prices for transferable electricity, heating and cooling loads; and They represent the transferable electricity, heating and cooling loads of the user side participating in demand response; e and λ h are the corresponding carbon emission quotas;

[0027] As a follower, the energy supplier, based on the energy price released by EHO, manages the reasonable output of various energy coupling devices in the area to maximize its own benefits. The objective function of the energy supplier is:

[0028]

[0029] Where: For EGO's energy sales revenue; Gas costs for gas turbines and gas boilers; is the operation and maintenance cost of each device in EGO; represents the carbon trading cost of EGO; the expressions are as follows:

[0030]

[0031] Where: r WT 、r PV 、r GT 、r WHB 、r GB 、r AC and r ERM They are the unit operation and maintenance costs of wind power, photovoltaic power, gas turbine, waste heat boiler, gas boiler, absorption chiller and electric chiller; Represent the output power of wind turbine and photovoltaic respectively; are the output power of absorption refrigerator and electric refrigerator respectively; a e , b e 、c e represents the cost factor of the gas turbine, a h , b h 、c h Cost coefficient of gas boiler;

[0032] The energy storage operator ESO includes electricity storage, heat storage and cold storage equipment. It realizes arbitrage between EHO and users through low charging and high discharging of energy. The objective function of the energy storage operator is:

[0033]

[0034]

[0035] Where: are the revenue and expenditure of the energy storage device respectively; The operation and maintenance costs of ESO; and are the discharge, heat, and cold powers of the ESO, respectively, where the subscript cha represents charging, dis represents discharging, and soc represents the state of charge of the energy storage device; and They are the electricity, heat and cooling sales prices of ESO respectively;

[0036] Considering the benefits brought by user load regulation, the user's objective function is as follows:

[0037]

[0038] It indicates the demand response subsidy given by EHO to users.

[0039] Furthermore, in step S3, the objective function for optimizing the efficiency of the integrated energy system is:

[0040]

[0041] Among them, T1 is the time period corresponding to the integrated energy system scheduling, the value is 24, and the time scale is 1h; f1, E 1,co2 They are the optimal benefits and carbon emissions of the integrated energy system under the optimal purchase and sale prices;

[0042] Constraints include power balance constraints, equipment output limits and ramp constraints, new energy consumption constraints, and energy storage equipment constraints;

[0043] Among them, the electrical, thermal, and cooling power output of the equipment at time t must meet the following constraints:

[0044]

[0045] Where: They represent the output power of the gas turbine and the power consumption of the electric refrigerator respectively; are the heat consumption of the absorption chiller and the heat output power of the waste heat boiler respectively;

[0046] Equipment output limitation and climbing constraints:

[0047]

[0048] Where: are the lower and upper limits of the output of the combined cooling, heating and power unit at time t, are the lower and upper limits of the output of the gas boiler and the electric refrigerator at time t, respectively. and are the lower and upper limits of the output of the refrigerator at time t respectively; are the lower and upper limits of the ramp rate of the combined cooling, heating and power unit at time t, are the lower and upper limits of the ramp rate of the gas boiler at time t, and They are the lower and upper limits of the climbing slope of the electric refrigerator at time t respectively;

[0049] Constraints on new energy consumption:

[0050]

[0051] Where: The maximum predicted values ​​of wind power and photovoltaic power output at time t are respectively;

[0052] Taking into account the self-loss and charging and discharging efficiency of energy storage, the energy storage device constraints are:

[0053]

[0054] Where: η loss , η cha and η dis They are the self-damage rate, charging efficiency, and discharging efficiency of the energy storage device; Respectively represent the minimum and maximum values ​​of charge energy; are the initial and final states of the charge energy respectively; They represent the lower and upper limits of the energy storage device's climbing slope at time t respectively; and the energy storage device can only be in one state of charging or discharging at a certain moment, represented by a 0-1 variable:

[0055]

[0056] Where: are the 0-1 state variables of energy storage device i at time t; They respectively represent the upper limits of the charging power and discharging power of the energy storage device i.

