Comprehensive energy system multi-time scale optimization scheduling method considering electricity-gas-carbon transaction asynchronism

Abstract

The invention discloses an integrated energy system multi-time scale optimization scheduling method and system considering electricity-gas-carbon transaction asynchronism. The method comprises the following steps: acquiring annual load data and transaction signal data of an integrated energy system; establishing an annual time scale optimization model by taking a month as a decision cycle; establishing a daily time scale optimization model by taking hours as a decision cycle; solving the annual time scale optimization model to obtain monthly electricity and carbon trading strategies of the park, including monthly purchased electricity quantity, monthly purchased natural gas quantity and monthly purchased carbon emission quantity; the electric quantity purchased every month and the natural gas quantity are decomposed into everyday; and solving the daily time scale optimization model to obtain a park daily operation scheduling strategy. The comprehensive energy system multi-time scale optimization scheduling method considering electricity-gas-carbon transaction asynchronization can effectively consider differences of electricity, natural gas and carbon quota transactions in settlement and transaction time, realizes cost minimization, improves economical efficiency and robustness of a scheduling scheme, and improves scheduling efficiency. And a new solution thought is provided for the multi-transaction participation strategy and operation scheduling of the integrated energy system.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy systems. Specifically, considering the transaction asynchrony of electricity, natural gas and carbon quota transactions and the uncertainty of load signal prediction, a multi-time scale optimization scheduling method for park integrated energy systems is proposed. Background Art

[0002] As the global energy transition deepens, integrated energy systems are becoming a crucial enabler for achieving a low-carbon economy. By integrating multiple energy sources—electricity, gas, heat, and cooling—IES leverages the synergistic effects of energy production, conversion, and storage equipment, improving energy efficiency while also adapting to the demand for a high proportion of renewable energy access. However, the efficient operation of IES relies on the coordinated optimization of external multi-energy transactions and internal multi-energy complementarity. This challenge is particularly acute in the complex context of coupled electricity, natural gas, and carbon quota trading.

[0003] Currently, multi-energy trading systems exhibit significant asynchronous characteristics. Electricity trading features long-term annual and monthly contracts, as well as a well-established spot trading mechanism. Natural gas trading, primarily based on annual and monthly contracts, exhibits a strong sense of periodicity and planning. Carbon trading, however, increases the time complexity of transactions due to the inter-annual nature of compliance requirements and the high volatility of trading signals. Furthermore, the uncertainty of multiple trading signals and carbon emissions verification further exacerbates the difficulty of scheduling decisions, resulting in significant limitations for traditional optimization methods in dealing with complex trading conditions. These challenges highlight the urgent need to construct an optimized scheduling framework that can adapt to the asynchrony and uncertainty of trading transactions to achieve the economical and efficient operation of IES in multiple trading environments. Summary of the Invention

[0004] Purpose of the Invention: To overcome the shortcomings of the prior art, the present invention proposes a multi-timescale optimization scheduling method for an integrated energy system that considers the asynchrony of electricity, gas, and carbon trading. The present invention also provides a multi-timescale optimization scheduling method system for an integrated energy system that considers the asynchrony of electricity, gas, and carbon trading. This method solves the problem of optimizing the scheduling of integrated energy systems in complex trading environments.

[0005] Technical Solution: According to a first aspect of the present invention, a multi-time-scale optimization scheduling method for an integrated energy system considering the asynchrony of electricity-gas-carbon trading is provided. The method comprises the following steps:

[0006] S1 obtains annual load data and trading signal data of the integrated energy system;

[0007] S2 establishes an annual time scale optimization model with monthly decision cycles;

[0008] S3 establishes a daily time scale optimization model with hourly decision cycles;

[0009] S4 solves the annual timescale optimization model to obtain the park's monthly electricity and carbon trading strategies, including monthly electricity purchases, monthly natural gas purchases, and monthly carbon emissions purchases;

[0010] S5 breaks down the monthly electricity and natural gas purchases into daily averages;

[0011] S6 solves the daily time-scale optimization model and obtains the daily operation scheduling strategy of the park.

[0012] Furthermore, the annual timescale optimization model in step S2 focuses on optimizing the park's decision-making regarding participation in annual and monthly contract transactions, including the decomposition of medium- and long-term electricity contracts, contract procurement plans for natural gas trading, and the multi-year carbon quota reserve and trading required by carbon trading compliance. In particular, to address the significant asynchrony between carbon trading compliance requirements and transaction timing, the model introduces carbon emission verification and compliance rules, closely linking the current year's carbon quota trading decisions with the next year's compliance requirements, ensuring the park's trading and compliance strategies are coordinated across different timescales.

