Multi-time scale optimization method and system of integrated energy system considering electro-carbon coupling

Abstract

A kind of comprehensive energy system multi-time scale optimization method and system considering electric-carbon coupling, comprising: constructing electric-carbon model of each device in comprehensive energy system;Electric-carbon coupling price model based on power spot market electricity price and floating carbon price is constructed;Based on the electric-carbon model of each device and electric-carbon coupling price model, multi-time scale regulation optimization model with the goal of minimizing system total cost is constructed;Under the condition of meeting the constraint set including system each device power balance constraint, reserve capacity constraint, electric-carbon coupling price constraint, day regulation constraint, real-time deviation constraint and power grid safety constraint, the multi-time scale regulation optimization model is solved, and the optimal dispatching scheme is obtained.The depth of fusion of electric-carbon coupling mechanism is realized, the time-of-use electricity price signal of power spot market and multi-time scale carbon price signal are coordinated to guide the optimal dispatching of comprehensive energy system, and the low-carbon resilience and operation stability of urban comprehensive energy system under power spot market are enhanced.

Description

Technical Field

[0001] This invention belongs to the field of joint regulation of electricity spot market and carbon market, and particularly relates to a multi-timescale optimization method and system for integrated energy system that takes into account electricity-carbon coupling. Background Technology

[0002] Against the backdrop of global energy transition, the goal of low-carbon energy is being accelerated, and integrated energy systems, with their multi-energy coupling and complementary characteristics, have become an important vehicle for achieving this goal. Currently, with the rapid development of the electricity spot market and the carbon market, more research is focusing on the coordinated response of various elements of an integrated energy system to electricity price and carbon price signals. However, the time-of-use electricity price signals in the electricity spot market tend to lead the system to pursue economic operation, while the tiered carbon price signals in the carbon market guide the system to meet low-carbon targets. Simultaneously, the deep coupling of energy flow and carbon flow within the integrated energy system further increases the difficulty of coordinating the regulation of electricity price and carbon price signals.

[0003] Currently, traditional mathematical modeling of integrated energy systems is one-sided, failing to fully reflect the linkage between carbon flow and current between equipment. In the three-tiered time-scale scheduling optimization objectives of the electricity spot market, only fixed carbon trading costs are considered, failing to integrate with multiple time scales. Carbon quota allocation is limited to a fixed quota method, making it difficult to respond to time-of-use price fluctuations in the electricity spot market, and lacking methods to decompose carbon quotas across multiple time scales. Electricity price signals and carbon price signals are considered separately in multi-time-scale optimization, neglecting their coupling relationship. An electricity-carbon price linkage mechanism has not yet been established, making it difficult for electricity price signals to effectively guide carbon emission reduction. Intraday and real-time scheduling lack closed-loop feedback and dynamic correction mechanisms for carbon emissions, making it difficult to adapt to the complex scenarios after a high proportion of renewable energy is integrated into urban integrated energy systems. Therefore, it is urgent to deeply analyze the coupling and synergistic relationship between electricity spot market prices and carbon trading market prices, using electricity-carbon coupled synergistic signals to guide the optimized scheduling of integrated energy systems, thereby achieving a balance between economic efficiency and low carbon emissions.

[0004] Patent CN117543538A proposes an energy system dispatch control method and system based on electricity-carbon coupling, along with a storage medium. By acquiring the predicted power of photovoltaic power plants participating in the dispatch and the power consumption of flexible loads within the energy system, along with the electricity and carbon prices in the joint electricity-carbon market, a two-layer dispatch model is established with constraints set. The two-layer dispatch model is then solved, and the dispatch scheme for the energy system is obtained based on the solution. Through iterative interaction between the joint market electricity and carbon prices and the energy system dispatch, the convergence of the energy system dispatch output and the joint market electricity and carbon prices is achieved. Based on the final solution, the dispatch output of distributed energy sources, the charging and discharging of energy storage systems, and the power consumption of flexible loads are optimized, along with the dispatch output of generators and renewable energy generation, thereby achieving balanced dispatch of the energy system and the joint market. Its shortcomings are: relying solely on day-ahead market electricity and carbon prices for dispatching, failing to construct an intraday, real-time dynamic feedback loop, unable to cope with the uncertainty of source and load caused by wind and solar fluctuations, using a fixed carbon price without considering a tiered carbon trading mechanism, failing to reflect the nonlinear impact of carbon emissions on costs, weakening the incentive effect of carbon emission reduction, and failing to model the electricity-carbon coupling characteristics of carbon capture (CCS) and carbon consumption (P2G) equipment, thus failing to quantify the contribution of carbon capture / consumption to system carbon emission reduction. The significant difference of this invention is: considering the energy supply and carbon emission characteristics of each device within the integrated energy system, constructing a physical model of the equipment, decomposing carbon quotas across multiple time scales based on a tiered carbon trading mechanism, constructing a multi-time-scale low-carbon economic regulation and optimization model, and dynamically correcting the carbon trading cost model based on the deviation between actual and projected carbon emissions.

