A multi-time scale scheduling method and device for a risk-averse integrated energy system in a park

Abstract

The present invention discloses a multi-time scale scheduling method and device for a risk-averse park integrated energy system. The method models the park integrated energy system by setting a modeling strategy to obtain a constraint model of the park integrated energy system; generates day-ahead scenarios through a scenario generation strategy and a scenario reduction strategy; generates intra-day scenarios according to the prediction data of the day-ahead scenarios through a scenario tree strategy; generates real-time scenarios according to the intra-day scenarios through a scenario tree strategy; respectively constructs a day-ahead three-stage stochastic optimization model considering CVaR, an intra-day two-stage stochastic rolling optimization model, and a real-time rolling optimization model according to the constraint model of the park integrated energy system; and solves the models by setting a solver respectively to obtain the day-ahead stage scheduling strategy, the intra-day stage scheduling strategy, and the real-time stage scheduling strategy of the park integrated energy system. The present invention can achieve economic and reliable scheduling at multiple time scales, and has the ability to avoid risks while improving economic efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of operation optimization of energy systems, and particularly to a multi-time scale scheduling method and device for a comprehensive energy system in a park with risk aversion. Background Art

[0002] With the intensification of the energy crisis and environmental deterioration, the disadvantages of low efficiency of traditional energy systems have gradually emerged. By organically coordinating and optimizing the user-side utilization links of different energies such as electricity, natural gas, and heating, PIES has a higher comprehensive energy utilization rate than traditional single-user-side energy systems.

[0003] Currently, in terms of multi-time scale optimal scheduling of PIES, a large number of studies have been carried out by domestic and foreign scholars. However, most studies adopt a two-stage optimal scheduling model, which makes there is still room for further improvement in the economy of multi-time scale scheduling. In addition, PIES involves multiple uncertainties such as renewable energy output, multi-energy load, and energy price. A large number of studies use stochastic programming methods to consider the influence of uncertain factors, which makes the scheduling plan often too optimistic and lacks the ability to avoid risks.

[0004] Therefore, how to invent a multi-time scale optimal scheduling method for PIES with risk aversion, which has the ability to avoid risks while improving economy, has become an urgent problem to be solved. Summary of the Invention

[0005] For this reason, the present invention provides a multi-time scale scheduling method and device for a comprehensive energy system in a park with risk aversion, which can realize economic and reliable scheduling under multiple time scales and has the ability to avoid risks while improving economy.

[0006] To achieve the above object, the present invention provides the following technical solutions: A multi-time scale scheduling method for a comprehensive energy system in a park with risk aversion, including:

[0007] Modeling the renewable energy output equipment, energy coupling links, energy storage equipment, and bus structure in the comprehensive energy system of the park through a set modeling strategy to obtain a constraint model of the comprehensive energy system of the park;

[0008] Generating a day-ahead scenario through a scenario generation strategy and a scenario reduction strategy; generating an intra-day scenario according to the prediction data of the day-ahead scenario through a scenario tree strategy; generating a real-time scenario according to the intra-day scenario through a scenario tree strategy;

[0009] Respectively constructing a day-ahead three-stage stochastic optimization model, an intra-day two-stage stochastic rolling optimization model, and a real-time rolling optimization model considering CVaR according to the constraint model of the comprehensive energy system of the park;

[0010] By setting the solver to solve the three-stage day-ahead random optimization model taking into account CVaR, the two-stage intraday random rolling optimization model and the real-time rolling optimization model respectively, the day-ahead stage scheduling strategy, the intraday stage scheduling strategy and the real-time stage scheduling strategy of the park's integrated energy system are obtained.

[0011] As a preferred scheme for a risk-averse multi-time scale scheduling method for a park integrated energy system, in the process of modeling the renewable energy output equipment in the park integrated energy system by setting a modeling strategy, the constraint model expression of the renewable energy output equipment is:

[0012]

[0013] In the formula, The actual output of PV; is the maximum output of PV;

[0014] In the process of modeling the energy coupling link in the park comprehensive energy system by setting the modeling strategy, the energy coupling link includes: cogeneration unit, ground source heat pump, electric boiler and gas boiler; the constraint model expression of the energy coupling link is:

[0015]

[0016] In the formula, and is the electrical, gas and thermal power of unit j; and is the real-time adjustment of the electric, gas and thermal power of unit j; It is the start and stop status of the CHP unit; and H j It is the upper and lower limits of the unit output; and Δ H j It is the upper and lower limits of the unit climbing rate; is the CHP gas-to-electricity conversion efficiency; is the CHP gas-to-heat conversion efficiency; is the HP electrothermal conversion coefficient; is the EB electrothermal conversion efficiency; is GB gas-to-heat conversion efficiency; A collection of energy coupling devices;

[0017] In the process of modeling the energy storage equipment in the park comprehensive energy system by setting the modeling strategy, the energy storage equipment includes: power storage equipment and heat storage equipment; the constraint model expression of the energy storage equipment is:

[0018]

[0019] In the formula, and are the energy storage charging and discharging power; and are the upper limits of the energy storage charging and discharging power; μ j is the self-discharging rate of the energy storage; and are the energy storage charging and discharging efficiency; W j is the capacity of the energy storage device; and SOC j are the upper and lower limits of the energy storage SOC; is the set of energy storage devices;

[0020] In the process of modeling the bus structure in the park integrated energy system by setting the modeling strategy, the constraint model expression of the bus structure is:

[0021]

[0022] In the formula, is the PIES day-ahead market electricity purchase volume; is the PIES intra-day market electricity purchase volume; is the PIES real-time market electricity purchase volume; is the PIES heat purchase volume; is the PIES gas purchase volume.

[0023] As an optimal solution of a multi-time scale scheduling method for a park integrated energy system with risk aversion, in the process of generating the day-ahead scenario through the scenario generation strategy and the scenario reduction strategy, according to the distribution information of photovoltaic output, electrical load and electricity price, through the scenario generation strategy, relevant samples are generated; according to the relevant samples, through the scenario reduction strategy, typical scenarios are clustered; the scenario generation strategy is a scenario generation strategy based on CD-LHS; the scenario reduction strategy is a scenario reduction strategy based on GMM clustering.

[0024] As an optimal solution of a multi-time scale scheduling method for a park integrated energy system with risk aversion, the expression of the day-ahead three-stage stochastic optimization model considering CVaR is:

[0025]

[0026] In the formula, is the day-ahead scheduling cost; is the intra-day scheduling cost; is the real-time scheduling cost; is the scenario in the day-ahead scenario set; is the day-ahead scenario set; is the set of time periods for the day-ahead scheduling model; is the expected value under the day-ahead scenario set; is the CVaR under the day-ahead scenario set; Γ is the risk preference coefficient;

[0027] The expression of the intra-day two-stage stochastic rolling optimization model is:

[0028]

[0029] In the formula, is the set of time periods for the intra-day scheduling model; is the intra-day scenario set; is the expected value under the intra-day scenario set;

[0030] The expression of the real-time rolling optimization model is:

[0031]

[0032] In the formula, is the set of time periods for the intra-day scheduling model.

