Comprehensive energy system low-carbon economic dispatching method considering source load uncertainty and multi-type energy storage

Abstract

The invention discloses an integrated energy system low-carbon economic dispatching method considering source load uncertainty and multi-type energy storage. In order to restrain the carbon emission of the integrated energy system and cope with the uncertainty problem of renewable energy sources and loads in the system, firstly, wind and light output correlation constraints are introduced to an energy supply side, and multi-type demand responses of cold, heat and electricity and multi-type energy storage resources on the load side are combined to form a wind and light output correlation constraint; and constructing a deterministic economic low-carbon scheduling model by taking the minimum operation cost and the minimum carbon transaction cost of the system as targets. Secondly, aiming at the uncertain factors at the source side and the load side of the system, modeling multiple uncertain factors by adopting an improved information gap decision-making method, and analyzing the uncertain parameters in the comprehensive energy system based on a robust model and an opportunity model; an expected scheduling target is made according to the scheduling plan, the fluctuation amplitude of the corresponding equivalent uncertain parameters under the expected cost is calculated, and the uncertainty and the scheduling cost are effectively combined.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy optimization scheduling, and in particular to a low-carbon economic scheduling method for an integrated energy system taking into account source-load uncertainty and multiple types of energy storage. Background Art

[0002] Integrated Energy System (IES) can integrate multiple resources such as electricity, gas, and heat from traditional energy systems, taking into account the low-carbon, economical, and safe nature of energy systems, and has become a research hotspot in the current energy field. Clean energy systems represented by wind power and photovoltaics can provide sufficient electricity for integrated energy systems, but their own uncertainties will also have a negative impact on the economic and stable operation of integrated energy systems. In addition, the reasonable scheduling of various energy storage devices in the integrated energy system can achieve the effect of peak shaving and valley filling. However, the existing integrated energy system lacks in-depth research on carbon emissions and the uncertainty of renewable energy and load within the integrated energy system.

[0003] To this end, we designed a low-carbon economic dispatch method for an integrated energy system that takes into account source-load uncertainty and multiple types of energy storage to solve the above problems. Summary of the invention

[0004] In order to constrain the carbon emissions of the integrated energy system and deal with the uncertainty of renewable energy and load within the integrated energy system, the present invention proposes a low-carbon economic dispatching strategy for the integrated energy system based on the improved information gap decision theory, which takes into account source-load uncertainty and contains various types of energy storage resources, and provides more accurate dynamic operation guidance for the multi-source integrated energy system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The low-carbon economic dispatch method of the integrated energy system considering source-load uncertainty and multi-type energy storage includes the following steps:

[0007] Step S1: introduce the correlation constraint of wind and solar output on the energy supply side, and combine the demand response characteristics of multiple types of resources such as electricity, cooling, and heat and energy storage resources on the load side to establish the electricity, cooling, and heat load and energy storage model of the integrated energy system;

[0008] Step S2: construct a deterministic economic low-carbon dispatch model with the goal of minimizing the operating cost of the integrated energy system and the lowest carbon trading cost;

[0009] In step S3, the uncertain factors of wind, light, electricity and gas at both ends of the source and load are modeled based on the improved information gap decision method;

[0010] In step S4, a robust model and an opportunity model are established and solved to achieve an effective combination of uncertainty and scheduling cost.

[0011] Furthermore, in step S1, among the multiple types of resources on the load side, namely electricity, cooling and heat, the output of the electric load is realized through the synergy of the power grid, photovoltaic, wind power new energy power generation system and gas turbine, the cooling load is realized by the electric refrigerator and absorption refrigerator, and the heat load is realized by the heat energy provided by the gas boiler;

[0012] Energy storage resources are realized by physical energy storage based on battery energy storage and air energy storage based on heat storage process.

[0013] Furthermore, in step S1, the electricity, cooling, heating load and energy storage model of the integrated energy system is established in the following manner:

[0014] The electric load is divided into rigid electric load and flexible electric load. The rigid electric load is the basic load P base , the flexible electric load is divided into the movable load P shift , transferable load P tran and the load that can be reduced P cut ;

[0015] The cooling load model adopts the equivalent thermal parameter model as follows:

[0016]

[0017] Where, T t，c-in and T t，c-out represent the indoor and outdoor temperatures for cooling, Q t，c represents the total cooling power of the integrated energy system, R and C represent the equivalent thermal resistance and equivalent heat capacity of the room, respectively, T t-1,c-in represents the indoor temperature of the cooling room at the last moment, and Δt represents the time interval;