[0005] Patent CN118842075A proposes a source-grid-load-storage coordinated planning method that considers electricity-carbon coupling and flexibility supply-demand balance. It establishes an electricity-carbon coupling model, a demand response model, and a flexibility supply-demand balance model. The upper layer aims to minimize the equivalent annual comprehensive cost of power system construction investment, while the lower layer aims to minimize the sum of the annual operating cost and annual carbon trading cost of the power system. This establishes a two-layer model for source-grid-load-storage coordinated planning, and an improved adaptive genetic algorithm is used to solve the established two-layer model. This method can improve the capacity of new energy power sources in the system, reduce carbon emissions of the power system, improve the utilization rate of new energy sources, enhance the system's flexibility, and improve the system's operational level. However, its shortcomings include: only considering long-term power planning and not involving multi-timescale scheduling strategies, making it difficult for the planning results to adapt to the dynamic allocation needs of carbon quotas in real-time operation; carbon trading costs are calculated annually without considering the rolling correction of intraday and real-time carbon emission deviations, making it unable to respond to short-term carbon market fluctuations; and electrical signals and carbon signals act independently on scheduling without constructing a dynamic correlation model between them. The significant difference of this invention lies in its design of a multi-timescale decomposition method for carbon allowances, which decomposes the total 24-hour carbon allowance into day-ahead, intraday, and real-time stages. It also proposes an electricity-carbon coupled pricing model that combines the tiered carbon price in the carbon market with the time-of-use electricity price in the spot market. The cost of electricity purchase is dynamically adjusted through a floating carbon price coefficient. Furthermore, the carbon trading cost model is corrected during the intraday and real-time optimization stages. Based on actual carbon emissions, the allowances and trading ranges for subsequent cycles are adjusted to form a closed-loop rolling optimization. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a multi-timescale optimization method and system for integrated energy systems that considers electricity-carbon coupling. Specific objectives include: constructing a refined mathematical model of energy coupling equipment, carbon capture systems, and carbon consumption equipment within an urban area, clarifying the coupling relationship between equipment energy supply and carbon emissions; analyzing the electricity-carbon coupling mechanism, establishing a dynamic carbon quota allocation method and a tiered carbon trading cost model, and realizing the linkage between the carbon trading market and the electricity spot market; constructing an optimization model for day-ahead low-carbon economic dispatch, intraday carbon quota adjustment, and real-time carbon emission feedback of the integrated energy system under the electricity spot market, proposing a low-carbon economic operation strategy for the integrated energy system, and achieving low-carbon operation of the urban integrated energy system under the joint operation of the electricity spot market and the carbon trading market.

[0007] To achieve the above-mentioned objectives, the present invention specifically adopts the following technical solution.

[0008] This invention discloses a multi-timescale optimization method for a comprehensive energy system considering electro-carbon coupling, comprising the following steps:

[0009] S1. Construct an electricity-carbon model for each device within the integrated energy system;

[0010] S2. Construct an electricity-carbon coupled pricing model based on electricity spot market prices and floating carbon prices;

[0011] S3. Based on the electricity-carbon model and electricity-carbon coupling price model of each device, construct a multi-timescale regulation and optimization model with the goal of minimizing the total system cost;

[0012] S4. Under the constraint set conditions including power balance constraints of each device in the system, reserve capacity constraints, electricity-carbon coupling price constraints, intraday adjustment constraints, real-time deviation constraints and grid security constraints, the multi-time-scale control optimization model is solved to obtain the optimized scheduling scheme.

[0013] More preferably,

[0014] The electric-carbon models for each device include: natural gas combined heat and power (CHP) units, gas boilers, electric chillers, wind power generation systems, photovoltaic power generation systems, and corresponding electric power models, carbon emission models, carbon capture system models, and carbon consumption equipment models for electric energy storage.

[0015] More preferably,

[0016] The construction of the electricity-carbon coupled pricing model based on electricity spot market prices and floating carbon prices specifically includes:

[0017] Calculate the day-ahead system carbon allowance of the integrated energy system, and decompose the day-ahead system carbon allowance into multiple time scales, including intraday and real-time, to obtain the system carbon allowance at each time scale.

[0018] Based on the system carbon quotas at each time scale, the length of the tiered carbon trading interval at each time scale is calculated. Combined with the carbon trading benchmark price and the tiered price increase, a tiered carbon trading cost model is constructed for each time scale.

[0019] Based on the electricity spot market price and the tiered carbon trading cost at various time scales, an electricity-carbon coupled price model is constructed.

[0020] More preferably,

[0021] The calculation of the day-ahead system carbon allowance of the integrated energy system, and the decomposition of the day-ahead system carbon allowance into intraday and real-time multi-timescales, specifically includes:

[0022] The day-ahead carbon allowance is the sum of the day-ahead carbon allowance for purchased electricity, the carbon allowance for natural gas combined heat and power (CHP) units, the carbon allowance for gas-fired boilers, and the carbon allowance for system gas load.

[0023] Intraday system carbon allowances and real-time system carbon allowances are calculated as follows:

[0024] ;

[0025] ;

[0026] In the formula, For intraday system carbon quotas, determined by the day-ahead scheduling period. t Decomposed into corresponding intraday scheduling periods t ; For the current system carbon allowance, For the system in recent days t Carbon emissions over a given period; For the corresponding scheduling period within the day t The projected carbon emissions; For real-time system carbon quotas, the daily scheduling period is used. t Decomposed into real-time scheduling periods r ; For the system's intraday scheduling period t The actual carbon emissions; To correspond to the scheduling period in real time r The projected carbon emissions.

[0027] More preferably,

[0028] The lengths of the tiered carbon trading intervals at each time scale include the daily tiered carbon trading interval length, the intraday tiered carbon trading interval length, and the real-time tiered carbon trading interval length; wherein the intraday tiered carbon trading interval length and the real-time tiered carbon trading interval length are calculated as follows:

[0029] ;

[0030] ;

[0031] In the formula, The length of the day-ahead tiered carbon trading range exists as a system decision variable and is obtained through day-ahead optimization. The length of the intraday tiered carbon trading range. This refers to the length of the real-time tiered carbon trading range.

[0032] More preferably,

[0033] The construction of the electricity-carbon coupling price model includes a day-ahead electricity-carbon coupling price model, an intraday electricity-carbon coupling price model, and a real-time electricity-carbon coupling price model, specifically as follows:

[0034] The current electricity-carbon coupling price model is as follows:

[0035] ;

[0036] In the formula, The current day's price for electro-carbon coupling; This refers to the pre-emptive electricity price. The current floating carbon price; The current average carbon price of the integrated energy system. For the day before t Influence coefficient of carbon emission factor at any given time;

[0037] The intraday electricity-carbon coupling price model is as follows:

[0038] ;

[0039] In the formula, The intraday price of electro-carbon coupling; The electricity price was cleared out a few days ago; The carbon price fluctuates within the day; The average carbon price of the integrated energy system during the day. Within the day Influence coefficient of carbon emission factor at any given time;

[0040] The real-time electrocarbon coupling price model is as follows:

[0041] ;

[0042] In the formula, For real-time electro-carbon coupling price; For real-time electricity prices; For real-time floating carbon prices; The real-time average carbon price of the integrated energy system. For real time r The impact coefficient of carbon emission factors at any given time.