[0033] As an optimal solution of the multi-time scale scheduling method for the park integrated energy system with risk aversion, the expression of the day-ahead stage scheduling strategy is:

[0034]

[0035] In the formula, is the day-ahead stage scheduling strategy; is the day-ahead market purchase electricity quantity of PIES; is the charging power of ES; is the discharging power of ES; is the charging energy power of HS; is the discharging energy power of HS; is the start-stop state of CHP;

[0036] The expression of the intra-day stage scheduling strategy is:

[0037]

[0038] In the formula, is the intra-day two-stage scheduling strategy; is the intra-day market purchase electricity quantity of PIES; is the electric power of CHP; is the electric power of HP; is the electric power of EB; is the gas power of GB;

[0039] The expression of the real-time stage scheduling strategy is:

[0040]

[0041] In the formula, is the real-time rolling scheduling strategy; is the real-time market electricity purchase quantity of PIES; is the gas purchase quantity of PIES; is the heat purchase quantity of PIES; is the PV accommodation power; is the CHP power adjustment value; is the HP power adjustment value; is the EB power adjustment value; is the GB power adjustment value.

[0042] The present invention also provides a multi-time scale scheduling device for a risk-averse park integrated energy system. Based on the above-mentioned multi-time scale scheduling method for a risk-averse park integrated energy system, it includes:

[0043] A park integrated energy system modeling module, which is used to model renewable energy output devices, energy coupling links, energy storage devices, and bus structures in the park integrated energy system by setting modeling strategies, and obtain a park integrated energy system constraint model;

[0044] A scenario generation module, which is used to generate day-ahead scenarios through scenario generation strategies and scenario reduction strategies; generate intra-day scenarios according to the prediction data of the day-ahead scenarios through scenario tree strategies; generate real-time scenarios according to the intra-day scenarios through scenario tree strategies;

[0045] An optimization model construction module, which is used to construct a day-ahead three-stage stochastic optimization model considering CVaR, an intra-day two-stage stochastic rolling optimization model, and a real-time rolling optimization model respectively according to the park integrated energy system constraint model;

[0046] A scheduling strategy acquisition module, which is used to solve the day-ahead three-stage stochastic optimization model considering CVaR, the intra-day two-stage stochastic rolling optimization model, and the real-time rolling optimization model respectively by setting a solver, and obtain the day-ahead stage scheduling strategy, intra-day stage scheduling strategy, and real-time stage scheduling strategy of the park integrated energy system.

[0047] As a preferred solution of a multi-time scale scheduling device for a risk-averse park integrated energy system, in the park integrated energy system modeling module, during the process of modeling renewable energy output devices in the park integrated energy system by setting modeling strategies, the constraint model expression of the renewable energy output device is:

[0048]

[0049] In the formula, is the actual output of the PV; is the maximum output of the PV;

[0050] In the process of modeling the energy coupling link in the park integrated energy system by setting a modeling strategy, the energy coupling link includes: a combined heat and power unit, a ground source heat pump, an electric boiler, and a gas boiler; the constraint model expression of the energy coupling link is:

[0051]

[0052] In the formula, and are the electrical, gas, and heat powers of unit j; and are the real-time adjustment amounts of the electrical, gas, and heat powers of unit j; is the start / stop state of the CHP unit; and H j are the upper and lower limits of the unit output; and Δ H j are the upper and lower limits of the unit ramp rate; is the CHP gas-electricity conversion efficiency; is the CHP gas-heat conversion efficiency; is the HP electric-heat conversion coefficient; is the EB electric-heat conversion efficiency; is the GB gas-heat conversion efficiency; is the set of energy coupling devices;

[0053] In the process of modeling the energy storage device in the park integrated energy system by setting a modeling strategy, the energy storage device includes: an electricity storage device and a heat storage device; the constraint model expression of the energy storage device is:

[0054]

[0055] In the formula, and are the charging and discharging powers of the energy storage; and are the upper limits of the charging and discharging energies of the energy storage; μ j is the self-discharging rate of the energy storage; and are the charging and discharging efficiencies of the energy storage; W j is the capacity of the energy storage device; and SOC j are the upper and lower limits of the energy storage SOC; is the set of energy storage devices;

[0056] In the process of modeling the bus structure in the integrated energy system of a park by setting a modeling strategy, the constraint model expression of the bus structure is as follows:

[0057]

[0058] In the formula, is the electricity purchase quantity in the day-ahead market of PIES; is the electricity purchase quantity in the intraday market of PIES; is the electricity purchase quantity in the real-time market of PIES; is the heat purchase quantity of PIES; is the gas purchase quantity of PIES.

[0059] As an optimal solution of a multi-time scale scheduling device for an integrated energy system of a park for risk aversion, in the scenario generation module, in the process of generating the day-ahead scenario through the scenario generation strategy and the scenario reduction strategy, according to the distribution information of photovoltaic output, electrical load and electricity price, relevant samples are generated through the scenario generation strategy; according to the relevant samples, typical scenarios are clustered through the scenario reduction strategy; the scenario generation strategy is a scenario generation strategy based on CD-LHS; the scenario reduction strategy is a scenario reduction strategy based on GMM clustering.

[0060] As an optimal solution of a multi-time scale scheduling device for an integrated energy system of a park for risk aversion, in the optimization model construction module, the expression of the day-ahead three-stage stochastic optimization model considering CVaR is as follows:

[0061]

[0062] In the formula, is the day-ahead scheduling cost; is the intraday scheduling cost; is the real-time scheduling cost; is the scenario in the day-ahead scenario set; is the day-ahead scenario set; is the time period set of the day-ahead scheduling model; is the expected value under the day-ahead scenario set; is the CVaR under the day-ahead scenario set; Γ is the risk preference coefficient;

[0063] The expression of the intraday two-stage stochastic rolling optimization model is as follows:

[0064]

[0065] In the formula, is the time period set of the intraday scheduling model; is the intraday scenario set; is the expected value under the intraday scenario set;

[0066] The expression of the real-time rolling optimization model is:

[0067]

[0068] In the formula, is the time period set of the intraday scheduling model.

[0069] As an optimal solution of the multi-time scale scheduling device for the park integrated energy system with risk aversion, in the scheduling strategy acquisition module, the expression of the day-ahead stage scheduling strategy is:

[0070]

[0071] In the formula, is the day-ahead stage scheduling strategy; is the day-ahead market purchase electricity quantity of PIES; is the charging power of ES; is the discharging power of ES; is the charging energy power of HS; is the discharging energy power of HS; is the start-stop state of CHP;

[0072] The expression of the intraday stage scheduling strategy is:

[0073]

[0074] In the formula, is the intraday two-stage scheduling strategy; is the intraday market purchase electricity quantity of PIES; is the electric power of CHP; is the electric power of HP; is the electric power of EB; is the gas power of GB;

[0075] The expression of the real-time stage scheduling strategy is:

[0076]

[0077] In the formula, is the real-time rolling scheduling strategy; is the real-time market purchase electricity quantity of PIES; is the gas purchase quantity of PIES; is the heat purchase quantity of PIES; is the PV consumption power; is the CHP power adjustment value; is the HP power adjustment value; is the EB power adjustment value; It is the GB power adjustment value.