[0018] The heat load model is described as follows using an autoregressive moving average time series:

[0019]

[0020] Where, T t，h Indicates the return water temperature of the heating network, T t，h-in represents the indoor temperature of the heating room, α j ,β j ,γ j ,θ1, κ1 are all physical parameters related to the heating system, order J is the thermal inertia of the heating system, T t,j,in represents the return water temperature at time tj, T t,j,g represents the water supply temperature at time tj, T t,j,out represents the outdoor temperature at time tj, Tt-1,h-in Indicates the indoor temperature at the last moment, T t-1,g Indicates the water supply temperature at the last moment, T t-1,out Indicates the outdoor temperature at the last moment;

[0021] Heating capacity of the integrated energy system H t，h Expressed as: H t，h =η(T t，g -T t，h )

[0022] Among them, η is the relationship coefficient between the heat supply and the supply and return water temperature of the heating network;

[0023] The air energy storage of the energy storage model makes full use of the thermal inertia and heat storage characteristics of air and satisfies the basic laws of thermodynamics. Its expression is:

[0024]

[0025] Where P VES is the energy storage charging and discharging power, C air and ρ air are the specific heat capacity and air density of air, V is the air volume capacity, VES t and VES max are the capacity and maximum capacity of air energy storage at time t, is the indoor temperature, is the indoor temperature at the previous moment, Δt is the time interval between obtaining the indoor temperature at this moment and the previous moment, T in,max and T in,min They are the upper and lower limits of the acceptable indoor temperature respectively;

[0026] The energy storage capacity and charging and discharging frequency of the physical energy storage model before and after charging and discharging are subject to the following constraints:

[0027]

[0028] Where P ES,t is the capacity state of the physical energy storage, and are the charging and discharging efficiency of physical energy storage, and are the input and output electrical power of the physical energy storage, and are the minimum and maximum charge states of the physical energy storage, respectively. and are the charge and discharge symbols of physical energy storage, P ES,t-1 is the capacity state of the physical energy storage at the previous moment, Δt ES The time interval between charging and discharging of physical energy storage.

[0029] Furthermore, in step S2, the objective function of constructing a deterministic economic low-carbon dispatch model is:

[0030]

[0031] In the formula, F IES is the total operating cost of the integrated energy system, F buy is the real-time energy purchase cost of the integrated energy system, F dev Equipment operation and maintenance costs, is the carbon trading cost of the comprehensive energy system, F pay Compensation benefits for users participating in demand response;

[0032] The carbon emissions of the integrated energy system are generated by purchased electricity and natural gas. A tiered carbon trading mechanism is used to limit carbon emissions. The carbon trading cost expression is:

[0033]

[0034] In the formula, is the initial carbon emission quota, is the carbon trading base price, l is the length of the carbon emission interval, and υ is the price growth rate.

[0035] Furthermore, the constraints of the deterministic economic low-carbon dispatch model include the following:

[0036] 1) Energy storage resource constraints,

[0037] The air energy storage and physical energy storage of energy storage resources have upper and lower limits on their capacity and charging and discharging power;

[0038] 2) Cooling and heating constraints,

[0039] In order to meet the comfort requirements and temperature upper and lower limits in the cooling and heating systems of the integrated energy system, the following constraints are made:

[0040]

[0041] In the formula, To quantify the thermal comfort of the integrated energy system, the average number of votes is used to predict the range of indicators, T t,h-in is the indoor temperature for heating, T t,h-in,min and T t,h-in,max are the lower and upper limits of the indoor temperature for heating, T t,c-in is the indoor temperature for cooling, T c-in,min and T c-in,max are the lower and upper limits of the indoor temperature for cooling, respectively, PMV To predict the average voting number indicator;

[0042] 3) Power balance constraints,

[0043] The electric power balance constraint is:

[0044]

[0045] Where P load Represents the total electrical load of the integrated energy system, P buy is the purchased power of the comprehensive energy system, P WT Represents wind power, P PV Represents photovoltaic power, P ES Represents the energy storage discharge power, P GT Represents the power generated by the gas turbine, P EC Indicates the power consumed by the electric refrigerator, P P2G Indicates the power consumption of the power-to-gas device; P base is the basic load of the rigid electric load, P shift , P tran and P cut They are the translatable load, transferable load and curtailable load of the flexible electric load;

[0046] The thermal power balance constraint is:

[0047] H load +H AC =H GB +H VES

[0048] In the formula, H VES The heat power released for air energy storage, H GB and H AC are the thermal power generated or consumed by the gas boiler and absorption chiller, respectively, H load It represents the total heat load of the integrated energy system;

[0049] The cooling power balance constraint is:

[0050] Q load =Q AC +Q EC

[0051] In the formula, Q AC and Q EC are the cooling power generated by the absorption refrigerator and the electric refrigerator, Q load It represents the total cooling load of the integrated energy system;

[0052] The gas power balance constraint is:

[0053] G load +G GT +G GB =G buy +G P2G

[0054] In the formula, G buy is the gas purchasing power of the comprehensive energy system, G GT , G GB and G P2G are the gas power consumed by the gas turbine, gas boiler and power-to-gas device, G load Represents the total gas load of the integrated energy system.