[0043] More preferably,

[0044] Recently t The impact coefficient of the carbon emission factor at any given time is calculated as follows:

[0045] ;

[0046] In the formula, , These are the upward and downward floating carbon price coefficients, respectively. , They are respectively t Carbon emission factor at any time, carbon emission factor threshold.

[0047] More preferably,

[0048] The multi-timescale control optimization model with the goal of minimizing the total system cost includes a day-ahead control optimization model, an intraday control optimization model, and a real-time control optimization model. The intraday control optimization model and the real-time control optimization model both use the difference between the actual carbon emissions and the expected carbon emissions in the current scheduling period to correct the expected carbon emissions in the next scheduling period, and calculate the corrected electricity-carbon coupling price based on the corrected expected carbon emissions. Then, the electricity-carbon coupling price is fed back to the objective function and constraints of the next scheduling cycle.

[0049] More preferably,

[0050] The electricity-carbon coupling price constraint specifically means that the average floating carbon price during the total scheduling period is the same as the average carbon price of the integrated energy system.

[0051] Another aspect of the present invention discloses a multi-timescale optimization system for a comprehensive energy system considering electricity-carbon coupling based on the aforementioned method, including an equipment electricity-carbon model construction module, an electricity-carbon coupling price model construction module, a multi-timescale regulation and optimization model construction module, and a scheduling scheme acquisition module;

[0052] The device electricity-carbon model building module is used to build electricity-carbon models for each device in an integrated energy system.

[0053] The electricity-carbon coupling price model construction module is used to build an electricity-carbon coupling price model based on the spot market electricity price and the floating carbon price.

[0054] The multi-timescale regulation and optimization model construction module is used to construct a multi-timescale regulation and optimization model with the goal of minimizing the total system cost, based on the electricity-carbon model of each device and the electricity-carbon coupling price model.

[0055] The scheduling scheme acquisition module is used to solve the multi-timescale control optimization model under the constraint set conditions, which includes power balance constraints of each device in the system, reserve capacity constraints, electricity-carbon coupling price constraints, intraday adjustment constraints, real-time deviation constraints and grid security constraints, so as to obtain the optimized scheduling scheme.

[0056] The beneficial effects of this invention are compared with those of the prior art:

[0057] By constructing refined mathematical models of electricity-carbon related equipment, unified modeling of energy coupling equipment, carbon capture systems, and carbon consumption equipment is achieved, providing a precise foundation for electricity-carbon coupling analysis. The established dynamic carbon quota allocation model solves the problem of mismatch between electricity spot market price signals and carbon trading market carbon price signals caused by traditional fixed quotas and single carbon prices, enhancing the guiding role of carbon costs in power dispatch. The multi-time-scale optimization model achieves synergistic optimization of low-carbon and economic objectives through day-ahead, intraday, and real-time carbon emission closed-loop feedback. Through the deep integration of the electricity-carbon coupling mechanism, the time-of-use electricity price signals of the electricity spot market and multi-time-scale carbon price signals are synergistically guided to optimize the dispatch of the integrated energy system, improving the system's ability to cope with renewable energy fluctuations and enhancing the low-carbon resilience and operational stability of urban integrated energy systems under the electricity spot market. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the multi-timescale optimization method for integrated energy systems that takes into account the coupling of electricity and carbon in this invention.

[0059] Figure 2 This is a schematic diagram of the solution process for the multi-timescale regulation and optimization model of this invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0061] like Figure 1 As shown, this invention proposes a multi-timescale optimization method for a comprehensive energy system considering electro-carbon coupling. This method includes the following steps:

[0062] S1. Construct an electricity-carbon model for each device within the integrated energy system;

[0063] Specifically, S1 includes the following steps:

[0064] S101: Construct equipment power model and carbon emission model;

[0065] 1) Combined Gas Power Generation (CCHP) Unit

[0066] As the most important energy coupling device in a comprehensive energy system, natural gas combined cooling, heating, and power (CCHP) system can utilize natural gas to produce electricity, cooling, and heating energy. Its mathematical model is as follows:

[0067] (1) Mathematical model for electric power production

[0068] (1);

[0069] In the formula, Power generation for CCHP; CCHP power generation efficiency; Power input for natural gas.

[0070] (2) Mathematical model for thermal power production

[0071] (2);

[0072] In the formula, This refers to the heating power of the CCHP. This refers to the heating efficiency of CCHP.

[0073] (3) Mathematical model for cold power production

[0074] (3);

[0075] In the formula, This refers to the cooling capacity of the CCHP. The coefficient of performance (COP) for cooling is 1 / 3 of the CCHP (Constant Refrigeration). The flow splitting coefficient controls the proportion of heat energy allocated to cooling.

[0076] (4) Mathematical model of carbon emissions

[0077] (4);

[0078] In the formula, for t Carbon emissions generated by CCHP units at any given time; It is a carbon emission factor for natural gas.

[0079] 2) Gas-fired boiler

[0080] (1) Equipment mathematical model

[0081] (5);

[0082] In the formula, This refers to the heating capacity of the gas-fired boiler. For the heating efficiency of gas-fired boilers; The amount of gas consumed for heating in a gas-fired boiler; It has a low calorific value, similar to natural gas.

[0083] (2) Mathematical model of carbon emissions

[0084] (6);

[0085] In the formula, For electric boilers t Carbon emissions at any given moment.

[0086] 3) Electric refrigeration unit

[0087] Electric chillers use electricity for cooling, thus falling under the electricity consumption end. They do not directly generate carbon emissions themselves; instead, carbon emissions are accounted for at the upstream power generation end. The mathematical model for this equipment is as follows:

[0088] (7);

[0089] In the formula, This refers to the cooling power of the electric refrigeration unit. The energy efficiency ratio of an electric refrigeration unit; This refers to the refrigeration energy consumption of an electric refrigeration system.

