Operation loss-energy efficiency-carbon emission balancing method and system of park comprehensive energy system and medium

Abstract

The invention discloses an operation loss-energy efficiency-carbon emission balancing method and system of a park integrated energy system, and a medium, and the method comprises the following steps: introducing a stepped carbon transaction mechanism for the park integrated energy system, and considering that a demand side has the capability of regulating and controlling demand response, the scheduling conditions of strong consumption and high efficiency of the park integrated energy system under a highly controllable mechanism are researched and determined, and energy efficiency models of an energy supply structure and various energy devices are established; and according to the energy efficiency model, solving by taking the lowest operation loss, the lowest carbon emission cost and the highest energy efficiency as targets, and obtaining an optimal scheduling scheme under the condition of ensuring that the operation loss, the lowest carbon emission cost and the highest energy efficiency are balanced. According to the scheme, after an adjustable and controllable load response mechanism is introduced, supply and demand coordination and multi-energy complementation can be flexibly carried out on the supply side and the demand side according to a scheduling instruction of the park, new energy consumption is promoted, and the energy efficiency is further improved.

Description

Technical Field

[0001] The present invention relates to the field of operation balance strategies for integrated energy systems in a park, and in particular to an operation loss-energy efficiency-carbon emission balance method, system and medium for an integrated energy system in a park. Background Art

[0002] With the increasing proportion of renewable energy power generation, human beings are facing increasingly severe energy consumption problems. In this context, the independent planning and operation mode adopted by traditional energy systems can no longer effectively cope with the growing global energy demand due to resource waste and low energy utilization efficiency. Therefore, it is particularly important to find new solutions. Existing research focuses on operational input scheduling or low-carbon scheduling. At present, less attention is paid to the energy efficiency of the park integrated energy system. In addition, most efficiency indicators have nonlinear characteristics, which are difficult to handle with conventional solution methods, resulting in a lack of effective technical means for coordination and balance with other optimization goals. The Park Integrated Energy System came into being and became a hot area in current energy research. The system adheres to the principle of "horizontal multi-energy complementarity and vertical source-grid-load-storage coordination", seeks a balance between operational input, high efficiency and cleanliness, and actively promotes the consumption of renewable energy power generation. This system integrates multiple resources such as electricity, heat, and natural gas within the park to achieve optimal allocation and utilization of resources and improve overall energy efficiency.

[0003] In order to promote the consumption of new energy, a variety of flexible energy conversion equipment are coupled to join the park's integrated energy system. Demand response. As a flexible means of regulation and control of IPES, demand response can stimulate users' energy flexibility and further optimize the rational operation of the park's integrated energy system. With the widespread application of the park's integrated energy system, demand response has also changed from single load demand response to comprehensive demand response of multiple loads. The potential dispatchability of multiple adjustable loads is taken into account, but the coupling relationship between adjustable loads and energy efficiency indicators is ignored. In fact, due to the efficiency problems of energy equipment within the park's integrated energy system, changes in load directly affect the efficiency of equipment output. Therefore, adjustable demand response affects the efficiency of the park's integrated energy system to a certain extent.

[0004] Existing research focuses on operational input scheduling or low-carbon scheduling, with a two-layer energy efficiency model. The upper layer optimization aims at rationality and environmental protection, and the lower layer optimization aims at operating costs; an optimization scheduling model for the park's integrated energy system with the goals of minimizing operating costs, abandoned energy power, and carbon emissions, and performs weighted optimization calculations; currently, less attention is paid to the energy efficiency of the park's integrated energy system, and most efficiency indicators have nonlinear characteristics, which are difficult to handle with conventional solution methods, resulting in a lack of effective technical means for coordination and balance with other optimization objectives. Summary of the invention

[0005] The purpose of the present invention is to overcome the problems existing in the prior art, and propose an operation loss-energy efficiency-carbon emission balance method, system and medium for a comprehensive energy system in a park. For a comprehensive energy system in a park containing electricity, gas and heat, its energy supply structure and energy efficiency models of various energy equipment are established, and an operation loss-energy efficiency-carbon emission multi-objective balance scheduling method that takes into account adjustable demand response is proposed. The method is solved with the goals of minimizing operating investment, carbon emission costs and maximizing energy efficiency, to ensure that the optimal scheduling plan is achieved when the three are balanced.

[0006] In order to achieve the above-mentioned object, the first aspect of the present invention provides an operation loss-energy efficiency-carbon emission balance method of a park integrated energy system, comprising the following steps:

[0007] For the park's comprehensive energy system including electricity, gas and heat, a tiered carbon trading mechanism is introduced, and considering the demand side's ability to regulate demand response, research is conducted to determine the strong absorption and high-efficiency dispatch of the park's comprehensive energy system under a highly regulated mechanism;

[0008] Based on the tiered carbon trading mechanism and scheduling, establish energy supply structure and energy efficiency models of various energy equipment;

[0009] According to the energy efficiency model, the solution is carried out with the goal of minimizing operating losses, carbon emission costs and maximizing energy efficiency to obtain the Pareto frontier. The fuzzy decision-making method is used to ensure that the optimal scheduling plan is obtained when the operating losses, carbon emission costs and energy efficiency are balanced.

[0010] A second aspect of the present invention provides an operation loss-energy efficiency-carbon emission balance method for a park integrated energy system, comprising the following steps:

[0011] The optimization introduction module is used to introduce a step-by-step carbon trading mechanism for the park's comprehensive energy system containing electricity, gas and heat. Considering the demand side's ability to respond to adjustable demand, the research determines the strong absorption and high-efficiency dispatch of the park's comprehensive energy system under a highly adjustable mechanism.