[0078] The present invention has the following advantages: By setting up a modeling strategy to model the renewable energy output devices, energy coupling links, energy storage devices and bus structures in the integrated energy system of the park, a constraint model of the integrated energy system of the park is obtained; By means of a scenario generation strategy and a scenario reduction strategy, a day-ahead scenario is generated; By means of a scenario tree strategy, an intra-day scenario is generated according to the prediction data of the day-ahead scenario; By means of a scenario tree strategy, a real-time scenario is generated according to the intra-day scenario; According to the constraint model of the integrated energy system of the park, a day-ahead three-stage stochastic optimization model considering CVaR, an intra-day two-stage stochastic rolling optimization model and a real-time rolling optimization model are respectively constructed; By setting a solver to solve the day-ahead three-stage stochastic optimization model considering CVaR, the intra-day two-stage stochastic rolling optimization model and the real-time rolling optimization model respectively, a day-ahead stage scheduling strategy, an intra-day stage scheduling strategy and a real-time stage scheduling strategy of the integrated energy system of the park are obtained. The present invention first models the renewable energy output devices, energy coupling devices, energy storage devices and park structures involved in the PIES; Furthermore, a Latin hypercube sampling method based on Cholesky decomposition (Cholesky decomposition-based Latin hypercubic sampling, CD-LHS) and a Gaussian mixture model (Gaussian mixture model, GMM) clustering method are used for scenario generation and scenario reduction to obtain relevant typical scenarios; On this basis, a day-ahead three-stage stochastic optimization model considering Conditional Value at Risk (CVaR), an intra-day two-stage stochastic rolling optimization model and a real-time rolling optimization model are constructed to achieve economic and reliable scheduling at multiple time scales. The present invention can have the ability to avoid risks while improving economic efficiency. Description of the Drawings

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained according to the provided drawings.

[0080] The structures, ratios, sizes, etc. shown in this specification are only used to match the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the implementation conditions of the present invention. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the ratio relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0081] Figure 1 It is a schematic flow chart of a multi-time scale scheduling method for a risk-averse integrated energy system in a park provided in Embodiment 1 of the present invention;

[0082] Figure 2 It is a schematic structural diagram of a typical electrical-thermal coupled PIES in a multi-time scale scheduling method for a risk-averse integrated energy system in a park provided in Embodiment 1 of the present invention;

[0083] Figure 3 It is a schematic diagram of the generation process of a three-stage scheduling scenario tree in a multi-time scale scheduling method for a risk-averse integrated energy system in a park provided in Embodiment 1 of the present invention;

[0084] Figure 4 It is a schematic diagram of a multi-time scale optimal scheduling framework in a multi-time scale scheduling method for a risk-averse integrated energy system in a park provided in Embodiment 1 of the present invention;

[0085] Figure 5 It is a schematic diagram of typical daily data in a possible embodiment provided in Embodiment 1 of the present invention;

[0086] Figure 6 It is a schematic diagram of the operating condition of the electric power of PIES in a possible embodiment provided in Embodiment 1 of the present invention;

[0087] Figure 7 It is a schematic diagram of the operating condition of the thermal power of PIES in a possible embodiment provided in Embodiment 1 of the present invention;

[0088] Figure 8 It is a schematic diagram of the architecture of a multi-time scale scheduling device for a risk-averse integrated energy system in a park provided in Embodiment 2 of the present invention. Detailed implementation manners

[0089] The following specific embodiments illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0090] Embodiment 1

[0091] See Figure 1 , Embodiment 1 of the present invention provides a multi-time scale scheduling method for a risk-averse integrated energy system in a park, including the following steps:

[0092] S1. Model the renewable energy output devices, energy coupling links, energy storage devices, and bus structures in the integrated energy system of the park through setting modeling strategies to obtain a constraint model of the integrated energy system of the park;

[0093] S2. Generate day-ahead scenarios through scenario generation strategies and scenario reduction strategies; generate intra-day scenarios according to the prediction data of the day-ahead scenarios through scenario tree strategies; generate real-time scenarios according to the intra-day scenarios through scenario tree strategies;

[0094] S3. Respectively construct a day-ahead three-stage stochastic optimization model considering CVaR, an intra-day two-stage stochastic rolling optimization model, and a real-time rolling optimization model according to the constraint model of the integrated energy system of the park;

[0095] S4. Solve the day-ahead three-stage stochastic optimization model considering CVaR, the intra-day two-stage stochastic rolling optimization model, and the real-time rolling optimization model respectively through setting solvers to obtain the day-ahead stage scheduling strategy, the intra-day stage scheduling strategy, and the real-time stage scheduling strategy of the integrated energy system of the park.

[0096] In this embodiment, in step S1, model the renewable energy output devices, energy coupling links, energy storage devices, and bus structures in the integrated energy system of the park through setting modeling strategies to obtain a constraint model of the integrated energy system of the park;

[0097] Specifically, the typical electrical-thermal coupling PIES structure is as Figure 2 shown, including modules such as renewable energy output devices, energy coupling links, energy storage devices, and bus structures.

[0098] Among them, the renewable energy output device in the PIES is a photovoltaic (PV) power generation device, and the constraint model expression of the renewable energy output device is:

[0099]

[0100] In the formula, is the actual output of PV; is the maximum output of PV; Equation (1) is the upper and lower limit constraint of PV output.

[0101] Among them, the energy coupling links in PIES include combined heat and power (CHP) units, ground source heat pumps (HP), electric boilers (EB), and gas boilers (GB). The constraint model expression of the energy coupling link is:

[0102]

[0103] In the formula, and are the electric, gas, and heat powers of unit j; and are the real-time adjustment amounts of the electric, gas, and heat powers of unit j; is the start-stop state of the CHP unit; and H j are the upper and lower limits of unit output; and Δ H j are the upper and lower limits of the unit ramp rate; is the gas-electric conversion efficiency of the CHP; is the gas-heat conversion efficiency of the CHP; is the electric-heat conversion coefficient of the HP; is the electric-heat conversion efficiency of the EB; is the gas-heat conversion efficiency of the GB; is the set of energy coupling devices;

[0104] Equation (2) is the upper and lower limit constraint of CHP output; Equation (3) is the upper and lower limit constraint of the output of energy coupling devices; Equation (4) is the ramp rate constraint of energy coupling devices; Equation (5) is the gas-electric conversion constraint of CHP; Equation (6) is the gas-heat conversion constraint of CHP; Equation (7) is the electric-heat conversion constraint of HP; Equation (8) is the electric-heat conversion constraint of EB; Equation (9) is the gas-heat conversion constraint of GB.

[0105] Among them, the energy storage devices in PIES include electric storage (ES) and heat storage (HS). The constraint model expression of the energy storage devices is:

[0106]

[0107] In the formula, and is the charge and discharge power of the energy storage; and is the upper limit of the charge and discharge energy of the energy storage; μ j is the self-discharge rate of the energy storage; and is the charge and discharge efficiency of the energy storage; W j is the capacity of the energy storage device; and SOC j are the upper and lower limits of the SOC of the energy storage; is the set of energy storage devices;

[0108] Equations (10)-(12) are the charge and discharge constraints of the energy storage device; Equation (13) is the state of charge (SOC) balance constraint of the energy storage device; Equation (14) is the upper and lower limit constraint of the SOC of the energy storage device; Equation (15) is the initial and final SOC constraint of the energy storage device.

[0109] Among them, due to the small scale of PIES, its topological structure is ignored, and the bus structure is used for modeling. The constraint model expression of the bus structure is:

[0110]

[0111] In the formula, is the amount of electricity purchased by PIES in the day-ahead market; is the amount of electricity purchased by PIES in the intraday market; is the amount of electricity purchased by PIES in the real-time market; is the amount of heat purchased by PIES; is the amount of gas purchased by PIES.

[0112] Equation (17) is the power balance constraint of the electric bus; Equation (18) is the power balance constraint of the heat bus; Equation (19) is the power balance constraint of the natural gas bus.