[0090] 4) Wind power generation

[0091] (8);

[0092] In the formula, , These are wind power generation capacity and wind power rated capacity, respectively. , , , These are the cut-in wind speed, rated wind speed, cut-out wind speed, and actual wind speed, respectively. This represents the characteristic coefficient of the wind turbine.

[0093] Wind power is a clean energy source with zero carbon emissions during operation.

[0094] 5) Photovoltaic power generation

[0095] (9);

[0096] In the formula, Photovoltaic power generation; Photoelectric efficiency; The area of ​​the photovoltaic panel; Light intensity; Temperature coefficient; , These are the actual temperature and the reference temperature, respectively.

[0097] Since photovoltaic power generation equipment only utilizes natural environmental resources to generate electricity during operation and does not involve carbon emissions, the carbon emissions during the operation phase are 0.

[0098] 6) Energy storage

[0099] Energy storage does not emit direct carbon emissions during operation; however, the source of its charging and discharging power may indirectly contribute to carbon emissions. Therefore, it is not allocated carbon emission credits. The mathematical model for the equipment is as follows:

[0100] (10);

[0101] In the formula, , They are respectively t time, t The amount of stored energy at time -1; , These are the charging and discharging efficiencies, respectively. , These represent charging and discharging power, respectively.

[0102] S102: Constructing a carbon capture system model

[0103] Carbon capture systems (CCS) can absorb carbon dioxide generated by various devices in an integrated energy system for power generation or cooling / heating, thereby achieving carbon emission reduction. The physical model of the equipment is as follows:

[0104] (11);

[0105] In the formula, Carbon capture amount; Carbon capture rate; This refers to the system's energy consumption.

[0106] S103: Constructing a carbon digestion equipment model

[0107] Electricity-to-gas (P2G) conversion enables the conversion of electricity into gas through water electrolysis to produce hydrogen and methanation. During operation, it does not generate carbon emissions but instead consumes some carbon dioxide. Its physical model is as follows:

[0108] (12);

[0109] In the formula, , These are hydrogen production capacity and gas production capacity, respectively. , These are electrolysis efficiency and methanation efficiency, respectively. Energy consumption for hydrogen production via water electrolysis; This represents the carbon consumption of the methanation reaction.

[0110] S2. Construct an electricity-carbon coupled pricing model based on electricity spot market prices and floating carbon prices;

[0111] Specifically, S2 includes the following steps:

[0112] S201: Calculate the day-ahead system carbon allowance of the integrated energy system, and decompose the day-ahead system carbon allowance into multiple time scales, including intraday and real-time, to obtain the carbon allowance at each time scale.

[0113] Currently, relevant agencies issue free carbon emission allowances to each carbon dioxide emitting device within an integrated energy system. When carbon emissions do not exceed the allowance, the system sells the allowance to generate revenue; when carbon emissions exceed the allowance, the system purchases more allowances, increasing carbon costs, thereby guiding the system towards low-carbon operation.

[0114] Specifically, the initial carbon emission allowance model is calculated as follows:

[0115] Based on the modeling of various devices within the S1 integrated energy system, it is known that, except for wind and solar power generation which has no carbon emissions during operation, all other devices (including the carbon capture system – where carbon emissions from electricity consumption are internalized into the devices) will generate carbon emissions during operation. Therefore, the carbon quota model is constructed as follows:

[0116] (13);

[0117] In the formula, , , , , These are respectively the day-ahead carbon quota, the carbon quota for purchased electricity, the carbon quota for natural gas combined heat and power (CHP) units, the carbon quota for gas-fired boilers, and the carbon quota for gas load in the system; , , These are the benchmark factors for carbon emissions per unit of electricity, heat, and cooling, respectively. Carbon emission factors for electricity used by electrical equipment; This refers to the system's externally purchased power. For system gas load; T This represents the total number of scheduling time slots for the day before.

[0118] The day-ahead system carbon allowance of the integrated energy system is calculated, and the day-ahead system carbon allowance is decomposed into intraday and real-time multi-timescale decompositions. The specific calculation and decomposition methods are as follows:

[0119] The current trading cycle is 24 hours, and the current carbon allowance can be calculated using equation (13). Since multi-timescale optimization scheduling is required, the carbon allowance is decomposed into various timescales here.

[0120] (1) Daily carbon allowance

[0121] The scheduling plan is formulated 1 hour in advance for the day, and the scheduling cycle is 4 hours. The daily carbon allowance can be calculated based on the previous day's carbon allowance, as follows:

[0122] (14);

[0123] In the formula, For the system in recent days t Carbon emissions over a given period; For the corresponding scheduling period within the day t The projected carbon emissions; The daytime scheduling period t Decomposed into corresponding intraday scheduling periods t Carbon quotas.

[0124] (2) Real-time carbon quota

[0125] The scheduling plan is prepared 15 minutes in advance, with a cycle of 1 hour. Real-time carbon allowances are determined based on intraday data, using the following method:

[0126] (15);

[0127] In the formula, For the system's intraday scheduling period t The actual carbon emissions; To correspond to the scheduling period in real time r The projected carbon emissions; For intraday scheduling period t Decomposed into real-time scheduling periods r Carbon quotas.

[0128] S202: Based on the system carbon allowances at each time scale, the length of the carbon trading interval for each time scale is calculated. Combined with the carbon trading benchmark price and tiered price increases, a tiered carbon trading cost model is constructed for each time scale. The specific construction method is as follows:

[0129] 1) Actual carbon emissions from integrated energy systems

[0130] The actual carbon emissions of the system are calculated based on the day-ahead, intraday, and real-time unit output status, as well as the system's power interaction with the external environment. The specific method is as follows:

[0131] (16);

[0132] In the formula, Total carbon emissions; Carbon emissions from purchased electricity; This represents the carbon emissions from the system's air load.

[0133] To calculate the actual carbon emissions at the day-ahead, intraday, and real-time stages, simply substitute the scheduling cycle at each time scale, the equipment output, and the system status within each time period into formula (16). This will not be elaborated further here.