[0055] Furthermore, in step S3, the uncertainty factors of wind, light, electricity and gas at both ends of the source and load are modeled based on the improved information gap decision method, including the following contents:

[0056] Use the envelope boundary interval to limit the range of wind, solar, and load power fluctuations:

[0057]

[0058] Among them, μ and are the fluctuation range and forecast value of new energy output and load demand, respectively. is the deviation range of the uncertainty adjustable parameter, Y represents the output of new energy or load demand in scheduling;

[0059] The weighted summation method is used to unify the uncertainties of wind, light, electricity and gas, and the weights of each uncertain parameter are assigned to obtain the fluctuation range of the equivalent uncertainty parameter. The modeling expression formula is as follows:

[0060]

[0061] In the formula, μ1, μ2, …, μ n is the deviation coefficient of the uncertain parameter, k1, k2, ..., k n is the weight of the fluctuation range of each uncertain parameter.

[0062] Furthermore, in step S4, the robust model and the chance model are established and the solution is as follows:

[0063] For the deterministic economic low-carbon dispatch model, the following expression is constructed in vector form:

[0064]

[0065] In the formula, f(X,Y), E(X,Y), and I(X,Y) represent the objective function, equality constraint, and inequality constraint, respectively, and X represents the decision variable;

[0066] The robust model and the chance model are expressed as follows:

[0067]

[0068] In the formula, δ m and ε m Denote the robust bias factor and the chance bias factor, respectively, F RM and F OM is the corresponding dispatch target cost, F0 is the benchmark value, that is, the optimal dispatch cost when the uncertain parameters take the predicted value, Represents any factor containing uncertainty;

[0069] The solution to the low-carbon economic dispatch of the integrated energy system is divided into two stages. The first stage is to obtain the optimal operating cost through a deterministic economic low-carbon dispatch model. The second stage is to consider the improved information gap decision-making method, based on the robust model and the opportunity model, to analyze the uncertainty parameters in the integrated energy system, and formulate the expected dispatching target according to the dispatching plan, calculate the fluctuation range of the corresponding equivalent uncertain parameters under the expected cost, and realize the effective combination of uncertainty and dispatching cost.

[0070] Compared with the prior art, the beneficial effects of the present invention are as follows: in order to constrain the carbon emissions of the integrated energy system and deal with the uncertainty of renewable energy and load within the integrated energy system, the present invention first introduces the correlation constraint of wind and solar output on the energy supply side, and combines the multi-type demand response of cold, heat and electricity on the load side and multi-type energy storage resources, and builds a deterministic economic low-carbon scheduling model with the minimum operating cost and the lowest carbon trading cost of the integrated energy system as the goal. Secondly, in view of the uncertainties at both ends of the source side and the load side of the integrated energy system, a variety of uncertainties are modeled based on an improved information gap decision method, a robust model and an opportunity model are established, and the expected scheduling target is formulated according to the scheduling plan to calculate the fluctuation range of the corresponding equivalent uncertain parameters under the expected cost, so as to achieve an effective combination of uncertainty and scheduling cost, explore the optimal scheduling strategy of the integrated energy system under different risk preference conditions, and provide guidance for the dynamic operation of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A flow chart of a low-carbon economic dispatching method for an integrated energy system considering source-load uncertainty and multiple types of energy storage proposed in the present invention. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0073] In this embodiment, a low-carbon economic dispatch method for an integrated energy system considering source-load uncertainty and multi-type energy storage is proposed. Figure 1As shown in the figure, the method is mainly divided into two stages and four steps. Stage one is deterministic scheduling optimization, and stage two is scheduling optimization based on information gap decision method. Each step mainly includes the following contents:

[0074] In step S1, the wind and solar power output correlation constraints are introduced on the energy supply side, and the electricity, cooling, heat load and energy storage model of the Integrated Energy System (IES) is established by combining the demand response characteristics of multiple types of resources such as electricity, cooling, and heat and energy storage resources on the load side.

