Comprehensive energy system operation optimization method and device, electronic equipment and storage medium

Abstract

The invention is suitable for the technical field of integrated energy system optimization, and provides an integrated energy system operation optimization method and device, electronic equipment and a storage medium, and the integrated energy system operation optimization method comprises the steps: obtaining price data, new energy output data and user load data; based on the price data, the new energy output data and the user load data, constructing a joint optimization model considering various uncertain factors; wherein the plurality of uncertainty factors comprise spot market transaction price uncertainty, new energy output and load demand uncertainty, and peak regulation market transaction period and price uncertainty; the joint optimization model is a double-layer optimization model with the minimum operation cost as a lower layer target and the maximum peak regulation capacity as an upper layer target; and solving the joint optimization model, and determining an optimization strategy of the integrated energy system. The operation optimization method provided by the invention can effectively cope with various uncertainties of an external spot market, a peak regulation market and an internal source load.

Description

Technical Field

[0001] The present application belongs to the technical field of integrated energy system optimization, and in particular, relates to an integrated energy system operation optimization method, device, electronic device and storage medium. Background Art

[0002] As a form of energy management with multi-energy complementation on the user side, the integrated energy system gradually highlights its multi-dimensional effects such as low carbon, clean and high efficiency. With the gradual increase in the penetration rate of new energy and the level of marketization, the operation of the integrated energy system faces various difficulties. On the one hand, the volatility of large-scale distributed new energy and the randomness of load are superimposed. The double randomness increases the difficulty of regulating the integrated energy system, and to a certain extent increases the system's dependence on the power grid. Balancing its own volatility through the power grid undoubtedly brings great pressure to the stability of the new power system; on the other hand, with the advancement of the marketization process, the integrated energy system needs to face the challenge of joint decision-making in multiple markets. Among them, the deviation assessment of the spot market increases the operating cost of the system randomness, and the peak-shaving market also creates a profitable environment for the system's flexible adjustment of resources, but multi-market transactions also face difficulties such as resource balance and market uncertainty.

[0003] In the process of uncertainty research, the methods for dealing with uncertainty in the power market mainly include three types of methods: stochastic optimization model, robust optimization model and information gap decision theory (IGDT). With the continuous development of the power market, the integrated energy system will also face the needs of trading strategy optimization and operation optimization under the market environment. However, in the relevant technologies for optimizing the operation strategy of the integrated energy system, there is a lack of optimization methods that can cope with the external spot market, peak-shaving market and various uncertainties of internal sources and loads. Summary of the invention

[0004] In order to overcome the problem in related technologies of lack of optimization methods that can cope with multiple uncertainties of external spot market, peak-shaving market and internal source and load, the embodiments of the present application provide a comprehensive energy system operation optimization method, device, electronic device and storage medium.

[0005] This application is implemented through the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing operation of an integrated energy system, comprising:

[0007] Obtain price data, renewable energy output data and user load data;

[0008] Based on the price data, the new energy output data and the user load data, a joint optimization model considering multiple uncertainties is constructed; wherein the multiple uncertainties include the uncertainty of spot market transaction prices, the uncertainty of new energy output and load demand, and the uncertainty of peak-shaving market transaction periods and prices; the joint optimization model is a two-layer optimization model with the minimum operating cost as the lower layer goal and the maximum peak-shaving capacity as the upper layer goal;

[0009] Solve the joint optimization model to determine the optimization strategy of the integrated energy system; the optimization strategy includes the output curves of energy conversion equipment, energy storage equipment and gas equipment in the integrated energy system, as well as the peak-shaving capacity curve.

[0010] In a second aspect, an embodiment of the present application provides a comprehensive energy system operation optimization device, comprising:

[0011] The acquisition module is used to obtain price data, new energy output data and user load data;

[0012] A construction module is used to construct a joint optimization model that takes into account multiple uncertainties based on the price data, the new energy output data, and the user load data; wherein the multiple uncertainties include uncertainty in spot market transaction prices, uncertainty in new energy output and load demand, and uncertainty in peak-shaving market transaction periods and prices; the joint optimization model is a two-layer optimization model that takes minimizing operating costs as a lower-level objective and maximizing peak-shaving capacity as an upper-level objective;

[0013] A solution module is used to solve the joint optimization model and determine the optimization strategy of the integrated energy system; the optimization strategy includes the output curves of energy conversion equipment, energy storage equipment and gas equipment in the integrated energy system, as well as the peak-shaving capacity curve.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the integrated energy system operation optimization method as described in any one of the first aspects is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the integrated energy system operation optimization method as described in any one of the first aspects is implemented.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the integrated energy system operation optimization method described in any one of the first aspects above.

[0017] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0018] Compared with the related art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application obtain price data, new energy output data and user load data; then, based on the price data, new energy output data and user load data, a joint optimization model is constructed that takes into account the uncertainty of spot market transaction prices, the uncertainty of new energy output and load demand, and the uncertainty of peak-shaving market transaction periods and prices; the joint optimization model is a two-layer optimization model with the minimum operating cost as the lower level goal and the maximum peak-shaving capacity as the upper level goal; by solving the joint optimization model, the optimization strategy of the integrated energy system is determined. The operation optimization method provided by the embodiments of the present application can effectively cope with various uncertainties in the external spot market, the peak-shaving market and the internal source and load, and can effectively reduce the difficulty of regulating the integrated energy system, and can increase the revenue space of the peak-shaving market under the premise of safe and reliable operation of the system.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 It is a flowchart of a comprehensive energy system operation optimization method provided in one embodiment of the present application;