[0012] An energy efficiency model building module, used to establish energy efficiency models of energy supply structure and various energy equipment based on the tiered carbon trading mechanism and scheduling conditions;

[0013] The calculation module is used to solve the energy efficiency model with the goal of minimizing operating loss, carbon emission cost and maximizing energy efficiency, obtain the Pareto frontier, and use the fuzzy decision-making method to ensure that the optimal scheduling plan is obtained when the operating loss, carbon emission cost and energy efficiency are balanced.

[0014] A third aspect of the present invention provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the steps of the above method.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. The present invention introduces a tiered carbon trading mechanism with a compensation coefficient to further constrain the carbon emissions of the park's comprehensive energy system and further reduce carbon emissions.

[0017] 2. After the adjustable load response mechanism is introduced into the present invention, both the supply and demand sides can flexibly coordinate supply and demand according to the dispatching instructions of the park, complement multiple energies, promote the consumption of new energy, and further improve energy efficiency.

[0018] 3. The present invention fully considers the balance effects among operating loss, energy efficiency and carbon emission reduction in the optimization scheduling, establishes a multi-objective energy efficiency model using the main objective function method, obtains a multi-objective balanced optimization plan by solving the Pareto solution set, and uses the fuzzy decision-making method to solve the optimal solution when operating loss, energy efficiency and carbon emission reduction are balanced. This is of certain significance for promoting the sustainable and balanced operation of the park's comprehensive energy system, and promoting the development of the park's comprehensive energy system in the direction of green and low-carbon. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0020] Figure 1 It is a schematic diagram of the optimization method for balancing operating losses, energy efficiency and carbon emissions;

[0021] Figure 2 This is a schematic diagram of the day-ahead data of load and wind and solar output;

[0022] Figure 3 Schematic diagram of comparison of new energy abandonment;

[0023] Figure 4 This is a schematic diagram of the scheduling results of scenario 1;

[0024] Figure 5 This is a schematic diagram of the scheduling results of scenario 2;

[0025] Figure 6 This is a schematic diagram of the scheduling results of scenario 3;

[0026] Figure 7 It is a schematic diagram of the Pareto solution set;

[0027] Figure 8 It is a schematic diagram of load regulation;

[0028] Fig. 9 It is a schematic diagram of the control scenario scheduling results;

[0029] Fig.10 This is a schematic diagram of the scheduling results of scenario 4;

[0030] Fig.11 It is a flow chart of the park’s comprehensive energy system operation loss-energy efficiency-carbon emission balance method. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0032] The first aspect of the embodiment of the present invention provides a method for balancing the operation loss, energy efficiency and carbon emission of a park comprehensive energy system, taking into account the participation of the park comprehensive energy system in the carbon trading market, and introducing a stepped carbon trading mechanism to further constrain the carbon emission of the park comprehensive energy system; secondly, considering the demand side's ability to control demand response, the strong absorption and high-efficiency scheduling of the park comprehensive energy system under a highly controllable mechanism is studied; for the park comprehensive energy system containing electricity, gas and heat, its energy supply structure and energy efficiency models of various energy equipment are established; finally, with the goal of minimizing the operation loss, carbon emission cost and maximizing the energy efficiency, the Pareto frontier is obtained, and the fuzzy decision method is used to obtain the final optimized scheduling of the operation loss, energy efficiency and carbon emission balance. The specific function implementation flow chart is as follows Fig.11 As shown;

[0033] S1: In order to further reduce carbon emissions, a tiered carbon trading mechanism with compensation coefficients is introduced to further constrain the carbon emissions of the park's integrated energy system. If the actual carbon emissions of the park's integrated energy system do not reach the upper limit of the allocated quota, the unused quota can be sold on the carbon emission rights trading market; if it exceeds, the corresponding number of carbon emission rights must be purchased on the carbon trading market;

[0034] S2: In view of the coupling between energy efficiency index and load, a demand-side adjustable load model is proposed. While considering that both electric and thermal loads can be reduced and transferred in time dimension, the demand side introduces the substitutability between the two loads. Both loads can achieve reduction and transfer in their respective time dimensions.

[0035] Aiming at the coupling between energy efficiency index and load, a demand-side adjustable load model is proposed. While considering the reduction of electric and thermal loads and the transfer in time dimension, the demand side introduces the substitutability between the two loads. Combining the characteristics of gas equipment and energy storage equipment, the goals are to minimize operating loss, carbon emission reduction and maximize energy efficiency. The former two are taken as target constraints, and energy efficiency is the main goal. The multi-objective Pareto frontier is solved, and the fuzzy decision method is used to obtain the final optimal scheduling.

[0036] S3: Gas equipment can respond quickly to load changes, provide flexible scheduling capabilities, effectively balance the instability of renewable energy, ensure the continuity and reliability of the energy system, achieve multi-energy complementarity, improve overall energy efficiency, and ensure stable energy supply in the park under different load conditions;

[0037] S4: With the goals of minimum operating loss, carbon emission reduction and maximum energy efficiency, and taking the first two as target constraints, energy efficiency as the main goal, solve the multi-objective Pareto frontier, and use fuzzy decision-making method to obtain the final optimal scheduling of operating loss-energy efficiency-carbon emission balance.