[0134] 2) Carbon trading cost time-series decomposition calculation model

[0135] (1) Day-ahead carbon trading costs

[0136] By comparing day-ahead carbon emissions with day-ahead carbon allowances, the carbon share eligible to participate in the carbon market can be determined, as shown in the following formula:

[0137] (17);

[0138] In the formula, The carbon share of the system participating in the carbon market in the current phase; This represents the carbon emissions for the current period.

[0139] The tiered carbon trading mechanism defines the trading ranges within the system and the unit price for carbon transactions in different ranges, forming a tiered carbon trading cost model. The greater the carbon emissions, the higher the cost. The current tiered carbon trading cost model is as follows:

[0140] (18);

[0141] In the formula, The system's day-ahead carbon trading costs; , , These are the carbon trading benchmark price, the tiered price increase, and the length of the day-ahead tiered carbon trading range; among which, the length of the day-ahead tiered carbon trading range... The length of the day-ahead tiered carbon trading range exists as a system decision variable and is obtained through day-ahead optimization. The length of the intraday tiered carbon trading range. This refers to the length of the real-time tiered carbon trading range.

[0142] (2) Intraday carbon trading costs

[0143] The interval length is determined based on the intraday carbon allowance:

[0144] (19);

[0145] In the formula, This refers to the length of the intraday tiered carbon trading range.

[0146] By comparing the actual daily carbon emissions with the daily carbon emission allowance, the carbon share eligible to participate in the carbon trading market can be determined. The calculation formula is as follows:

[0147] (20);

[0148] In the formula, The carbon share of the system participating in the carbon market during the intraday phase; This represents the carbon emissions during the day.

[0149] The intraday carbon trading cost model is as follows:

[0150] (twenty one);

[0151] In the formula, This represents the system's intraday carbon trading costs.

[0152] (3) Real-time carbon trading costs

[0153] Similarly, the interval length is determined based on the real-time carbon quota:

[0154] (twenty two);

[0155] In the formula, This refers to the length of the real-time tiered carbon trading range.

[0156] By comparing real-time actual carbon emissions with real-time carbon emission allowances, the carbon share eligible to participate in the carbon trading market can be determined. The calculation formula is as follows:

[0157] (twenty three);

[0158] In the formula, The carbon share for the real-time stage system to participate in the carbon trading market; This represents the actual carbon emissions of the system in the real-time phase.

[0159] The real-time carbon trading cost model is as follows:

[0160] (twenty four);

[0161] In the formula, This refers to the real-time carbon trading cost of the system.

[0162] S203: Based on electricity spot market prices and tiered carbon trading costs at various time scales, construct an electricity-carbon coupled pricing model; the construction method is as follows:

[0163] 1) Calculate the average carbon price of the integrated energy system

[0164] The average carbon price of the system is calculated based on the concept of averaging.

[0165] (1) The current average carbon price of the integrated energy system is calculated as follows:

[0166] (25);

[0167] In the formula, This represents the current average carbon price of the integrated energy system.

[0168] (2) The calculation method for the daily average carbon price of the integrated energy system is as follows:

[0169] (26);

[0170] In the formula, This represents the average carbon price of the integrated energy system during the day.

[0171] (3) The calculation method for the real-time integrated energy system average carbon price is as follows:

[0172] (27);

[0173] In the formula, The average carbon price of the integrated energy system in real time.

[0174] 2) Constructing an electrocarbon coupling price model

[0175] By combining the day-ahead clearing tariff, the day-ahead clearing tariff, the real-time tariff, and the carbon price across multiple time scales, the electricity-carbon coupled pricing model is as follows:

[0176] (1) The current electricity-carbon coupling price model is:

[0177] (28);

[0178] (29);

[0179] In the formula, The current day's price for electro-carbon coupling; This refers to the pre-emptive electricity price. The current floating carbon price; For the day before t Influence coefficient of carbon emission factor at any given time; , These are the upward and downward floating carbon price coefficients, respectively. , They are respectively t Carbon emission factor at any time, carbon emission factor threshold.

[0180] Specifically, the upward and downward carbon price coefficients are determined based on carbon emissions. When carbon emissions exceed a predetermined value, an upward carbon price coefficient is set, increasing carbon costs to penalize high-emission behavior. When carbon emissions fall below the predetermined value, a downward carbon price coefficient is set to incentivize low-carbon energy use. The value is determined based on the cost required to reduce unit carbon emissions. The carbon emission factor threshold is based on the average carbon emissions of the regional integrated energy system. The carbon emission factor is determined by a weighted average of the unit carbon emission factors of each energy device within the regional integrated energy system, combined with real-time power output.

[0181] (2) The intraday electricity-carbon coupling price model is as follows:

[0182] (30);

[0183] In the formula, The intraday price of electro-carbon coupling; The electricity price was cleared out a few days ago; For intraday floating carbon prices, Within the day The influence coefficient of carbon emission factors at any given time. for Carbon emission factors at all times.

[0184] (3) The real-time electricity-carbon coupling price model is as follows:

[0185] (31);

[0186] In the formula, For real-time electro-carbon coupling price; For real-time electricity prices; For real-time floating carbon prices; For real time r The influence coefficient of carbon emission factors at any given time. for r Carbon emission factors at all times.

[0187] S3. Based on the electricity-carbon model and electricity-carbon coupling price model of each device, construct a multi-timescale regulation and optimization model with the goal of minimizing the total system cost;

[0188] Specifically, the multi-timescale control optimization model with the goal of minimizing the total system cost includes a day-ahead control optimization model, an intraday control optimization model, and a real-time control optimization model. The intraday control optimization model and the real-time control optimization model both use the difference between the actual carbon emissions and the expected carbon emissions in the current scheduling period to correct the expected carbon emissions in the next scheduling period, and calculate the corrected electricity-carbon coupling price based on the corrected expected carbon emissions. Then, the electricity-carbon coupling price is fed back to the objective function and constraints of the next scheduling cycle.