[0075] In step S2, a deterministic economic low-carbon scheduling model is constructed with the goal of minimizing the operating cost of the integrated energy system and the lowest carbon trading cost.

[0076] In step S3, the uncertain factors of wind, light, electricity and gas at both ends of the source and load are modeled based on the improved information gap decision method (IGDT).

[0077] In step S4, a robust model and an opportunity model are established and solved to obtain the expected cost of the integrated energy system and the fluctuation range of equivalent uncertain parameters under different risk preference conditions, thus achieving an effective combination of uncertainty and scheduling cost.

[0078] A large number of new controllable loads are connected to the energy distribution network. Through reasonable regulation, they can achieve the effect of cross-time load transfer similar to energy storage. At the same time, indoor air energy storage in the integrated energy system park can also play a role in load regulation. These, together with physical energy storage devices, constitute multiple types of energy storage resources in the integrated energy system.

[0079] Specifically, in step S1 of this embodiment, among the multiple types of resources of electricity, cold and heat on the load side of the integrated energy system, the output of electric load is achieved through the synergy of new energy power generation systems such as grid electricity, photovoltaic (PV), wind power (WT) and gas turbine (GT), the cold load is achieved by electric refrigerators (EC) and absorption refrigerators (AC), the gas load is converted by gas grid, power to gas (P2G) device, etc., and the heat load is provided by the heat energy of gas boiler (GB).

[0080] Energy storage resources are realized by physical energy storage (Energy Storage, ES) based on battery energy storage and air energy storage based on the heat storage process. The heat storage process is mainly realized by indoor air energy storage in the park.

[0081] The following methods are used to establish the electricity, cooling, heating load and energy storage model of the integrated energy system:

[0082] The electric load in this embodiment is divided into rigid electric load and flexible electric load. The rigid electric load is the basic load P base , the flexible electric load is divided into the movable load P shift , transferable load P tran and the load that can be reduced P cut .

[0083] The cooling load model adopts the equivalent thermal parameter model as follows:

[0084]

[0085] Where, T t，c-in and T t，c-out represent the indoor and outdoor temperatures for cooling, Q t，c represents the total cooling power of the integrated energy system, R and C represent the equivalent thermal resistance and equivalent heat capacity of the room, respectively, T t-1,c-in represents the indoor temperature of the cooling room at the last moment, and Δt represents the time interval;

[0086] The heat load model is described as follows using an autoregressive moving average time series:

[0087]

[0088] Where, T t，h Indicates the return water temperature of the heating network, T t，h-in represents the indoor temperature of the heating room, α j ,β j ,γ j ,θ1, κ1 are all physical parameters related to the heating system, order J is the thermal inertia of the heating system, T t,j,in represents the return water temperature at time tj, T t,j,g represents the water supply temperature at time tj, T t,j,out represents the outdoor temperature at time tj, T t-1,h-in Indicates the indoor temperature at the last moment, T t-1,g Indicates the water supply temperature at the last moment, T t-1,out Indicates the outdoor temperature at the last moment;

[0089] Heating capacity of the integrated energy system H t，h Expressed as: H t，h＝ η(T t，g -T t，h )

[0090] Among them, η is the relationship coefficient between the heat supply and the supply and return water temperature of the heating network, and its value is closely related to the water flow rate of the heating network.

[0091] Take the predicted mean vote (PMV) indicator λ PMV , the thermal comfort of the integrated energy system is quantified, so that the heat load can be converted from a fixed value to a flexible value. In the integrated energy system, cooling is mostly used for cold storage, and only the upper and lower temperature limits need to be met.

[0092] The air energy storage of the energy storage model makes full use of the thermal inertia and heat storage characteristics of air and satisfies the basic laws of thermodynamics. Its model expression is:

[0093]

[0094] Where P VES is the energy storage charging and discharging power, C air and ρ air are the specific heat capacity and air density of air, V is the air volume capacity, VES t and VES max are the capacity and maximum capacity of air energy storage at time t, is the indoor temperature, is the indoor temperature at the previous moment, Δt is the time interval between obtaining the indoor temperature at this moment and the previous moment, T in,max and T in,min They are the upper and lower limits of the acceptable indoor temperature respectively;

[0095] The energy storage capacity and charging and discharging frequency of the physical energy storage model before and after charging and discharging are subject to the following constraints:

[0096]

[0097] Where P ES,t is the capacity state of the physical energy storage, and are the charging and discharging efficiency of physical energy storage, and are the input and output electrical power of the physical energy storage, and are the minimum and maximum charge states of the physical energy storage, respectively. and are the charge and discharge symbols of physical energy storage, P ES,t-1 is the capacity state of the physical energy storage at the previous moment, Δt ES The time interval between charging and discharging of physical energy storage.

