Low-carbon scheduling model construction method and device of comprehensive energy industrial park considering stepped carbon-electricity linkage cost

Abstract

The invention discloses a method and a device for constructing a low-carbon dispatching model of an integrated energy industrial park considering stepped carbon-electricity linkage cost. The method comprises the following steps: establishing a wind power output model and a photovoltaic power generation output model; constructing a reward and punishment type stepped carbon cost model; based on the reward and punishment type stepped carbon cost model, introducing a carbon-electricity price linkage coefficient so as to construct a stepped carbon-electricity linkage cost model; according to the wind power output model, the photovoltaic power generation output model and the reward and punishment type stepped carbon cost model, establishing a carbon transaction model of each main body in the park to determine the carbon transaction income of each main body; determining a total revenue function in the industrial park based on the obtained carbon transaction revenue of each main body and the boiler revenue; and according to the obtained total revenue function, taking the maximization of the total revenue of the park as an optimization target, and combining constraint conditions to construct a low-carbon scheduling model of the comprehensive energy industrial park. By applying the model, a low-carbon scheduling result of the comprehensive energy industrial park can be obtained.

Description

Technical Field

[0001] The present invention relates to a method and device for constructing a low-carbon scheduling model of an integrated energy industrial park considering the stepped carbon-electricity linkage cost, and belongs to the field of low-carbon scheduling of integrated energy systems. Background Art

[0002] The rapid development of the current social economy comes at the great expense of the environment. In the face of the great pressure on the ecological environment, the goals of "carbon peak" and "carbon neutrality" have been put forward. At the same time, policy mechanisms such as carbon tax and Carbon Emission Trading (CET) mechanism have emerged as the times require. With the development of the carbon trading mechanism, it is also crucial to improve the carbon trading mechanism and reasonably organize high-emission industrial loads to participate in the carbon trading market.

[0003] In existing research, the scheduling strategies for introducing carbon trading in integrated energy systems mostly introduce a stepped carbon trading mechanism or a coupling mechanism of carbon trading and green certificate trading to construct a low-carbon scheduling model and improve the low-carbon performance of the system, which has a positive impact on the low-carbon transformation of the system. However, existing research mostly focuses on carbon trading modeling for power generation-side entities and ignores the situation of industrial loads on the load side participating in the carbon trading market, and there is still room for improvement in the research on low-carbon strategies for industrial parks.

[0004] Demand response is an effective means for the scheduling system to guide loads to participate in system scheduling. Therefore, a large number of studies on demand response mechanisms start from price response mechanisms, including electricity price response mechanisms and carbon price response mechanisms. By modifying the electricity price at different time periods, or setting different electricity prices and carbon emission costs for the electricity consumption and carbon emissions of different entities, users are guided to transfer loads. The above methods can well transfer the load peak-valley willingness, but they are all one-sided guidance and do not specifically reflect the coupling relationship between the electricity market and the carbon market. Therefore, it is very meaningful to develop a low-carbon scheduling model that can comprehensively consider the relationship between the electricity market and the carbon market. Summary of the Invention

[0005] The present invention provides a method for constructing a low-carbon scheduling model of an integrated energy industrial park considering the stepped carbon-electricity linkage cost. The establishment of this model takes into account the certainty of conventional units and the uncertainty of new energy units such as wind power and photovoltaic power, and incorporates the linkage relationship between the carbon market and the electricity market.

[0006] The technical solution of the present invention is as follows:

[0007] According to the first aspect of the present invention, there is provided a method for constructing a low-carbon scheduling model of an integrated energy industrial park considering the stepped carbon-electricity linkage cost, including the following steps:

[0008] Step 1: Establish a wind power output model and a photovoltaic power output model;

[0009] Step 2: Construct a reward and punishment-based stepped carbon cost model; based on the reward and punishment-based stepped carbon cost model, introduce a carbon-electricity price linkage coefficient to construct a stepped carbon-electricity linkage cost model.

[0010] Step 3: Based on the wind power output model, photovoltaic power generation output model, and reward and punishment-based stepped carbon cost model, establish a carbon trading model for each entity in the park to determine the carbon trading revenue of each entity; among them, the carbon trading model for each entity includes a thermal power carbon trading model, a renewable energy carbon trading model, and a load carbon trading model.