[0022] Figure 2 It is a schematic diagram of the comprehensive energy system load and new energy equipment output curve;

[0023] Figure 3 This is a typical curve diagram of the spot market;

[0024] Figure 4 It is a schematic diagram of the medium- and long-term market transaction curve and energy price curve;

[0025] Figure 5 This is a schematic diagram of the typical peak-shaving capacity response relationship of the spot market;

[0026] Figure 6 It is a schematic diagram of the Pareto solution set of the typical spot market curve 1;

[0027] Figure 7(a) is a schematic diagram of spot trading volume and peak-shaving capacity at turning point 1;

[0028] Figure 7(b) is a schematic diagram of spot trading volume and peak-shaving capacity at turning point 2;

[0029] Figure 8 It is a schematic diagram of the relationship between the peak load regulation capacity and the comprehensive daily operation cost provided in the embodiment of the present application;

[0030] Fig. 9 It is a structural schematic diagram of the integrated energy system operation optimization device provided in an embodiment of the present application;

[0031] Fig.10 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0033] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0034] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0035] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0036] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0037] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0038] In the operation optimization and transaction optimization of integrated energy systems, uncertainty problems are usually solved through robust optimization, stochastic programming, IGDT and other methods, including risk aversion models based on safety and opportunity benefit models based on benefits. However, in the optimization of integrated energy systems facing multi-level markets, simple risk aversion or opportunism will result in excessive costs or risks. Therefore, there is an urgent need for an optimization method that simultaneously considers the uncertainties of the external spot market, the peak-shaving market, and the internal source and load.

[0039] Based on the above problems, the embodiment of the present application proposes a method for optimizing the operation of an integrated energy system. The present application proposes corresponding solution strategies according to different risk types and characteristics, and constructs a joint optimization model that considers multiple uncertainties. The present application constructs a joint optimization model for the uncertainties of new energy output, load demand, spot market, and peak-shaving market faced by the integrated energy system. By solving the joint optimization model, an optimization strategy for the integrated energy system can be obtained. The present application provides an optimization method that can cope with multiple uncertainties in the external spot market, peak-shaving market, and internal source and load (i.e., new energy output and load demand), and the optimization method provided by the present application can effectively reduce the difficulty of regulating the integrated energy system.

[0040] Figure 1 is a flow chart of a comprehensive energy system operation optimization method provided by an embodiment of the present application, referring to Figure 1 , the operation optimization method of the integrated energy system is described in detail as follows:

[0041] In S101 , price data, new energy output data, and user load data are obtained.

[0042] In S102, a joint optimization model considering multiple uncertain factors is constructed based on price data, new energy output data and user load data.

[0043] Among them, the various uncertain factors include the uncertainty of spot market transaction prices, the uncertainty of new energy output and load demand, and the uncertainty of peak-shaving market transaction periods and prices; the joint optimization model is a two-layer optimization model with minimizing operating costs as the lower-level goal and maximizing peak-shaving capacity as the upper-level goal.

[0044] It should be understood that the integrated energy system, as an integrated production and sales user, faces multiple uncertainties in the process of participating in market transactions. These include uncertainty in the supply and demand of sources and loads within the system, uncertainty in spot market transaction prices, and uncertainty in peak-shaving transaction periods and prices. Among them:

[0045] In terms of the uncertainty of spot market transaction prices, it faces strong random characteristics, and as an important indicator for daily transaction cost decisions, risk aversion and opportunity returns cannot fully quantify and evaluate its impact on system optimization. Therefore, this application adopts an uncertainty scenario simulation method based on a multi-level norm to simulate and screen typical scenarios of the spot market price curve, and make decisions on system trading plans under different spot price scenarios.

[0046] In terms of source-load uncertainty, this application uses a risk scale model to solve the source-load uncertainty problem in terms of energy supply and demand, and uses the robust optimization solution idea to carry out optimization.

[0047] In terms of trading hours and price uncertainty in the peak-shaving market, it faces the dual requirements of strong uncertainty and profit acquisition. The integrated energy system has high flexibility and adjustability, and has profitability in peak-shaving transactions. In addition, the power system faces a high proportion of new energy access, and market-based means are also needed to fully mobilize user-side resource interactions. Therefore, in terms of peak-shaving market transactions, the risk aversion model will have an adverse impact on both the integrated energy system's revenue and the macro power system. Therefore, this application adopts the opportunity benefit model to solve the peak-shaving market transaction optimization problem.

[0048] Based on the above analysis, this application proposes corresponding optimization methods for each uncertainty, as shown in Table 1 below.

[0049] Table 1 Analysis and treatment methods of uncertain factors in integrated energy system

[0050]

[0051] (1) For spot transaction price uncertainty:

[0052] In terms of spot market price processing, this application takes into account the strong uncertainty of the market and the differences in price fluctuation ranges in different periods and seasons. The price combinations it faces are diverse, and the results of its scenario simulation grow exponentially. Therefore, this application simulates the spot electricity price curves of different periods and prices through the Monte Carlo method, clusters the scenario set, uses the K-medoids method to achieve price clustering, selects the cluster center s through the Davies-Bouldin index, and analyzes the scenario probability K of different cluster centers. s .

[0053] Taking into account the serious uncertainty of electricity prices, the 1-norm and ∞-norm are combined to constrain the electricity price scenarios and construct the uncertainty fuzzy set of typical spot electricity price scenarios:

[0054]

[0055] in, is the uncertainty fuzzy set of spot electricity price scenario; K s is the true probability of the full scenario of the sth scenario; is the initial probability of the clustering scenario of the sth scenario; N is the number of typical spot electricity price curves; θ1 and θ ∞ They represent the 1-norm and ∞-norm thresholds in the constraints respectively.