[0098] Wind power and photovoltaic power have strong statistical laws and correlations. The Copula function can be used to link the joint distribution of random variables and their respective marginal distributions to reflect the correlation characteristics of the joint distribution of wind and photovoltaic output. Wind power and photovoltaic output in the same area have a certain correlation, so the Copula function is used to simulate this correlation, making the final dispatch result more realistic. The specific implementation steps are as follows:

[0099] 1) Based on several days of historical wind and solar data, a non-parametric kernel density estimation method is used to select a Gaussian function to fit the probability distribution function of wind power and photovoltaic output. and

[0100] 2) On this basis, the cumulative distribution function of wind power and photovoltaic power is solved, and the Frank-Copula function is used to describe the joint distribution of wind and photovoltaic power to generate joint distribution samples:

[0101]

[0102] In the formula, is the joint distribution function of wind power and photovoltaic power, and are the cumulative distribution numbers of wind power and photovoltaic power respectively; K represents the Copula connection function.

[0103] in,

[0104] u t and v t Respectively represent the above and χ t The relevant parameters of this function.

[0105] 3) Use the cubic spline interpolation method to sample the joint distribution function of each time period and calculate the wind and solar power output corresponding to each time period;

[0106] First, the cubic spline interpolation method is used to obtain the cubic spline function in the full cumulative probability range:

[0107]

[0108] In the formula, and Respectively and The inverse function of d 、b d 、c d , l d and a' d , b' d 、c'd , l' d Respectively and They represent the relevant parameters of the inverse function, u and u 2 、u 3 Respectively and its quadratic and cubic powers; v, v 2 、v 3 Respectively and its quadratic and cubic powers; F is the total number of samples, let f be the number of sampling points of the cumulative distribution function of wind and solar output, f = 1, 2, ..., F;

[0109] Will and Substituting into their respective cubic spline polynomials, we can obtain the wind power and photovoltaic output curves for each time period.

[0110] 4) On this basis, a clustering algorithm is used to reduce the wind power and photovoltaic output scenarios, and the occurrence probability of each scenario is estimated to better describe and simulate the changing characteristics of wind and photovoltaic scenarios.

[0111] The above 1)-4) parts complete the scenario generation and reduction considering the correlation between wind and solar power to obtain typical wind and solar power output scenarios, providing a wind and solar power data basis for subsequent scheduling.

[0112] In step S2, the objective function of constructing a deterministic economic low-carbon dispatch model with the minimum operating cost of the integrated energy system and the lowest carbon trading cost as the goal is:

[0113]

[0114] In the formula, F IES is the total operating cost of the integrated energy system, F buy is the real-time energy purchase cost of the integrated energy system, F dev Equipment operation and maintenance costs, is the carbon trading cost of the comprehensive energy system, F pay Compensation benefits for users participating in demand response;

[0115] The carbon emissions of the integrated energy system are generated by purchased electricity and natural gas. A tiered carbon trading mechanism is used to limit carbon emissions. The carbon trading cost expression is:

[0116]

[0117] In the formula, is the initial carbon emission quota, is the carbon trading base price, l is the length of the carbon emission interval, and υ is the price growth rate.

[0118] The constraints of the deterministic economic low-carbon dispatch model include the following:

[0119] 1) Energy storage resource constraints,

[0120] The air energy storage and physical energy storage of energy storage resources have upper and lower limits on their capacity and charging and discharging power;

[0121] 2) Cooling and heating constraints,

[0122] In order to meet the comfort requirements and temperature upper and lower limits in the cooling and heating systems of the integrated energy system, the following constraints are made:

[0123]

[0124] In the formula, To quantify the thermal comfort of the integrated energy system, the average number of votes is used to predict the range of indicators, T t,h-in is the indoor temperature for heating, T t,h-in,min and T t,h-in,max are the lower and upper limits of the heating indoor temperature, T t,c-in is the indoor temperature for cooling, T c-in,min and T c-in,max are the lower and upper limits of the indoor temperature for cooling, respectively, PMV To predict the average voting number indicator;

[0125] 3) Power balance constraints,

[0126] The electric power balance constraint is:

[0127]