[0013] Specifically, the objective function of the annual time scale optimization model is expressed as:

[0014] minC total =C c +C g +C e (46)

[0015]

[0016] Where C d 、C g 、C e The costs of purchasing carbon allowances, natural gas, and electricity for the park; are the annual contract signals for electricity and gas in year y respectively; are the monthly contract signals for electricity and gas in month t of year y respectively; are the annual contract trading volumes of electricity and natural gas in year y respectively; are the monthly contract trading volumes of electricity and natural gas in month t in year y, respectively; are the carbon quota signals of month t in year y+1 and year y, respectively; They are the trading volumes of carbon quotas in the past of year y (i.e., carbon emissions in year y-1 traded in year y), the current period of year y, and the past period of year y+1 (i.e., the amount of carbon quotas required this year purchased next year); T is the number of months in a year, 12

[0017] Specifically, the constraints of the annual time scale optimization model include:

[0018] (1) Power Constraints:

[0019] Annual power consumption constraints:

[0020]

[0021] Monthly power consumption limit:

[0022]

[0023] Where, The monthly breakdown ratio of annual electricity consumption; The monthly electricity consumption limit; This is the annual electricity consumption limit.

[0024] (2) Natural gas quantity constraints:

[0025] Annual natural gas volume constraints:

[0026]

[0027] Monthly natural gas volume constraints:

[0028]

[0029] Where, is the monthly breakdown ratio of annual natural gas volume; The monthly natural gas volume limit; The annual natural gas volume cap.

[0030] (3) Carbon quota constraints:

[0031] Carbon emissions calculation:

[0032]

[0033] Current carbon quota demand constraints:

[0034]

[0035] Previous carbon quota demand constraints:

[0036]

[0037]

[0038] Carbon compliance constraints:

[0039]

[0040] Where, The free carbon quota corresponding to the monthly electricity consumption in year y; is the carbon quota corresponding to the electricity consumption at the beginning of year y; B e 、B h These are the carbon emission benchmarks for gas-fired power generation and heating respectively; is the gas-to-electricity conversion efficiency of the CHP unit; is the electricity-to-heat ratio of the CHP unit; η GB is the gas-to-electricity conversion efficiency of the GB unit; is the monthly carbon emissions of the park; is the carbon emissions corresponding to the contracted electricity consumption of the park at the beginning of the year; CHP is the carbon emission calculation coefficient of the CHP unit; α GB D is the carbon emission calculation coefficient of GB; IES,y,t is the monthly net carbon emissions of the park in year y; D IES,y,0 is the net carbon emissions of the park at the beginning of the year y; is the carbon emission demand at the beginning of year y; is the carbon emission demand in the tth month of the yth year; is the carbon emission quota demand at the beginning of year y; is the carbon emission demand in the tth month of the past period of year y; where, is the carbon emission demand for the 12th month of year y.

[0041] (4) Energy balance constraints:

[0042]

[0043] Where λ g2e ,λ h2e ,λ c2e These are the equivalent conversion coefficients between gas-electricity, heat-electricity, and cold-electricity energy; W t M,load 、 They are the monthly electricity, heating and cooling capacity forecast values ​​respectively; is the monthly wind and solar forecast value; η EB is the electric-to-heat conversion efficiency of the electric boiler; η P2G is the electrical conversion efficiency of the power-to-gas equipment; η LBR is the efficiency of absorption refrigeration equipment.

[0044] Specifically, the annual timescale model introduces conditional value risk to quantify and control the economic risks caused by the volatility of multiple trading signals and the uncertainty of carbon emission verification, ensuring the robustness and economic efficiency of the optimization plan. The process is as follows:

[0045] Through Monte Carlo simulation, a series of possible trading signals and load scenarios are generated to construct a discretized scenario set S = {s1, s2, ..., s n}, each scenario corresponds to a multi-transaction signal prediction value and load value. Assume that the probability of each scenario is p s , the following constraints need to be met:

[0046]

[0047] Where n is the number of scenes; p s is the probability of the sth scenario.

[0048] Through the results of Monte Carlo simulation, the tail excess loss can be expressed in discrete form:

[0049]

[0050] Among them, CVaR(C total ) is the quantitative risk of scenario s; C total,s is the total cost of scenario s; The optimization result is defined as the VaR value of the cost; α is the confidence level.

[0051] Then the objective function can be reformulated as:

[0052]

[0053] Among them, λ CVaR is the risk preference coefficient.

[0054] Furthermore, it includes: Step S3: The daily time scale optimization model is further combined with the annual scale optimization results on the daily time scale to refine the operation scheduling and spot trading participation of the equipment within the park, focusing on responding to the impact of short-term load fluctuations and renewable energy output deviations.

[0055]

[0056]

[0057] Where, They are the carbon purchase cost, natural gas purchase cost, electricity purchase cost, operation and maintenance cost, and wind and solar power curtailment cost within a day; It is the signal for purchasing electricity in spot transactions; P t d It is the spot electricity trading volume; Provides gas purchase signals for spot trading; The first spot natural gas trading volume; Provide trading signals for carbon quotas; is the daily trading volume of carbon quotas; T d is the number of time periods in a day, 24; u GB (t),u CHP(t) are the start and stop state variables of CHP unit GB; C GB 、C CHP are the single startup costs of CHP unit GB; j is the maintenance cost of equipment j; P j (t) is the power of device j at time t; φ is the set of devices in the park; The cost of curtailed wind and solar power; Penalties for unit power abandonment; is the amount of abandoned wind; The amount of discarded light.