[0057] Compared with the prior art, the present invention has the following significant effects:

[0058] 1. The present invention establishes a multi-agent distributed collaborative optimization operation method with an integrated energy service provider as the leader and energy suppliers, energy storage operators and multiple users as followers. The integrated energy service provider guides energy suppliers, energy storage operators and multiple users to reasonably formulate energy equipment output and energy use strategies by formulating the purchase and sale prices of energy;

[0059] 2. The present invention gives full play to the role of the energy market in optimizing resource allocation by considering the interaction between stakeholders on the energy supply side and the demand side. The impact of energy storage devices and the energy coupling and coordinated optimization among multiple subjects are considered, and a reward-penalty ladder carbon trading mechanism is introduced to constrain the carbon emissions of the system. With the goal of minimizing the total operating cost of the system, the flexible demand response resources under real-time scheduling can be fully tapped to achieve a balance of interests among various subjects and economic and low-carbon operation of the system;

[0060] 3. Take into account the interests of multiple investment entities in the integrated energy system and the impact of various uncertain factors on the system, and formulate reasonable trading strategies based on energy price fluctuations; at the same time, establish respective mathematical models according to the characteristics of each entity, and use optimization algorithms to solve the established models; while considering the demands of multiple stakeholders, use energy storage devices to make real-time corrections to the scheduling strategy, so that the system has a greater ability to smooth random source and load fluctuations; finally, the effectiveness of the proposed model is verified through example analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of a real-time scheduling energy consumption mode of the integrated energy system of the present invention;

[0062] Figure 2 This is a schematic diagram of the real-time scheduling trading strategy for the integrated energy system in the example;

[0063] Figure 3 This is a schematic diagram of the real-time dispatching of photovoltaic, wind power output and electric heating and cooling load curves on a typical winter day for the integrated energy system in the example;

[0064] Figure 4 This is the electric load balance diagram under the comprehensive optimal strategy for real-time dispatch of the integrated energy system in the example;

[0065] Figure 5 It is the heat load balance diagram under the comprehensive optimal strategy of real-time scheduling of the integrated energy system in the example;

[0066] Figure 6 It is the cooling load balance diagram under the comprehensive optimal strategy of real-time dispatch of the integrated energy system in the example;

[0067] Figure 7 It is a trend chart of the change of the transferable amount of flexible load resources under the comprehensive optimal strategy of real-time scheduling of the integrated energy system in the example;

[0068] Figure 8 This is the trend chart of electricity price changes under the comprehensive optimal strategy for real-time scheduling of the integrated energy system in the example;

[0069] Fig. 9 It is the heat price change trend chart under the comprehensive optimal strategy of real-time dispatch of the integrated energy system in the example;

[0070] Fig.10 This is a cooling price change trend chart under the comprehensive optimal strategy for real-time scheduling of the integrated energy system in the example. DETAILED DESCRIPTION

[0071] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0072] The real-time dispatching method of the integrated energy system under multiple subjects and taking into account price fluctuations of the present invention comprises the following steps:

[0073] Step 1: Establish the various entities and energy consumption patterns of the integrated energy system and introduce a model of reward-penalty tiered carbon trading mechanism to constrain system carbon emissions;

[0074] The main bodies in the integrated energy system include integrated energy service providers (Energy Handling Operator, EHO), energy suppliers (Energy Generation Operator, EGO), energy storage operators (Energy Storage Operator, ESO) and multi-energy users (USER). Energy-consuming equipment includes photovoltaic units, wind turbines, gas turbines (GT) and gas boilers (GB); energy coupling equipment includes electric refrigerators, absorption refrigerators, waste heat boilers; energy storage equipment includes batteries, heat storage tanks, etc.