[0113] In this embodiment, in step S2, a day-ahead scenario is generated through a scenario generation strategy and a scenario reduction strategy; an intraday scenario is generated according to the prediction data of the day-ahead scenario through a scenario tree strategy; a real-time scenario is generated according to the intraday scenario through a scenario tree strategy;

[0114] Specifically, the present invention uses the scenario method to model the integrated environment. The photovoltaic output follows a Beta distribution, the electric and thermal loads follow a normal distribution, and the electricity price follows a normal distribution. There is a positive correlation among the photovoltaic output, the electric load, and the electricity price, and the three are negatively correlated with the thermal load. According to the distribution information, samples with correlation are generated through the CD-LHS method. The number of scenarios generated by scenario generation is large. To avoid excessive computational complexity, the samples are clustered into typical scenarios through the GMM clustering method.

[0115] Among them, the scenario generation strategy based on CD-LHS is as follows:

[0116] Suppose we need to sample M in an N-dimensional scenario. Then the initial sample X0| M×N can be expressed as:

[0117]

[0118] In the formula, is the inverse function of the probability density function of the uncertain factors in the scenario; U m,n is a random variable and satisfies U m,n ~U(0,1); R n is a sequence randomly shuffled from the natural number sequence 1, 2,..., M.

[0119] For the correlation coefficient matrix C0 of the initial sample| N×N and the target correlation coefficient matrix C| N×N performing Cholesky decomposition gives:

[0120]

[0121] C = LU = U T U (21)

[0122] In the formula, U0 is the upper triangular matrix after the decomposition of C0; L0 is the lower triangular matrix after the decomposition of C0; U is the upper triangular matrix after the decomposition of C; L is the lower triangular matrix after the decomposition of C.

[0123] Through the upper triangular matrices U0 and U after decomposition, the initial sample can be transformed into a sample X with the target correlation coefficient| M×N :

[0124]

[0125] Among them, the scenario reduction strategy based on GMM clustering is as follows:

[0126] Suppose we need to reduce M N-dimensional scenarios to K. Then the probability density function of the Gaussian function corresponding to each scenario after reduction is:

[0127]

[0128] In the formula, γ i = [μ i , Σ i , p i is the scenario parameter; μ i is the scenario mean; Σ i is the diagonal matrix of the scenario variance; p i is the scenario probability.

[0129] The reduced K scenes can form a GMM:

[0130]

[0131] Where, γ={γ1,γ2,...,γ K} are GMM parameters.

[0132] For the sample before reduction X = {x1, x2, ..., x M}, its likelihood function can be expressed as:

[0133]

[0134] By finding the parameters that maximize the likelihood function, we can get the GMM parameters. This problem can usually be solved using the expectation maximization (EM) method.

[0135] In this embodiment, unlike the traditional two-stage scheduling method, since the three stages of day-ahead, intraday and real-time need to be considered, it is impossible to obtain a typical scenario through only one scenario generation and scenario reduction. Therefore, the day-ahead scenario is obtained through the scenario tree method; secondly, the intraday scenario is generated based on the day-ahead forecast data; and then the real-time scenario is generated based on the intraday scenario, such as Figure 3 As shown. If M is generated ID Intraday scenarios, each intraday forecast scenario generates M RT For real-time scenarios, the number of day-ahead scenarios is:

[0136] M DA =M ID M RT (27)

[0137] Where M DA is the number of day-ahead scenarios.

[0138] In this embodiment, in step S3, according to the park integrated energy system constraint model, a three-stage random optimization model for the day before, a two-stage random rolling optimization model for the day, and a real-time rolling optimization model taking into account CVaR are respectively constructed;

[0139] Specifically, the multi-time scale stochastic optimization scheduling model considering CVaR consists of three optimization models: a three-order stochastic optimization model considering CVaR, a two-stage stochastic rolling optimization model within the day, and a real-time rolling optimization model, such as Figure 4As shown in the figure. During the day-ahead scheduling process, the prediction errors of photovoltaic output, electro-thermal load, and electricity market price are relatively large, which may lead to an overly optimistic scheduling strategy and too high a scheduling cost. Therefore, CVaR is used to avoid scheduling risks. During the intra-day scheduling process, the intra-day prediction error is small, and the scheduling risk also decreases. In order to obtain a more economical intra-day market power purchase plan, a stochastic rolling optimization model is adopted. During the real-time scheduling process, to ensure the balance of real-time power, a deterministic rolling optimization model is adopted to obtain the output plan as soon as possible.

[0140] Among them, the day-ahead three-stage stochastic optimization model considering CVaR is as follows:

[0141] Let the time scale of the day-ahead scheduling model be Δt DA , and its optimization range is the entire scheduling day. Then the time period set of the day-ahead scheduling model can be expressed as:

[0142]

[0143] Through scenario generation and scenario reduction, the day-ahead scenario set can be expressed as:

[0144]

[0145] In the formula, is the scenario in the day-ahead scenario set; is the maximum output value of the photovoltaic under the MPPT mode; is the electric load power; is the heat load power; is the intra-day market electricity price; is the real-time market electricity price.

[0146] In the day-ahead scheduling model, only the day-ahead scheduling strategy is actually executed, and the intra-day and real-time scheduling strategies are not executed. Therefore, each scenario corresponds to a different intra-day scheduling strategy and real-time scheduling strategy and correspondingly generates different intra-day scheduling costs and real-time scheduling costs Based on the above settings, the day-ahead three-stage stochastic optimization model considering CVaR can be expressed as:

[0147]

[0148]

[0149] Equations (1)-(18),,

[0150] In the formula, is the day-ahead scheduling cost; is the intra-day scheduling cost; is the real-time scheduling cost; is the scenario in the day-ahead scenario set; is the day-ahead scenario set; is the time period set of the day-ahead scheduling model; is the expected value under the day-ahead scenario set; is the CVaR under the day-ahead scenario set; Γ is the risk preference coefficient; is the day-ahead electricity price; is the intra-day electricity price; is the real-time electricity price; p Gas is the gas purchase price; p Heat is the heat purchase price; p Mai,j is the maintenance cost per unit power of equipment j; p Mai,j is the single start-up cost of equipment j; is the energy storage depreciation cost; is the start-up cost of equipment j; is the depth of discharge of ES; is the investment cost per unit capacity of ES; δ ES is the depreciation rate of ES; a, b, c are the ES cycle life parameters.

[0151] Equation (32) is the day-ahead cost expression; Equation (33) is the intra-day cost expression; Equation (34) is the real-time cost expression; Equations (35)-(36) are the CHP start-up cost expressions; Equation (37) is the ES depreciation cost expression; Equation (38) is the CVaR calculation expression; Equation (39) is the day-ahead scheduling non-foresight constraint.

[0152] Among them, the intra-day two-stage stochastic rolling optimization model is:

[0153] The intra-day optimization model rolls with the actual operation of the system and gives the intra-day power purchase strategy within a few minutes before each clearing. Let its time scale be Δt ID , and the optimization range is l ID Δt ID . The rolling optimization is executed once every Δt ID time, and its optimization range can be divided into a control domain and a prediction domain: the control domain is Δt ID time period after each optimization moment, and the intra-day strategy in the control domain will be executed in this optimization; the prediction domain is (l ID -1)Δt ID time period after the control domain, and the intra-day strategy in the prediction domain is not executed but executed in the subsequent rolling process. If the intra-day optimization model is called at time τ, then its optimization time period set can be expressed as:

[0154]

[0155] At time τ, an intra-day scenario set can be obtained through a scenario generation and reduction method:

[0156]

[0157] In the intra-day optimization model, real-time strategies are not executed. Therefore, each scenario corresponds to a different real-time strategy Accordingly, different real-time costs are generated Based on the above settings, the intra-day two-stage stochastic optimization model can be expressed as:

[0158]

[0159] Equations (1)-(18), (32)-(37),

[0160] In the formula, is the set of time periods of the intra-day scheduling model; is the intra-day scenario set; is the expected value under the intra-day scenario set.

