Day-ahead Optimal Scheduling Method for Integrated Energy Systems

Abstract

The present application discloses a method for optimizing the day-ahead scheduling of an integrated energy system, comprising the following steps: S1: constructing a CET cost calculation model and a GCT cost calculation model according to the parameters of the integrated energy system; S2: constructing a risk value model according to the output power of renewable energy; S3: taking the minimum comprehensive operating cost of the integrated energy system as the goal, constructing an optimization scheduling model using the risk value model, the CET cost calculation model and the GCT cost calculation model, and constructing constraint conditions using the parameters of the integrated energy system; S4: obtaining the actual renewable energy output and the parameters of the integrated energy system, and outputting the day-ahead optimization scheduling strategy of the integrated energy system using the optimization scheduling model and executing it. The beneficial effects of the present application: improving the energy utilization rate of renewable energy units while reducing carbon emissions.

Description

Technical Field

[0001] The present application relates to the technical field of day-ahead scheduling of integrated energy systems, and in particular to a day-ahead optimization scheduling method for integrated energy systems. Background Art

[0002] The installed capacity of renewable energy is sufficient and maintains a rapid growth trend, but the existing power system structure and system are difficult to support a higher proportion of renewable energy consumption. In terms of IES scheduling constraints, the relevant technology further considers the minimum start and stop time, inter-flow constraints, thermal power unit ramp rate and other constraints on the original scheduling stability constraints to achieve the purpose of improving system stability.

[0003] With a high proportion of renewable energy connected to the power system, the stability of the power system is seriously affected. When the uncertainty range of wind and solar power exceeds the safe boundary of the system, it will threaten the reliability of the system. Conventional peak-shaving strategies can no longer meet the peak-shaving needs of IES. The main obstacles to the consumption of renewable energy are its uncontrollability and volatility. Large-scale wind and solar power directly connected to the grid will have a severe impact on the reliability of power supply and threaten the safe operation of the system. However, the traditional unit combination concept and method can no longer effectively cope with the current situation of high penetration of renewable energy.

[0004] The Chinese patent "A method for optimizing the operation of an integrated energy system taking into account the carbon-green certificate trading mechanism", publication number: CN 113222465A, publication date: August 6, 2021, specifically discloses step S1) studying the concept of carbon-green certificate trading, analyzing the carbon emission rights and green certificate trading mechanism and the feasibility of implementing the mechanism in IES; step S2) establishing a carbon-green certificate cost model and a market value risk model taking into account carbon emission rights and green certificate trading; step S3) establishing an IES optimization model including carbon-green certificate trading and performing case analysis. This scheme takes into account the requirements of the quota system and the existing market risks, but does not take into account the risks brought by the unstable output of new energy.

[0005] The Chinese patent "An integrated energy system optimization scheduling method considering the carbon-green certificate trading mechanism", publication number: CN 115689736A, publication date: February 3, 2023, specifically discloses the establishment of a carbon-green certificate joint trading market framework based on the CET and GCT mechanisms; comprehensively considers the IES's electricity purchase cost, gas purchase cost, and equipment operation and maintenance cost, establishes a carbon-green certificate joint trading model that considers conditional risk value, and introduces it into the integrated energy system optimization scheduling model. Similarly, this plan only considers the quota system requirements and existing market risks, and does not consider the risks brought by the unstable output of new energy. Summary of the invention

[0006] In view of the fact that a high proportion of renewable energy is connected to the power system in the prior art, which may cause instability of the integrated energy system due to the uncertainty of wind and solar power, this application provides a method for optimizing the scheduling of the integrated energy system on the day before, calculates the optimized scheduling strategy of the integrated energy system with low carbon emissions by establishing a CET cost calculation model and a GCT cost calculation model, and constructs a risk value model through the output power of renewable energy to obtain the wind and solar power consumption losses that may be caused by the instability of renewable energy output, constructs an optimized scheduling model, and outputs an optimized scheduling strategy to achieve the lowest wind and solar power abandonment losses while ensuring the lowest possible carbon emissions, thereby reducing carbon emissions while improving the energy utilization rate of renewable energy units.