[0038] Furthermore, in step S1, if the actual carbon emissions of the park's integrated energy system do not reach the upper limit of the allocated quota, the unused quota can be sold on the carbon emission rights trading market; if it exceeds, the corresponding number of carbon emission rights must be purchased on the carbon trading market; the above carbon trading mechanism mainly consists of the following three parts: initial quota of carbon emission rights, actual carbon emissions and carbon trading costs;

[0039] S1-1: The main sources of carbon emissions in the park's integrated energy system are: upper-level coal-fired power units, gas turbines, and gas boilers; the initial quota model for carbon emission rights is:

[0040]

[0041] Where: E a is the initial carbon quota of the system; e , χ g is the carbon quota coefficient of power supply and heat supply output per unit; e =0.798kg / kWh, χ g =0.382kg / kWh; P e,buy (t) is the power purchased by the park at time t; H gb (t) is the heating power of the gas boiler at time t; P CHP,h (t), P CHP,e (t) is the heating and power supply power of the cogeneration unit at time t;

[0042] S1-2: Power-to-gas will consume CO2, so its carbon emissions are negative. The actual carbon emission model is:

[0043]

[0044] Where: E P is the actual carbon emission of the system; E e,buy The actual carbon emissions of electricity purchased from the superior; E total is the total actual carbon emissions of the cogeneration unit and gas boiler; E P2G is the amount of CO2 actually absorbed by power-to-gas conversion; P total (t) is the equivalent output power of the cogeneration unit and gas boiler during period t; a1, b1, c1 are the carbon emission calculation parameters of the coal-fired power unit; a2, b2, c2 are the carbon emission calculation parameters of the natural gas-consuming unit; is the parameter of CO2 absorption in the power-to-gas process of the power-to-gas equipment; H MR (t) is the heating power of the power-to-gas methanation process at time t;

[0045] S1-3: The existing tiered carbon trading mechanism divides the carbon emission interval into purchase intervals. In order to further constrain the carbon emission of the industrial park, a compensation coefficient α is introduced, that is, when the actual carbon emission is lower than the initial quota, the market gives a certain subsidy as compensation; based on this, a tiered carbon trading model with a compensation coefficient is constructed:

[0046]

[0047] E IES =E P -E a (4)

[0048] Where: E IES is the actual carbon emissions of the park, E P With the initial carbon quota E a The difference between is the carbon trading cost of the system at time t, a positive value indicates the purchase of quotas, and a negative value indicates the use of remaining quotas to obtain subsidies; k is the carbon trading base price of the day; α is the compensation coefficient; β is the price increase; d is the length of the carbon emission interval corresponding to each step.

[0049] Furthermore, in step S2, a demand-side adjustable load model is proposed. While considering that both electric and thermal loads can be reduced and transferred in the time dimension, the demand side introduces the substitutability between the two loads. Both loads can achieve reduction and transfer in their respective time dimensions. Any load can be divided into four parts: fixed load, reducible load, transferable load and substitutable load, namely:

[0050]

[0051] Where: k represents the load type, k∈{e,h}; P k,Load(t) represents the value of the kth load in period t; represents the fixed load of the kth load in period t; represents the load that can be reduced for the kth load in period t; represents the transferable load of the kth load in period t; represents the alternative load of the kth load in period t;

[0052] S2-1: Fixed load Fixed loads such as electricity and heat loads do not participate in regulation and dispatch;

[0053] S2-2: Load reduction possible Reducible loads Some loads can be reduced to a certain extent as needed:

[0054]

[0055] Where: θ cut (t) is a 0-1 variable; γ t is the reduction factor; and To reduce the power of front and rear loads; θ min and θ max is the minimum and maximum load reduction times, N max The maximum number of consecutive reductions;

[0056] S2-3: Transferable load Transferable loads can be transferred in the time dimension within the scheduling cycle:

[0057]

[0058] Where: It represents the power of the kth load before load regulation in period t; and respectively represent the transferable load interaction power of the kth load in period t; are binary variables, representing the transfer-in and transfer-out parameters of the k-th load transferable type load in period t; They represent the transfer-in and transfer-out power of the kth load in period t respectively; Formula (11) indicates that the total amount of various transferable loads remains unchanged in a scheduling cycle; They represent the upper and lower limits of the amount of interaction of the kth transferable load in each period. This constraint is used to ensure the energy quality requirements of users.

[0059] S2-4: Replaceable load Users can choose different energy supply methods to meet their own energy needs during the same period. The loads that are partially or completely replaced are:

[0060]

[0061] Where: Respectively represent the electric and thermal substitution load values ​​after interaction; They represent the electric and thermal substitution load powers before interaction respectively; are binary variables, representing the transfer-in and transfer-out parameters of the electrical and thermal loads in period t, respectively; They represent the transfer-in and transfer-out power of the electric and heat substitution loads during period t respectively.