[0075] There are three main types of carbon emission sources in the reward-penalty tiered carbon trading mechanism: power purchase from higher authorities, GB and GT. At present, the domestic main method is to use the baseline method to provide free quotas to each emission entity, and in this embodiment, it is assumed that power purchase from higher authorities comes from coal-fired units. The carbon emission function actually participated by each entity in the comprehensive energy system is as follows:

[0076]

[0077] Where: x∈{EHO,EGO}, E cx The carbon emissions of each entity actually participating in the market; is the actual total carbon emission for a cycle; is the total carbon emission quota obtained by each entity; T is the total operation cycle, in hours (h). EHO bears the carbon emission expenditure generated by purchasing electricity from the power grid, and the rest of the carbon emission expenditure is borne by EGO.

[0078] The carbon emission quota function of each entity is as follows:

[0079]

[0080] Where: and They are the free carbon emission quotas of RIES (Regional Integrated Energy System) service providers, energy suppliers and energy storage operators; e , g They are the carbon emission quotas per unit electricity consumption of coal-fired units and per unit natural gas consumption of natural gas-fired units; They are the amount of electricity purchased by EHO from the upper grid and the amount of electricity supplied by the gas turbine at time t respectively; are the heating power of the gas boiler and waste heat boiler at time t respectively. h It is the conversion factor for converting power generation into heat supply.

[0081] The actual carbon emission function of the integrated energy system is:

[0082]

[0083] Where: and The actual carbon emissions of RIES service providers, energy suppliers and energy storage operators respectively; is the actual total carbon emissions of all participating entities in a cycle. total (t) is the equivalent output power of GB and GT at time t; a1, b1, c1 are the actual carbon emission model calculation parameters of coal-fired units; a2, b2, c2 are the actual carbon emission model calculation parameters of natural gas-consuming energy supply equipment.

[0084] In order to promote carbon emission reduction, each entity shall establish a reward and punishment ladder carbon trading cost based on actual carbon emissions and taking into account the length of the carbon emission interval. The objective function is as follows: when the actual carbon emissions of each entity are lower than the carbon quota, subsidies will be given; otherwise, the entity will need to purchase excess carbon emission quotas.

[0085]

[0086] Where: x∈{EHO,EGO}, is the carbon emissions of each stakeholder actually participating in the market at time t; χ is the carbon trading base price; L is the length of the carbon emission interval; δ and h are the reward and penalty coefficients of carbon trading.

[0087] Step 2: Establish a multi-agent transaction model with the integrated energy service provider as the leader and the other entities as followers;

[0088] As a leader, the integrated energy service provider is a carrier of flexible interaction between the supply and demand sides, reasonably guides energy transactions with two-way energy flow, and has the function of coordinated optimization of source-grid-load-storage. The integrated energy service provider formulates the purchase and sale prices based on the time-of-use electricity price, reasonably guides the equipment output, and adjusts the energy demand of users; smoothes the power fluctuations of the source and load, takes the sum of the energy sales income, energy purchase cost, subsidy cost and carbon trading cost as the optimization target, and obtains the maximum benefit through game interaction with other entities. The objective function of the integrated energy service provider is as follows:

[0089]

[0090] Where: It represents the revenue from energy sales by EHO to ESO and users; They represent the energy purchase costs from EHO to EGO and external power grid respectively; They represent the demand response subsidy and carbon subsidy given by EHO to users respectively; represents the carbon trading cost of EHO; the superscript t represents the time.

[0091] The expressions for the above items are:

[0092]

[0093] Where: and They represent the electricity, heat and cooling energy loads on the user side at time t respectively; and They represent the electricity, heat and cooling obtained by EHO from EGO, that is, when the electricity consumption is greater than the supply, it is necessary to purchase electricity from the upper grid; They represent the electricity and heat sold by EHO to ESO respectively; and They represent the energy selling prices announced by EHO to ESO and users respectively; and They represent the energy purchase price of EHO from EGO respectively; are the grid connection and electricity sales prices at time t respectively; To provide users with compensation prices for transferable electricity, heating and cooling loads; and They represent the transferable electricity, heating and cooling loads of the user side participating in demand response; e and λ h are the corresponding carbon emission quotas respectively; Δt represents a certain period of system operation.