[0007] To achieve the above technical objectives, a technical solution provided by the present application is a method for optimizing the scheduling of an integrated energy system on the day before, comprising the following steps: S1: constructing a CET cost calculation model and a GCT cost calculation model based on the parameters of the integrated energy system; S2: constructing a risk value model based on the output power of renewable energy; S3: with the goal of minimizing the comprehensive operating cost of the integrated energy system, constructing an optimization scheduling model using the risk value model, the CET cost calculation model and the GCT cost calculation model, and constructing constraint conditions using the parameters of the integrated energy system; S4: obtaining the actual renewable energy output and the parameters of the integrated energy system, and using the optimization scheduling model to output the optimized scheduling strategy of the integrated energy system on the day before and execute it.

[0008] Furthermore, the CET cost calculation model is: ΔE=E p -E q Among them, C CET Costs for the system to participate in carbon trading; is the carbon market trading base price; l is the length of the carbon emission interval; is the price growth rate; E q is the system carbon quota; E p is the system carbon emissions; ΔE is the difference between the carbon emissions of the carbon emission control unit and the carbon quota.

[0009] Furthermore, the GCT cost calculation model is: GCT =C green (Q gain -Q RPS ), where G GCT is the transaction cost of GCT; C green is the transaction price of green certificates; Q gain Number of green certificates obtained for IES; Q RPS The number of green certificates required to meet RPS.

[0010] Furthermore, constructing a risk value model based on the output power of renewable energy includes: determining the relationship between the output power of renewable energy and the maximum power of comprehensive energy consumption, and constructing a risk value model.

[0011] Furthermore, the risk value model is: Among them, C CVaR,j is the risk value of each renewable energy unit; N R is the number of installed capacity of renewable energy; T is the dispatching period; i is the i-th renewable energy unit; t is the time period.

[0012] Furthermore, the optimization scheduling model is: min(C T +C R +C CET +C GCT +C ren +C CVaR ), where C T is the operating cost of fossil energy generators; C R is the operating cost of renewable energy generators; C CET G is the cost of the system participating in carbon trading; GCT is the transaction cost of GCT; C ren Penalize costs for curtailing renewable energy.

[0013] Furthermore, the operating cost of the fossil energy power generation unit, i.e. the operating cost of the coal-fired unit, is: Among them, P i,t is the output of fossil energy generator set i in period t; is the coal consumption cost of unit i; is the startup cost of unit i; is the shutdown cost of unit i; T is the scheduling period; t is the time; N T is the installed capacity of fossil energy.

[0014] Furthermore, the operating cost of the renewable energy generator set is: Among them, P j,t is the output of renewable energy generator j in period t; is the maintenance cost of unit j; is the startup cost of unit j; is the shutdown cost of unit j; N R is the installed capacity of renewable energy; T is the scheduling period; t is the time.

[0015] Furthermore, the constraint conditions constructed with comprehensive energy system parameters include: constructing a coupled system power balance constraint based on load demand and the number of load nodes; constructing a spinning reserve constraint based on a spinning reserve coefficient; constructing a unit output constraint based on the maximum output power and the minimum output power of the unit; constructing a unit climbing constraint based on the unit's ramp-up rate and ramp-down rate; constructing a unit start-up and shutdown cost constraint based on the unit's single startup cost and single shutdown cost; and constructing a flow safety constraint based on the flow constraints of each branch.

[0016] Furthermore, the power balance constraint of the coupling system is constructed according to the load demand and the number of load nodes as follows: Among them, u i,t is the start and stop status of unit i in period t; ρ is the spinning reserve coefficient; N is the total number of units; P i,max , is the maximum output power of the unit, P i,min is the minimum output power of the unit; P d,t is the load demand of node d in period t; P i,t is the output of fossil energy generator i in period t.

