A low-carbon dispatch method for integrated energy systems considering carbon price uncertainty

Abstract

The present invention discloses a low-carbon dispatching method for an integrated energy system considering the uncertainty of carbon prices, and belongs to the field of integrated energy dispatching. The method first analyzes the calculation method of carbon trading costs under carbon price uncertainty; secondly, the carbon trading costs after considering the uncertainty of carbon prices are included in the day-ahead dispatching model of the electric-thermal integrated energy system; then, the established dispatching model is converted into a mixed integer linear dispatching model by using the smoothing method; finally, the day-ahead forecasting data of residential electric load and wind power are imported into the mixed integer linear dispatching model, and the model is solved by calling CPLEX using YALMIP to obtain the low-carbon dispatching result of the integrated energy system.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy scheduling, and in particular to a low-carbon scheduling method for an integrated energy system taking into account the uncertainty of carbon prices. Background Art

[0002] Integrated energy system (IES) has become one of the key technologies for solving energy problems around the world. Although the access technologies of new energy sources such as wind power and photovoltaic power to the power grid have made great progress, their intermittency and determinism still affect their access to the large power grid. The optimization research of IES has gradually shifted from traditional economic dispatch to low-carbon economic dispatch. In low-carbon economic dispatch, the cost of carbon trading must be considered. In the establishment of a low-carbon economic dispatch model, the price of carbon is usually set to a fixed value, while the carbon price in the actual carbon trading market fluctuates with the market, which leads to the fact that the established low-carbon economic dispatch model cannot truly reflect the market process of carbon trading. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention proposes a low-carbon scheduling method for an integrated energy system taking into account the uncertainty of carbon prices.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A low-carbon scheduling method for an integrated energy system comprises the following steps:

[0006] Step S1: Use the conditional value at risk (CvaR) method to calculate the carbon trading cost after considering the uncertainty of carbon price;

[0007] Step S2: Incorporating the carbon trading cost after considering the uncertainty of carbon price into the day-ahead dispatch model of the electric-thermal integrated energy system;

[0008] Step S3: using a smoothing method to transform the scheduling model established in step S2 into a mixed integer linear scheduling model;

[0009] Step S4: Import the day-ahead forecast data of residential power load and wind power into the mixed integer linear scheduling model, and use the optimization tool to solve the model to obtain the day-ahead scheduling result of the integrated energy system.

[0010] Optionally, the step S1 specifically includes the following steps: before considering the uncertainty of the carbon price, the carbon price is fixed to π, and the carbon price π′ after considering the uncertainty is:

[0011] π′=π+ξ

[0012] Where ξ is the carbon price prediction error, which obeys the normal distribution N(0,δ), and takes δ=0.1π. The probability density function of the carbon price prediction error is y(ξ);

[0013] CHP carbon emission initial quota C chp for:

[0014]

[0015] C chp is the initial carbon emission quota of the CHP unit, χ h represents the carbon emission quota coefficient per unit heat generated by the CHP unit, 0.385 kg / (kW·h), χ p,h The conversion parameter representing electrical power and thermal power, 6×10 3 kJ / kW, and are the electrical power and thermal power of the CHP unit respectively;

[0016] Calculate the actual carbon emissions C of the CHP unit chp,a :

[0017]

[0018] a 1 ,b 1 ,c 1 is the carbon emission calculation coefficient of the CHP unit.

[0019] The carbon trading cost after considering the uncertainty of carbon price can be expressed as:

[0020]

[0021] Calculate the carbon trading cost after considering the uncertainty of carbon price using the conditional value at risk (CvaR) method for:

[0022]

[0023] Among them, β is the confidence level, z is the auxiliary variable, and E means to find the expected value.

[0024] Optionally, in step S3, the random variable ξ is subjected to Monte Carlo sampling to extract I samples, and an auxiliary variable s=[s 1 ,...s I ], finally, the carbon trading cost considering the uncertainty of carbon price can be linearized as

[0025]

[0026] Optionally, the constraints of the model in step S2 include power grid constraints and heating network constraints.

[0027] Optionally, the grid constraints include: power balance constraints, power purchase constraints from the main grid, CHP constraints, battery charging and discharging constraints, wind power output constraints, heat pump power constraints and carbon trading constraints due to carbon price uncertainty.

[0028] Optionally, the heat network constraints include: heat source node heat energy acquisition constraints, heat energy balance constraints, heat storage constraints, temperature drop and pipeline delay constraints.

[0029] A computer-readable storage medium stores computer instructions, which, when executed, can implement any of the scheduling methods described above.