[0189] Specifically, S3 includes the following steps:

[0190] S301: Constructing a Day-ahead Regulation Optimization Model

[0191] Assuming the safe unit combination is already determined in the current phase, only safety-constrained economic dispatch optimization is performed, with the objective of minimizing the total system cost. This considers unit output constraints, system power balance constraints, transmission line constraints, reserve constraints, and unit ramping constraints, and takes into account electricity-carbon coupling prices, adjusting the electricity purchase cost in the cost item. The specific optimization model is as follows:

[0192] 1) Objective function

[0193] The goal is to minimize the total system cost.

[0194] (32);

[0195] In the formula, , , They are respectively from the day before t The unit operation and maintenance costs, energy purchase costs, and wind and solar curtailment costs are calculated at specific times. The specific calculation method is as follows:

[0196] (33);

[0197] (34);

[0198] (35);

[0199] In the formula, , , , , , , , These are the unit operation and maintenance costs of natural gas combined heat and power (CHP) units, gas-fired boilers, electric chillers, wind power generation systems, photovoltaic power generation systems, electric energy storage, carbon capture systems, and carbon digestion equipment, respectively. , , These are the power generation, heating, and cooling capacities of the current-day natural gas combined cycle power (CCHP) unit. , , , , , , , These are, respectively, the day-ahead heating power of gas-fired boilers, the refrigeration power of electric refrigeration units, the power generation of wind turbines, the power generation of photovoltaic power, the charging power of energy storage systems, the discharging power of energy storage systems, the carbon capture capacity of carbon capture systems, and the gas production capacity of carbon digestion equipment. For the day before t The system's external power consumption; , , These are the penalty coefficients for wind and solar curtailment, and the day-ahead... t The amount of wind and solar power abandoned at any given moment.

[0200] 2) Constraints

[0201] (1) Power balance constraint

[0202] Power balance:

[0203] (36);

[0204] In the formula, For the system t Constant electrical load.

[0205] Thermal power balance:

[0206] (37);

[0207] In the formula, For the system t Constant heat load.

[0208] Cold power balance:

[0209] (38);

[0210] In the formula, For the system t Always keep the load low.

[0211] Gas power balance:

[0212] (39);

[0213] In the formula, For the system t Constant air load.

[0214] (2) Transmission line power constraints

[0215] (40);

[0216] In the formula, , Each is a comprehensive energy system in t Minimum and maximum power exchange with the external power grid at all times.

[0217] (3) Equipment output constraints

[0218] (41);

[0219] In the formula, , In the integrated energy system i The equipment is t The output power at any given time and the upper limit of the output power, among which .

[0220] (4) Energy storage state constraints

[0221] (42);

[0222] In the formula, , These are the lower and upper limits of the energy storage capacity, respectively. , These represent the initial and final states of electrical energy storage, respectively.

[0223] (5) Unit ramping constraints

[0224] (43);

[0225] In the formula, , , Each is within the integrated energy system i The equipment is t Output power at time -1 i The equipment's lower and upper ramp limits.

[0226] (6) Reserve capacity constraints

[0227] (44);

[0228] In the formula, For the system in t The required reserve capacity at any time.

[0229] (7) Price constraints of electro-carbon coupling

[0230] (45).

[0231] S302: Constructing an Intraday Regulation Optimization Model

[0232] 1) Construct an intraday optimized scheduling model

[0233] Intraday optimization aims to minimize the total system cost, specifically including intraday unit operation and maintenance costs, intraday energy purchase costs, intraday wind and solar curtailment costs, and intraday unit regulation costs. Among these, intraday energy purchase costs and unit regulation costs are the primary considerations. The remaining costs are calculated similarly to those calculated in the day-ahead model, and the specific calculations will not be elaborated here. The constraints to be considered are intraday unit ramp-up constraints, intraday regulation constraints, and electricity-carbon coupling price constraints. The remaining constraints can be obtained by modifying the equipment status and output based on the day-ahead model, and will not be elaborated here.

[0234] (1) Objective function

[0235] (46);

[0236] In the formula, , , , intraday tThe unit's operation and maintenance costs, energy purchase costs, wind and solar curtailment costs, and regulation costs at all times; M This represents the total scheduling timeframe for the day. The specific calculation method is as follows:

[0237] (47);

[0238] (48);

[0239] In the formula, Within the day t The system's externally purchased electricity consumption at any given time; , , , , , , , These are the daily adjustment costs for each piece of equipment in the integrated energy system; , , , , , , , , , , For each device within a day t The difference between the output power at a given time and the output power at a given day.

[0240] (2) Constraints

[0241] Intraday unit ramp-up constraints:

[0242] (49);

[0243] Intraday adjustment constraints:

[0244] (50);

[0245] In the formula, The system allows for intraday adjustment rates; , respectively equipment i Within the day t Compared to the previous day's output adjustment, the previous day t The planned output value at any given moment.

[0246] Intraday price constraints for electro-carbon coupling:

[0247] (51).

[0248] 2) Constructing an intraday carbon trading correction model

[0249] Due to significant uncertainties in the source and load of integrated energy systems, there will always be a certain deviation between the projected daily carbon emissions and the actual emissions. The difference between actual and projected carbon emissions is used to revise the carbon cost model for the next period, thereby adjusting the intraday tiered carbon trading quotas and trading range. The specific method is as follows:

[0250] (52);

[0251] In the formula, For the system in Real-time scheduling of actual carbon emissions during specific time periods; This is the revised value for carbon emissions during the day's scheduling.

[0252] Based on the carbon emission correction value of the previous intraday scheduling cycle, the predicted carbon emission value for the next intraday scheduling cycle can be calculated, making carbon emission forecasting more accurate. The calculation method is as follows:

[0253] (53);

[0254] In the formula, For scheduling time one t The sum of carbon emission revisions prior to 1; Carbon emissions projected for the revised intraday scheduling cycle (scheduling from) t (Starting at moment 1).

[0255] Will Substituting into equations (14) and (19), the adjusted intraday tiered carbon trading cost model can be calculated, and the adjusted electricity-carbon coupling price can be obtained, assumed to be... The price is then fed back into the objective function and constraints of the next scheduling cycle within the day, forming a closed-loop rolling optimization for intraday carbon cost adjustment.