[0062] Furthermore, it can effectively balance the instability of renewable energy, ensure the continuity and reliability of the energy system, achieve multi-energy complementarity, improve overall energy efficiency, and ensure stable energy supply in the park under different load conditions;

[0063] S3-1: Gas equipment model:

[0064] (1) Power-to-gas includes two processes: hydrogen production from water electrolysis and methanogenesis. Hydrogen production from water electrolysis is the precursor reaction of methanogenesis. Assuming that all the H2 generated from hydrogen production from water electrolysis is used in the methanogenesis process, the conversion relationship between the methane yield of power-to-gas and the heat release of the methanogenesis reaction per unit time is:

[0065]

[0066] Where: is the CH4 yield; is the heat release power of the methanation process per unit power consumption of power-to-gas; is the H2 production rate; is the density of H2 and CH4; is the molar mass of H2 and CH4; The efficiency of using heat released by the power-to-gas methanation process for heating;

[0067] (2) The cogeneration unit consists of a gas turbine and a waste heat boiler. The gas turbine generates electricity by burning natural gas, and the waste heat is recovered by the waste heat boiler for heating. Its operation model is:

[0068]

[0069] Where: P CHP,e (t) is the power generation capacity of the gas turbine; P CHP,h (t) is the heating power of the waste heat boiler; η e is the power generation efficiency of the gas turbine; G CHP,g (t) is the natural gas consumed by the gas turbine; Q GT (t) is the exhaust waste heat of the gas turbine at time t; η loss is the heat loss rate; η h is the waste heat recovery efficiency; COP,h is the flue gas recovery rate of the waste heat boiler;

[0070] Gas boilers are used for heating by burning natural gas. Its operation model is as follows:

[0071] H gb (t) = η gb,h H L G gb,g (t) (19)

[0072] Where: H gb (t) is the heating power of the gas boiler; η gb,h is the heating efficiency of the gas boiler; G gb,g (t) natural gas consumed by gas boilers;

[0073] S3-2: Energy storage equipment mainly includes batteries and heat storage tanks. Operation model:

[0074]

[0075] Where: is the state of charge of the battery at time t; is the charging and discharging power of the battery at time t, kW; η bt,chg , η bt,dis is the charging and discharging efficiency of the battery at time t; is the thermal energy of the heat storage tank at time t; is the heat storage and release power of the heat storage tank at time t; γ h is the energy self-loss rate of the heat storage tank; η hst,chg , η hst,dis is the heat storage and release efficiency of the heat storage tank;

[0076] S3-3: Power Constraints:

[0077] (1) Considering that the distribution network in actual industry does not allow reverse power transmission, the integrated energy system of the industrial park does not consider selling electricity to the power grid company:

[0078]

[0079] Where: It is the limit of purchased electricity for the park’s integrated energy system in each time period.

[0080] (2) Thermal power balance constraints:

[0081] H gb (t)+P CHP,h (t)+H MR (t)+P hst,dis (t) = P h,Load (t)+P hst,chg (t) (23)

[0082] (3) Natural gas balance constraints:

[0083]

[0084] (4) Climbing rate constraint:

[0085]

[0086] Where: It is the upper and lower limits of the power supply of the cogeneration unit; It is the upper and lower limits of the ramp rate of the cogeneration unit;

[0087] (5) Actual operating power constraints:

[0088]

[0089] Where: The upper and lower limits of the heating power of the gas boiler;

[0090] (6) When batteries and heat storage tanks are in operation, they must meet capacity constraints, mutual exclusion constraints, charging and discharging constraints, and ramp constraints:

[0091]

[0092] Where: It is a variable of 0 to 1, indicating the charge and discharge status of the battery and heat storage tank; and The upper and lower limits of the charging and discharging power of batteries and heat storage tanks; and It is the upper and lower limit of the charging and discharging energy climbing rate of the battery and heat storage tank.

[0093] Furthermore, with the goals of operating loss, minimum carbon emission reduction and maximum energy efficiency, and taking the first two as target constraints, and energy efficiency as the main goal, the multi-objective Pareto frontier is solved, and the fuzzy decision method is used to obtain the final optimal scheduling. The relationship between operating loss, minimum carbon emission reduction and maximum energy efficiency is as follows: Figure 1 As shown;

[0094] S4-1: Operation loss: Operation loss is an important factor in ensuring the sustainable development of the park's integrated energy system. It can reduce environmental impact and improve the park's overall competitiveness by optimizing operations. Operation loss target F1 includes the energy interaction cost C between the park and the power grid and natural gas company. buy , Equipment operation and maintenance cost C O , Energy sales cost C sell and demand response compensation cost C DR :

[0095]

[0096] Where: C e is the unit electricity purchase price; C g , C gs G is the price of gas purchased and sold by the unit; g,buy , G g,sell K is the gas purchase and sales volume of the park at time t; i is the unit operation and maintenance cost of equipment i; P i (t) is the working power of device i at time t; D is the compensation coefficient of the adjustable load change, D∈{r,t,c};

[0097]

[0098] Where: The load change power that can be reduced at time t; is the load change power that can be transferred at time t; is the power change of the alternative load at time t;

[0099] S4-2: Highest energy efficiency: Due to the loss when converting energy types, in order to further improve the energy utilization efficiency of the park's comprehensive energy system, the goal is to maximize the comprehensive energy efficiency of each energy source:

[0100]

[0101] Where: P Load (t) is the load power considering energy quality; P chg (t) and P dis (t) are the energy storage charging and discharging powers considering energy quality; P B (t) The purchased power taking into account the energy quality;

[0102] S4-3: Minimum carbon emission reduction: The operation of the park's integrated energy system should consider minimizing system carbon emissions, and the carbon emission target F3 is expressed through tiered carbon trading:

[0103]

[0104] Where: See formula (3) for details; in summary, the solution steps are:

[0105] (1) Input basic information of the park's integrated energy system, load, and wind and solar day-ahead data;

[0106] (2) Establishing the park's comprehensive energy system scheduling objectives, including operating loss F1, energy efficiency F2, and carbon emission cost F3;

[0107] (3) Linearized fractional energy efficiency F2 and segmented carbon emission cost F3;

[0108] (4) Convert the sub-goals into constraints to obtain multiple sub-problems:

[0109] Max F2(33)

[0110]

[0111] Where: ε 1n , ε 3n are the constraint parameters for operating losses and carbon emission costs;

[0112] (5) Based on the Matlab modeling toolbox Yalmip, load operation model, gas operation model, energy storage operation model and optimization scheduling model of each target sub-problem were constructed, and the Pareto frontier was obtained using GUROBI solution;

[0113] (6) Use fuzzy decision-making method to find the final optimal solution of Pareto frontier and obtain the scheduling plan.