[0011] Step 4: Based on the carbon trading revenue of each entity obtained in Step 3, plus the boiler revenue, determine the total revenue function in the industrial park; according to the obtained total revenue function, with the maximization of the total park revenue as the optimization goal, combined with the constraint conditions, construct a low-carbon scheduling model for the integrated energy industrial park.

[0012] Further, Step 1 is specifically: use the Weibull distribution to fit the uncertainty of wind power, and establish a wind power output model; use the Beta distribution to fit the uncertainty of photovoltaic power generation, and establish a photovoltaic power generation output model.

[0013] Further, the stepped carbon-electricity linkage cost model is specifically:

[0014]

[0015] In the formula, c e,t is the stepped electricity price at time t; P c,t represents the electricity consumption of the entity at time t; Y0 represents the step size of the decline interval of the stepped carbon-electricity linkage cost; Y1 represents the step size of the increase interval of the stepped carbon-electricity linkage cost; P av represents the average daily load of the system; P all represents the total daily load of the system; T represents the scheduling period; c e,0 is the electricity price base price under the carbon trading mode; τ1 represents the stepped electricity price increase coefficient; ξ1 represents the stepped electricity price decrease coefficient.

[0016] Further, the establishment of a carbon trading model for each entity in the park based on the wind power output model, photovoltaic power generation output model, and reward and punishment-based stepped carbon cost model to determine the carbon trading revenue of each entity is specifically: establish a thermal power carbon trading model and a load carbon trading model based on the reward and punishment-based stepped carbon cost model; establish a renewable energy carbon trading model based on the wind power output model and the photovoltaic power generation output model.

[0017] Further, the expression of the objective function is:

[0018]

[0019]

[0020] where π f,t is the revenue of the thermal power unit at time t; π whp,t is the revenue of the fuel cell, wind power, and photovoltaic power at time t; π g,t is the revenue of the boiler at time t; π load,t is the comprehensive revenue of the load at time t; P f,t is the thermal power output at time t; c f is the on-grid electricity price of the thermal power unit; a, b, and c are the fuel cost coefficients of the thermal power unit; is the carbon trading revenue of the thermal power unit obtained based on the thermal power carbon trading model; is to recover the heat of the thermal power unit using the waste heat recovery system; c hw is the price at which the heating company purchases heat in the "by heat amount" pricing method; P w,t is the wind power on-grid power at time t; c w is the wind power on-grid electricity price; P H,t is the hydrogen energy storage on-grid power at time t; c H is the fuel cell on-grid electricity price; P pv,t is the photovoltaic on-grid power at time t; c pv is the photovoltaic on-grid electricity price; is the carbon trading revenue of the renewable energy unit obtained based on the renewable energy carbon trading model; η Hh is the fuel cell thermal efficiency; c store is the hydrogen storage cost coefficient; M store,t is the hydrogen storage mass at time t; c qf is the wind curtailment penalty coefficient; P qf,t is the wind curtailment power at time t; c qpv is the PV curtailment penalty coefficient; P qpv,t is the PV curtailment power at time t; P sr,t is the heat selling energy at time t, c sr is the heat selling revenue, P g,t is the electric boiler operating power at time t; c e,t is the time-of-use electricity price for the t period; n is the total number of load users; is the electrical load power of the i-th load at time t; is the heat load power of the i-th load at time t; is the economic benefit generated per degree of electricity by load i; is the carbon emission revenue of the load obtained based on the load carbon trading model.

[0021] Furthermore, the constraint conditions include system power balance constraints, thermal power unit output constraints, thermal power unit ramping and sliding constraints, wind power unit output constraints, electrolyzer ramping constraints, hydrogen storage constraints, fuel cell and electrolyzer output constraints, thermal load power balance constraints, and heat pump conversion power constraints.

[0022] According to the second aspect of the present invention, there is provided a device for constructing a low-carbon scheduling model of an integrated energy industrial park considering the stepped carbon-electricity linkage cost, including a module for the method of constructing a low-carbon scheduling model of an integrated energy industrial park considering the stepped carbon-electricity linkage cost as described in any one of the above.

[0023] According to the third aspect of the present invention, there is provided a processor for performing operations including executing the method of constructing a low-carbon scheduling model of an integrated energy industrial park considering the stepped carbon-electricity linkage cost as described in any one of the above.