[0056] In the above formula, the probability distribution of electricity price scenarios still needs to satisfy the confidence interval constraint:

[0057]

[0058] Among them, α1 and α ∞ They are the confidence intervals of the 1-norm and ∞-norm of the s-th scenario respectively.

[0059] Through multi-level norm constraints, forward reduction of typical electricity price scenarios can be achieved, low-probability scenarios and abnormal electricity prices can be eliminated, and effective screening of typical scenarios can be achieved.

[0060] (2) For source load uncertainty:

[0061] In the integrated energy system, source-load uncertainty is an important issue to ensure the physical constraints of system supply-demand balance. This paper adopts the distribution of source-load uncertainty based on Wasserstein metric and evaluates the possible probability distribution by constructing historical experience distribution.

[0062]

[0063] Among them, f N is the empirical distribution based on historical data; ξ is s i The Dirac measure of the sample in the historical actual curve; The probability distribution f N Centered on ε w Is the Wasserstein sphere with radius E f is the probability distribution f N The expected value of; W is the Wasserstein distance; ξ is the probability distribution f N A random variable.

[0064] In solving the source-load uncertainty problem in the integrated energy system, considering that the system supply-demand balance is a physical safety constraint, ensuring the safe and stable operation of the system is a rigid constraint for system optimization. Therefore, the IGDT risk avoidance model is usually adopted to deal with the source-load uncertainty problem. The typical optimization structure is as follows:

[0065]

[0066] in, is the uncertainty factor of source and load; f is the system operation index under the uncertainty factor; X is the optimization decision variable; H and G are the equality constraint and inequality constraint respectively.

[0067] The IGDT risk avoidance model usually seeks to maximize the uncertainty space within a certain range of operating indicator changes. In the operation of the integrated energy system, it is usually converted into a robust optimization solution to solve it, that is, to meet the most split in the source-load uncertainty space and seek the optimal operating indicator, as shown in the following formula:

[0068]

[0069] (3) Regarding peak trading hours and price uncertainty:

[0070] Peak-shaving trading is a trading method that the integrated energy system participates in during the day to ensure the safe and stable operation of the power system. The uncertainty of its trading period and price is constructed through the above scenario simulation method. The uncertainty of peak-shaving trading is essentially an economic problem, and will not cause internal safety problems in the integrated energy system due to the deviation of peak-shaving prices and periods. Therefore, this application adopts the IGDT opportunity benefit model to solve the peak-shaving uncertainty problem.

[0071] The opportunity return model seeks the minimum uncertainty space range within a certain upper limit of the return space, as shown in the following formula:

[0072]

[0073] In the process of comprehensive energy system optimization, this application seeks to maximize the opportunity benefit under a certain uncertainty fuzzy set, so the above formula is transformed into:

[0074]

[0075] Optionally, this application integrates three uncertainty optimization methods, constructs a multi-market joint transaction optimization architecture based on IDGT risk-opportunity fusion under typical scenario simulation, and proposes a joint optimization model for peak-shaving market space and system operation costs, and the joint optimization model is a max-min model, as shown in the following formula:

[0076]

[0077] Among them, P α is the peak-shaving space of the integrated energy system, α is the peak-shaving space under the regulation strategy of the integrated energy system, D is the relatively minimum set of operating costs of the integrated energy system, is the expected value of the probability distribution of the operating cost of the integrated energy system, is the operating cost of the integrated energy system, X is the optimization decision variable, R is the spot price curve, is the uncertainty factor of the source load, H and G are the equality constraint and inequality constraint respectively, is a collection of typical spot price curves, is the uncertainty range of the source charge.

[0078] Optionally, the lower objective function of the joint optimization model is:

[0079]

[0080] The upper objective function of the joint optimization model is:

[0081]

[0082] Among them, C tran is the long-term market transaction cost of the integrated energy system, C spot is the transaction cost of the spot market of the integrated energy system, C gas is the other fuel cost of the comprehensive energy system, T Reg is the peak load period of the comprehensive energy system, P tran (t) is the long-term market transaction power curve of the integrated energy system, P spot (t) is the spot market transaction power curve of the integrated energy system, P sys (t) supply power to other units in the integrated energy system; infP E (t) is the maximum lower bound of the new energy output in the integrated energy system; supP L (t) is the minimum upper bound of the load of the integrated energy system.

[0083] Optionally, the medium and long-term market transaction costs of the integrated energy system are:

[0084]

[0085] The transaction cost of the spot market of the integrated energy system is:

[0086]

[0087] Other fuel costs for the integrated energy system are:

[0088]

[0089] in, P M&L (t) is the power curve of medium and long-term market transactions, p M&L (t) is the price curve of medium and long-term market transactions, t tran,end is the end time of medium and long-term market transactions, t tran,init is the start time of medium and long-term market transactions, P spot (t) is the power curve of spot market transactions, p spot (t) is the price curve of spot market transactions, t spot,end is the end time of spot market transactions, t spot,init is the spot market trading start time, Q gas (t) is the hourly fuel consumption of the integrated energy system, p gas (t) is the price curve of other fuels, t gas,end The end time of using other fuels for the integrated energy system, t gas,init The starting time for using other fuels in the integrated energy system, D EL is the distribution space of the price curve of spot market transactions and the peak-shaving capacity curve, The uncertainty range of renewable energy output in the integrated energy system, is the uncertainty range of the load of the integrated energy system.

[0090] Optionally, the equality constraints of the joint optimization model include an electric load balance constraint, a thermal load balance constraint, a cooling load balance constraint, and a natural gas balance constraint.