[0058] Specifically, the daily time scale optimization model constraints are:

[0059] (1) Electric power balance

[0060]

[0061] Where, The contract electricity is decomposed into the value of the tth hour of each day, assuming that the situation is the same every day; For spot purchase of electricity; P load,t is the electricity load for t hours; It is the ratio of monthly electricity consumption to daily consumption.

[0062] Gas power balance:

[0063]

[0064] Where, The contract electricity is decomposed into the value of the tth hour of each day, assuming that the situation is the same every day; Purchase electricity for spot purchase; It is the ratio of monthly electricity consumption to daily consumption.

[0065] (2) Thermal power balance:

[0066]

[0067] Where H load,t is the heat load per hour.

[0068] Cold power balance:

[0069] C AC,t +C LBR,t =C load,t (83)

[0070] Among them, C load,t For cooling load.

[0071] (3) Carbon quota balance:

[0072]

[0073] If we further add the completion of the monthly plan, the constraints are expressed as:

[0074]

[0075] in, The ratio of monthly carbon quota to days; The carbon quota demand for the park in one day is calculated as follows:

[0076]

[0077] in, These are the daily carbon quota and carbon emissions of the CHP unit and GB respectively.

[0078] On the other hand, the present invention also provides a multi-time-scale optimization scheduling system for an integrated energy system that considers the asynchrony of electricity-gas-carbon trading, including:

[0079] The annual time-scale decision-making module is used to build and solve the annual time-scale optimization model to obtain the monthly electricity, natural gas and carbon emission decisions of the park;

[0080] The daily time-scale decision module is used to build and solve the daily time-scale optimization model to obtain the operation scheduling decision of the equipment in the park every hour;

[0081] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0082] The present invention constructs an optimized scheduling technology for a park integrated energy system that takes into account the asynchrony of electricity, natural gas, and carbon quota transactions. It optimizes the energy purchasing decisions of the integrated energy system on a monthly basis on an annual time scale and the operation scheduling decisions on an hourly basis on a daily time scale. It achieves cost minimization on the basis of being closer to the real environment of electricity and carbon trading, provides a more practical decision-making method for the integrated energy system to participate in multi-transaction decisions, and provides new ideas for the economic operation of the integrated energy system under the participation of multiple transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0084] Figure 1 This is a flowchart of multi-time-scale optimization scheduling of an integrated energy system considering the asynchrony of electricity-gas-carbon trading according to an exemplary embodiment;

[0085] Figure 2 is a schematic diagram of a multi-transaction integrated energy system according to an exemplary embodiment;

[0086] Figure 3 is a schematic diagram of a comprehensive energy system park structure according to an exemplary embodiment;

[0087] Figure 4 is a flowchart of an integrated energy system optimization solution according to an exemplary embodiment;

[0088] Figure 5 This is a diagram showing the decomposition of annual contracted electricity and monthly contracted electricity purchase results for a comprehensive energy system according to an exemplary embodiment;

[0089] Figure 6 The following is an example of an integrated energy system showing annual contracted natural gas decomposition and monthly natural gas purchase results according to an exemplary embodiment;

[0090] Figure 7 is a graph showing annual carbon quota purchase results for an integrated energy system according to an exemplary embodiment;

[0091] Figure 8 is a diagram showing an electric power balance diagram of an integrated energy system according to an exemplary embodiment;

[0092] Figure 9 is a gas power balance diagram of an integrated energy system according to an exemplary embodiment;

[0093] Figure 10 The figure shows a cooling power balance diagram of an integrated energy system according to an exemplary embodiment. DETAILED DESCRIPTION

[0094] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0095] like Figure 1 As shown in Figure 1, a multi-time-scale optimization scheduling framework for a campus integrated energy system includes the following steps:

[0096] S1 obtains annual load data and trading signal data of the integrated energy system;

[0097] S2 establishes an annual time scale optimization model with monthly decision cycles;