[0024] The beneficial effects of the present invention are as follows: The low-carbon scheduling model of the integrated energy industrial park established by the present invention considers the coupling relationship between electricity cost and carbon cost. On the one hand, it simultaneously considers the demand response mechanism of the coordinated electricity price and carbon price, which can further improve the low-carbon optimization effect of the park and adapt to the low-carbon reform of the electricity market and carbon market. Further, adding industrial loads to the carbon trading market can adapt to the multi-subject development trend of the carbon trading market. On the other hand, the present invention simultaneously considers the certainty of conventional units in the park and the uncertainty of the output of wind and solar units, which can adapt to the development of the new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0026] Figure 2 It is a schematic diagram of the linkage relationship between the carbon market and the electricity market in the present invention;

[0027] Figure 3 It is a structural diagram of the park in the embodiment of the present invention;

[0028] Figure 4 It is a diagram of the optimized scheduling result of the thermal power unit in the embodiment of the present invention;

[0029] Figure 5 It is a diagram of the optimized scheduling result of the wind power unit in the embodiment of the present invention;

[0030] Figure 6 It is a diagram of the optimized scheduling result of the industrial load in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention. It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily.

[0032] Embodiment 1: As Figures 1-6 shown, according to the first aspect of the embodiments of the present invention, a method for constructing a low-carbon scheduling model for an integrated energy industrial park considering the stepped carbon-electricity linkage cost is provided, including the following steps:

[0033] Step 1: Use the Weibull distribution to fit the uncertainty of wind power and establish a wind power output model; use the Beta distribution to fit the uncertainty of photovoltaic power generation and establish a photovoltaic power generation output model;

[0034] Step 2: Construct a reward and punishment type stepped carbon cost model; based on the reward and punishment type stepped carbon cost model, introduce a carbon-electricity price linkage coefficient to construct a stepped carbon-electricity linkage cost model;

[0035] Step 3: Based on the wind power output model, photovoltaic power generation output model, and reward and punishment type stepped carbon cost model, establish a carbon trading model for each entity in the park to determine the carbon trading income of each entity; among them, the carbon trading model for each entity includes a thermal power carbon trading model, a renewable energy carbon trading model, and a load carbon trading model;

[0036] Step 4: Based on the carbon trading income of each entity obtained in Step 3, plus the boiler income, determine the total income function in the industrial park; according to the obtained total income function, with the maximization of the total park income as the optimization goal, combined with the constraint conditions, construct a low-carbon scheduling model for the integrated energy industrial park.

[0037] Further, the specific content of Step 1 is:

[0038] The mathematical expression of the Weibull distribution function f(ν) of wind speed is:

[0039]

[0040] In the formula, is the actual wind speed at the hub position of the fan; k is the Weibull distribution shape factor; c is the Weibull distribution scale factor; v0 is the wind speed at height h0; h is the hub height of the fan, and n is the wind speed variation coefficient with height, which is a parameter related to the atmospheric stability and ground roughness.

[0041] The output of wind power is proportional to the cube of the wind speed. After considering the simplified correction relationship of the wind speed distribution with the vertical height, a complete wind power output model can be constructed as shown in the following equation:

[0042]

[0043] In the formula, ν IN is the cut-in wind speed of the wind turbine; ν OUT is the cut-out wind speed of the wind turbine; ν N is the rated wind speed of the wind turbine; is the rated output of the wind turbine; P W,t is the total power generation of the wind power generation unit, which is divided into the grid-connected power P w,t , the grid-connected power of hydrogen energy storage P H,t , and the curtailed wind power P qf,t ;

[0044] The mathematical expression of the selected Beta distribution function f(s; α; β) is:

[0045]

[0046] In the formula, s is the current light intensity, with the unit of W / m 2 ; both α and β are the current Beta distribution shape factors, and both are greater than zero; Γ(α), Γ(β), and Γ(α + β) are all gamma functions;

[0047] After comprehensively considering the influence of the operating temperature of the photovoltaic cell on the photovoltaic output, the photovoltaic power generation output model is obtained as:

[0048]

[0049] In the formula, P PV,t is the photovoltaic power generation at time t, which is divided into the grid-connected power P pv,t and the curtailed light power P qpv,t ; η PV is the photovoltaic output attenuation factor, which is used to describe the performance attenuation of the photovoltaic power generation system affected by factors such as light intensity change, temperature change, and component aging during actual operation, and reflects the difference between the actual output and the theoretical output; N s is the number of series-connected photovoltaic arrays; N p is the number of parallel-connected photovoltaic arrays; P STC is the maximum output power of the photovoltaic cell under standard environmental conditions; s STC is the light intensity under standard conditions, generally 1000W / m 2 ; k T is the power temperature coefficient; T is the operating temperature of the photovoltaic cell; T STC is the surface temperature under standard conditions; T ENV is the environmental temperature at the working location.