[0094] As a follower, the energy supplier EGO maximizes its own benefits by managing the reasonable output of various energy coupling devices in the region based on the energy prices released by EHO. The objective function of the energy supplier is:

[0095]

[0096] Where: For EGO's energy sales revenue; is the gas cost of GT and GB; is the operation and maintenance cost of each device in EGO; Represents the carbon trading cost of EGO. The specific expression is as follows:

[0097]

[0098] Where: r WT 、r PV 、r GT 、r WHB 、r GB 、r AC and r ERM They are the unit operation and maintenance costs of wind power, photovoltaic, gas turbine, waste heat boiler (WHB), gas boiler, absorption chiller (AC) and electric refrigerator (ERM); Represent the output power of wind turbine and photovoltaic respectively; are the output powers of the absorption refrigerator and the electric refrigerator respectively. e 、b e 、c e represents the cost factor of the gas turbine, a h 、b h 、c h Cost factor of gas boiler.

[0099] Followers also include energy storage operators and multi-energy users. ESO includes electricity storage, heat storage and cold storage equipment. Arbitrage is achieved between EHO and users through low charging and high discharging of energy. The objective function of the energy storage operator is:

[0100]

[0101] Where: are the revenue and expenditure of the energy storage device respectively; is the operation and maintenance cost of ESO, which is taken as 10% of the charging expenditure in this embodiment; and are the discharge, heat, and cold powers of the ESO, respectively, where the subscript cha represents charging, dis represents discharging, and soc represents the state of charge of the energy storage device; and These are ESO’s electricity, heat and cooling sales prices respectively.

[0102] Considering the benefits brought by user load regulation, the user's objective function is as follows:

[0103]

[0104] User-side benefits mainly come from subsidies brought by demand response.

[0105] Step 3: Based on the day-ahead market, combined with the operation optimization objectives and constraints, a real-time economic optimization dispatch model for the integrated energy system taking into account price fluctuations is constructed under multiple subjects.

[0106] The objective function for optimizing the efficiency of the integrated energy system is:

[0107]

[0108] Among them, the time period corresponding to the integrated energy system scheduling is T1, the value is 24, and the time scale is 1h. 1,co2 They are respectively the optimal benefits and carbon emissions of the integrated energy system under the optimal energy purchase and sales prices.

[0109] Constraints include power balance constraints, equipment output limits and ramp constraints, new energy consumption constraints, and energy storage equipment constraints;

[0110] Among them, the electrical, thermal, and cooling power output by the equipment in the system at time t must meet the following constraints:

[0111]

[0112] Where: They represent the output power of the gas turbine and the power consumption of the ERM (Electric Refrigeration Machine). They are the heat consumption of the absorption chiller and the heat output power of the waste heat boiler respectively.

[0113] Among them, equipment output limit and climbing constraint:

[0114]

[0115] Where: and are the upper and lower limits of the output of the combined cooling, heating and power unit, gas boiler and electric refrigerator at time t respectively; and They are the upper and lower limits of the climbing of the combined cooling, heating and power unit, gas boiler and electric refrigerator at time t respectively.

[0116] Among them, new energy consumption constraints:

[0117]

[0118] Where: and are the maximum predicted outputs of wind power and photovoltaic power at time t.

[0119] Among them, considering the self-loss and charging and discharging efficiency of energy storage, the energy storage device constraints are:

[0120]

[0121] Where: The energy storage device S includes an electrical storage device and a heat storage tank, η loss , η cha and η dis They are the self-damage rate, charging efficiency, and discharging efficiency of the energy storage device; Respectively represent the minimum and maximum values ​​of charge energy; are the initial and final states of the charge energy respectively; They represent the lower and upper limits of the climbing capacity of the energy storage device at time t respectively.