[0098] Specifically, such as Figure 2 As shown, the park's integrated energy system (IES) participates in electricity trading primarily through annual contracts, monthly contracts, and spot trading. Monthly contracts provide flexibility for mid-term adjustments. The park can dynamically optimize its electricity purchase plan based on load forecasts and monthly electricity signal fluctuations. Combined with energy storage systems for timing adjustments, it can reduce purchases during periods of high electricity signals, further improving economic efficiency. Spot trading complements short-term trading, addressing actual load fluctuations or deviations in renewable energy output. By flexibly adjusting purchases or selling excess power, it complements long- and medium-term contracts. By participating in multi-tiered electricity trading, the IES achieves efficient and economical resource allocation while meeting load demand. The park's integrated energy system (IES) participates in natural gas trading primarily through annual contracts, monthly contracts, and off-contract purchases, reflecting the long-term and contractual nature of natural gas trading. Annual natural gas contracts typically serve as the foundation for the park's natural gas supply. By signing long-term contracts with suppliers, the park locks in annual supply volumes with relatively stable signals, providing reliable fuel support for equipment such as gas boilers and CHP units. Monthly contracts are adjusted based on seasonal load characteristics and fluctuations in monthly trading signals. For example, gas purchases can be increased during periods of high heating demand in winter or high cooling loads in summer, optimizing resource allocation. The park's Integrated Energy System (IES) participates in carbon trading primarily through daily carbon allowance trading, annual carbon emissions verification, and compliance carbon allowance trading. Daily carbon allowance trading provides a means for the park to adjust its carbon emission quotas. The park can flexibly purchase or sell carbon allowances based on fluctuations in carbon signals and its own emission expectations, stockpiling them appropriately when carbon signals are low and optimizing its sales strategy when they are high, thereby generating economic benefits. Annual carbon emissions verification is a key component of carbon trading, typically completed mid-year. It verifies the park's actual carbon emissions from the previous year. This characteristic creates a clear temporal separation between carbon trading and compliance. This means that the park can flexibly adjust its carbon allowance reserves before compliance through multi-year trading strategies.

[0099] Specifically, the objective function of the annual time scale optimization model is expressed as:

[0100] minC total =C c +C g +C e (91)

[0101]

[0102]

[0103] Where C d 、C g 、C eThe costs of purchasing carbon allowances, natural gas, and electricity for the park; are the annual contract signals for electricity and gas in year y respectively; are the monthly contract signals for electricity and gas in month t of year y respectively; are the annual contract trading volumes of electricity and natural gas in year y respectively; are the monthly contract trading volumes of electricity and natural gas in month t in year y, respectively; are the carbon quota signals of month t in year y+1 and year y, respectively; They are the carbon quota trading volumes of the past period of year y (i.e., carbon emissions of year y-1 traded in year y), the current period of year y, and the past period of year y+1 (i.e., the amount of carbon quota demand this year purchased next year); T is the number of months in a year, which is 12.

[0104] Specifically, the constraints of the annual time scale optimization model include:

[0105] (1) Power Constraints:

[0106] Annual power consumption constraints:

[0107]

[0108] Monthly power consumption limit:

[0109]

[0110] Where, The monthly breakdown ratio of annual electricity consumption; The monthly electricity consumption limit; This is the annual electricity consumption limit.

[0111] (2) Natural gas quantity constraints:

[0112] Annual natural gas volume constraints:

[0113]

[0114] Monthly natural gas volume constraints:

[0115]

[0116] Where, is the monthly breakdown ratio of annual natural gas volume; The monthly natural gas volume limit; The annual natural gas volume cap.

[0117] (3) Carbon quota constraints:

[0118] Carbon emissions calculation:

[0119]

[0120] Current carbon quota demand constraints:

[0121]

[0122] Previous carbon quota demand constraints:

[0123]

[0124] Carbon compliance constraints:

[0125]

[0126] Where, The free carbon quota corresponding to the monthly electricity consumption in year y; is the carbon quota corresponding to the electricity consumption at the beginning of year y; B e 、B h These are the carbon emission benchmarks for gas-fired power generation and heating respectively; is the gas-to-electricity conversion efficiency of the CHP unit; is the electricity-to-heat ratio of the CHP unit; η GB is the gas-to-electricity conversion efficiency of the GB unit; is the monthly carbon emissions of the park; is the carbon emissions corresponding to the contracted electricity consumption of the park at the beginning of the year; CHP is the carbon emission calculation coefficient of the CHP unit; α GB D is the carbon emission calculation coefficient of GB; IES,y,t is the monthly net carbon emissions of the park in year y; D IES,y,0 is the net carbon emissions of the park at the beginning of the year y; is the carbon emission demand at the beginning of year y; is the carbon emission demand in the tth month of the yth year; is the carbon emission quota demand at the beginning of year y; is the carbon emission demand in the tth month of the past period of year y; where, is the carbon emission demand for the 12th month of year y.

[0127] (4) Energy balance constraints:

[0128]

[0129] Where λ g2e ,λ h2e ,λ c2e These are the equivalent conversion coefficients between gas-electricity, heat-electricity, and cold-electricity energy; W t M,load 、 They are the monthly electricity, heating and cooling capacity forecast values ​​respectively; is the monthly wind and solar forecast value; η EB is the electric-to-heat conversion efficiency of the electric boiler; η P2G is the electrical conversion efficiency of the power-to-gas equipment; η LBR is the efficiency of absorption refrigeration equipment.