[0161] Among them, the real-time rolling optimization model is:

[0162] The rolling process of the real-time optimization model is similar to that of the intra-day optimization model. Let its time scale be Δt RT , and the optimization range is l RT Δt RT . If the real-time optimization model is called at time τ, the set of its optimization time periods can be expressed as:

[0163]

[0164] Real-time scenarios are obtained through real-time measurements and market information:

[0165]

[0166] Based on the above settings, the real-time rolling optimization model can be expressed as:

[0167]

[0168] Equations (1)-(18), (32)-(37),

[0169] In the formula, is the set of time periods of the intra-day scheduling model.

[0170] In this embodiment, in step S4, the day-ahead three-stage stochastic optimization model considering CVaR, the intra-day two-stage stochastic rolling optimization model, and the real-time rolling optimization model are respectively solved by a solver to obtain the day-ahead stage scheduling strategy, the intra-day stage scheduling strategy, and the real-time stage scheduling strategy of the integrated energy system in the park.

[0171] Specifically, the day-ahead three-stage stochastic optimization model considering CVaR, the intra-day two-stage stochastic rolling optimization model, and the real-time rolling optimization model can all be transformed into MILP problems, and various MILP solvers can be used to solve the above problems.

[0172] Among them, in the three-stage scheduling model: the day-ahead stage decision variables include the start-stop state of the CHP, the output of the energy storage device, and the day-ahead market electricity purchase quantity; the intra-day stage decision variables include the output of the unit and the intra-day market electricity purchase quantity; the real-time stage decision variables include the unit output adjustment amount, the output of the photovoltaic, the real-time market electricity purchase quantity, the gas purchase quantity, and the heat purchase quantity. Therefore, the expression of the day-ahead stage scheduling strategy is:

[0173]

[0174] In the formula, is the day-ahead stage scheduling strategy; is the day-ahead market electricity purchase quantity of PIES; is the charging power of ES; is the discharging power of ES; is the charging energy power of HS; is the discharging energy power of HS; is the start-stop state of CHP;

[0175] The expression of the intra-day stage scheduling strategy is:

[0176]

[0177] In the formula, is the intra-day two-stage scheduling strategy; is the intra-day market electricity purchase quantity of PIES; is the electric power of CHP; is the electric power of HP; is the electric power of EB; is the gas power of GB;

[0178] The expression of the real-time stage scheduling strategy is:

[0179]

[0180] In the formula, is the real-time rolling scheduling strategy; is the electricity purchase quantity in the PIES real-time market; is the gas purchase quantity in the PIES; is the heat purchase quantity in the PIES; is the PV accommodation power; is the CHP power adjustment value; is the HP power adjustment value; is the EB power adjustment value; is the GB power adjustment value.

[0181] In a possible embodiment, a dispatching method for an electric-gas-thermal coupled PIES in a certain area in northern China is provided as follows:

[0182] Taking an electric-gas-thermal coupled PIES in a certain area in northern China as an example, the capacity configurations of various devices in the PIES are as follows: PV capacity is 650 kW, CHP capacity is 350 kW, HP capacity is 300 kW, EB capacity is 100 kW, GB capacity is 100 kW, ES capacity is 500 kW·h, HS capacity is 600 kW·h, the maximum power of the electric load is 550 kW, and the maximum power of the heat load is 800 kW. The park's electricity market refers to the day-ahead-intraday-real-time three-layer architecture of the European electricity market. The average intraday electricity price is 1.2 times the day-ahead electricity price, and the average real-time electricity price is 1.8 times the intraday electricity price. The park adopts a fixed gas price: 0.28 yuan / kW; a fixed heat price: 0.6 yuan / kW. The CVaR confidence level β = 0.90. The day-ahead dispatching time scale is 1 h, and the optimization range is 24 h; the intraday dispatching time scale is 15 min, and the optimization range is 2 h; the real-time dispatching time scale is 5 min, and the optimization range is 30 min.

[0183] The day-ahead data of PV output, electric load, heat load, and electricity price adopt the data of a typical winter day, such as Figure 5 shown. The intraday data is generated into 100 groups of data through the CD-LHS method on this basis to verify the intraday scenarios. The correlation coefficient between PV output and electric load is 0.80, the correlation coefficient between PV output and heat load is -0.50, the correlation coefficient between PV output and electricity price is 0.40, the correlation coefficient between electric load and heat load is -0.70, the correlation coefficient between electric load and electricity price is 0.60, and the correlation coefficient between heat load and electricity price is -0.30.

[0184] After calculation, the operating conditions of the PIES electric power can be obtained as shown in Figure 6 shown, and the operating conditions of the thermal power are as shown in Figure 7 shown. Analyzing the dispatching process, the following conclusions can be obtained:

[0185] First, the device EB is in a shutdown state throughout the process and does not participate in the dispatching process. This is because EB converts electric energy into heat energy with low efficiency and the comprehensive energy utilization rate is low, so it is abandoned by the dispatching method.

[0186] II. At 3:00 in the morning, the electricity price is relatively low and the electricity demand is small, so the equipment HP and GB cooperate to supply energy; at 22:00-23:00 in the evening, the heat demand decreases, and the equipment CHP and GB cooperate to supply energy; in other periods, the load demand is large, and the equipment CHP, HP, and GB work together to complete the energy supply. The above process shows that the scheduling method described in the present invention can flexibly mobilize equipment to cooperate according to demand and cost to complete the economic and reliable supply of multi-energy loads.

[0187] III. The energy storage devices ES and HS can effectively smooth the peak and fill the valley of the electric and heat loads, which helps the rest of the ligands to complete system integration and can effectively reduce the system integration cost. The output time of the renewable energy power generation device PV is more in line with the electric and heat load demands, and it can provide clean electric energy for the system better.

[0188] IV. The electricity purchase in the park is mainly carried out during the day-ahead scheduling process, and the purpose of electricity purchase during this period is to meet the energy demand of the park; while the purpose of electricity purchase during the intra-day scheduling and real-time scheduling processes is to cope with prediction errors and fluctuations of uncertain factors.

[0189] Four scenarios are set in this embodiment for comparison:

[0190] Scenario 1: Use the three-stage risk-averse stochastic optimization model described in this article for scheduling;

[0191] Scenario 2: Use the traditional two-stage risk-averse stochastic optimization model for scheduling;

[0192] Scenario 3: Use the three-stage stochastic optimization model without considering risk aversion for scheduling;

[0193] Scenario 4: Use the three-stage robust optimization model for scheduling;

[0194] After calculation, the daily operating cost, CVaR of the daily operating cost, day-ahead scheduling cost, intra-day operating cost, and CVaR of the intra-day operating cost of each scenario can be obtained, as shown in Table 1.

[0195] Cost item / yuan Scenario 1 Scenario 2 Scenario 3 Scenario 4 Daily operating cost / yuan 10930 10974 10934 11270 CVaR of daily operating cost / yuan 11080 11125 11092 11324 Day-ahead scheduling cost / yuan 5401 5533 5322 7311 Intraday scheduling cost / yuan 5529 5441 5612 3959 CVaR of intraday scheduling cost / yuan 5679 5592 5770 4013

[0196] Table 1 Comparison of scenario data

[0197] By comparing the daily operating costs in each scenario, it can be seen that the daily operating cost of Scenario 1 is basically the same as that of Scenario 3, 0.40% lower than that of Scenario 2, and 3.11% lower than that of Scenario 4; the daily operating CVaR of Scenario 1 is 0.41% lower than that of Scenario 2, 0.11% lower than that of Scenario 3, and 2.20% lower than that of Scenario 4. This shows that Scenario 1 has better economy than other scenarios and lower cost tail risk.