[0091] The inequality constraints of the joint optimization model include market constraints, physical constraints of energy storage equipment, and ramp constraints of energy conversion equipment.

[0092] Optionally, market constraints include spot market deviation assessment constraints and peak-shaving market access capacity constraints.

[0093] In the spot market trading rules, when the declared curve is greater than the actual deviation curve, no fee will be refunded for the excess outside the standard line. Therefore, it is necessary to constrain the declared volume and declared curve of the spot market, that is, it is necessary to set the spot market deviation assessment constraint. The spot market deviation assessment constraint is:

[0094]

[0095] Among them, P spot (t) is the power curve of spot market transactions, P act (t) is the actual usage of the spot market, η is the proportion of excess capacity allowed per unit time, η' is the average proportion of excess capacity allowed during the assessment period, and T is the period during which the integrated energy system has the peak load regulation capability;

[0096] In the peak-shaving market, there are access constraints on the access capacity of peak-shaving resources and the basic market, that is, it is necessary to set the access capacity constraints of the peak-shaving market. The access capacity constraints of the peak-shaving market are:

[0097]

[0098] Among them, D is the time period set when the comprehensive energy system has the peak load regulation capability, P reg (t) is the peak-shaving capacity of the comprehensive energy system at time t, M is the minimum peak-shaving capacity for access to the peak-shaving market, c is the minimum trading period length for participating in the peak-shaving market, and card(D)>c means that the lengths of each peak-shaving period in set D are greater than the minimum trading period length for participating in the peak-shaving market.

[0099] Optionally, the energy storage device includes an electric storage device, a heat storage device and a cold storage device.

[0100] The electrical load balance constraint is:

[0101]

[0102] in, is the electric load demand on the user side in the integrated energy system in time period t, in kW; is the electric load demand of electric drive equipment other than power storage equipment in the comprehensive energy system in time period t, in kW; is the charge amount of the energy storage device in time period t, in kW; is the power purchased by the integrated energy system in time period t, in kW; is the discharge amount of the energy storage device in time period t, in kW; is the output of the i-th power generation equipment in the integrated energy system in time period t, in kW; E is the set of power generation equipment in the integrated energy system.

[0103] The heat load balance constraint is:

[0104]

[0105] in, is the heat load demand on the user side in the integrated energy system at time period t, in kW; is the heat storage capacity of the heat storage equipment in time period t, in kW; is the heat loss of the heating equipment in the integrated energy system during time period t, in kW; is the heat release of the heat storage equipment in time period t, in kW; is the output of the i-th heating equipment in the integrated energy system in time period t, in kW; H is the set of heating equipment in the integrated energy system.

[0106] The cooling load balancing constraints are:

[0107]

[0108] in, is the cooling load demand on the user side in the integrated energy system in time period t, in kW; is the cooling capacity of the cooling equipment in time period t, in kW; is the cooling loss of the cooling equipment in the integrated energy system in time period t, in kW; is the cooling capacity of the cold storage equipment in time period t, in kW; is the output of the i-th cooling equipment in the integrated energy system in time period t, in kW; C is the set of cooling equipment in the integrated energy system.

[0109] The natural gas balance constraint is:

[0110]

[0111] in, is the amount of natural gas purchased by the integrated energy system from the external natural gas market in period t, in m 3 / h; is the output of the i-th gas equipment in the integrated energy system in time period t, in m 3 / h; η i is the output efficiency of the i-th gas equipment, which is a percentage; G is the set of gas equipment.

[0112] The physical constraints of energy storage devices are:

[0113]

[0114] Among them, SOC i(t) is the residual energy state of the i-th energy storage device in the integrated energy system in time period t, and the residual energy state is a percentage; is the lower limit of the residual energy state of the i-th energy storage device in the integrated energy system in time period t, and the lower limit of the residual energy state is a percentage; is the upper limit of the remaining energy state of the i-th energy storage device in the integrated energy system in time period t, and the upper limit of the remaining energy state is a percentage; δ i is the self-consumption rate of the i-th energy storage device in the comprehensive energy system, in % / h; is the charging power of the i-th energy storage device in the integrated energy system at time period t; s i (t) is the state of the i-th energy storage device in the integrated energy system at time period t: i When (t) = 1, it means that the energy storage device is turned on; when s i When (t) = 0, it means that the energy storage device is turned off; is the energy storage efficiency of the i-th energy storage device in the integrated energy system, which is expressed as a percentage; is the discharge power of the i-th energy storage device in the integrated energy system in time period t; is the discharge efficiency of the i-th energy storage device in the integrated energy system, which is expressed as a percentage; Δt is the time it takes for the energy storage device to update its state, in h; Cap i is the maximum capacity of the i-th energy storage device in the integrated energy system, in kWh; ener(i) is the type of energy stored in the i-th energy storage device in the integrated energy system; is the gap in energy stored by the i-th energy storage device in the integrated energy system in time period t, in kW; is the upper limit of the discharge power of the i-th energy storage device in the integrated energy system in time period t; It is the upper limit of the charging power of the i-th energy storage device in the integrated energy system in time period t.