[0114] Based on the same inventive concept, the second aspect of the embodiment of the present invention proposes a park comprehensive energy system operation loss-energy efficiency-carbon emission balance system, including the following steps:

[0115] The optimization introduction module is used to introduce a step-by-step carbon trading mechanism for the park's comprehensive energy system containing electricity, gas and heat. Considering the demand side's ability to respond to adjustable demand, the research determines the strong absorption and high-efficiency dispatch of the park's comprehensive energy system under a highly adjustable mechanism.

[0116] An energy efficiency model building module, used to establish energy efficiency models of energy supply structure and various energy equipment based on the tiered carbon trading mechanism and scheduling conditions;

[0117] The calculation module is used to solve the energy efficiency model with the goal of minimizing operating loss, carbon emission cost and maximizing energy efficiency, obtain the Pareto frontier, and use the fuzzy decision-making method to ensure that the optimal scheduling plan is achieved while minimizing operating loss, carbon emission cost and balancing energy efficiency.

[0118] Based on the same inventive concept, a third aspect of an embodiment of the present invention proposes a machine-readable storage medium, characterized in that instructions are stored on the machine-readable storage medium, and the instructions are used to enable a machine to execute the above method steps.

[0119] Test example:

[0120] In order to enable those skilled in the art to better understand the present invention, the example analysis includes the following components:

[0121] 1. Case description and simulation results analysis

[0122] A scheduling cycle is 24 hours a day. Figure 2 The day-ahead data of wind power output and electricity and heat load demand within the park's integrated energy system; the operating parameters of each device and energy storage are shown in Tables 1 and 2; the electricity price is 0.2871 yuan / kWh in the valley period from 0:00 to 8:00, 0.6314 yuan / kWh in the flat period from 12:00 to 17:00 and 21:00 to 24:00, and 1.0557 yuan / kWh in the peak period from 8:00 to 12:00 and 17:00 to 21:00; the carbon trading base price k = 0.232 yuan / kg, the interval length d = 300kg, the compensation coefficient α = 0.2, the price increase range β = 0.25, and the unit wind and solar abandonment penalty input are both 0.3 yuan / kWh; the load-side adjustable resources of the park account for 30%. The calculation is done on a computer with a processor model Intel Core i9-13900k 3.00GHz and a memory of 128 gas boilers. The program language is MATLABR2021b, and the optimizer uses GUROBI 11.0.

[0123] Energy equipment parameters and energy storage equipment parameters are shown in Table 1 and Table 2:

[0124] Table 1

[0125]

[0126] Table 2

[0127]

[0128] The initial storage capacity of the battery is 90kW, and the charging and discharging efficiency is 0.96; the initial heat storage capacity of the heat storage tank is 80kW, and the charging and discharging efficiency is 0.98, and the energy self-loss rate is 0.025. In order to analyze the rationality and effectiveness of the multi-objective energy efficiency model and consider the optimization of adjustable resources, 5 optimization examples are set for comparative analysis:

[0129] Comparison scenario: Add adjustable resource optimization on both the source and load sides, comprehensively consider multi-objective optimization of operating losses, energy efficiency and carbon emission costs, and select the Pareto optimal solution.

[0130] Scenario 1: Adding adjustable resource optimization on both the source and load sides, and only considering the optimization of the operating loss target;

[0131] Scenario 2: Adding adjustable resource optimization on both the source and load sides, comprehensively considering multi-objective optimization of operation loss and energy efficiency;

[0132] Scenario 3: Adding adjustable resource optimization on both the source and load sides, and only considering energy efficiency optimization;

[0133] Scenario 4: Without considering the optimization of load-side adjustable resources, the multi-objective optimization of operation loss, energy efficiency and carbon emission cost is comprehensively considered, and the Pareto optimal solution is selected. The optimization results are shown in Table 3.

[0134] Table 3

[0135]

[0136]

[0137] Depend on Figure 3 From the new energy abandonment situation, it can be seen that the abandonment phenomenon in scenario 3 is the most serious. This is because scenario 3 only considers the extreme situation of energy efficiency targets. Although adjustable loads are added, the power-to-gas equipment is not enabled, and it does not have the ability to absorb new energy. Compared with scenario 3, other scenarios take into account the operating losses of wind turbines and photovoltaics, and basically absorb all wind and solar power generation with adjustable loads. Although scenario 4 uses multi-objective optimization to solve the Pareto frontier, it does not introduce adjustable loads for demand response. During the period when wind and solar resources are abundant, the power-to-gas is almost fully generated, which also causes a large amount of wind and solar power to be abandoned. The control scenario uses all wind and solar power generation, reduces the power supply pressure of the cogeneration unit, and in terms of heat load, makes its heat output lower within a certain range. In addition, due to the existence of alternative loads, the heat load in this period is replaced by the electric load, and most of the heat load is met by gas boilers with higher efficiency. Therefore, taking into account the demand response of adjustable loads can effectively improve the absorption capacity of the park's comprehensive energy system for wind and solar power.