[0050] Further, step 2 is specifically as follows:

[0051] 1) Reward and punishment type stepped carbon cost model

[0052] The carbon trading mechanism is based on the establishment of legal carbon emission rights, allowing each entity within the system to trade carbon emission rights in the market, realizing the "more refund and less make-up" behavior of carbon quotas, and conducting cost subsidies or profit increases, thereby controlling carbon emissions and achieving low-carbon dispatching. In China's current carbon emission mechanism, the main carbon quota allocation method adopted is the free allocation method. Local regulatory departments allocate carbon quotas to different carbon emission sources, and each producer adjusts its production plan according to its own allocated quota. If the actual carbon emission is higher than the allocated carbon quota, it is necessary to bid to purchase carbon quotas in the carbon market; conversely, if the actual carbon emission is lower than the allocated carbon quota, the surplus part can be sold in the carbon market in exchange for income.

[0053] In order to further limit carbon emissions in the multi-energy coupling system, the present invention uses a reward and punishment type stepped carbon cost model to describe and depict the cost unit price of each entity participating in carbon emission rights trading, replacing the CEA unit price. Different from the traditional carbon quota pricing mechanism, the reward and punishment type stepped carbon cost model divides multiple price intervals. The more carbon quotas are sold or purchased, the higher the unit price within the corresponding interval will be. The reward and punishment type stepped carbon cost model is as follows:

[0054]

[0055] In the formula, is the carbon cost at time t; represents purchasing carbon quotas from the carbon market, generating carbon purchase costs, and conversely represents selling carbon quotas to the carbon market, generating carbon sale revenues; λ1 is the carbon cost base price; α1 is the stepped carbon cost growth coefficient; β1 is the stepped carbon cost decline coefficient; l is the interval for dividing carbon emission units; E i,t is the carbon emission of load i at time t.

[0056] 2) Carbon-electricity price linkage coefficient

[0057] The stepped electricity price is dynamically adjusted according to the fluctuations of the total system load and the carbon trading price to achieve global optimal control of the multi-energy coupling system. By introducing the electricity price increase coefficient τ1 and the decrease coefficient ξ1, the initial electricity price is combined with the carbon cost base price to describe the stepped electricity price under the influence of the stepped carbon price.

[0058]

[0059] In the formula, c e,0is the base price of electricity under the carbon trading mode; τ1 represents the step electricity price increase coefficient; ξ1 represents the step electricity price decrease coefficient; τ0 and ξ0 respectively represent the initial step electricity price increase and decrease coefficients without considering the carbon-electricity coupling relationship; λ0 represents the carbon cost base price without considering the carbon-electricity coupling relationship; α0 is the initial step carbon cost increase coefficient without considering the carbon-electricity coupling relationship; β0 is the initial step carbon cost decrease coefficient without considering the carbon-electricity coupling relationship.

[0060] 3) Step carbon-electricity linkage cost model

[0061] The step electricity price c e (t) is expressed as follows:

[0062]

[0063] In the formula, c e,t is the step electricity price at time t; P c,t represents the electricity consumption of the entity at time t; Y0 represents the step length of the step carbon-electricity linkage cost decrease interval; Y1 represents the step length of the step carbon-electricity linkage cost increase interval; P av represents the average daily load of the system; P all represents the total daily load of the system; T represents the scheduling period, usually taken as 24 hours.

[0064] The step carbon-electricity linkage cost model is introduced to replace the electricity purchase price of different entities from the large power grid. The step electricity price under the carbon trading mechanism is closely related to the carbon trading base price. When the carbon cost base price λ1 changes, c e,t under the carbon trading mechanism will also change accordingly. The peak electricity price of the step electricity price is affected by the carbon cost increase coefficient α1. Similarly, the valley electricity price of the step electricity price is affected by the carbon cost decrease coefficient β1.

[0065] Furthermore, the specific content of step 3 is as follows: The carbon trading models of each entity mainly include the thermal power carbon trading model, the renewable energy carbon trading model, and the load carbon trading model.