[0017] The beneficial effects of this application are as follows: by establishing a CET cost calculation model and a GCT cost calculation model to calculate the day-ahead optimal dispatching strategy for an integrated energy system with low carbon emissions, and by constructing a risk value model through the output power of renewable energy to obtain the wind and solar power consumption losses that may be caused by the instability of renewable energy output, an optimal dispatching model is constructed, and an optimal dispatching strategy is output to ensure the lowest wind and solar power abandonment losses under the lowest possible carbon conditions, thereby improving the energy utilization rate of renewable energy units while reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the method for optimizing the scheduling of the integrated energy system in this application.

[0019] Figure 2 Schematic diagram of the risk probability of wind and solar power abandonment of renewable energy for the day-ahead optimization scheduling method of the integrated energy system applied in this application.

[0020] Figure 3 Schematic diagram of the optimization scheduling framework of the day-ahead optimization scheduling method for the integrated energy system in this application.

[0021] Figure 4 Schematic diagram of the improved IEEE-30 node system structure for the current optimization scheduling method of the integrated energy system in this application.

[0022] Figure 5 This is a schematic diagram of the forecast of wind turbines, photovoltaic units and IES loads for the day-ahead optimization scheduling method of the integrated energy system in this application.

[0023] Figure 6 Schematic diagram of fossil energy and renewable energy output for the current optimized scheduling method of the integrated energy system in this application.

[0024] Figure 7 This is a schematic diagram of the impact of CET price on IES operating costs for the day-ahead optimization scheduling method of the integrated energy system applied in this application.

[0025] Figure 8 This is a schematic diagram of the impact of green certificates and renewable energy consumption coefficients on IES operating costs for the day-ahead optimized scheduling method of the integrated energy system applied in this application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the drawings and examples. It should be understood that the specific implementation method described here is only an optimal embodiment of the present application, which is only used to explain the present application and does not limit the scope of protection of the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0027] like Figure 1 As shown, as the first embodiment of the present application, the day-ahead optimization scheduling method of the integrated energy system includes the following steps:

[0028] S1: Construct CET cost calculation model and GCT cost calculation model based on comprehensive energy system parameters;

[0029] S2: Construct a risk value model based on the output power of renewable energy;

[0030] S3: With the goal of minimizing the comprehensive operating cost of the integrated energy system, the optimal scheduling model is constructed using the risk value model, the CET cost calculation model, and the GCT cost calculation model, and the constraints are constructed using the parameters of the integrated energy system;

[0031] S4: Obtain the actual renewable energy output and integrated energy system parameters, use the optimization scheduling model to output the day-ahead optimization scheduling strategy for the integrated energy system and execute it.

[0032] In this application, based on the CET and GCT mechanisms, i.e., based on the carbon emission rights trading and green certificate trading mechanisms, a low-carbon economic dispatch model is established in the integrated energy system IES that takes into account the carbon-green certificate trading mechanism. Combining the properties of CET and GCT, based on the uncertainty of renewable energy output, the dispatch results under different scenarios are simulated, and the impact of different CET prices and green certificate prices on IES operation is analyzed. At the same time, the CET and GCT costs and the operational risks of renewable energy units are considered, so that the output of the integrated energy system can optimize the dispatch strategy to meet the low-carbon demand and the low energy loss demand.

[0033] Specifically, the CET cost calculation model and the GCT cost calculation model are constructed based on the comprehensive energy system parameters, including:

[0034] Construct a CET cost calculation model based on the output parameters of coal-fired units;

[0035] The GCT cost calculation model is constructed based on the electricity consumption parameters of the integrated energy system and the renewable energy generation absorption coefficient.

[0036] In this embodiment, the comprehensive energy system parameters include at least the output parameters of the coal-fired units, the electricity consumption parameters of the comprehensive energy system, and the renewable energy power generation absorption coefficient that the comprehensive energy system needs to complete according to its obligations.