[0198] The day-ahead scheduling cost of Plan 2 is 2.44% higher than that of Plan 1, and the intra-day scheduling cost is 1.59% higher than that of Plan 1. This is because Plan 2 is a two-stage scheduling method. Compared with the three-stage scheduling method, the intra-day scheduling process is ignored, so the additional costs and risks brought by the fluctuations of uncertain factors cannot be avoided through the intra-day market. Therefore, its scheduling cost and scheduling risk are both too high.

[0199] The day-ahead operating cost of Plan 3 is 1.46% lower than that of Plan 1, and the intra-day operating cost is 1.50% higher than that of Plan 1. This is because Plan 2 does not consider risk aversion during the day-ahead scheduling process and purchases less electricity during the day-ahead period. During the intra-day period, due to its overly optimistic plan, it needs to bear higher intra-day operating costs, resulting in a higher scheduling risk than Plan 1.

[0200] The day-ahead scheduling cost of Plan 4 is 35.36% higher than that of Plan 1, and the intra-day scheduling cost is 28.40% lower than that of Plan 1. This is because Plan 4 adopts a robust scheduling method. Therefore, during the day-ahead scheduling stage, Plan 4 will consider the worst intra-day scenarios and thus needs to pay a high cost. Due to the relatively conservative strategy adopted in the day-ahead scheduling, the intra-day scheduling cost of Plan 4 is lower, but this still makes the total scheduling cost too high and the economy of this plan poor.

[0201] In summary, the present invention models renewable energy output devices, energy coupling links, energy storage devices, and bus structures in the integrated park energy system by setting up a modeling strategy to obtain a constraint model of the integrated park energy system; generates day-ahead scenarios through a scenario generation strategy and a scenario reduction strategy; generates intra-day scenarios based on the prediction data of the day-ahead scenarios through a scenario tree strategy; generates real-time scenarios based on the intra-day scenarios through a scenario tree strategy; respectively constructs a day-ahead three-stage stochastic optimization model considering CVaR, an intra-day two-stage stochastic rolling optimization model, and a real-time rolling optimization model according to the constraint model of the integrated park energy system; and obtains a day-ahead stage scheduling strategy, an intra-day stage scheduling strategy, and a real-time stage scheduling strategy of the integrated park energy system by setting up a solver to solve the day-ahead three-stage stochastic optimization model considering CVaR, the intra-day two-stage stochastic rolling optimization model, and the real-time rolling optimization model respectively. First, the present invention models the renewable energy output devices, energy coupling devices, energy storage devices, and park structure involved in the PIES; furthermore, a Cholesky decomposition-based Latin hypercubic sampling (CD-LHS) method and a Gaussian mixture model (GMM) clustering method are used for scenario generation and scenario reduction to obtain relevant typical scenarios; on this basis, a day-ahead three-stage stochastic optimization model considering Conditional Value at Risk (CVaR), an intra-day two-stage stochastic rolling optimization model, and a real-time rolling optimization model are constructed to achieve economic and reliable scheduling at multiple time scales. The present invention can have the ability to avoid risks while improving economy.

[0202] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this case of a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0203] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order from that in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0204] Example 2

[0205] See Figure 8 , Example 2 of the present invention also provides a multi-time scale scheduling device for a risk-averse integrated park energy system, including:

[0206] An integrated park energy system modeling module 001, configured to model renewable energy output devices, energy coupling links, energy storage devices, and bus structures in the integrated park energy system by setting modeling strategies, to obtain an integrated park energy system constraint model;

[0207] A scenario generation module 002, configured to generate day-ahead scenarios through scenario generation strategies and scenario reduction strategies; generate intra-day scenarios according to the prediction data of the day-ahead scenarios through scenario tree strategies; generate real-time scenarios according to the intra-day scenarios through scenario tree strategies;

[0208] An optimization model construction module 003, configured to respectively construct a day-ahead three-stage stochastic optimization model considering CVaR, an intra-day two-stage stochastic rolling optimization model, and a real-time rolling optimization model according to the integrated park energy system constraint model;

[0209] A scheduling strategy acquisition module 004, configured to solve the day-ahead three-stage stochastic optimization model considering CVaR, the intra-day two-stage stochastic rolling optimization model, and the real-time rolling optimization model respectively by setting a solver, to obtain a day-ahead stage scheduling strategy, an intra-day stage scheduling strategy, and a real-time stage scheduling strategy of the integrated park energy system.

[0210] In this embodiment, in the integrated park energy system modeling module 001, during the process of modeling the renewable energy output devices in the integrated park energy system by setting modeling strategies, the constraint model expression of the renewable energy output devices is:

[0211]

[0212] In the formula, is the actual PV output; is the maximum PV output;

[0213] During the process of modeling the energy coupling links in the integrated park energy system by setting modeling strategies, the energy coupling links include: combined heat and power units, ground source heat pumps, electric boilers, and gas boilers; the constraint model expression of the energy coupling links is:

[0214]

[0215]

[0216] In the formula, and are the electrical, gas, and heat powers of unit j; and are the real-time adjustment amounts of the electrical, gas, and heat powers of unit j; is the start / stop status of the CHP unit; and H j are the upper and lower limits of the unit output; and Δ H j are the upper and lower limits of the unit ramp rate; is the CHP gas-electricity conversion efficiency; is the CHP gas-heat conversion efficiency; is the HP electric-heat conversion coefficient; is the EB electric-heat conversion efficiency; is the GB gas-heat conversion efficiency; is the set of energy coupling devices;

[0217] In the process of modeling the energy storage devices in the integrated energy system of the park by setting the modeling strategy, the energy storage devices include: electrical energy storage devices and heat storage devices; the constraint model expression of the energy storage devices is:

[0218]

[0219] In the formula, and are the charging and discharging powers of the energy storage; and are the upper limits of the charging and discharging energies of the energy storage; μ j is the self-discharging rate of the energy storage; and are the charging and discharging efficiencies of the energy storage; W j is the capacity of the energy storage device; and SOC j are the upper and lower limits of the SOC of the energy storage; is the set of energy storage devices;

[0220] In the process of modeling the bus structure in the integrated energy system of the park by setting the modeling strategy, the constraint model expression of the bus structure is:

[0221]

[0222] In the formula, is the electricity purchase volume of the PIES day-ahead market; is the electricity purchase volume of the PIES intraday market; is the electricity purchase volume of the PIES real-time market; is the heat purchase volume of the PIES; It is the gas purchase volume for PIES.

[0223] In this embodiment, in the scenario generation module 002, during the process of generating the day-ahead scenario through the scenario generation strategy and the scenario reduction strategy, according to the distribution information of photovoltaic output, electrical load, and electricity price, samples with correlation are generated through the scenario generation strategy; based on the samples with correlation, typical scenarios are clustered through the scenario reduction strategy; the scenario generation strategy is a scenario generation strategy based on CD-LHS; the scenario reduction strategy is a scenario reduction strategy based on GMM clustering.