[0066] Currently, there are two types of products in China's carbon trading market. One is the carbon emission allowances allocated by the government to enterprises (China Emission Allowance, CEA), and the other is the Chinese Certified Emission Reductions (CCER). Entities participating in carbon trading can voluntarily choose CEA or CCER for trading.

[0067] 1) Thermal power carbon trading model

[0068] According to the national carbon market trading price, it can be seen that the CEA price in the carbon emission trading market is much higher than that of CCER. Therefore, while completing the established power generation plan, thermal power units can sell 5% of the carbon emission quotas at the CEA price and buy an equal amount of carbon emission right quotas at the CCER price to earn the price difference. After considering the reward and punishment-based stepped carbon cost model, replacing the CEA unit price with the proposed carbon cost model, the carbon trading revenue of thermal power units is:

[0069]

[0070] M f,t = λ f P f,t ;

[0071] In the formula, P f,t is the thermal power output at time t, M f,t is the carbon emission quality of the thermal power unit at time t; λ f is the benchmark emission value of the thermal power unit for power supply, with the unit of t(CO2) / MWh; E a is the quota applied according to the dispatching plan before the day, and each power plant distributes it to each dispatching cycle according to the power generation plan; c ccer is the CCER market price.

[0072] 2) Renewable energy carbon trading model

[0073] The output characteristics of renewable energy such as wind-solar units and fuel cells are all random, and the carbon trading strategies are the same. Therefore, a unified model is established here. Distributed photovoltaic power plants, wind farms and fuel cells sell all CCERs in the carbon emission trading market to obtain profits. The carbon trading revenue of renewable energy units is:

[0074]

[0075] In the formula, P w,t is the wind power grid connection power at time t; P pv,t is the photovoltaic grid connection power at time t; P H,t is the hydrogen energy storage grid connection power at time t; CM is the marginal emission factor of the grid benchmark scenario.

[0076] 3) Load carbon trading model

[0077] The carbon trading of the load is similar to the trading model of thermal power generation, both of which are to sell 5% of the free quotas and buy CCERs to earn the price difference. As the largest consumer of CEA, the load will buy CEA to avoid punishment when the carbon emissions exceed the quota. Therefore, the trading strategy is to first buy 5% of the CCER quotas, and then buy CEA according to the emission results to complete the carbon trading clearance. The carbon emission revenue of the load is as follows:

[0078]

[0079] In the formula, is the free allocation quota of the i-th load allocated to each time period according to the scheduling cycle, i is the number of loads, and μ i is the "electricity-carbon index" of the i-th load, which refers to the carbon emission corresponding to each kilowatt-hour of electricity consumption, with the unit of kg / kwh; is the electrical load power of the i-th load at time t.

[0080] Furthermore, the specific content of step 4 is as follows: The objective function of the model is the total revenue of the park, including the revenue of thermal power units, the revenue of fuel cells and wind-solar units, the revenue of boilers, and the comprehensive revenue of loads.

[0081] 1) Revenue of thermal power units

[0082]

[0083] In the formula, π f,t is the revenue of the thermal power unit at time t; c f is the on-grid electricity price of the thermal power unit; a, b, and c are the fuel cost coefficients of the thermal power unit; is the fuel cost, is the heating revenue, is to recover the heat of the thermal power unit using the waste heat recovery system; c hw is the price at which the heating company purchases heat in the way of "calculating according to the amount of heat". Among them:

[0084]

[0085] In the formula, is the recovery efficiency of the heat pump in the waste heat recovery system.

[0086] 2) Revenue of fuel cells and wind-solar units

[0087]

[0088] In the formula, π whp,t is the revenue of fuel cells and wind-solar units at time t; c w is the on-grid electricity price of wind power; c H is the on-grid electricity price of fuel cells; c pv is the on-grid electricity price of photovoltaic power; η Hh is the thermal efficiency of the fuel cell; c store is the hydrogen storage cost coefficient; M store,t is the hydrogen storage mass at time t; c qf is the penalty coefficient for curtailed wind; P qf,t is the curtailed wind power at time t; c qpvis the curtailment penalty coefficient; P qpv,t is the curtailment power at time t;

[0089] 3) Boiler revenue

[0090]

[0091] In the formula, π g,t is the boiler revenue at time t; P sr,t is the heat sales energy at time t, c sr is the heat sales revenue, P g,t is the operating power of the electric boiler at time t.