[0256] S303: Constructing a Real-Time Control and Optimization Model

[0257] 1) Construct a real-time optimized scheduling model

[0258] Real-time optimization aims to minimize the total system cost, specifically including real-time equipment operation and maintenance costs, real-time energy purchase costs, real-time wind and solar curtailment costs, real-time regulation costs, and real-time deviation costs. Among these, real-time energy purchase costs, real-time regulation costs, and real-time deviation costs are the primary considerations. The calculation methods for the remaining costs are similar to those used in day-ahead calculations, and the specific calculations will not be elaborated here. The constraints mainly consider output deviation constraints, real-time unit ramp-up constraints, and grid security constraints. The remaining constraints are the same as those used in day-ahead calculations, and only the equipment status needs to be replaced with the real-time status, which will not be elaborated here.

[0259] (1) Objective function

[0260] (54);

[0261] In the formula, , , , , Real-time r The operating and maintenance costs, energy purchase costs, wind and solar curtailment costs, regulation costs, and deviation costs of the generating units at each time point; R The total time period for real-time scheduling is calculated as follows:

[0262] (55);

[0263] (56);

[0264] (57);

[0265] In the formula, For real-time stage r The time-based system purchases electricity from external sources; , , , , , , , , , , Real-time for each device r The difference between the time of day and the daily power output; This is the deviation penalty coefficient; , , , , , , , , , , Real-time stage r The difference between the output of each device in the timekeeping system and the daily plan.

[0266] (2) Constraints

[0267] Real-time unit ramp-up constraints:

[0268] (58);

[0269] Real-time power deviation constraints:

[0270] (59);

[0271] In the formula, For real time r The deviation between the current output and the planned output; This is the real-time deviation penalty coefficient.

[0272] Power grid security constraints:

[0273] (60);

[0274] In the formula, , , Real-time data for regional integrated energy systems r Frequency at any given time, rated frequency, and maximum permissible deviation frequency; , , Real-time r Time Node l Voltage, upper and lower voltage limits.

[0275] 2) Construct and revise the real-time carbon trading cost model

[0276] Similarly, the carbon trading cost model also needs to be revised based on the actual situation of the real-time equipment. The specific revision methods are as follows:

[0277] (61);

[0278] In the formula, For the system in r Real-time scheduling of actual carbon emissions during specific time periods; This is a real-time carbon emission correction value.

[0279] Based on the carbon emission correction value of the previous real-time scheduling cycle, the predicted carbon emission value for the next real-time scheduling cycle can be calculated as follows:

[0280] (62);

[0281] In the formula, For scheduling time two r The sum of carbon emission revisions prior to 1; Carbon emissions are projected for the revised real-time scheduling cycle (scheduling from...). r (Starting at moment 1).

[0282] Will Substituting into equations (15) and (22), the modified real-time carbon trading cost model is obtained, and then the modified real-time electricity-carbon coupling price is derived, assuming it to be... This price will be fed back into the objective function of the next time period in real-time scheduling, forming a real-time carbon cost correction closed-loop optimization scheduling.

[0283] S4. Under the constraint set conditions including power balance constraints of each device in the system, reserve capacity constraints, electricity-carbon coupling price constraints, intraday adjustment constraints, real-time deviation constraints and grid security constraints, the multi-time-scale control optimization model is solved to obtain the optimized scheduling scheme.

[0284] Specifically, the solution process is as follows: Figure 2 As shown.

[0285] This invention also claims protection for a multi-timescale optimization system for an integrated energy system considering electricity-carbon coupling based on the aforementioned method, including an equipment electricity-carbon model construction module, an electricity-carbon coupling price model construction module, a multi-timescale regulation and optimization model construction module, and a scheduling scheme acquisition module;

[0286] The device electricity-carbon model building module is used to build electricity-carbon models for each device in an integrated energy system.

[0287] The electricity-carbon coupling price model construction module is used to build an electricity-carbon coupling price model based on the spot market electricity price and the floating carbon price.

[0288] The multi-timescale regulation and optimization model construction module is used to construct a multi-timescale regulation and optimization model with the goal of minimizing the total system cost, based on the electricity-carbon model of each device and the electricity-carbon coupling price model.

[0289] The scheduling scheme acquisition module is used to solve the multi-timescale control optimization model under the constraint set conditions, which includes power balance constraints of each device in the system, reserve capacity constraints, electricity-carbon coupling price constraints, intraday adjustment constraints, real-time deviation constraints and grid security constraints, so as to obtain the optimized scheduling scheme.

[0290] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A multi-timescale optimization method for a comprehensive energy system considering electro-carbon coupling, characterized in that, Includes the following steps: S1. Construct an electricity-carbon model for each device within the integrated energy system; S2. Construct an electricity-carbon coupling price model based on electricity spot market prices and floating carbon prices; calculate the day-ahead system carbon allowance of the integrated energy system, and decompose the day-ahead system carbon allowance into intraday and real-time multi-timescales to obtain the system carbon allowance at each timescale; calculate the length of the tiered carbon trading interval at each timescale based on the system carbon allowance at each timescale, and construct a tiered carbon trading cost model at each timescale by combining the carbon trading benchmark price and the tiered price increase; construct an electricity-carbon coupling price model based on electricity spot market prices and tiered carbon trading costs at each timescale. S3. Based on the electricity-carbon model and electricity-carbon coupling price model of each device, construct a multi-timescale regulation and optimization model with the goal of minimizing the total system cost; S4. Under the constraint set conditions that include power balance constraints of each device in the system, reserve capacity constraints, electricity-carbon coupling price constraints, intraday adjustment constraints, real-time deviation constraints and grid security constraints, the multi-time-scale control optimization model is solved to obtain the optimized scheduling scheme; Intraday system carbon allowances and real-time system carbon allowances are calculated as follows: ； In the formula, For intraday system carbon quotas, determined by the day-ahead scheduling period. t Decomposed into corresponding intraday scheduling periods τ ; For the current system carbon allowance, For the system in recent days t Carbon emissions over a given period; For the corresponding scheduling period within the day τ The projected carbon emissions; For real-time system carbon quotas, the daily scheduling period is used. τ Decomposed into real-time scheduling periods r ; For the system's intraday scheduling period τ The actual carbon emissions; To correspond to the scheduling period in real time r The projected carbon emissions.