[0037] More specifically, the actual carbon emissions of IES p Determined by the output of the coal-fired unit, the system carbon emissions are shown in formula (1):

[0038]

[0039] Where: E p is the system carbon emissions; N T is the installed capacity of fossil energy; T is the dispatching period; b i is the coal consumption of coal-fired units (tce / (MW·h)); σ is the carbon emission coefficient of coal-fired units converted to standard coal (t / tce); P i,t is the actual output of coal-fired unit i during period t.

[0040] The output parameters of coal-fired units include at least the number of fossil energy installed capacity, the coal consumption of coal-fired units for power supply, the carbon emission coefficient of coal-fired units converted to standard coal, and the actual output of coal-fired unit i in time period t.

[0041] The baseline method is used to allocate the carbon quota quantity. The allocation of carbon emission quota is shown in formula (2):

[0042]

[0043] Where: E q is the system carbon quota; B i is the power supply carbon emission benchmark value (t / (MW·h)); f i is the correction factor.

[0044] Tiered carbon trading means dividing carbon dioxide emissions into multiple intervals, with different carbon prices in different intervals. The higher the carbon dioxide emissions, the higher the unit cost of carbon dioxide emissions from the system. The use of tiered carbon trading is more conducive to the output of renewable energy units and low-carbon units, and is more helpful in reducing IES carbon emissions. The tiered CET cost calculation model is shown in equations (3) and (4):

[0045]

[0046] ΔE=E p -E q (4)

[0047] Where: C CET Costs for the system to participate in carbon trading; is the carbon market trading base price; l is the length of the carbon emission interval; is the price growth rate; ΔE is the difference between the carbon emissions and the carbon quota of the carbon emission control unit. When it is negative, it means that the actual carbon emissions of the system are lower than the allocated carbon quota, and the excess quota can be sold at the initial carbon price.

[0048] The GCT mechanism is an auxiliary policy tool of the Renewable Portfolio Standard (RPS), in which the renewable energy quota index refers to the government's requirement that the amount of renewable energy consumed by power sales companies or power users must reach a certain proportion of the total power generation. The green certificate is a certificate issued to renewable energy power generators. The actual consumption ratio completed by market entities such as power sales companies and power users who bear the consumption obligation should not be lower than the consumption responsibility weight index formulated by the province. In order to complete the renewable energy consumption index, IES will choose to purchase the missing number of green certificates from the GCT market.

[0049] When the number of green certificates obtained by the subject exceeds the number required for the consumption responsibility weight assessment, the subject will choose to sell the excess green certificates. Otherwise, the subject will purchase the corresponding number of missing green certificates to complete the consumption responsibility. According to the definition of green certificates, the number of green certificates required to meet the RPS for IES is shown in formula (5):

[0050]

[0051] Where: Q RPS The number of green certificates required to meet RPS; P total is the predicted value of total electricity consumption of IES; It is the consumption coefficient of renewable energy power generation that IES needs to complete according to its obligations. The total power consumption forecast of the integrated energy system can be obtained according to the power consumption parameters of the integrated energy system.

[0052] For every 1000 kWh of electricity generated by a renewable energy unit, one certificate can be obtained in the green certificate market. The amount of green certificates obtained is shown in formula (6):

[0053]

[0054] Where: Q gain Number of green certificates obtained for IES; P j,tis the actual output of renewable energy unit j in period t; N R is the installed capacity of renewable energy; T is the scheduling period.

[0055] The cost of IES participating in GCT is shown in formula (7):

[0056] G GCT =C green (Q gain -Q RPS ) (7)

[0057] Where: G GCT is the transaction cost of GCT; C green The transaction price of green certificates.

[0058] In order to encourage the consumption of more renewable energy, penalty measures are imposed on the amount of renewable energy that is not consumed. The penalty cost can be expressed as shown in formula (8):

[0059]

[0060] Where: C ren Penalty cost for curtailing renewable energy; C pun Penalty fees for renewable energy curtailment units; j,t,max is the predicted output of renewable energy unit j in period t; P j,t is the actual output of renewable energy unit j during period t.