[0128] Where P load Represents the total electrical load of the integrated energy system, P buy is the purchased power of the comprehensive energy system, P WT Represents wind power, P PV Represents photovoltaic power, P ES Represents the energy storage discharge power, P GT Represents the power generated by the gas turbine, P EC Indicates the power consumed by the electric refrigerator, P P2G Indicates the power consumption of the power-to-gas device; P base is the basic load of the rigid electric load, P shift , P tran and P cut They are the translatable load, transferable load and curtailable load of the flexible electric load;

[0129] The thermal power balance constraint is:

[0130] H load +HAC =H GB +H VES

[0131] In the formula, H VES The heat power released for air energy storage, H GB and H AC are the thermal power generated or consumed by the gas boiler and absorption chiller, respectively, H load It represents the total heat load of the integrated energy system;

[0132] The cooling power balance constraint is:

[0133] Q load =Q AC +Q EC

[0134] In the formula, Q AC and Q EC are the cooling power generated by the absorption refrigerator and the electric refrigerator, Q load It represents the total cooling load of the integrated energy system;

[0135] The gas power balance constraint is:

[0136] G load +G GT +G GB =G buy +G P2G

[0137] In the formula, G buy is the gas purchasing power of the comprehensive energy system, G GT , G GB and G P2G are the gas power consumed by the gas turbine, gas boiler and power-to-gas device, G load Represents the total gas load of the integrated energy system.

[0138] In step S3, the uncertainty factors of wind, light, electricity and gas at both ends of the source and load are modeled based on the improved information gap decision method, including the following:

[0139] Use the envelope boundary interval to limit the range of wind, solar, and load power fluctuations:

[0140]

[0141] Among them, μ and are the fluctuation range (uncertainty) and predicted value of new energy output and load demand, respectively. is the deviation range of the uncertainty adjustable parameter, Y represents the output of new energy or load demand in scheduling;

[0142] The traditional IGDT method is only applicable to the fluctuation of single-factor uncertain parameters, so it is necessary to improve the traditional IGDT method. The weighted summation method is used to unify the uncertainties of wind, light, electricity and gas, and the weights of each uncertain parameter are assigned to obtain the fluctuation amplitude of the equivalent uncertain parameter. The modeling expression formula is as follows:

[0143]

[0144] In the formula, μ1, μ2, …, μ n is the deviation coefficient of the uncertain parameter, k1, k2, ..., k n is the weight of the fluctuation range of each uncertain parameter. μ is the fluctuation range of the equivalent uncertain parameter, that is, the fluctuation range of the new energy output and load demand.

[0145] Considering that the changes in uncertainty factors and the risk attitude of decision makers will affect the final cost and dispatch strategy of the system, the dispatch of integrated energy systems under uncertain conditions is divided into two categories: robustness model (RM) and opportunity model (OM). The RM strategy starts from the perspective of risk aversion. In order to reduce the probability of model assessment risk, it means that a higher risk decision information cost needs to be borne. The OM strategy believes that the uncertainty of new energy and load can make the dispatch develop in an economically favorable direction and minimize the risk brought by the pursuit of uncertainty.

[0146] In step S4, the robust model and the chance model are established and the solution is as follows:

[0147] For the deterministic economic low-carbon dispatch model, the following expression is constructed in vector form:

[0148]

[0149] In the formula, f(X,Y), E(X,Y), and I(X,Y) represent the objective function, equality constraint, and inequality constraint, respectively, and X represents the decision variable;

[0150] The robust model and the chance model are expressed as follows:

[0151]

[0152] In the formula, δ m and ε m Denote the robust bias factor and the chance bias factor, respectively, F RM and F OM is the corresponding dispatch target cost, F0 is the benchmark value, that is, the optimal dispatch cost when the uncertain parameters take the predicted value, Represents any factor containing uncertainty;

[0153] The solution to the low-carbon economic dispatch of the integrated energy system is divided into two stages. The first stage is to obtain the optimal operating cost through a deterministic economic low-carbon dispatch model. The second stage is to consider the improved information gap decision-making method, based on the robust model and the opportunity model, to analyze the uncertainty parameters in the integrated energy system, and formulate the expected dispatching target according to the dispatching plan, calculate the fluctuation range of the corresponding equivalent uncertain parameters under the expected cost, and realize the effective combination of uncertainty and dispatching cost.