[0122]

[0123] Where: are the 0-1 state variables of energy storage device i at time t; Respectively represent the upper limits of the charging power and discharging power of energy storage device i. 0-1 variable, indicating that the device can only exist in one state of charging or discharging at a certain moment, that is, energy storage and discharging cannot coexist at the same time.

[0124] Step 4, solving the economic optimization scheduling model;

[0125] The economic optimization scheduling model is solved by calling the CPLEX toolbox using Matlab.

[0126] The present invention establishes the system energy consumption mode and introduces a reward-and-punishment ladder carbon trading mechanism to constrain the system's carbon emissions. Secondly, a multi-subject trading model is established with the energy service provider as the leader and the other entities as followers, and the real-time optimal purchase (sale) energy price is obtained based on the master-slave game decision. Then, based on the day-ahead market, combined with the operation optimization objectives and constraints, an economic optimization scheduling model is constructed. While considering the demands of multiple stakeholders, the energy storage device is used to modify the scheduling strategy in real time, so that the system has a greater ability to smooth random source and load fluctuations, and Matlab is used to call the CPLEX toolbox to solve the proposed model. The present invention aims to minimize the total operating cost of the system, and can fully tap the flexible demand response resources under real-time scheduling to achieve a balance of interests among various entities and economic and low-carbon operation of the system.

[0127] In order to analyze in detail the actual optimization operation effect of the real-time scheduling method of an integrated energy system taking into account price fluctuations under multiple subjects proposed in the present invention, a typical integrated energy system is selected as an example scenario. The main parameters involved in the example calculation process are shown in Tables 1 and 2:

[0128] Table 1 Equipment parameters of regional integrated energy system

[0129]

[0130]

[0131] Table 2 Time-of-use electricity price parameters

[0132] Time Electricity price / [yuan / kW·h] 00:00-08:00,23:00-24:00 0.4 08:00-11:00,15:00-19:00,22:00-23:00 0.8 11:00-15:00,19:00-22:00 1.25

[0133] The structure of the integrated energy system is as follows Figure 1 As shown in the figure, it contains a variety of energy coupling equipment, which can enhance the absorption capacity of new energy and reduce the carbon emissions of the system through joint scheduling. The external energy input is the gas grid and the power grid. The system energy production equipment includes photovoltaic arrays (Photovoltaic, PV), wind turbines (Wind Turbine, WT), gas turbines (Gas Turbine, GT) and gas boilers (Gas Boiler, GB); energy coupling equipment includes electric refrigerators (Electric Refrigeration Machine, ERM), absorption chillers (Absorption Chiller, AC), waste heat boilers (Waste Heat Boiler, WHB); energy storage devices include storage batteries (Storage Battery, SB), heat storage tanks (Heat Storage Tank, HST); among them, the combined cold, heat and power (Combined Cold, Heat and Power, CCHP) units mainly include GT, WHB and AC; among them, the optimization strategy is as follows Figure 2 shown.

[0134] Winter electricity, heating, cooling loads, photovoltaic and wind power forecast curves are Figure 3 As shown, the system has obvious load changes within 24 hours. The demand for heat load is lower during the day, and the demand for cooling load is higher, while the opposite is true at night.

[0135] Depend on Figure 3 It can be seen that the typical daily cooling, heating and power load curve of the system in winter shows the opposite characteristics to that in summer. The demand for cooling load and freezing load is very low, while the demand for space heat load and hot water load is very large, among which the space heat load occupies a large part of the energy proportion.

[0136] analyze Figure 4 , Figure 5 , Figure 6 From the power balance diagram of electricity, heat and cooling loads, we can see that new energy sources such as photovoltaic and wind power are fully absorbed. At the same time, the cooperation between various entities and the superior energy purchasing authorities effectively meets the various energy needs of users.