[0224] In this embodiment, in the optimization model construction module 003, the expression of the day-ahead three-stage stochastic optimization model considering CVaR is as follows:

[0225]

[0226] In the formula, It is the day-ahead scheduling cost; It is the intra-day scheduling cost; It is the real-time scheduling cost; It is the scenario in the day-ahead scenario set; It is the day-ahead scenario set; It is the time period set of the day-ahead scheduling model; It is the expected value under the day-ahead scenario set; It is the CVaR under the day-ahead scenario set; Γ is the risk preference coefficient;

[0227] The expression of the intra-day two-stage stochastic rolling optimization model is as follows:

[0228]

[0229] In the formula, It is the time period set of the intra-day scheduling model; It is the intra-day scenario set; It is the expected value under the intra-day scenario set;

[0230] The expression of the real-time rolling optimization model is as follows:

[0231]

[0232] In the formula, It is the time period set of the intra-day scheduling model.

[0233] In this embodiment, in the scheduling strategy acquisition module 004, the expression of the day-ahead stage scheduling strategy is as follows:

[0234]

[0235] In the formula, is the day-ahead stage scheduling strategy; is the day-ahead market electricity purchase quantity of PIES; is the charging power of ES; is the discharging power of ES; is the charging energy power of HS; is the discharging energy power of HS; is the start-stop state of CHP;

[0236] The expression of the intra-day stage scheduling strategy is:

[0237]

[0238] In the formula, is the two-stage intra-day scheduling strategy; is the intra-day market electricity purchase quantity of PIES; is the electric power of CHP; is the electric power of HP; is the electric power of EB; is the gas power of GB;

[0239] The expression of the real-time stage scheduling strategy is:

[0240]

[0241] In the formula, is the real-time rolling scheduling strategy; is the real-time market electricity purchase quantity of PIES; is the gas purchase quantity of PIES; is the heat purchase quantity of PIES; is the PV accommodation power; is the CHP power adjustment value; is the HP power adjustment value; is the EB power adjustment value; is the GB power adjustment value.

[0242] It should be noted that for the information interaction, execution process, etc. among the above system modules, since they are based on the same concept as the method embodiment in Embodiment 1 of the present application, the technical effects brought by them are the same as those of the method embodiment of the present application. For the specific content, reference can be made to the description in the method embodiment shown above in the present application, and details are not described herein again.

[0243] Embodiment 3

[0244] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which program code of a multi-time scale scheduling method for a risk-averse integrated energy system in a park is stored, and the program code includes instructions for executing the multi-time scale scheduling method for a risk-averse integrated energy system in a park according to Embodiment 1 or any possible implementation thereof.

[0245] The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that integrates one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state disk (SSD)), etc.

[0246] Embodiment 4

[0247] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0248] The processor and the memory complete communication with each other through a bus; the memory stores program instructions executable by the processor, and the processor can execute a multi-time scale scheduling method for a risk-averse integrated energy system in a park according to Embodiment 1 or any possible implementation thereof by calling the program instructions.

[0249] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in the memory. The memory can be integrated in the processor or can exist independently outside the processor.

[0250] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.).

[0251] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing system. They can be concentrated on a single computing system or distributed on a network composed of multiple computing systems. Optionally, they can be implemented with program codes executable by the computing system. Thus, they can be stored in a storage system and executed by the computing system. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to be implemented. In this way, the present invention is not limited to any specific combination of hardware and software.

[0252] Although the present invention has been described in detail above with general descriptions and specific embodiments, based on the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention.

Claims

1. A multi-time scale scheduling method for a risk-averse integrated energy system in a park, characterized in that, Including: Modeling renewable energy output equipment, energy coupling links, energy storage equipment, and bus structures in the park's integrated energy system through a set modeling strategy to obtain a constraint model of the park's integrated energy system; Generating day-ahead scenarios through a scenario generation strategy and a scenario reduction strategy; Generating intra-day scenarios based on the prediction data of the day-ahead scenarios through a scenario tree strategy; Generating real-time scenarios based on the intra-day scenarios through a scenario tree strategy; Respectively constructing a day-ahead three-stage stochastic optimization model considering CVaR, an intra-day two-stage stochastic rolling optimization model, and a real-time rolling optimization model according to the constraint model of the park's integrated energy system; Solving the day-ahead three-stage stochastic optimization model considering CVaR, the intra-day two-stage stochastic rolling optimization model, and the real-time rolling optimization model respectively through a set solver to obtain the day-ahead stage scheduling strategy, intra-day stage scheduling strategy, and real-time stage scheduling strategy of the park's integrated energy system; During the process of modeling the energy coupling link in the park's integrated energy system through a set modeling strategy, the energy coupling link includes: combined heat and power units, ground source heat pumps, electric boilers, and gas boilers; During the process of generating the day-ahead scenarios through the scenario generation strategy and the scenario reduction strategy, according to the distribution information of photovoltaic output, electrical load, and electricity price, generating relevant samples through the scenario generation strategy; clustering to obtain typical scenarios according to the relevant samples through the scenario reduction strategy; the scenario generation strategy is a scenario generation strategy based on CD-LHS; the scenario reduction strategy is a scenario reduction strategy based on GMM clustering; In the day-ahead scheduling model, only the day-ahead scheduling strategy is actually executed, while the intra-day and real-time scheduling strategies are not; each scenario corresponds to a different intra-day scheduling strategy and real-time scheduling strategy Accordingly, different intra-day scheduling costs and real-time scheduling costs The intraday optimization model is carried out in a rolling manner as the system actually operates, and an intraday power purchase strategy is given within a few minutes before each clearing. Let its time scale be Δt ID , and the optimization range is l ID Δt ID . The rolling optimization is executed once every Δt ID time. The optimization range is divided into a control domain and a prediction domain: the control domain is Δt ID time period after each optimization moment, and the intraday strategy in the control domain will be executed in this optimization; the prediction domain is (l ID -1)Δt ID time period. The intraday strategy in the prediction domain is not executed but will be executed in subsequent rolling processes.

2. A multi-time scale scheduling method for a risk-averse integrated energy system in a park according to claim 1, characterized in that, During the process of modeling the renewable energy output equipment in the park's integrated energy system through a set modeling strategy, the constraint model expression of the renewable energy output equipment is: In the formula, is the actual PV output; is the maximum PV output; The constraint model expression of the energy coupling link is: wherein, and are the electrical, gas, and heat powers of unit j; and are the real-time adjustment amounts of the electrical, gas, and heat powers of unit j; is the start-stop state of the CHP unit; and H j are the upper and lower limits of the unit output; and ΔH j are the upper and lower limits of the unit ramp rate; is the CHP gas-electricity conversion efficiency; is the CHP gas-heat conversion efficiency; is the HP electric-heat conversion coefficient; is the EB electric-heat conversion efficiency; is the GB gas-heat conversion efficiency; is the set of energy coupling devices; During the process of modeling the energy storage equipment in the park's integrated energy system through a set modeling strategy, the energy storage equipment includes: electricity storage equipment and heat storage equipment; the constraint model expression of the energy storage equipment is: In the formula, and are the charging and discharging power of the energy storage; and are the upper limits of the charging and discharging energy power of the energy storage; μ j is the self-discharging rate of the energy storage; and are the charging and discharging efficiencies of the energy storage; W j is the capacity of the energy storage device; and SOC j are the upper and lower limits of the SOC of the energy storage; is the set of energy storage devices; During the process of modeling the bus structure in the park's integrated energy system through a set modeling strategy, the constraint model expression of the bus structure is: In the formula, is the electricity purchase volume in the PIES day-ahead market; is the electricity purchase volume in the PIES intraday market; is the electricity purchase volume in the PIES real-time market; is the heat purchase volume of PIES; is the gas purchase volume of PIES.