[0092] 4) Load comprehensive revenue

[0093]

[0094] In the formula, n is the total number of load users; π load,t is the load comprehensive revenue at time t; is the electrical load power of the i-th load at time t; is the heat load power of the i-th load at time t; is the production revenue index, which refers to the economic benefit generated per degree of electricity by load i and is calculated through the annual electricity cost and revenue of the load.

[0095] 5) Total revenue function

[0096]

[0097] In the formula, π f,t is the thermal power unit revenue at time t; π whp,t is the fuel cell, wind power revenue and photovoltaic revenue at time t; π g,t is the boiler revenue at time t; π load,t is the load comprehensive revenue at time t.

[0098] Furthermore, considering the security of system operation, the low-carbon scheduling model needs the following constraints:

[0099] 1) System power balance constraint

[0100] P f,t +P w,t +P H,t =P load,t

[0101] In the formula, P load,t is the predicted load within the scheduling unit period, and the system does not consider line losses.

[0102] 2) Thermal power unit output constraint:

[0103]

[0104] In the formula, are the upper and lower limits of the output of the thermal power unit respectively.

[0105] 3) Ramp-up and ramp-down constraints of the thermal power unit

[0106]

[0107] In the formula, is the maximum ramp-up and ramp-down rate of the thermal power unit. Here, a simplified treatment is made, assuming that the maximum values of the ramp-up and ramp-down rates are the same constant value.

[0108] 4) Output constraints of the wind power unit

[0109]

[0110] In the formula, are the upper and lower limits of the output of the wind power unit respectively; P W,t is the total power generation of the wind turbine; P wH,t is the power consumption of the electrolyzer;

[0111] 5) Ramp-up constraint of the electrolyzer

[0112]

[0113] In the formula, is the maximum value of the ramp-up rate of the electrolyzer. The electrolyzer needs to effectively absorb the fluctuating power of the wind power, without considering absorbing additional grid power to maintain a certain power of the electrolyzer.

[0114] 6) Hydrogen storage constraint

[0115]

[0116] In the formula, is the maximum value of the hydrogen storage mass converted under standard conditions.

[0117] 7) Output constraints of the fuel cell and the electrolyzer

[0118]

[0119] In the formula, are the upper and lower limits of the output of the electrolyzer respectively; are the upper and lower limits of the output of the fuel cell respectively; ω is a 0-1 variable, controlling that the electrolyzer and the fuel cell do not work simultaneously.

[0120] 8) Thermal load power balance constraint

[0121]

[0122] In the formula, Let \(Q_{th}\) be the heat load at time \(t\). In this paper, the heat load system is simplified, and the hot water flow rate and losses during transportation are not considered.

[0123] 9) Heat pump conversion power constraint

[0124]

[0125] In the formula, are the upper and lower limits of the heat pump output of the thermal power unit respectively; are the upper and lower limits of the heat pump output of the fuel cell respectively.

[0126] Furthermore, it also includes step 5, and the specific content of step 5 is as follows: construct an example sample, and use MATLAB for simulation calculation according to the models constructed in step 2 and step 4 to obtain the low-carbon scheduling results of the integrated energy industrial park under different strategies.

[0127] Specifically: use MATLAB to call the Yalmip toolbox and Gurobi solver, and based on the models in step 2 and step 4, set the corresponding parameters for simulation calculation to obtain the output of each unit and industrial load in the integrated energy industrial park considering the stepped carbon-electricity linkage cost.

[0128] As Figure 2 shown, as energy trading markets, there is an inherent coupling relationship between the carbon trading market and the electricity trading market, and there is a linkage relationship between their prices. If the market aims at low carbon, it will increase the proportion of green electricity use, thereby causing a decrease in electricity prices and a reduction in carbon quota demand, resulting in a decrease in carbon prices and carbon emission costs. At this time, in order to maximize their own interests, each market entity will increase the utilization rate of carbon quotas and increase the demand for carbon quotas, thus tightening the quotas and increasing the carbon price. The increase in the carbon price brings an increase in the power generation cost of coal-fired power enterprises, and then the electricity price rises, forming another feedback path. The electricity price and the carbon price fluctuate alternately and influence each other in this process.