[0138] Depend on Figure 4 It can be seen that in the power dispatch, the battery is charged and discharged during the period when the new energy is sufficient and insufficient, which has obvious operation loss benefits. Similarly, the power-to-gas also operates during the period when the new energy is sufficient. The above results show that the power-to-gas and battery equipment can improve the system operation loss benefits through time sequence dispatch under the background of new energy.

[0139] contrast Figure 4 and Figure 5 It can be found that when carbon emissions are considered, the use of cogeneration units and gas boilers is directly affected, and the scheduling of electricity and heat is significantly affected. In thermal energy scheduling, after the gas turbine is started, the gas boiler first produces heat to supply energy to the load, and the remaining heat demand is met by the cogeneration unit and the power-to-gas. During this period, the power-to-gas is basically in full-fire state when it is started, which alleviates the heating pressure of the cogeneration unit and the gas boiler to a certain extent. The reason for the above results is that when the carbon emission cost is taken into account, the output of the cogeneration unit and the gas boiler with carbon emissions is controlled. The above analysis shows that the introduction of carbon emission costs may have a significant impact on the scheduling strategy of the park.

[0140] contrast Figure 6 and Figure 4 , Figure 5 It can be found that when comprehensive energy efficiency is the goal, the power grid purchases electricity significantly and the new energy consumption capacity decreases sharply. The main reason is that energy efficiency has little to do with the source of energy, so the source of energy is in a disordered state and the consumption of new energy is not considered first. Comparing scenarios 2 and 3, it can be found that with the improvement of comprehensive energy efficiency, the total investment increases, indicating that there is a conflict between the two goals of the highest energy efficiency and the lowest total investment.

[0141] Considering the cost and energy efficiency of the comparison scenario, the total cost upper limit is taken as a constraint. When selecting the upper limit, the operation loss cost range [9280.35, 19622.13] yuan and the carbon emission cost range [-1117.08, 578.94] yuan are traversed to solve the comprehensive energy efficiency optimization goal. The Pareto optimal frontier is as follows Figure 8 In practice, park managers can make decisions based on demand or preference. According to the results of this paper, the fuzzy decision method is used to select Figure 8 The marked points are used as a compromise. This scenario not only fully reflects the new energy consumption capacity, but also improves the comprehensive energy efficiency by 7.46% compared with scenario 2; compared with scenario 3, the carbon emission cost is reduced by 164.97%, and the total cost is reduced by 13.09%. In summary, the multi-objective optimization strategy that takes into account operation loss, energy efficiency, and carbon emissions truly realizes the balanced operation of PIES and promotes sustainable development. In order to analyze the adjustable operation mechanism of the demand side of the park under the participation of the tiered carbon trading, the scenario 4 and the control scenario are compared and analyzed. Fig. 9 To compare the scene scheduling results, Fig.10 Scheduling result graph for scenario 4.

[0142] By comparing the control scenario with scenario 4, we can see that Figure 8 As shown, since new energy has strong anti-peak characteristics, in order to consume wind power locally as much as possible and reduce the energy ladder cost in the power-to-gas process, and at the same time affected by time-of-use electricity prices and alternative heat load, the electric load has increased to varying degrees.

[0143] From the observation of the heat load regulation, it can be seen that the demand for heat load is higher during the daytime and lower during the nighttime. In addition, since the park participates in the carbon trading market, the system is constrained by the carbon trading cost. At the same time, combined with the actual carbon emission model, it can be seen that since the natural gas-consuming equipment needs to meet the higher heat load demand during the daytime, it is in a high carbon emission state. Therefore, the system chooses to transfer part of the heat load demand during the daytime to the nighttime to reduce the carbon emissions of the entire scheduling cycle. In addition, since the electric load demand during the daytime in the control scenario has increased to a certain extent compared with scenario 4, the new energy consumption is more sufficient. At this time, reducing the heat load demand during the daytime can reduce the output of the cogeneration unit. Most of the heat load demand can be met by more efficient gas boilers, further reducing the total investment of the system. Since part of the heat load is replaced by the electric load to further absorb the wind power, part of the carbon emissions are reduced, and the high carbon emission state of coal-fired power units and natural gas-consuming equipment is alleviated. Therefore, the total carbon emissions of the system have been reduced to a certain extent, and the total operation investment has also been reduced.

[0144] Depend on Figure 3 It can be seen that scenario 4 is dispatched only through supply-side equipment, and the problem of new energy consumption has not been completely solved. However, during the dispatch period of the control scenario, Fig. 9 Through the flexible coordination of various internal equipment and the adjustable load on the demand side, all wind power is absorbed by the park; during the daytime, part of the heat in the park is replaced by electricity load, so that the electricity load demand curve is as close to the new energy electricity power curve as possible, while realizing local absorption and reducing energy ladder costs, it alleviates the high carbon emissions during the daytime due to the large amount of heat load demand supplied by natural gas-consuming equipment; at the same time, due to the high electricity price during the 19:00-21:00 period, reducing the electricity load demand can reduce the power purchase consumption of superiors; due to the increase in electricity load demand during the daytime, it can meet the absorption of new energy and reduce the output of the cogeneration unit. At this time, the thermal power demand can be supplied by more efficient gas boilers, which can further improve the energy efficiency of the park.