[0030] Beneficial effects of the present invention:

[0031] The present invention establishes a low-carbon scheduling method for an integrated energy system that takes into account the uncertainty of carbon prices. The scheduling method takes into account the uncertainty of carbon prices in real carbon trading, can more realistically reflect the carbon trading process, and is conducive to the realization of low-carbon economic scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described below in conjunction with the accompanying drawings.

[0033] Figure 1 It is a structural diagram of the electric-thermal integrated energy system of the present invention;

[0034] Figure 2 The wind power and electric load forecast data and errors of the present invention;

[0035] Figure 3 This is the optimization result of the scheduling model of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] In some examples of the present invention, the day-ahead scheduling method of the integrated energy system, the evaluation method comprises the following steps:

[0038] Step S1: Use the conditional value at risk (CvaR) method to calculate the carbon trading cost after considering the uncertainty of carbon price;

[0039] 1) Before considering the uncertainty of carbon price, the carbon price is fixed to π, and it is assumed that the uncertain carbon price conforms to the normal distribution. Therefore, the carbon price π′ after considering the uncertainty is:

[0040] π′=π+ξ (1)

[0041] Where ξ is the carbon price prediction error, which obeys the normal distribution N(0,δ), and takes δ=0.1π. The probability density function of the carbon price prediction error is y(ξ).

[0042] 2) According to the carbon emission target, the initial carbon emission quota should be formulated first. For CHP, its initial carbon emission quota C chp for:

[0043]

[0044] C chp is the initial carbon emission quota of the CHP unit, χ h represents the carbon emission quota coefficient per unit heat generated by the CHP unit, 0.385 kg / (kW·h), χ p,h The conversion parameter representing electrical power and thermal power, 6×10 3 kJ / kW, and They are the electrical power and thermal power of the CHP unit respectively.

[0045] After the initial carbon emission quota is formulated, the actual carbon emission of each unit of the comprehensive energy system must be determined. For the CHP unit, its actual carbon emission C chp,a Usually expressed as follows:

[0046]

[0047] a 1 ,b 1 ,c 1 is the carbon emission calculation coefficient of the CHP unit.

[0048] 3) The carbon trading cost after considering the uncertainty of carbon price can be expressed as:

[0049]

[0050] 4) Calculate the carbon trading cost after considering the uncertainty of carbon price using the conditional value at risk (CvaR) method for:

[0051]

[0052] Among them, β is the confidence level, z is the auxiliary variable, and E means to find the expected value.

[0053] Step S2: Incorporate the carbon trading cost after considering the uncertainty of carbon price into the day-ahead dispatch model of the electric-thermal integrated energy system.

[0054] 1) The objective function of the dispatch model that takes into account the carbon trading cost with carbon price uncertainty is:

[0055]

[0056]

[0057] Where: F chp is the operating cost of the CHP unit; F grid is the net cost after exchanging electricity with the main grid; F m Operation and maintenance costs for CHP, heat pump, fan, battery, and thermal storage; is the carbon trading cost after considering the uncertainty of carbon price; P gas , For gas price and gas quantity; P t grid is the power and unit price of transactions with the main network; P t chp , P t hp , P t w , P t bt，chr , P t bt ，dis , They are the power of the combined heat and power generation unit, heat pump, fan, battery charging, battery discharging, heat storage charging, and heat storage releasing. It is the operating unit price of the combined heat and power generation unit, heat pump, fan, battery and heat storage.

[0058] 2) The model constraints that take into account carbon price uncertainty are divided into two types: power grid and heat network. Power grid constraints mainly include: power balance constraints, power purchase constraints from the main grid, CHP constraints, battery charging and discharging constraints, wind power output constraints, heat pump power constraints, and carbon trading constraints due to carbon price uncertainty. Among them, the carbon trading constraints due to carbon price uncertainty are shown in equations (1)-(5). Heat network constraints include: heat source node heat energy acquisition constraints, heat energy balance constraints, heat storage constraints, temperature drop and pipeline delay constraints, etc.

[0059] Step S3: transform the scheduling model established in step S2 into a mixed integer linear scheduling model by using piecewise linearization and smoothing methods;

[0060] Perform Monte Carlo sampling on the random variable ξ and extract I samples. At the same time, introduce the auxiliary variable s=[s 1 ,...s I ], finally, the carbon trading cost equation (5) considering the uncertainty of carbon price can be linearized as

[0061]

[0062] Step S4: Import the day-ahead forecast data of residential power load and wind power into the mixed integer linear scheduling model, and use YALMIP to call CPLEX to solve the model to obtain the day-ahead scheduling result of the integrated energy system.

[0063] Example

[0064] The present invention takes a small-scale IES as an example to illustrate the specific implementation method.