3. The multi-time scale scheduling method for a risk-averse integrated energy system in a park according to claim 2, wherein, The expression of the day-ahead three-stage stochastic optimization model considering CVaR is: In the formula, is the day-ahead scheduling cost; is the intra-day scheduling cost; is the real-time scheduling cost; is the scenario in the day-ahead scenario set; is the day-ahead scenario set; is the time period set of the day-ahead scheduling model; is the expected value under the day-ahead scenario set; is the CVaR under the day-ahead scenario set; Γ is the risk preference coefficient; The expression of the intra-day two-stage stochastic rolling optimization model is: In the formula, is the set of time periods of the intraday scheduling model; is the intraday scenario set; is the expected value under the intraday scenario set; The expression of the real-time rolling optimization model is: In the formula, is the set of time periods of the intraday scheduling model.

4. A multi-time scale scheduling method for a risk-averse integrated energy system in a park according to claim 3, characterized in that The expression of the day-ahead stage scheduling strategy is: In the formula, is the daily-ahead stage scheduling strategy; is the daily-ahead market electricity purchase quantity of PIES; is the charging power of ES; is the discharging power of ES; is the charging energy power of HS; is the discharging energy power of HS; is the start-stop state of CHP; The expression of the intra-day stage scheduling strategy is: In the formula, is the intraday stage scheduling strategy; is the intraday market electricity purchase quantity of PIES; is the electric power of CHP; is the electric power of HP; is the electric power of EB; is the gas power of GB; The expression of the real-time stage scheduling strategy is: Wherein, is the real-time stage scheduling strategy; is the real-time market electricity purchase volume of PIES; is the gas purchase volume of PIES; is the heat purchase volume of PIES; is the PV accommodation power; is the CHP power adjustment value; is the HP power adjustment value; is the EB power adjustment value; is the GB power adjustment value.

5. A multi-time scale scheduling device for a risk-averse park integrated energy system, which adopts the multi-time scale scheduling method for a risk-averse park integrated energy system according to any one of claims 1-4, characterized in that, Including: A park integrated energy system modeling module for modeling renewable energy output equipment, energy coupling links, energy storage equipment, and bus structures in the park's integrated energy system through a set modeling strategy to obtain a constraint model of the park's integrated energy system; A scenario generation module for generating day-ahead scenarios through a scenario generation strategy and a scenario reduction strategy; Generating intra-day scenarios based on the prediction data of the day-ahead scenarios through a scenario tree strategy; Generating real-time scenarios based on the intra-day scenarios through a scenario tree strategy; An optimization model construction module, for constructing a three-stage stochastic optimization model for the day before, a two-stage stochastic rolling optimization model for the day, and a real-time rolling optimization model taking into account CVaR according to the constraint model of the park comprehensive energy system; The scheduling strategy acquisition module is used to solve the three-stage random optimization model taking into account CVaR, the two-stage random rolling optimization model within the day, and the real-time rolling optimization model by setting a solver to obtain the day-ahead stage scheduling strategy, the intraday stage scheduling strategy, and the real-time stage scheduling strategy of the park's comprehensive energy system.

6. The multi-time scale scheduling device for a risk-averse integrated energy system in a park according to claim 5, characterized in that In the park comprehensive energy system modeling module, in the process of modeling the renewable energy output equipment in the park comprehensive energy system by setting the modeling strategy, the constraint model expression of the renewable energy output equipment is: Wherein, is the actual PV output; is the maximum PV output; In the process of modeling the energy coupling link in the park comprehensive energy system by setting the modeling strategy, the energy coupling link includes: cogeneration unit, ground source heat pump, electric boiler and gas boiler; the constraint model expression of the energy coupling link is: Wherein, and are the electrical, gas, and heat powers of unit j; and are the real-time adjustment amounts of the electrical, gas, and heat powers of unit j; is the start-stop state of the CHP unit; and H j are the upper and lower limits of the unit output; and ΔH j are the upper and lower limits of the unit ramp rate; is the CHP gas-electricity conversion efficiency; is the CHP gas-heat conversion efficiency; is the HP electric-heat conversion coefficient; is the EB electric-heat conversion efficiency; is the GB gas-heat conversion efficiency; is the set of energy coupling devices; In the process of modeling the energy storage equipment in the park comprehensive energy system by setting the modeling strategy, the energy storage equipment includes: power storage equipment and heat storage equipment; the constraint model expression of the energy storage equipment is: In the formula, and are the charge-discharge power of the energy storage; and are the upper limits of the charge-discharge energy power of the energy storage; μ j is the self-discharge rate of the energy storage; and are the charge-discharge efficiency of the energy storage; W j is the capacity of the energy storage device; and SOC j are the upper and lower limits of the SOC of the energy storage; is the set of energy storage devices; In the process of modeling the busbar structure in the park integrated energy system by setting the modeling strategy, the constraint model expression of the busbar structure is: Wherein, is the electricity purchase volume in the PIES day-ahead market; is the electricity purchase volume in the PIES intraday market; is the electricity purchase volume in the PIES real-time market; is the heat purchase volume of PIES; is the gas purchase volume of PIES.

7. A multi-time-scale scheduling device for a risk-averse integrated energy system in a park according to claim 6, characterized in that, In the scenario generation module, in the process of generating the day-ahead scenario through the scenario generation strategy and the scenario reduction strategy, a sample of correlation is generated through the scenario generation strategy according to the distribution information of photovoltaic output, electric load and electricity price; based on the sample of correlation, a typical scenario is clustered through the scenario reduction strategy; the scenario generation strategy is a scenario generation strategy based on CD-LHS; The scene reduction strategy is a scene reduction strategy based on GMM clustering.

8. A multi-time scale scheduling device for a risk-averse integrated energy system in a park according to claim 7, characterized in that In the optimization model construction module, the expression of the three-stage stochastic optimization model taking into account CVaR is: wherein is the day-ahead scheduling cost; is the intra-day scheduling cost; is the real-time scheduling cost; is the scenario in the day-ahead scenario set; is the day-ahead scenario set; is the time period set of the day-ahead scheduling model; is the expected value under the day-ahead scenario set; is the CVaR under the day-ahead scenario set; Γ is the risk preference coefficient; The expression of the intraday two-stage stochastic rolling optimization model is: Wherein, is the set of time periods of the intra-day scheduling model; is the intra-day scenario set; is the expected value under the intra-day scenario set; The expression of the real-time rolling optimization model is: In the formula, is the set of time periods of the intraday scheduling model.

9. A multi-time scale scheduling device for a risk-averse integrated energy system in a park according to claim 8, characterized in that, In the scheduling strategy acquisition module, the expression of the day-ahead scheduling strategy is: In the formula, is the stage dispatching strategy for the current day; is the electricity purchase quantity in the PIES day-ahead market; is the charging power of the ES; is the discharging power of the ES; is the energy charging power of the HS; is the energy discharging power of the HS; is the start-stop state of the CHP; The expression of the intraday stage scheduling strategy is: In the formula, is the two-stage scheduling strategy within a day; is the electricity purchase quantity in the PIES intraday market; is the electric power of the CHP; is the electric power of the HP; is the electric power of the EB; is the gas power of the GB; The expression of the real-time stage scheduling strategy is: In the formula, is the real-time rolling scheduling strategy; is the real-time market electricity purchase quantity of PIES; is the gas purchase quantity of PIES; is the heat purchase quantity of PIES; is the PV accommodation power; is the CHP power adjustment value; is the HP power adjustment value; is the EB power adjustment value; is the GB power adjustment value.

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