[0145] Combined with Table 3, it can be seen that on the basis of considering the demand response on the demand side, the energy efficiency result of the park's comprehensive energy system is 0.9543, which is 7.9% higher than that of scenario 4. At this time, the operation mode will become more flexible, and the high efficiency, low carbon and operation loss of the park operation will be further optimized, which verifies the effectiveness of the park optimization scheduling strategy considering the demand-side controllable capacity mentioned in the paper.

[0146] In summary, compared with the prior art, the beneficial effects of the technical solution of the present invention are: 1) The present invention introduces a step-by-step carbon trading mechanism with a compensation coefficient to further constrain the carbon emissions of the park's integrated energy system and further reduce carbon emissions. 2) After the present invention introduces an adjustable load response mechanism, both the supply and demand sides can flexibly coordinate supply and demand according to the park's dispatch instructions, complement multiple energies, promote the consumption of new energy, and further improve energy efficiency. 3) The present invention fully considers factors such as operating loss, energy efficiency, and carbon emission reduction in optimization scheduling, and establishes a multi-objective energy efficiency model using the main objective function method. By solving the Pareto frontier, a multi-objective equilibrium optimization plan is obtained. The fuzzy decision method is used to solve the optimal solution of the three while considering each other. It has certain significance for promoting the sustainable operation of the park's integrated energy system and promoting the development of the park's integrated energy system in the direction of green and low-carbon.

[0147] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity, or apparatus.

[0148] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for balancing operating losses, energy efficiency and carbon emissions of a comprehensive energy system in a park, characterized in that: The following steps are involved: For the park's comprehensive energy system including electricity, gas and heat, a tiered carbon trading mechanism is introduced, and considering the demand side's ability to regulate demand response, research is conducted to determine the strong absorption and high-efficiency dispatch of the park's comprehensive energy system under a highly regulated mechanism; Based on the tiered carbon trading mechanism and scheduling, establish energy supply structure and energy efficiency models of various energy equipment; According to the energy efficiency model, the solution is carried out with the goal of minimizing operating losses, carbon emission costs and maximizing energy efficiency to obtain the Pareto frontier. The fuzzy decision-making method is used to ensure that the optimal scheduling plan is obtained when the operating losses, carbon emission costs and energy efficiency are balanced.

2. The method according to claim 1, characterized in that The introduction of the tiered carbon trading mechanism specifically includes: introducing a tiered carbon trading mechanism with a compensation coefficient to constrain the carbon emissions of the park's integrated energy system. If the actual carbon emissions of the park's integrated energy system do not reach the upper limit of the allocated quota, the unused quota can be sold on the carbon emission rights trading market; If it exceeds the limit, a corresponding amount of carbon emission rights must be purchased in the carbon trading market.

3. The method according to claim 2, characterized in that The above-mentioned consideration of the demand side's ability to respond to adjustable demand, the study determines the strong absorption and high-efficiency dispatching of the park's comprehensive energy system under a highly adjustable mechanism. Specifically, in view of the coupling between energy efficiency indicators and loads, a demand-side adjustable load model is proposed. The demand side introduces two types of loads that are substitutable while considering that both electric and thermal loads have the ability to be reduced and transferred in the time dimension. Both loads can achieve reduction and transfer in their respective time dimensions. Any of the two types of loads introduced can be divided into four parts: fixed load, reducible load, transferable load and substitutable load, namely: Where: k represents the load type, k∈{e,h}; P k,Load (t) represents the value of the kth load in period t; represents the fixed load of the kth load in period t; represents the load that can be reduced for the kth load in period t; represents the transferable load of the kth load in period t; Represents the alternative load of the kth load in period t.

4. The method according to claim 2, characterized in that: The step-by-step carbon trading mechanism with compensation coefficients is composed of the following parts: initial carbon emission quota, actual carbon emissions and carbon trading costs; The initial quota model of carbon emission rights is: Where: E a is the initial carbon quota of the system; e , χ g are the carbon quota coefficients per unit power supply and heat supply output; P e,buy (t) is the power purchased by the park at time t; H gb (t) is the heating power of the gas boiler at time t; P CHP,h (t), P CHP,e (t) are the heating and power supply power of the cogeneration unit at time t; The actual carbon emission model is: Where: E P is the actual carbon emission of the system; E e,buy The actual carbon emissions of electricity purchased from the superior; E total is the total actual carbon emissions of the cogeneration unit and gas boiler; E P2G is the amount of CO2 actually absorbed by power-to-gas conversion; P total (t) is the equivalent output power of the cogeneration unit and gas boiler during period t; a1, b1, c1 are the carbon emission calculation parameters of the coal-fired power unit; a2, b2, c2 are the carbon emission calculation parameters of the natural gas-consuming unit; is the parameter of CO2 absorption in the power-to-gas process of the power-to-gas equipment; H MR (t) is the heating power of the power-to-gas methanation process at time t; The carbon trading cost model is: AND IES =And P -AND a Where: E IES is the actual carbon emissions of the park, E P With the initial carbon quota E a The difference between is the carbon trading cost of the system at time t, a positive value indicates the purchase of quotas, and a negative value indicates the use of remaining quotas to obtain subsidies; k is the carbon trading base price of the day; α is the compensation coefficient; β is the price increase; d is the length of the carbon emission interval corresponding to each step.

5. The method according to claim 4, characterized in that Refers to the load that is partially or completely replaced by users who can choose different energy supply methods to meet their own energy needs in the same period of time: Where: Respectively represent the electric and thermal substitution load values ​​after interaction; They represent the electric and thermal substitution load powers before interaction respectively; are binary variables, representing the transfer-in and transfer-out parameters of the electrical and thermal loads in period t, respectively; They represent the transfer-in and transfer-out power of the electric and heat substitution loads during period t respectively.