[0130] Specifically, the annual timescale model introduces conditional value risk to quantify and control the economic risks caused by the volatility of multiple trading signals and the uncertainty of carbon emission verification, ensuring the robustness and economic efficiency of the optimization plan. The process is as follows:

[0131] Through Monte Carlo simulation, a series of possible trading signals and load scenarios are generated to construct a discretized scenario set S = {s1, s2, ..., s n}, each scenario corresponds to a multi-transaction signal prediction value and load value. Assume that the probability of each scenario is p s , the following constraints need to be met:

[0132]

[0133] Where n is the number of scenes; p s is the probability of the sth scenario.

[0134] Through the results of Monte Carlo simulation, the tail excess loss can be expressed in discrete form:

[0135]

[0136] Among them, CVaR(C total ) is the quantitative risk of scenario s; C total,s is the total cost of scenario s; The optimization result is defined as the VaR value of the cost; α is the confidence level.

[0137] Then the objective function can be reformulated as:

[0138]

[0139] Among them, λ CVaR is the risk preference coefficient.

[0140] Specifically, such as Figure 3 As shown, a comprehensive energy system park structure, in one feasible manner, includes wind and solar new energy units, cogeneration units, gas boilers, electric boilers, electric refrigeration equipment, absorption refrigeration equipment, power-to-gas equipment, as well as electric energy storage, heat storage tanks, and gas storage tanks.

[0141] Specifically, the device models are as follows:

[0142] Electric boiler:

[0143] H EB (t) = η EB P EB (t) (117)

[0144] Where H EB (t) The thermal power of the electric boiler at time t, P EB (t) is the electric power of the electric boiler at time t, η EB is the electric-to-heat conversion efficiency of the electric boiler.

[0145] Power-to-gas equipment:

[0146] G P2G (t) = η P2G P P2G (t) (118)

[0147] Where G P2G (t) The gas power output of the power-to-gas device at time t, P P2G (t) is the electric power absorbed by the power-to-gas equipment at time t, η P2G It is the electrical conversion efficiency of the power-to-gas equipment.

[0148] Electric refrigeration equipment:

[0149] C AC (t) = η AC P AC (t) (119)

[0150] C LBR (t) = η LBR C LBR (t) (120)

[0151] Where C AC (t) is the output cooling power of the electric refrigeration equipment; η AC is the cooling efficiency of the electric refrigeration equipment; P AC (t) is the electric power consumed by the electric refrigeration equipment; C LBR (t) is the output cooling power of the absorption refrigeration equipment; η LBR is the efficiency of absorption refrigeration equipment; P LBR (t) is the thermal power consumed by the absorption refrigeration equipment.

[0152] (2) Energy storage devices

[0153] Energy storage devices are used to store electricity, heat, and gas within the park, enabling the temporal translation of different energy forms. Each energy device shares the same principles, differing in the storage medium. These devices include electrical energy storage, gas storage tanks, and thermal storage tanks. The specific model is as follows:

[0154] Electric energy storage:

[0155]

[0156] Where, is the rated power of the energy storage, is the charging power of the energy storage at time t, is the discharge power of the energy storage at time t, u ES (t) represents the charge and discharge state of the energy storage. ES (t) = 1 means charging, u ES (t) = 0 means discharge, is the rated capacity of the electric energy storage, E ES (t) represents the amount of energy stored at time t, Respectively represent the upper and lower limits of SOC of electric energy storage, σ ES is the self-consumption rate of the energy storage, η ES,c ,η ES,d They represent the charging and discharging efficiency of the energy storage.

[0157] Gas Storage Tank (GST):

[0158]

[0159] Where, is the rated power of the gas tank, is the charging power of the gas tank at time t, is the deflation power of the gas tank at time t, u GST (t) represents the filling and discharging state of the gas tank. GST (t) = 1 means inflation, u GST (t) = 0 means deflation, is the rated capacity of the gas tank, E GST (t) represents the gas storage capacity of the gas tank at time t, Respectively represent the upper and lower limits of the SOC of the gas tank, σ GST is the self-consumption rate of the gas tank due to leakage, η GST,c ,η GST,d They represent the charging and discharging efficiency of the gas tank respectively.

[0160] Heat storage tank:

[0161]

[0162] Where, is the rated power of the heat storage tank, is the thermal storage power of the electric energy storage at time t, is the heat release power of the electric energy storage at time t, u HST (t) represents the charging and discharging state of the heat storage tank. HST(t)=1 means heat storage, u HST (t) = 0 means heat release, is the rated capacity of the heat storage tank, E HST (t) represents the amount of heat that the heat storage tank can store at time t, Respectively represent the upper and lower limits of the SOC of the heat storage tank, σ HST is the self-consumption rate of the heat storage tank, η HST,c ,η HST,d Respectively represent the charging and discharging efficiency of the heat storage tank.