[0137] Depend on Figure 4 It can be seen that gas turbines are mainly used to supply electricity during the day, while new energy sources account for a higher proportion at night; photovoltaic power consumption occurs between 7:00 and 18:00, and wind power consumption is mainly concentrated at night; from 9:00 to 15:00, all units in the system are in working condition, and users' demand for load gradually increases. Figure 4 The several power consumption peaks are mainly concentrated around 12:00, 16:00 and 24:00. During this period, in order to maintain the power balance of the system, gas turbine power generation becomes the main energy supply mode of EGO. When there is a power shortage, EHO purchases electricity from the superior power grid for the shortfall; at the same time, ESO participates in regulation and control, basically realizing the "low charging and high discharging" of stored electricity.

[0138] Figure 5 It shows that waste heat boilers and gas boilers are the main sources of thermal power, and gas boilers account for a large proportion and are the main heating method. The user's heat load demand at night is greater than that during the day. At around 18:00, EGO increases the heating of gas boilers because the temperature during this period is lower than that of the day. Most user residents have just got off work and are resting, and the heat load demand is the highest. The heat load demand is the lowest at noon, and the cold load demand is high. At the same time, ESO plays the role of low-heat charging and high-heat discharging of the heat storage tank.

[0139] Figure 6 It can be found that EGO mainly uses air conditioning for cooling. When the air conditioning load is high during the daytime working hours, the shortfall is compensated by the absorption chiller's heat consumption cooling; because the absorption chiller does not require electricity for cooling, it only needs waste heat to work, which is more economical and low-carbon. Air conditioning refrigeration is more flexible and can be put into operation at any time to make up for the power shortage. The reasonable planning of the absorption chiller and air conditioning effectively meets the user's cooling load demand.

[0140] Depend on Figure 7 It can be seen that compared with night, the fluctuation of the transfer trend during the day is gentle. Considering that the energy consumption at night is less than that during the day and the satisfaction has little impact on users, users have greater adjustment potential during this period, so the load fluctuation is larger. According to the changing trends of various types of transferable loads, it can provide a certain reference for the reasonable scheduling of technicians.

[0141] as follows Figure 8 It can be seen that in order to increase the proportion of new energy consumption in the system and the benefits of the source and load sides, EHO's purchase and sales prices always fluctuate within a certain range. The fluctuation trend of EHO's sales price changes with market trading conditions, while the purchase price trend is close to the changes in the actual load of users.

[0142] Fig. 9 and Fig.10The optimization of heat price is similar to that of electricity price. The changing trend of heat and cold purchase prices is consistent with the changing trend of users' heat and cold loads. In most cases, heat and cold sale prices are higher than heat and cold purchase prices. The above shows that the reasonable formulation of energy purchase and sale prices can effectively promote EGO's energy supply and users' reasonable energy use.

[0143] From the above analysis, it can be seen that the real-time scheduling method of the integrated energy system taking into account price fluctuations under multiple subjects of the present invention has high calculation efficiency of the scheduling strategy, which can effectively improve the benefits of each subject. While considering the demands of multiple stakeholders, it enables the system to have a greater ability to smooth random source and load fluctuations, reduce the total operating cost of the system, and fully tap the flexible load resources under real-time scheduling, achieve a balance of interests among various subjects and economic and low-carbon operation of the system, and provide a practical and effective operation plan for the operation optimization of the energy market.

[0144] The above examples are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A real-time dispatching method for an integrated energy system under multiple agents taking into account price fluctuations, characterized in that: The following steps are involved: S1, establish the various entities of the integrated energy system and their energy consumption patterns, and introduce a model of reward and punishment tiered carbon trading mechanism to constrain system carbon emissions; S2, with the integrated energy service provider as the leader and the other entities as followers, a multi-agent transaction model is established; S3, constructs an economic optimization dispatch model for the integrated energy system based on optimization objectives, constraints and price fluctuations; S4, calling the CPLEX toolbox in MATLAB software to solve the economic optimization scheduling model and obtain the optimal real-time scheduling strategy for the integrated energy system.