[0129] The present invention uses the topological structure as Figure 3 shown for example calculation and analysis. There is a 150MW photovoltaic unit in the system. The thermal power units are adjusted to one 150MW unit, one 250MW unit and one 300MW unit, and a 140MW wind turbine unit. In the system structure, the fuel cell, electrolyzer and hydrogen storage tank are simplified into a hydrogen energy system, and the heat pump and boiler are simplified into a waste heat recovery system.

[0130] The specific configuration of this embodiment is as follows: Set Policy 1 as the optimal scheduling policy that does not consider any mechanism; Set Policy 2 as the optimal scheduling policy that only considers carbon trading; Set Policy 3 as the optimal scheduling policy that considers carbon trading and time-of-use electricity price; Set Policy 4 as the optimal scheduling policy that considers carbon trading, time-of-use electricity price, and stepped carbon price; Set Policy 5 as the optimal scheduling policy proposed by the present invention. This embodiment calls the Gurobi solver on the MATLAB platform for solution, and the obtained optimal results of unit output are as Figure 4 and Figure 5 shown, indicating that the present invention can reduce the output of CHP units and increase the output of wind turbines, thereby reducing the carbon emissions of the park while meeting the load demand; the optimal results of industrial load are as Figure 6 shown, indicating that among many scheduling policies, the present invention can bring the highest economic benefits while minimizing carbon emissions to the greatest extent.

[0131] According to the second aspect of the embodiments of the present invention, there is provided a device for constructing a low-carbon scheduling model of an integrated energy industrial park considering stepped carbon-electricity linkage cost, including modules of the method for constructing a low-carbon scheduling model of an integrated energy industrial park considering stepped carbon-electricity linkage cost described in any one of the above. Specifically, it includes: a first module for executing Step 1 to establish a wind power output model and a photovoltaic power output model; a second module for executing Step 2 to construct a reward and punishment type stepped carbon cost model; based on the reward and punishment type stepped carbon cost model, introducing a carbon-electricity price linkage coefficient to construct a stepped carbon-electricity linkage cost model; a third module for executing Step 3 to establish a carbon trading model for each entity in the park according to the wind power output model, photovoltaic power output model, and reward and punishment type stepped carbon cost model to determine the carbon trading benefits of each entity; among them, the carbon trading model of each entity includes a thermal power carbon trading model, a renewable energy carbon trading model, and a load carbon trading model; a fourth module for executing Step 4 to determine the total revenue function in the industrial park based on the carbon trading benefits of each entity obtained in Step 3 plus the boiler revenue; according to the obtained total revenue function, with the maximization of the total revenue of the park as the optimization goal and combined with the constraint conditions, constructing a low-carbon scheduling model of the integrated energy industrial park. For the parts not detailed in the above modules, reference can be made to other related descriptions of this embodiment.

[0132] According to the third aspect of the embodiments of the present invention, there is provided a processor, and the processor is used to execute operations, and the operations include executing the method for constructing a low-carbon scheduling model of an integrated energy industrial park considering stepped carbon-electricity linkage cost described in any one of the above.

[0133] The specific implementation manners of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above implementation manners. Within the knowledge scope of those of ordinary skill in the art, various changes can be made without departing from the purpose of the present invention.

Claims

1. A method for constructing a low-carbon scheduling model for an integrated energy industrial park considering the step-by-step carbon-electricity linkage cost, characterized in that: The following steps are involved: Step 1: Establish wind power output model and photovoltaic power output model; Step 2: Construct a reward-and-penalty ladder carbon cost model. Based on the reward-and-penalty ladder carbon cost model, introduce the carbon-electricity price linkage coefficient to construct a ladder carbon-electricity linkage cost model. Step 3: Based on the wind power output model, photovoltaic power output model, and reward-penalty step-by-step carbon cost model, establish carbon trading models for each entity in the park to determine the carbon trading benefits of each entity; among them, the carbon trading models for each entity include thermal power carbon trading model, renewable energy carbon trading model, and load carbon trading model; Step 4: Based on the carbon trading income of each entity obtained in step 3, plus the boiler income, the total revenue function of the industrial park is determined; based on the obtained total revenue function, taking maximizing the total revenue of the park as the optimization goal and combining the constraints, a low-carbon scheduling model for the comprehensive energy industrial park is constructed.

2. The method for constructing a low-carbon scheduling model for a comprehensive energy industrial park considering the step-by-step carbon-electricity linkage cost according to claim 1 is characterized in that: The step 1 is specifically as follows: using Weibull distribution to fit the uncertainty of wind power and establish a wind power output model; using Beta distribution to fit the uncertainty of photovoltaic power generation and establish a photovoltaic power generation output model.