[0065] Take a small-scale IES as an example. Figure 1 The proposed dispatch model is verified as shown in Figure 2. The day-ahead forecast data of residential power load and wind power are shown in Figure 2. Figure 2 As shown in Figure 1, the day-ahead forecast data of residential power load and wind power are imported into the established dispatch model, and YALMIP is used to call the CPLEX solver for solution to obtain the main grid power purchase, wind power, CHP unit output, and battery storage and release power as shown in Figure 1. Figure 3 The detailed composition of the optimal operating cost is shown in Table 1.

[0066] Table 1 Detailed composition of optimal operating cost (yuan)

[0067]

[0068] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0069] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A low-carbon scheduling method for an integrated energy system, characterized in that: The following steps are involved: Step S1: Use the conditional value at risk (CvaR) method to calculate the carbon trading cost after considering the uncertainty of carbon price; Step S2: Incorporating the carbon trading cost after considering the uncertainty of carbon price into the day-ahead dispatch model of the electric-thermal integrated energy system; Step S3: using a smoothing method to transform the scheduling model established in step S2 into a mixed integer linear scheduling model; Step S4: importing the day-ahead forecast data of residential power load and wind power into the mixed integer linear dispatch model, and solving the model using an optimization tool to obtain the day-ahead dispatch result of the integrated energy system; Wherein, step S1 includes the following steps: 1) before considering the uncertainty of carbon price, the carbon price is fixed to π, assuming that the uncertain carbon price conforms to the normal distribution; the carbon price π′ after considering the uncertainty is: π′=π+ξ (1) Where ξ is the carbon price prediction error, which obeys the normal distribution N(0,δ), and takes δ=0.1π. The probability density function of the carbon price prediction error is y(ξ); 2) According to the carbon emission target, the initial carbon emission quota should be formulated first. For CHP, its initial carbon emission quota C chp for: C chp is the initial carbon emission quota of the CHP unit, χ h represents the carbon emission quota coefficient per unit heat generated by the CHP unit, which is 0.385 kg / (kW·h), p,h The conversion parameter between electrical power and thermal power is 6×10 3 kJ / kW, P t chp and are the electrical power and thermal power of the CHP unit respectively; For CHP units, the actual carbon emissions C chp,a It is expressed by the following formula: a1, b1, c1 are the carbon emission calculation coefficients of the CHP unit; 3) The carbon trading cost after considering the uncertainty of carbon price is expressed as: 4) Calculate the carbon trading cost after considering the uncertainty of carbon price using the conditional value at risk (CvaR) method for: Among them, β is the confidence level, z is the auxiliary variable, and E represents the expected value; Step S2 includes the following steps: incorporating the carbon trading cost after considering the uncertainty of carbon price into the day-ahead dispatch model of the electric-thermal integrated energy system; 1) The objective function of the dispatch model that takes into account the carbon trading cost with carbon price uncertainty is: Where: F chp is the operating cost of the CHP unit; F grid is the net cost after exchanging electricity with the main grid; F m Operation and maintenance costs for CHP, heat pump, fan, battery, and thermal storage; is the carbon trading cost after considering the uncertainty of carbon price; p gas , For gas price and gas quantity; P t grid is the power and unit price of transactions with the main network; P t chp , P t hp , P t w , P t bt，chr , P t bt，dis , They are the power of the combined heat and power generation unit, heat pump, fan, battery charging, battery discharging, heat storage charging, and heat storage releasing. The operating unit price of the combined heat and power generation unit, heat pump, fan, battery, and heat storage; The step S3 comprises the following steps: performing Monte Carlo sampling on the random variable ξ, extracting I samples, and introducing an auxiliary variable s=[s1,...s I ], the carbon trading cost equation (5) considering the uncertainty of carbon price is linearized as 2. The low-carbon scheduling method for an integrated energy system according to claim 1, characterized in that: The constraints of the model in step S2 include power grid constraints and heating network constraints.

3. The low-carbon scheduling method for an integrated energy system according to claim 2, characterized in that: Grid constraints include: power balance constraints, power purchase constraints from the main grid, CHP constraints, battery charging and discharging constraints, wind power output constraints, heat pump power constraints and carbon trading constraints due to carbon price uncertainty.

4. The low-carbon scheduling method for an integrated energy system according to claim 2, characterized in that: The heat network constraints include: heat source node heat energy acquisition constraints, heat energy balance constraints, heat storage constraints, temperature drop and pipeline delay constraints.

5. The low-carbon scheduling method for an integrated energy system according to claim 1, characterized in that: The optimization tool is CPLEX called by YALMIP.

6. A computer-readable storage medium storing computer instructions, characterized in that: When the instruction is executed, the scheduling method described in any one of claims 1 to 5 can be implemented.

Images