[0115] In the integrated energy system, energy conversion equipment such as CCHP and heat pump cannot achieve fast response, so its ramp rate needs to be constrained. The ramp rate constraint of energy conversion equipment is:

[0116]

[0117] in, is the output of the i-th energy conversion equipment in the integrated energy system in time period t, in kW; is the lower limit of the output of the i-th energy conversion equipment in the integrated energy system in time period t, in kW; is the upper limit of the output of the i-th energy conversion equipment in the integrated energy system in time period t, in kW; is the ramp rate of the i-th energy conversion equipment in the integrated energy system in time period t, in kW; is the lower limit of the ramp rate of the i-th energy conversion equipment in the integrated energy system in time period t, in kW; is the upper limit of the ramp rate of the i-th energy conversion equipment in the integrated energy system in time period t, in kW; is the state of the i-th energy conversion equipment in the comprehensive energy system at time period t: When , it means that the energy conversion equipment is turned on; when When , it means that the energy conversion equipment is turned off.

[0118] In S103, the joint optimization model is solved to determine the optimization strategy of the integrated energy system.

[0119] Among them, the optimization strategy includes the output curves of energy conversion equipment, energy storage equipment and gas equipment in the integrated energy system, as well as the peak-shaving capacity curve.

[0120] In an embodiment of the present application, after obtaining price data, new energy output data and user load data, the above-mentioned joint optimization model taking into account multiple uncertainties is constructed based on the price data, new energy output data and user load data. By solving the joint optimization model, the optimization strategy of the integrated energy system can be obtained, that is, the output curves of energy conversion equipment, energy storage equipment and gas equipment, as well as the peak-shaving capacity curve are obtained. The present application provides an optimization method that can cope with multiple uncertainties in the external spot market, the peak-shaving market and the internal source and load (i.e., new energy output and load demand), and the optimization method provided by the present application can effectively reduce the difficulty of regulating the integrated energy system.

[0121] In some embodiments of the present application, when solving the joint optimization model and determining the optimization strategy of the integrated energy system, the joint optimization model can be solved based on a heuristic algorithm and a CPLEX solver to determine the optimization strategy of the integrated energy system.

[0122] The joint optimization model constructed by the present application is a multi-stage, nonlinear complex problem to solve. The complexity and correlation of its decision variables are high. Usually, a heuristic algorithm is adopted, or the model is linearized and then solved by a solver. However, the joint optimization model constructed by the present application is a two-layer optimization model. The upper layer solves the optimization of the market joint trading strategy, and the lower layer solves the optimization of the system scheduling scheme. Therefore, it is necessary to solve the model through algorithm nesting. The heuristic algorithm has high requirements on computing power, and the two-layer heuristic nesting is less applicable to solving the time-sensitive joint trading and regulation optimization problems. Solving through a solver can generally only solve a single target, and it is impossible to achieve the fusion of the risk aversion model and the opportunity benefit model. Therefore, the present application proposes to solve the model based on the heuristic algorithm-CPLEX dual solution architecture.

[0123] In the heuristic algorithm, this application selects the honeypot algorithm based on multi-strategy fusion to achieve upper-level dual-objective optimization, which has great advantages in solving high-dimensional complex problems. On this basis, this application has made adaptive adjustments to the model solving requirements. First, chaos theory and reverse learning theory are introduced to construct the initialization process of the Honey Badger Algorithm (HBA) algorithm to increase uniformity; second, a dynamic adaptation factor is introduced into the density calculation factor to ensure a smooth transition between exploration and development and enhance local search capabilities; third, a quantum space search method is introduced to mutate and update the honeypot position of the global search to improve the global search capability.

[0124] During the solution process, the joint optimization model constructed in this application solves the problem of joint optimization of upper-level multi-market risk aversion and opportunity benefits through the HBA algorithm. After determining the market transaction plan, the CPLEX solver is used to optimize the internal equipment scheduling strategy of the integrated energy system, and the system operating cost is fed back to the upper layer to support the target calculation of the system operating cost.

[0125] This application selects a comprehensive energy system as a scenario sample to verify the effectiveness and credibility of the comprehensive energy system operation optimization method for spot and peak-shaving market transactions. The scenario includes a variety of distributed energy equipment such as new energy, CCHP, heat pumps, and multi-energy storage.

[0126] The joint optimization model of the integrated energy system constructed in this application optimizes the equipment operation strategy, spot market trading strategy and peak load capacity retention strategy on the day before and in the middle of the day based on the completed medium- and long-term trading curves. When optimizing the system, it is necessary to obtain the load and new energy equipment output information on the optimization day, such as Figure 2 shown.

[0127] The park participates in various market trading modes outside. The medium- and long-term trading curve is regarded as a fixed external power source for the park. Due to differences in trading rules and clearing time, accurate data cannot be obtained during optimization between spot trading and peak-shaving trading. This application simulates typical spot market curves based on multi-level norms, such as Figure 3 As shown in the figure, peak load regulation is carried out based on the three time periods of 0:00-4:00, 6:00-10:00, and 16:00-20:00. When optimizing the operation of the integrated energy system, the medium- and long-term transaction prices, transaction curves, and gas transaction prices have been determined, which are key parameters for calculating the operation optimization cost, such as Figure 4 shown.

[0128] The operating cost and technical parameters of the equipment are important factors affecting the optimization target and directly determine the operation strategy of the equipment. Table 2 shows the installed capacity and operating cost of different energy equipment in IES.