6. The method according to claim 2, characterized in that The energy supply structure and various energy equipment include gas turbines, waste heat boilers, batteries and heat storage tanks, among which: The cogeneration unit consists of a gas turbine and a waste heat boiler. The gas turbine generates electricity by burning natural gas, and the waste heat is recovered by the waste heat boiler for heating. The operation model of the cogeneration unit is: Where: P CHP,e (t) is the power generation capacity of the gas turbine; P CHP,h (t) is the heating power of the waste heat boiler; η e is the power generation efficiency of the gas turbine; G CHP,g (t) is the natural gas consumed by the gas turbine; Q GT (t) is the exhaust waste heat of the gas turbine at time t; η loss is the heat loss rate; η h is the waste heat recovery efficiency; C OP,h is the flue gas recovery rate of the waste heat boiler; Gas boilers burn natural gas for heating, and the operating model is: H gb (t)=η gb,h H L G gb,g (t) Where: H gb (t) is the heating power of the gas boiler; η gb,h is the heating efficiency of the gas boiler; G gb,g (t) natural gas consumed by gas boilers; Batteries and thermal storage tanks, run the model: Where: is the state of charge of the battery at time t; are the charging and discharging power of the battery at time t, kW; η bt,chg , η bt,dis are the charging and discharging efficiencies of the battery at time t respectively; is the thermal energy of the heat storage tank at time t; are the heat storage and heat release powers of the heat storage tank at time t; γ h is the energy self-loss rate of the heat storage tank; η hst,chg , η hst,dis are the heat storage and release efficiencies of the heat storage tank respectively.

7. The method according to claim 6, characterized in that The energy supply structure and various energy devices include the following power constraints: (1) Considering that the distribution network in actual industry does not allow reverse power transmission, the integrated energy system of the industrial park does not consider selling electricity to the power grid company: Where: The limit of power purchased by the park's integrated energy system in each period; (2) Thermal power balance constraints: H gb (t)+P CHP，h (t)+H MR (t)+P hst，dis (t)=P h，Load (t)+P hst，chg (t) (3) Natural gas balance constraints: (4) Climbing rate constraint: Where: It is the upper and lower limits of the power supply of the cogeneration unit; It is the upper and lower limits of the ramp rate of the cogeneration unit; (5) Actual operating power constraints: Where: The upper and lower limits of the heating power of the gas boiler; (6) When batteries and heat storage tanks are in operation, they must meet capacity constraints, mutual exclusion constraints, charging and discharging constraints, and ramp constraints: Where: It is a variable of 0 to 1, indicating the charge and discharge status of the battery and heat storage tank; and The upper and lower limits of the charging and discharging power of batteries and heat storage tanks; and It is the upper and lower limit of the charging and discharging energy climbing rate of the battery and heat storage tank.

8. The method according to any one of claims 2 to 7, characterized in that: The optimal scheduling scheme to ensure the lowest operating loss, lowest carbon emission cost and highest energy efficiency is achieved by taking each other into consideration is as follows: taking the lowest operating loss and carbon emission reduction as the target constraints, taking energy efficiency as the highest energy efficiency as the main target, solving the multi-objective Pareto frontier, and using the fuzzy decision method to obtain the final optimal scheduling, and obtaining a set of park comprehensive energy system operation loss-energy efficiency-carbon emission balance schemes; among them, The operating loss target F1 includes the energy interaction cost C between the park and the power grid and natural gas company. buy , Equipment operation and maintenance cost C O , Energy sales cost C sell and demand response compensation cost C DR : Where: C e is the unit electricity purchase price; C g , C gs G is the price of gas purchased and sold by the unit; g,buy , G g,sell K is the gas purchase and sales volume of the park at time t; i is the unit operation and maintenance cost of equipment i; P i (t) is the working power of device i at time t; D is the compensation coefficient of the adjustable load change, D∈{r,t,c}; Where: The load change power that can be reduced at time t; is the load change power that can be transferred at time t; is the power change of the alternative load at time t; Maximum energy efficiency maxF2: Where: P Load (t) is the load power considering energy quality; P chg (t) and P dis (t) are the energy storage charging and discharging powers considering energy quality; P B (t) The purchased power taking into account the energy quality; Carbon emission reduction minimum minF3: Carbon emission target F3 is expressed through tiered carbon trading:

9. A method for balancing operating losses, energy efficiency and carbon emissions of a comprehensive energy system in a park, characterized in that: The following steps are involved: The optimization introduction module is used to introduce a step-by-step carbon trading mechanism for the park's comprehensive energy system containing electricity, gas and heat. Considering the demand side's ability to respond to adjustable demand, the research determines the strong absorption and high-efficiency dispatch of the park's comprehensive energy system under a highly adjustable mechanism. An energy efficiency model building module, used to establish energy efficiency models of energy supply structure and various energy equipment based on the tiered carbon trading mechanism and scheduling conditions; The calculation module is used to solve the energy efficiency model with the goal of minimizing operating loss, carbon emission cost and maximizing energy efficiency, obtain the Pareto frontier, and use the fuzzy decision-making method to ensure that the optimal scheduling plan is obtained when the operating loss, carbon emission cost and energy efficiency are balanced.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for causing a machine to execute the steps of any one of the methods described in claims 1-8.

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