3. The method for constructing a low-carbon scheduling model for a comprehensive energy industrial park considering the step-by-step carbon-electricity linkage cost according to claim 1 is characterized in that: The step-by-step carbon-electricity linkage cost model is specifically: In the formula, c e,t P is the tiered electricity price during period t; c,t represents the electricity consumption of the subject at time t; Y0 represents the step length of the step-by-step carbon-electricity linkage cost decrease interval; Y1 represents the step length of the step-by-step carbon-electricity linkage cost increase interval; P av Represents the average daily load of the system; P all Indicates the total daily load of the system; T represents the scheduling period; c e,0 is the base price of electricity under the carbon trading model; τ1 represents the increase coefficient of the step electricity price; ξ1 represents the decrease coefficient of the step electricity price.

4. The method for constructing a low-carbon scheduling model for an integrated energy industrial park considering the step-by-step carbon-electricity linkage cost according to claim 1 is characterized in that: The carbon trading models of various entities in the park are established based on the wind power output model, photovoltaic power generation output model, and the reward-and-penalty step-by-step carbon cost model to determine the carbon trading income of each entity. Specifically: a thermal power carbon trading model and a load carbon trading model are established based on the reward-and-penalty step-by-step carbon cost model; a renewable energy carbon trading model is established based on the wind power output model and the photovoltaic power generation output model.

5. The method for constructing a low-carbon scheduling model for a comprehensive energy industrial park considering the step-by-step carbon-electricity linkage cost according to claim 1 is characterized in that: The objective function expression is: In the formula, π f,t is the revenue of thermal power unit at time t; whp,t is the fuel cell, wind power and photovoltaic income at time t; π g,t is the boiler revenue at time t; π load,t is the comprehensive load benefit at time t; P f,t is the thermal power output at time t; c f is the on-grid electricity price of thermal power units; a, b, c are the fuel cost coefficients of thermal power units; Carbon trading income of thermal power units obtained based on the thermal power carbon trading model; To use the waste heat recovery system to recover the heat of thermal power units; c hw The price at which the heat company purchases heat in the form of "calorie-based pricing"; P w,t is the wind power grid-connected power at time t; c w is the on-grid electricity price of wind power; P H,t c is the hydrogen energy storage grid-connected power at time t; H is the fuel cell grid electricity price; P pv,t is the photovoltaic grid-connected power at time t; c pv The photovoltaic grid-connected electricity price; Carbon trading income of renewable energy units obtained based on the renewable energy carbon trading model; η Hh is the thermal efficiency of the fuel cell; c store is the hydrogen storage cost coefficient; M store,t is the hydrogen storage mass at time t; c qf P is the wind abandonment penalty coefficient; qf,t is the wind power abandoned at time t; c qpv is the light abandonment penalty coefficient; P qpv,t is the abandoned optical power at time t; P sr,t is the heat energy sold at time t, c sr is the sales revenue, P g,t is the operating power of the electric boiler at time t; c e,t is the tiered electricity price for period t; n is the total number of load users; is the electric load power of the i-th load at time t; is the heat load power of the i-th load at time t; is the economic benefit generated by each kilowatt-hour of load i; The carbon emission benefits of the load obtained based on the load carbon trading model.

6. The method for constructing a low-carbon scheduling model for an integrated energy industrial park considering the step-by-step carbon-electricity linkage cost according to claim 1 is characterized in that: The constraints include system power balance constraints, thermal power unit output constraints, thermal power unit climbing and landslide constraints, wind turbine unit output constraints, electrolyzer climbing constraints, hydrogen storage constraints, fuel cell and electrolyzer output constraints, heat load power balance constraints, and heat pump conversion power constraints.

7. A low-carbon dispatch model construction device for a comprehensive energy industrial park considering the step-by-step carbon-electricity linkage cost, characterized in that: A module comprising a method for constructing a low-carbon scheduling model for an integrated energy industrial park taking into account the step-by-step carbon-electricity linkage cost as described in any one of claims 1-6.

8. A processor, characterized in that: The processor is used to execute operations, including executing a method for constructing a low-carbon scheduling model for an integrated energy industrial park that takes into account the step-by-step carbon-electricity linkage cost as described in any one of claims 1-6.

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