[0129] Table 2 Parameters of distributed energy equipment in integrated energy system

[0130]

[0131] This application solves the system trading scheme and strategy based on the above-mentioned equipment and market price parameters, combined with the joint optimization model of the integrated energy system constructed by this application. Among them, considering the uncertainty of spot market transactions, it is divided into typical curves based on multi-level norms. This application analyzes the relationship between the peak-shaving capacity and operating cost of the integrated energy system transaction and operation scheme under various typical curves. Through analysis, it is concluded that under different typical curves of spot market prices, the daily operating cost and peak-shaving capacity are mutually exclusive, and the upper limit of the system peak-shaving capacity is not obviously differentiated. This is because the system peak-shaving capacity is mainly related to the medium- and long-term, the total amount of spot market participation, and the internal cold, heat, gas and other energy sources. In the transaction optimization and operation regulation link, the system peak-shaving capacity can be increased by changing the spot market transaction volume. In addition, different spot curves also lead to differences in the relative minimum daily operating cost and peak-shaving capacity. For example, the peak-shaving capacity corresponding to the relative minimum cost of typical curve 1 is relatively high. This is because the spot price of typical curve 1 is relatively low during the peak-shaving period. Considering the optimization of cost targets will also increase the proportion of power purchases in the system during this period, while increasing some peak-shaving capacity. In order to more intuitively analyze the differences in peak-shaving capabilities under different typical curves, the average peak-shaving capability response relationship of different typical curves in different peak-shaving periods is plotted, such as Figure 5 shown.

[0132] Based on the above results, this paper selects typical curve 1 for specific analysis, and its Pareto curve is as follows: Figure 6As shown. As can be seen from the figure, from a macro perspective, the peak-shaving capacity and cost are mutually exclusive. However, in the plan with a lower cost, it also has a certain peak-shaving capacity, that is, the operation plan obtained by optimizing with the lowest cost as the single objective also has a certain peak-shaving capacity, and the basic peak-shaving capacity is 16654.54kWh. As the reserved peak-shaving capacity gradually increases, the daily operating cost also gradually increases. It can be seen from the figure that when the peak-shaving capacity is low, the cost change caused by the increase in peak-shaving capacity is not obvious, and the overall trend is rapid. When the peak-shaving capacity is high, the increase in peak-shaving capacity leads to a significant change in cost, and the cost impact of the unit peak-shaving capacity is large. It shows a gentle increase trend overall, and the maximum peak-shaving capacity reaches 49217.78kWh.

[0133] Taking the peak-shaving capacity as the standard, this application compares the relationship between the peak-shaving capacity and daily operating cost of the minimum and maximum peak-shaving scenarios and the turning points 1 and 2, as shown in Table 3. It can be seen from the table that with the improvement of the peak-shaving capacity, the marginal peak-shaving cost changes greatly, and the peak-shaving capacity has a large spatial range and a significant span of the decision interval.

[0134] Table 3 Pareto curve key data

[0135]

[0136] This application compares the operation mode without peak-shaving at turning point 1 with the operation mode after participating in peak-shaving, and analyzes the impact of participating in the peak-shaving market on system scheduling: When the trading strategies of the medium- and long-term markets and the spot market are fixed, whether to participate in the peak-shaving market has a great impact on the system operation strategy in the three time periods of 0:00-4:00, 6:00-10:00, and 16:00-20:00 invited by the peak-shaving market. During the peak-shaving invitation period, the power load brought by electric refrigeration and air source heat pumps is significantly reduced, and is gradually replaced by power storage, gas boilers, and cold and hot energy storage to improve the system's peak-shaving capacity. For multi-energy storage equipment, after releasing energy during the peak-shaving period, it is recharged during the non-invitation period to ensure the normal execution of subsequent strategies.

[0137] In order to compare the impact of different trading strategies on peak-shaving capacity, this application compares the difference between spot trading volume and peak-shaving capacity at turning point 1 and turning point 2, as shown in Figure 7(a) and Figure 7(b). It can be seen from the figure that during the peak-shaving trading period, turning point 2 increased some spot trading volume to a certain extent to improve the peak-shaving capacity of the corresponding period, but such behavior increased the daily operating cost. As can be seen from Table 3, the marginal peak-shaving costs of turning point 1 and turning point 2 are 0.0753 yuan / kWh and 0.5290 yuan / kWh respectively.

[0138] Therefore, appropriately increasing the spot market transaction volume during the peak-shaving period can increase the system's peak-shaving capacity with a small increase in cost. If the peak-shaving capacity needs to be greatly increased, the cost increase will be more obvious. It is necessary to consider the comprehensive benefits of the peak-shaving market and analyze its break-even point. Figure 8 shown.

[0139] The break-even point is analyzed with an average peak-shaving income of 0.35 yuan / kWh. As can be seen from the above figure, when the peak-shaving capacity is 43715.66kWh, the comprehensive daily operating cost is the lowest, which is 22441.87 yuan, achieving the optimal daily cost and maximizing the opportunity income. Compared with not considering the opportunity income, the opportunity income brought by the peak-shaving market is 33060.25 yuan, which increases the profit margin by 59.57%.

[0140] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0141] Corresponding to the integrated energy system operation optimization method described in the above embodiment, Fig. 9 A structural block diagram of the integrated energy system operation optimization device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0142] See also Fig. 9 The integrated energy system operation optimization device in the embodiment of the present application may include an acquisition module 901, a construction module 902 and a solution module 903.

[0143] Among them, the acquisition module 901 is used to obtain price data, new energy output data and user load data.

[0144] Construction module 902 is used to construct a joint optimization model that takes into account multiple uncertainty factors based on price data, new energy output data and user load data; wherein the multiple uncertainty factors include the uncertainty of spot market transaction prices, the uncertainty of new energy output and load demand, and the uncertainty of peak-shaving market transaction periods and prices; the joint optimization model is a two-layer optimization model with minimizing operating costs as the lower-level goal and maximizing peak-shaving capacity as the upper-level goal.

[0145] The solution module 903 is used to solve the joint optimization model and determine the optimization strategy of the integrated energy system; the optimization strategy includes the output curves of energy conversion equipment, energy storage equipment and gas equipment in the integrated energy system, as well as the peak-shaving capacity curve.