[0163] (3) Combined heat and power units

[0164]

[0165] |P CHP (t)-P CHP (t-1)|≤ΔP CHP (136)

[0166] Where, P CHP (t) is the output power of the CHP unit at time t; G CHP (t) is the input gas power at time t; H CHP (t) is the output thermal power of the CHP unit at time t; is the gas-to-electricity conversion efficiency; is the electricity-to-heat ratio of the CHP unit; are the upper and lower limits of the CHP unit output respectively; u CHP (t) is the start and stop status of the CHP unit, 1 is running, 0 is shut down; ΔP CHP is the climbing power of the CHP unit.

[0167] (4) Gas boiler

[0168] H GB (t) = η GB G GB V HV (137)

[0169]

[0170] |H GB (t)-H GB (t-1)|≤ΔH GB (139)

[0171] Where H GB (t) is the output thermal power of the gas boiler at time t; G GB (t) is the input gas power of the gas boiler at time t; V HV is the calorific value of natural gas combustion; η GBis the gas-to-electricity conversion efficiency; are the upper and lower limits of the gas boiler output respectively; u GB (t) is the start and stop status of the gas boiler, 1 is running, 0 is shut down; ΔH GB is the climbing power of the gas boiler.

[0172] (5) Wind and solar power output

[0173]

[0174] Where, are wind and solar power forecast values ​​respectively; P w,t 、P s,t are the actual output values ​​of wind and solar power respectively.

[0175] S3 establishes a daily time scale optimization model with hourly decision cycles;

[0176] Specifically, the daily time scale optimization model is further combined with the annual scale optimization results to refine the operation scheduling and spot trading participation of equipment within the park, focusing on coping with the impact of short-term load fluctuations and renewable energy output deviations.

[0177]

[0178] Where, They are the carbon purchase cost, natural gas purchase cost, electricity purchase cost, operation and maintenance cost, and wind and solar power curtailment cost within a day; It is the signal for purchasing electricity in spot transactions; P t d It is the spot electricity trading volume; Provides gas purchase signals for spot trading; The first spot natural gas trading volume; Provide trading signals for carbon quotas; is the daily trading volume of carbon quotas; T d is the number of time periods in a day, 24; u GB (t),u CHP (t) are the start and stop state variables of CHP unit GB; C GB 、C CHP are the single startup costs of CHP unit GB; j is the maintenance cost of equipment j; P j (t) is the power of device j at time t; φ is the set of devices in the park; The cost of curtailed wind and solar power; Penalties for unit power abandonment; is the amount of abandoned wind; The amount of discarded light.

[0179] Specifically, the daily time scale optimization model constraints are:

[0180] (1) Electric power balance

[0181]

[0182] Where, The contract electricity is decomposed into the value of the tth hour of each day, assuming that the situation is the same every day; For spot purchase of electricity; P load,t is the electricity load for t hours; It is the ratio of monthly electricity consumption to daily consumption.

[0183] Gas power balance:

[0184]

[0185] Where, The contract electricity is decomposed into the value of the tth hour of each day, assuming that the situation is the same every day; Purchase electricity for spot purchase; It is the ratio of monthly electricity consumption to daily consumption.

[0186] (2) Thermal power balance:

[0187]

[0188] Where H load,t is the heat load per hour.

[0189] Cold power balance:

[0190] C AC,t +C LBR,t =C load,t (153)

[0191] Among them, C load,t For cooling load.

[0192] (3) Carbon quota balance:

[0193]

[0194] If we further add the completion of the monthly plan, the constraints are expressed as:

[0195]

[0196] in, The ratio of monthly carbon quota to days; The carbon quota demand for the park in one day is calculated as follows:

[0197]

[0198] in, These are the daily carbon quota and carbon emissions of the CHP unit and GB respectively.

[0199] S4 solves the annual timescale optimization model to obtain the park's monthly electricity and carbon trading strategies, including monthly electricity purchases, monthly natural gas purchases, and monthly carbon emissions purchases;

[0200] S5 breaks down the monthly electricity and natural gas purchases into daily averages;

[0201] S6 solves the daily time-scale optimization model and obtains the daily operation scheduling strategy of the park.

[0202] Specifically, such as Figure 4 As shown, in this example, first, on an annual timescale, with the goal of minimizing the annual total energy procurement cost, the park's annual electricity and gas contract trading volumes and carbon quota reserve strategy are optimized to determine the annual contract trading volumes for electricity, natural gas, and carbon quotas. The optimization output from this stage provides the input and boundary conditions for long-term planning on a daily timescale.

[0203] Secondly, on the daily time scale, the model decomposes the monthly electricity and natural gas purchases into daily averages based on the optimization results of the first stage.

[0204] Finally, based on the monthly electricity and natural gas purchases, the daily time-scale optimization model is solved month by month to obtain the operation scheduling plan and spot trading participation strategy for the equipment in the park.

[0205] The following example specifically illustrates the low-carbon economic optimization scheduling method for a virtual power plant park taking into account the uncertainty of electricity-carbon trading in the present invention. The parameters of each component are shown in Table 1.