[0154] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A low-carbon economic dispatching method for an integrated energy system considering source-load uncertainty and multi-type energy storage, characterized in that: The following steps are involved: Step S1: introduce the correlation constraint of wind and solar output on the energy supply side, and combine the demand response characteristics of multiple types of resources such as electricity, cooling, and heat and energy storage resources on the load side to establish the electricity, cooling, and heat load and energy storage model of the integrated energy system; Step S2: construct a deterministic economic low-carbon dispatch model with the goal of minimizing the operating cost of the integrated energy system and the lowest carbon trading cost; In step S3, the uncertain factors of wind, light, electricity and gas at both ends of the source and load are modeled based on the improved information gap decision method; In step S4, a robust model and an opportunity model are established and solved to achieve an effective combination of uncertainty and scheduling cost.

2. The low-carbon economic dispatching method for an integrated energy system considering source-load uncertainty and multi-type energy storage according to claim 1 is characterized in that: Step S1: Among the multiple types of resources on the load side, such as electricity, cooling and heat, the output of electric load is realized through the synergy of power grid electricity, photovoltaic, wind power new energy power generation system and gas turbine, the cooling load is realized by electric refrigerator and absorption refrigerator, and the heat load is realized by the heat energy provided by gas boiler; Energy storage resources are realized by physical energy storage based on battery energy storage and air energy storage based on heat storage process.

3. The low-carbon economic dispatching method for an integrated energy system considering source-load uncertainty and multi-type energy storage according to claim 2 is characterized in that: In step S1, the electricity, cooling, heating load and energy storage model of the integrated energy system is established in the following way: The electric load is divided into rigid electric load and flexible electric load. The rigid electric load is the basic load P base , the flexible electric load is divided into the movable load P shift , transferable load P tran and the load that can be reduced P out ; The cooling load model adopts the equivalent thermal parameter model as follows: Where, T t，c-in and T t，c-out represent the indoor and outdoor temperatures for cooling, Q t，c represents the total cooling power of the integrated energy system, R and C represent the equivalent thermal resistance and equivalent heat capacity of the room, respectively, T t-1，c-in represents the indoor temperature of the cooling room at the last moment, and Δt represents the time interval; The heat load model is described as follows using an autoregressive moving average time series: Where, T t，h Indicates the return water temperature of the heating network, T t，h-in Indicates the indoor temperature of the heating room. are all physical parameters related to the heating system. Order J is the thermal inertia of the heating system, T t，j，in represents the return water temperature at time tj, T t，j，g represents the water supply temperature at time tj, T t，j，out represents the outdoor temperature at time tj, T t-1，h-in Indicates the indoor temperature at the last moment, T t-1，g Indicates the water supply temperature at the last moment, T t-1，out Indicates the outdoor temperature at the last moment; Heating capacity of the integrated energy system H t，h Expressed as: H t，h =η(T t，g -T t，h ) Among them, η is the relationship coefficient between the heat supply and the supply and return water temperature of the heating network; The air energy storage of the energy storage model makes full use of the thermal inertia and heat storage characteristics of air and satisfies the basic laws of thermodynamics. Its expression is: Where P VES is the energy storage charging and discharging power, C air and ρ air are the specific heat capacity and air density of air, V is the air volume capacity, VES t and VES max are the capacity and maximum capacity of air energy storage at time t, is the indoor temperature, is the indoor temperature at the previous moment, Δt is the time interval between obtaining the indoor temperature at this moment and the previous moment, T in,max and T in,min They are the upper and lower limits of the acceptable indoor temperature respectively; The energy storage capacity and charging and discharging frequency of the physical energy storage model before and after charging and discharging are subject to the following constraints: Where P ES,t is the capacity state of the physical energy storage, and are the charging and discharging efficiency of physical energy storage, and are the input and output electrical power of the physical energy storage, and are the minimum and maximum charge states of the physical energy storage, respectively. and are the charge and discharge symbols of physical energy storage, P ES,t-1 is the capacity state of the physical energy storage at the previous moment, Δt ES The time interval between charging and discharging of physical energy storage.

4. The low-carbon economic dispatching method for an integrated energy system considering source-load uncertainty and multi-type energy storage according to claim 1 is characterized in that: In step S2, the objective function of constructing a deterministic economic low-carbon dispatch model is: In the formula, F IES is the total operating cost of the integrated energy system, F buy is the real-time energy purchase cost of the integrated energy system, F dev is the equipment operation and maintenance cost, F co2 is the carbon trading cost of the comprehensive energy system, F pay Compensation benefits for users participating in demand response; The carbon emissions of the integrated energy system are generated by purchased electricity and natural gas. A tiered carbon trading mechanism is used to limit carbon emissions. The carbon trading cost expression is: In the formula, is the initial carbon emission quota, is the carbon trading base price, l is the length of the carbon emission interval, and υ is the price growth rate.