2. The multi-timescale optimization method for integrated energy systems considering electro-carbon coupling according to claim 1, characterized in that, The electric-carbon models for each device include: natural gas combined heat and power (CHP) units, gas boilers, electric chillers, wind power generation systems, photovoltaic power generation systems, and corresponding electric power models, carbon emission models, carbon capture system models, and carbon consumption equipment models for electric energy storage.

3. The multi-timescale optimization method for integrated energy systems considering electro-carbon coupling according to claim 2, characterized in that: The day-ahead carbon allowance is the sum of the day-ahead carbon allowance for purchased electricity, the carbon allowance for natural gas combined cycle power units, the carbon allowance for gas-fired boilers, and the carbon allowance for system gas load.

4. The multi-timescale optimization method for integrated energy systems considering electro-carbon coupling according to claim 3, characterized in that, The lengths of the tiered carbon trading intervals at each time scale include the daily tiered carbon trading interval length, the intraday tiered carbon trading interval length, and the real-time tiered carbon trading interval length; wherein the intraday tiered carbon trading interval length and the real-time tiered carbon trading interval length are calculated as follows: In the formula, The length of the day-ahead tiered carbon trading range exists as a system decision variable and is obtained through day-ahead optimization. The length of the intraday tiered carbon trading range. This refers to the length of the real-time tiered carbon trading range.

5. The multi-timescale optimization method for integrated energy systems considering electro-carbon coupling according to claim 4, characterized in that, The construction of the electricity-carbon coupling price model includes a day-ahead electricity-carbon coupling price model, an intraday electricity-carbon coupling price model, and a real-time electricity-carbon coupling price model, specifically as follows: The current electricity-carbon coupling price model is as follows: In the formula, The current day's price for electro-carbon coupling; This refers to the pre-emptive electricity price. The current floating carbon price; The current average carbon price of the integrated energy system. For the day before t Influence coefficient of carbon emission factor at any given time; The intraday electricity-carbon coupling price model is as follows: In the formula, The intraday price of electro-carbon coupling; The electricity price was cleared out a few days ago; The carbon price fluctuates within the day; The average carbon price of the integrated energy system during the day. Within the day Influence coefficient of carbon emission factor at any given time; The real-time electrocarbon coupling price model is as follows: In the formula, For real-time electro-carbon coupling price; For real-time electricity prices; For real-time floating carbon prices; The real-time average carbon price of the integrated energy system. For real time r The impact coefficient of carbon emission factors at any given time.

6. The multi-timescale optimization method for integrated energy systems considering electro-carbon coupling according to claim 5, characterized in that, Recently t The impact coefficient of the carbon emission factor at any given time is calculated as follows: In the formula, , These are the upward and downward floating carbon price coefficients, respectively. , They are respectively t Carbon emission factor at any time, carbon emission factor threshold.

7. The multi-timescale optimization method for integrated energy systems considering electro-carbon coupling according to claim 6, characterized in that, The multi-timescale control optimization model with the goal of minimizing the total system cost includes a day-ahead control optimization model, an intraday control optimization model, and a real-time control optimization model. The intraday control optimization model and the real-time control optimization model both use the difference between the actual carbon emissions and the expected carbon emissions in the current scheduling period to correct the expected carbon emissions in the next scheduling period, and calculate the corrected electricity-carbon coupling price based on the corrected expected carbon emissions. Then, the electricity-carbon coupling price is fed back to the objective function and constraints of the next scheduling cycle.

8. The multi-timescale optimization method for integrated energy systems considering electro-carbon coupling according to claim 7, characterized in that, The electricity-carbon coupling price constraint specifically means that the average floating carbon price during the total scheduling period is the same as the average carbon price of the integrated energy system.

9. A multi-timescale optimization system for an integrated energy system considering electricity-carbon coupling based on the method of any one of claims 1-8, comprising an equipment electricity-carbon model construction module, an electricity-carbon coupling price model construction module, a multi-timescale regulation and optimization model construction module, and a scheduling scheme acquisition module, characterized in that: The device electricity-carbon model building module is used to build electricity-carbon models for each device in an integrated energy system. The electricity-carbon coupling price model construction module is used to build an electricity-carbon coupling price model based on the electricity spot market price and floating carbon price; calculate the day-ahead system carbon quota of the integrated energy system, and decompose the day-ahead system carbon quota into intraday and real-time multi-time scales to obtain the system carbon quota at each time scale; calculate the length of the tiered carbon trading interval at each time scale based on the system carbon quota at each time scale, and construct the tiered carbon trading cost model at each time scale by combining the carbon trading benchmark price and the tiered price increase; and construct the electricity-carbon coupling price model based on the electricity spot market price and the tiered carbon trading cost at each time scale. The multi-timescale regulation and optimization model construction module is used to construct a multi-timescale regulation and optimization model with the goal of minimizing the total system cost, based on the electricity-carbon model of each device and the electricity-carbon coupling price model. The scheduling scheme acquisition module is used to solve the multi-timescale control optimization model under the constraint set conditions, which includes power balance constraints of each system device, reserve capacity constraints, electricity-carbon coupling price constraints, intraday adjustment constraints, real-time deviation constraints and grid security constraints, and obtain the optimized scheduling scheme. Intraday system carbon allowances and real-time system carbon allowances are calculated as follows: ； In the formula, For intraday system carbon quotas, determined by the day-ahead scheduling period. t Decomposed into corresponding intraday scheduling periods τ ; For the current system carbon allowance, For the system in recent days t Carbon emissions over a given period; For the corresponding scheduling period within the day τ The projected carbon emissions; For real-time system carbon quotas, the daily scheduling period is used. τ Decomposed into real-time scheduling periods r ; For the system's intraday scheduling period τ The actual carbon emissions; To correspond to the scheduling period in real time r The projected carbon emissions.

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