[0206] Table 1 Operating parameters of each unit

[0207]

[0208]

[0209] The annual contract electricity rate is 0.65 yuan / kWh, and the contract gas rate is 0.32 yuan / kWh. Figure 5 The chart shows the breakdown of annual contracted electricity and the purchase of monthly contracted electricity. Since monthly contracted electricity signals are generally higher than annual contracted electricity signals in power trading, annual electricity purchases are prioritized when load demand is high enough, and then broken down into monthly units based on load characteristics.

[0210] Figure 6The chart shows the breakdown of annual natural gas contract electricity consumption and the purchase volume of monthly natural gas contracts. Similar to the electricity purchase volume, due to the lower signal for annual contracts, annual contracts are purchased first and then broken down into monthly units. Monthly gas contracts are then signed based on the monthly load. Compared to the cost of electricity purchased through electricity trading, the cost of gas purchased through the park's natural gas trading is lower. This is partly due to the lower signal for natural gas compared to the signal for electricity, and partly due to the lower purchase volume of natural gas trading compared to electricity trading. This is because, in addition to the gas purchase cost itself, natural gas trading also incurs the additional cost of purchasing carbon allowances. This means that the equivalent unit cost of purchasing gas is higher than the unit cost of purchasing electricity. Therefore, the purchase volume of natural gas trading is lower than the purchase volume of electricity trading.

[0211] Figure 7 The monthly carbon allowance purchase plan is displayed. Previous allowances are determined by the previous year's emissions, while this year's allowance is fixed. Therefore, purchase decisions are primarily influenced by carbon signals, with purchases concentrated in January, when the carbon signal is low. Current allowance purchases are made based on signals from both this year and next, reflecting the ability to trade carbon allowances across years. Due to discrepancies between third-party verification and internal verification, demand for carbon allowances surges in July each year, reflecting the uncertainty of carbon emissions verification.

[0212] The balance of electric power, heating power and cooling power in the park is as follows: Figure 8 As shown in the figure, the power load is significantly higher during the day than at night, especially during working hours, which results in significant fluctuations. The park mainly regulates power to meet load demand through external power purchases from the power grid.

[0213] Figure 9 This chart illustrates the thermal power balance within the park. Cogeneration units and gas-fired boilers, as the primary heat supply equipment, bear the majority of the heat load. Output fluctuations closely track load fluctuations. The electric boilers maintain a relatively stable output, operating at a fixed power level daily. The heat storage tanks operate infrequently, operating only twice, at peak and lowest load times.

[0214] The gas power balance in the park is as follows Figure 10 As shown, the park has no direct gas load, with the combined heat and power units being the largest natural gas consumers. Natural gas is primarily supplied through external purchases and power-to-gas equipment. As analyzed during the annual trading process, due to the costs of purchasing natural gas and the carbon emissions associated with natural gas consumption, the park opts to purchase more electricity directly, with a portion of its natural gas supply covered by power-to-gas equipment.

Claims

1. A multi-time-scale optimization scheduling method for an integrated energy system considering the asynchrony of electricity-gas-carbon trading, characterized by: The method comprises the following steps: S1 obtains annual load data and trading signal data of the integrated energy system; S2 establishes an annual time scale optimization model with monthly decision cycles; S3 establishes a daily time scale optimization model with hourly decision cycles; S4 solves the annual timescale optimization model to obtain the park's monthly electricity and carbon trading strategies, including monthly electricity purchases, monthly natural gas purchases, and monthly carbon emissions purchases; S5 breaks down the monthly electricity and natural gas purchases into daily amounts; S6 solves the daily time-scale optimization model and obtains the daily operation scheduling strategy of the park.

2. A multi-time-scale optimization scheduling method for an integrated energy system considering the asynchrony of electricity-gas-carbon trading according to claim 1, characterized in that: The objective function of the annual time scale optimization model is expressed as: my C total =C d +C g +C e (1) Where C total is the total cost of the park, C d 、C g 、C e The costs of purchasing carbon allowances, natural gas, and electricity for the park; The carbon quota trading signals for the tth month in year y+1 and year y, respectively; The carbon quota trading volumes for the past period of year y, the current period of year y, and the past period of year y+1 are respectively; They are the annual contract trading signals for electricity and gas in year y respectively; The monthly contract signals for electricity and gas in month t of year y respectively; are the annual contract trading volumes of electricity and natural gas in year y respectively; are the monthly contract trading volumes of electricity and natural gas in month t of year y, respectively; T is the number of months in a year, which is 12.