[0146] Optionally, the joint optimization model is:

[0147]

[0148] Among them, P α is the peak-shaving space of the integrated energy system, α is the peak-shaving space under the regulation strategy of the integrated energy system, D is the relatively minimum set of operating costs of the integrated energy system, E P is the expected value of the probability distribution of the operating cost of the integrated energy system, f is the operating cost of the integrated energy system, X is the optimization decision variable, R is the spot price curve, is the uncertainty factor of the source load, H and G are the equality constraint and inequality constraint respectively, is a collection of typical spot price curves, is the uncertainty range of the source charge.

[0149] Optionally, the lower objective function of the joint optimization model is:

[0150]

[0151] The upper objective function of the joint optimization model is:

[0152]

[0153] Among them, C tran is the long-term market transaction cost of the integrated energy system, C spot is the transaction cost of the spot market of the integrated energy system, C gas is the other fuel cost of the comprehensive energy system, T Reg is the peak load period of the comprehensive energy system, P tran (t) is the long-term market transaction power curve of the integrated energy system, P spot (t) is the spot market transaction power curve of the integrated energy system, P sys (t) supply power to other units in the integrated energy system; infP E (t) is the maximum lower bound of the new energy output in the integrated energy system; supP L (t) is the minimum upper bound of the load of the integrated energy system.

[0154] Optionally, the medium and long-term market transaction costs of the integrated energy system are:

[0155]

[0156] The transaction cost of the spot market of the integrated energy system is:

[0157]

[0158] Other fuel costs for the integrated energy system are:

[0159]

[0160] in, P M&L (t) is the power curve of medium and long-term market transactions, p M&L (t) is the price curve of medium and long-term market transactions, t tran,end is the end time of medium and long-term market transactions, t tran,init is the start time of medium and long-term market transactions, P spot (t) is the power curve of spot market transactions, p spot (t) is the price curve of spot market transactions, t spot,end is the end time of spot market transactions, t spot,init is the spot market trading start time, Q gas (t) is the hourly fuel consumption of the integrated energy system, p gas (t) is the price curve of other fuels, t gas,end The end time of using other fuels for the integrated energy system, t gas,init The starting time for using other fuels in the integrated energy system, D EL is the distribution space of the price curve of spot market transactions and the peak-shaving capacity curve, The uncertainty range of renewable energy output in the integrated energy system, is the uncertainty range of the load of the integrated energy system.

[0161] Optionally, the equality constraints of the joint optimization model include electric load balance constraints, thermal load balance constraints, cooling load balance constraints and natural gas balance constraints; the inequality constraints of the joint optimization model include market constraints, energy storage equipment physical constraints and energy conversion equipment ramping constraints.

[0162] Optionally, market constraints include spot market deviation assessment constraints and peak-shaving market access capacity constraints.

[0163] The spot market deviation assessment constraints are:

[0164]

[0165] Among them, P spot (t) is the power curve of spot market transactions, P act (t) is the actual usage of the spot market, η is the proportion of excess capacity allowed per unit time, η' is the average proportion of excess capacity allowed during the assessment period, and T is the period when the integrated energy system has the peak-shaving capacity.

[0166] The capacity constraint for peak load regulation market access is:

[0167]

[0168] Among them, D is the time period set when the comprehensive energy system has the peak load regulation capability, P reg (t) is the peak-shaving capacity of the integrated energy system at time t, M is the minimum peak-shaving capacity for access to the peak-shaving market, and D 1～n is a set of n time periods allowed to participate in the peak-shaving market, D(t) is..., D(t-1) is..., c is the minimum trading period length for participating in the peak-shaving market, and card(D)>c means that the length of each peak-shaving period in the set D is greater than the minimum trading period length for participating in the peak-shaving market.

[0169] Optionally, the solving module 903 is specifically used to solve the joint optimization model based on the heuristic algorithm and the CPLEX solver to determine the optimization strategy of the integrated energy system.

[0170] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0171] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0172] The present application also provides an electronic device, see Fig.10 The electronic device 1000 may include: at least one processor 1010, a memory 1020, and a computer program stored in the memory 1020 and executable on the at least one processor 1010. When the processor 1010 executes the computer program, the steps in any of the above-mentioned method embodiments are implemented, for example: Figure 1 S101 to S103 in the embodiment shown. Alternatively, when the processor 1010 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Fig. 9 Functions of modules 901 to 903 are shown.

[0173] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 1020, and executed by the processor 1010 to complete the present application. The one or more modules / units may be a series of computer program segments that can complete specific functions, and the program segments are used to describe the execution process of the computer program in the electronic device 1000.

[0174] Those skilled in the art will understand that Fig.10 These are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0175] The processor 1010 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0176] The memory 1020 may be an internal storage unit of the electronic device, or an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 1020 is used to store the computer program and other programs and data required by the electronic device. The memory 1020 may also be used to temporarily store data that has been output or is to be output.

[0177] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0178] The integrated energy system operation optimization method provided in the embodiment of the present application can be applied to electronic devices such as computers, tablet computers, laptops, netbooks, personal digital assistants (PDAs), augmented reality (AR) / virtual reality (VR) devices, and mobile phones. The embodiment of the present application does not impose any restrictions on the specific types of electronic devices.

[0179] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the steps in each embodiment of the above-mentioned integrated energy system operation optimization method.

[0180] An embodiment of the present application provides a computer program product. When the computer program product is executed on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned integrated energy system operation optimization method when executing the computer program product.