5. The low-carbon economic dispatching method for an integrated energy system considering source-load uncertainty and multi-type energy storage according to claim 4 is characterized in that: The constraints of the deterministic economic low-carbon dispatch model include the following: 1) Energy storage resource constraints, The air energy storage and physical energy storage of energy storage resources have upper and lower limits on their capacity and charging and discharging power; 2) Cooling and heating constraints, In order to meet the comfort requirements and temperature upper and lower limits in the cooling and heating systems of the integrated energy system, the following constraints are made: In the formula, To quantify the thermal comfort of the integrated energy system, the average number of votes is used to predict the range of indicators, T t,h-in is the indoor temperature for heating, T t,h-in,min and T t,h-in,max are the lower and upper limits of the heating indoor temperature, T t,c-in is the indoor temperature for cooling, T c-in,min and T c-in,max are the lower and upper limits of the indoor temperature for cooling, respectively, PMV To predict the average voting number indicator; 3) Power balance constraints, The electric power balance constraint is: Where P load Represents the total electrical load of the integrated energy system, P buy is the purchased power of the comprehensive energy system, P WT Represents wind power, P PV Represents photovoltaic power, P ES Represents the energy storage discharge power, P GT Represents the power generated by the gas turbine, P EC Indicates the power consumed by the electric refrigerator, P P2G Indicates the power consumption of the power-to-gas device; P base is the basic load of the rigid electric load, P shift , P tran and P cut They are the translatable load, transferable load and curtailable load of the flexible electric load; The thermal power balance constraint is: H load +H AC =H GB +H VES In the formula, H VES The heat power released for air energy storage, H GB and H AC are the thermal power generated or consumed by the gas boiler and absorption chiller, respectively, H load It represents the total heat load of the integrated energy system; The cooling power balance constraint is: Q load =Q AC +Q EC In the formula, Q AC and Q EC are the cooling power generated by the absorption refrigerator and the electric refrigerator, Q load It represents the total cooling load of the integrated energy system; The gas power balance constraint is: G load +G GT +G GB =G buy +G P2G In the formula, G buy is the gas purchasing power of the comprehensive energy system, G GT , G GB and G P2G are the gas power consumed by the gas turbine, gas boiler and power-to-gas device, G load Represents the total gas load of the integrated energy system.

6. The low-carbon economic dispatching method for an integrated energy system considering source-load uncertainty and multi-type energy storage according to claim 1 is characterized in that: In step S3, the uncertainty factors of wind, light, electricity and gas at both ends of the source and load are modeled based on the improved information gap decision method, including the following: Use the envelope boundary interval to limit the range of wind, solar, and load power fluctuations: Among them, μ and are the fluctuation range and forecast value of new energy output and load demand, respectively. is the deviation range of the uncertainty adjustable parameter, Y represents the output of new energy or load demand in scheduling; The weighted summation method is used to unify the uncertainties of wind, light, electricity and gas, and the weights of each uncertain parameter are assigned to obtain the fluctuation range of the equivalent uncertainty parameter. The modeling expression formula is as follows: In the formula, μ1, μ2, …, μ n is the deviation coefficient of the uncertain parameter, k1, k2, ..., k n is the weight of the fluctuation range of each uncertain parameter.

7. The low-carbon economic dispatching method for an integrated energy system considering source-load uncertainty and multi-type energy storage according to claim 1 is characterized in that: In step S4, the robust model and the chance model are established and the solution is as follows: For the deterministic economic low-carbon dispatch model, the following expression is constructed in vector form: In the formula, f(X,Y), E(X,Y), and I(X,Y) represent the objective function, equality constraint, and inequality constraint, respectively, and X represents the decision variable; The robust model and the chance model are expressed as follows: In the formula, δ m and ε m Denote the robust bias factor and the chance bias factor, respectively, F RM and F OM is the corresponding dispatch target cost, F0 is the benchmark value, that is, the optimal dispatch cost when the uncertain parameters take the predicted value, Represents any factor containing uncertainty; The solution to the low-carbon economic dispatch of the integrated energy system is divided into two stages. The first stage is to obtain the optimal operating cost through a deterministic economic low-carbon dispatch model. The second stage is to consider the improved information gap decision-making method, based on the robust model and the opportunity model, to analyze the uncertainty parameters in the integrated energy system, and formulate the expected dispatching target according to the dispatching plan, calculate the fluctuation range of the corresponding equivalent uncertain parameters under the expected cost, and realize the effective combination of uncertainty and dispatching cost.

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