3. The multi-time-scale optimization scheduling method for an integrated energy system considering the asynchrony of electricity-gas-carbon trading according to claim 2 is characterized in that: The constraints of the annual time scale optimization model include electricity quantity constraint, natural gas quantity constraint, carbon quota constraint, and energy balance constraint: Specifically, the power constraint is Where, The monthly breakdown ratio of annual electricity consumption; The monthly electricity consumption limit; The annual electricity consumption limit; Specifically, the natural gas constraints are: Where, is the monthly breakdown ratio of annual natural gas volume; The monthly natural gas volume limit; The annual natural gas volume cap; Specifically, the carbon quota constraints are: Where, The free carbon quota corresponding to the monthly electricity consumption in year y; is the carbon quota corresponding to the electricity consumption at the beginning of year y; B e 、B h These are the carbon emission benchmarks for gas-fired power generation and heating respectively; is the gas-to-electricity conversion efficiency of the CHP unit; is the electricity-to-heat ratio of the CHP unit; η GB is the gas-to-electricity conversion efficiency of the GB unit; is the monthly carbon emissions of the park; is the carbon emissions corresponding to the contracted electricity consumption of the park at the beginning of the year; CHP is the carbon emission calculation coefficient of the CHP unit; α GB D is the carbon emission calculation coefficient of GB; IES,y,t is the monthly net carbon emissions of the park in year y; D IES,y,0 is the net carbon emissions of the park at the beginning of the year y; is the carbon emission demand at the beginning of year y; is the carbon emission demand in the tth month of the yth year; is the carbon emission quota demand at the beginning of year y; is the carbon emission demand in the tth month of the past period of year y; where, is the carbon emission demand for the 12th month of year y. Specifically, the power balance constraint is: Where λ g2e ,λ h2e ,λ c2e These are the equivalent conversion coefficients between gas-electricity, heat-electricity, and cold-electricity energy; They are the monthly electricity, heating and cooling capacity forecast values ​​respectively; is the monthly wind and solar forecast value; η EB is the electric-to-heat conversion efficiency of the electric boiler; η P2G is the electrical conversion efficiency of the power-to-gas equipment; η LBR is the efficiency of absorption refrigeration equipment.

4. The multi-time-scale optimization scheduling method for an integrated energy system considering the asynchrony of electricity-gas-carbon trading according to claim 3 is characterized in that: The value-at-risk considerations in the annual timescale optimization model are as follows: Through Monte Carlo simulation, a series of possible trading signals and load scenarios are generated to construct a discretized scenario set S = {s1, s2, ..., s n }, each scenario corresponds to a multi-transaction signal prediction value and load value; assuming that the probability of each scenario is p s , the following constraints need to be met: Where n is the number of scenes; p s is the probability of the sth scenario. Through the results of Monte Carlo simulation, the tail excess loss is expressed in discrete form: Among them, CVaR(C total ) is the quantitative risk of scenario s; C total,s is the total cost of scenario s; the optimization result of ζ is defined as the VaR value of the cost; α is the confidence level. Then the objective function can be reformulated as: Among them, λ CVaR is the risk preference coefficient.

5. The multi-time-scale optimization scheduling method for an integrated energy system considering the asynchrony of electricity-gas-carbon trading according to claim 1 is characterized in that: The objective function of the daily time scale optimization model is expressed as: Where, They are the carbon purchase cost, natural gas purchase cost, electricity purchase cost, operation and maintenance cost, and wind and solar power curtailment cost within a day; Power purchase signals for spot trading; It is the spot electricity trading volume; Provides gas purchase signals for spot trading; The first spot natural gas trading volume; Provide trading signals for carbon quotas; is the daily trading volume of carbon quotas; T d is the number of time periods in a day, 24; u GB (t),u CHP (t) are the start and stop state variables of CHP unit GB; C GB 、C CHP are the single startup costs of CHP unit GB; j is the maintenance cost of equipment j; P j (t) is the power of device j at time t; φ is the set of devices in the park; The cost of curtailed wind and solar power; Penalties for unit power abandonment; is the amount of abandoned wind; The amount of discarded light.

6. A multi-time-scale optimization scheduling method for an integrated energy system considering the asynchrony of electricity-gas-carbon trading according to claim 5, characterized in that: The daily time scale optimization model constraints include: electric power balance, thermal power balance, gas power balance, and carbon quota balance; Specifically, the electric power balance is: Where, The contract electricity is decomposed into the value of the tth hour of each day, assuming that the situation is the same every day; For spot purchase of electricity; P load,t is the electricity load for t hours; The ratio of monthly electricity consumption to daily consumption; Specifically, the gas power balance is: Where, The contract electricity is decomposed into the value of the tth hour of each day, assuming that the situation is the same every day; Purchase electricity for spot purchase; The ratio of monthly electricity consumption to daily consumption; Specifically, the thermal power balance is: Where H load,t is the heat load for t hours; Specifically, the cooling power balance is: C AC,t +C LBR,t =C load,t (38) Among them, C load,t is the cooling load; Specifically, the carbon quota balance is: Where, The ratio of monthly carbon quota to days; The carbon quota demand for the park for one day; They are the daily carbon quota and carbon emissions of the CHP unit and GB respectively.

Images