2. The real-time dispatching method for an integrated energy system taking into account price fluctuations under multiple subjects according to claim 1 is characterized in that: The entities include integrated energy service provider EHO, energy supplier EGO, energy storage operator ESO and multi-energy user USER. Energy-using equipment includes photovoltaic units, wind turbines, gas turbines GT and gas boilers GB; energy coupling equipment includes electric refrigerators, absorption refrigerators and waste heat boilers; energy storage equipment includes batteries and heat storage tanks; The main sources of carbon emissions in the aforementioned reward-and-penalty tiered carbon trading mechanism are: electricity purchases from higher authorities, gas boilers and gas turbines; The carbon emission function actually participated by each subject is as follows: Where: x∈{EHO,EGO}, E cx The carbon emissions of each entity actually participating in the market; is the actual total carbon emission for a cycle; The total carbon emission quota obtained by each entity; Its integrated energy service provider bears the carbon emission expenditure generated by purchasing electricity from the power grid, and the remaining carbon emission expenditure is borne by the energy supplier; T is the total operating cycle.

3. The real-time dispatching method for an integrated energy system taking into account price fluctuations under multiple subjects according to claim 2 is characterized in that: The carbon emission quota function of each entity is as follows: Where: and They are free carbon emission quotas for regional integrated energy system service providers, energy suppliers and energy storage operators; e , g They are the carbon emission quotas per unit electricity consumption of coal-fired units and per unit natural gas consumption of natural gas-fired units; They are the amount of electricity purchased from the upper power grid and the amount of power supplied by the gas turbine by the integrated energy service provider at time t; are the heating power of the gas boiler and the waste heat boiler at time t; η h The conversion factor for converting power generation into heat supply; The actual carbon emission function of the integrated energy system is: Where: and are the actual carbon emissions of regional integrated energy system service providers, energy suppliers and energy storage operators; H total (t) is the equivalent output power of GB and GT at time t; a1, b1, c1 are the actual carbon emission model calculation parameters of coal-fired units; a2, b2, c2 are the actual carbon emission model calculation parameters of natural gas-consuming energy supply equipment; When the actual carbon emissions of each entity are lower than the carbon quota, subsidies will be given. Otherwise, the entity will need to purchase the excess carbon emission quota. This will establish a reward-penalty ladder-type carbon trading cost. The objective function is as follows: Where: x∈{EHO,EGO}, is the carbon emissions of each stakeholder actually participating in the market at time t; χ is the carbon trading base price; L is the length of the carbon emission interval; δ and h are the reward and penalty coefficients of carbon trading.