[0181] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0182] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0183] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0184] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for optimizing the operation of an integrated energy system, characterized in that: include: Obtain price data, renewable energy output data and user load data; Based on the price data, the new energy output data and the user load data, a joint optimization model considering multiple uncertainties is constructed; wherein the multiple uncertainties include the uncertainty of spot market transaction prices, the uncertainty of new energy output and load demand, and the uncertainty of peak-shaving market transaction periods and prices; the joint optimization model is a two-layer optimization model with the minimum operating cost as the lower layer goal and the maximum peak-shaving capacity as the upper layer goal; Solve the joint optimization model to determine the optimization strategy of the integrated energy system; the optimization strategy includes the output curves of energy conversion equipment, energy storage equipment and gas equipment in the integrated energy system, as well as the peak-shaving capacity curve.

2. The integrated energy system operation optimization method according to claim 1, characterized in that: The joint optimization model is: Among them, P α is the peak-shaving space of the integrated energy system, α is the peak-shaving space under the regulation strategy of the integrated energy system, D is the relatively minimum set of operating costs of the integrated energy system, is the expected value of the probability distribution of the operating cost of the integrated energy system, is the operating cost of the integrated energy system, X is the optimization decision variable, R is the spot price curve, is the uncertainty factor of the source load, H and G are the equality constraint and inequality constraint respectively, is a collection of typical spot price curves, is the uncertainty range of the source charge.

3. The integrated energy system operation optimization method according to claim 2, characterized in that: The lower objective function of the joint optimization model is: The upper objective function of the joint optimization model is: Among them, C tran is the long-term market transaction cost of the integrated energy system, C spot is the spot market transaction cost of the integrated energy system, C gas is the other fuel cost of the comprehensive energy system, T Reg is the peak load period of the comprehensive energy system, P tran (t) is the long-term market transaction power curve of the comprehensive energy system, P spot (t) is the spot market transaction power curve of the integrated energy system, P sys (t) supplying power to other units of the integrated energy system; infP E (t) is the maximum lower bound of the new energy output in the integrated energy system; supP L (t) is the minimum upper bound of the load of the integrated energy system.

4. The integrated energy system operation optimization method according to claim 3, characterized in that: The long-term market transaction cost of the comprehensive energy system is: The transaction cost of the spot market of the integrated energy system is: Other fuel costs for the integrated energy system are: in, P M&L (t) is the power curve of medium and long-term market transactions, p M&L (t) is the price curve of medium and long-term market transactions, t tran,end is the end time of medium and long-term market transactions, t tran,init is the start time of medium and long-term market transactions, P spot (t) is the power curve of spot market transactions, p spot (t) is the price curve of spot market transactions, t spot,end is the end time of spot market transactions, t spot,init is the spot market trading start time, Q gas (t) is the hourly fuel consumption of the integrated energy system, p gas (t) is the price curve of other fuels, t gas,end is the termination time of using other fuels in the comprehensive energy system, t gas,init is the starting time of using other fuels in the comprehensive energy system, D EL is the distribution space of the price curve of the spot market transaction and the peak load capacity curve, is the uncertainty range of the new energy output in the integrated energy system, is the uncertainty range of the load of the integrated energy system.

5. The integrated energy system operation optimization method according to claim 2, characterized in that: The equation constraints of the joint optimization model include an electric load balance constraint, a heat load balance constraint, a cooling load balance constraint, and a natural gas balance constraint; The inequality constraints of the joint optimization model include market constraints, physical constraints of energy storage equipment, and ramp constraints of energy conversion equipment.

6. The integrated energy system operation optimization method according to claim 5, characterized in that: The market constraints include spot market deviation assessment constraints and peak-load market access capacity constraints; The spot market deviation assessment constraints are: Among them, P spot (t) is the power curve of spot market transactions, P act (t) represents the actual usage of the spot market, η represents the percentage of excess capacity allowed per unit time, η' represents the average percentage of excess capacity allowed during the assessment period, and T represents the period during which the comprehensive energy system has the peak load regulation capability; The peak load market access capacity constraint is: Where D is the set of time periods during which the integrated energy system has the peak load regulation capability, P reg (t) is the peak-shaving capacity of the comprehensive energy system at time t, M is the minimum peak-shaving capacity for peak-shaving market access, and D 1～n is a set of n time periods allowed to participate in the peak-shaving market, c is the minimum trading period length for participating in the peak-shaving market, and card(D)>c means that the length of each peak-shaving period in the set D is greater than the minimum trading period length for participating in the peak-shaving market.

7. The integrated energy system operation optimization method according to claim 1, characterized in that: The step of solving the joint optimization model and determining the optimization strategy of the integrated energy system includes: Based on the heuristic algorithm and CPLEX solver, the joint optimization model is solved to determine the optimization strategy of the integrated energy system.

8. An integrated energy system operation optimization device, characterized in that: include: The acquisition module is used to obtain price data, new energy output data and user load data; A construction module is used to construct a joint optimization model that takes into account multiple uncertainties based on the price data, the new energy output data, and the user load data; wherein the multiple uncertainties include uncertainty in spot market transaction prices, uncertainty in new energy output and load demand, and uncertainty in peak-shaving market transaction periods and prices; the joint optimization model is a two-layer optimization model that takes minimizing operating costs as a lower-level objective and maximizing peak-shaving capacity as an upper-level objective; A solution module is used to solve the joint optimization model and determine the optimization strategy of the integrated energy system; the optimization strategy includes the output curves of energy conversion equipment, energy storage equipment and gas equipment in the integrated energy system, as well as the peak-shaving capacity curve.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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