4. The real-time dispatching method for an integrated energy system taking into account price fluctuations under multiple subjects according to claim 2 is characterized in that: In step S2, the integrated energy service provider sets the purchase and sale prices based on the time-of-use electricity price, takes the sum of the energy sale income, energy purchase cost, subsidy cost and carbon trading cost as the optimization target, and obtains the maximum benefit through game interaction with other entities. The objective function of the integrated energy service provider is as follows: Where: It represents the revenue from energy sales by EHO to ESO and users; They represent the energy purchase costs from EHO to EGO and external power grid respectively; They represent the demand response subsidy and carbon subsidy given by EHO to users respectively; represents the carbon trading cost of EHO; the superscript t represents the time, and T is the total operation cycle; and They represent the electricity, heat and cooling energy loads on the user side at time t respectively; and They represent the electricity, heat and cooling that EHO obtains from EGO. When the electricity consumption is greater than the power supply, it is necessary to purchase electricity from the upper power grid; They represent the electricity and heat sold by EHO to ESO respectively; and They represent the energy selling prices announced by EHO to ESO and users respectively; and They represent the energy purchase price of EHO from EGO respectively; are the grid connection and electricity sales prices at time t respectively; To provide users with compensation prices for transferable electricity, heating and cooling loads; and They represent the transferable electricity, heating and cooling loads of the user side participating in demand response; e and λ h are the corresponding carbon emission quotas; As a follower, the energy supplier, based on the energy price released by EHO, manages the reasonable output of various energy coupling devices in the area to maximize its own benefits. The objective function of the energy supplier is: Where: For EGO's energy sales revenue; Gas costs for gas turbines and gas boilers; is the operation and maintenance cost of each device in EGO; represents the carbon trading cost of EGO; the expressions are as follows: Where: r WT 、r PV 、r GT 、r WHB 、r GB 、r AC and r ERM They are the unit operation and maintenance costs of wind power, photovoltaic power, gas turbine, waste heat boiler, gas boiler, absorption chiller and electric chiller; Represent the output power of wind turbine and photovoltaic respectively; are the output power of absorption refrigerator and electric refrigerator respectively; a e 、b e 、c e represents the cost factor of the gas turbine, a h 、b h 、c h Cost coefficient of gas boiler; The energy storage operator ESO includes electricity storage, heat storage and cold storage equipment. It realizes arbitrage between EHO and users through low charging and high discharging of energy. The objective function of the energy storage operator is: Where: are the revenue and expenditure of the energy storage device respectively; The operation and maintenance costs of ESO; and are the discharge, heat, and cold powers of the ESO, respectively, where the subscript cha represents charging, dis represents discharging, and soc represents the state of charge of the energy storage device; and They are the electricity, heat and cooling sales prices of ESO respectively; Considering the benefits brought by user load regulation, the user's objective function is as follows: It indicates the demand response subsidy given by EHO to users.

5. The real-time dispatching method for an integrated energy system taking into account price fluctuations under multiple subjects according to claim 4 is characterized in that: In step S3, the objective function for optimizing the efficiency of the integrated energy system is: Among them, T1 is the time period corresponding to the integrated energy system scheduling, the value is 24, and the time scale is 1h; f1, E 1,co2 They are the optimal benefits and carbon emissions of the integrated energy system under the optimal purchase and sale prices; Constraints include power balance constraints, equipment output limits and ramp constraints, new energy consumption constraints, and energy storage equipment constraints; Among them, the electrical, thermal, and cooling power output of the equipment at time t must meet the following constraints: Where: They represent the output power of the gas turbine and the power consumption of the electric refrigerator respectively; are the heat consumption of the absorption chiller and the heat output power of the waste heat boiler respectively; Equipment output limitation and climbing constraints: Where: are the lower and upper limits of the output of the combined cooling, heating and power unit at time t, are the lower and upper limits of the output of the gas boiler and the electric refrigerator at time t, respectively. and are the lower and upper limits of the output of the refrigerator at time t respectively; are the lower and upper limits of the ramp rate of the combined cooling, heating and power unit at time t, are the lower and upper limits of the ramp rate of the gas boiler at time t, and They are the lower and upper limits of the climbing slope of the electric refrigerator at time t respectively; Constraints on new energy consumption: Where: The maximum predicted values ​​of wind power and photovoltaic power output at time t are respectively; Taking into account the self-loss and charging and discharging efficiency of energy storage, the energy storage device constraints are: Where: η loss , η cha and η dis They are the self-damage rate, charging efficiency, and discharging efficiency of the energy storage device; Respectively represent the minimum and maximum values ​​of charge energy; are the initial and final states of the charge energy respectively; They represent the lower and upper limits of the energy storage device's climbing slope at time t respectively; and the energy storage device can only be in one state of charging or discharging at a certain moment, represented by a 0-1 variable: Where: are the 0-1 state variables of energy storage device i at time t; They respectively represent the upper limits of the charging power and discharging power of the energy storage device i.

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