[0061] The risk value model based on renewable energy output includes:

[0062] A risk value model is constructed based on the output power of renewable energy and the maximum power of comprehensive energy consumption.

[0063] like Figure 2 As shown in the figure, the high proportion of renewable energy units connected increases the difficulty of day-ahead scheduling due to their uncontrollability. IES must invest reasonable costs to meet the requirements of power supply reliability and deal with the operational risks brought by wind and solar. The average loss caused by the renewable energy power exceeding the maximum uncertainty range of wind and solar that the system can absorb is defined as C CVaR Under a given confidence level β, if the actual renewable energy power P is within the permissible region If the power of renewable energy x can be absorbed by the system without risk, the power fluctuation of renewable energy will not cause any loss.

[0064] Determine the relationship between the output power of renewable energy and the maximum power of comprehensive energy consumption, and build a risk value model:

[0065] If the output power of renewable energy is greater than the maximum power that IES can absorb, There will be a phenomenon of wind and solar power abandonment, and the amount of wind and solar power abandoned The formula is C, which corresponds to the wind and solar power abandonment CVaR for As shown in formula (9) and (10):

[0066]

[0067]

[0068] in, is the adjustable upper limit of renewable energy power; p max The maximum output of renewable energy.

[0069] If the renewable energy output Load shedding will occur, load shedding amount = Formula (11), corresponding to the load shedding C CVaR for As shown in formula (11) and (12):

[0070]

[0071]

[0072] Where: is the adjustable lower limit of renewable energy power; f(x) is the probability density function of wind power. It can be understood that the output power of renewable energy here is the actual power of renewable energy.

[0073] The IES active power imbalance caused by the wind and solar forecast error requires the thermal power unit to provide spinning reserve to maintain the active power balance of the system. Therefore, the risk value of the renewable energy unit is C CVaR,j As shown in formula (13):

[0074]

[0075] All renewable energy generation capacity CVaR,j Add together to get the system's C CVaR , as shown in formula (14):

[0076]

[0077] Where: C pun Penalty fees for renewable energy curtailment units; C down N is the price coefficient for calling the spinning reserve of thermal power units; R is the installed capacity of renewable energy; T is the scheduling period.

[0078] like Figure 3As shown, with the goal of minimizing the comprehensive operating cost of the integrated energy system, the optimal scheduling model is constructed using the risk value model, the CET cost calculation model and the GCT cost calculation model, which also includes: constructing an optimal scheduling model using the penalty cost of renewable energy power abandonment, the operating cost of fossil energy power generation units and the operating cost of renewable energy power generation units.

[0079] Taking IES including photovoltaic power generation, wind power generation and fossil energy generating units as the scheduling object and minimizing the comprehensive operating cost of IES within 1 day as the goal, an optimization scheduling model is established.

[0080] The optimization scheduling model is shown in formula (15):

[0081] min(C T +C R +C CET +C GCT +C ren +C CVaR ) (15)

[0082] Among them, C T is the operating cost of fossil energy generators; C R The operating cost of renewable energy generators.

[0083] Calculate the operating cost of fossil energy generating units, i.e. coal-fired units:

[0084] The objective function of the operating cost of fossil energy power generation units is constructed based on the coal consumption cost of the unit and the start-up and shutdown cost of the unit, as shown in formula (16):

[0085]

[0086] Where: P i,t is the output of fossil energy generator set i in period t; is the coal consumption cost of unit i; is the startup cost of unit i; is the shutdown cost of unit i. The power generation cost of fossil energy power generation, that is, the coal consumption cost of unit i, can be regarded as a quadratic function, as shown in formula (17):

[0087]

[0088] Where: a i ,b i ,c i are the coal consumption coefficients of unit i respectively.

[0089] Calculate the operating cost of renewable energy generators:

[0090] The objective function of the operation cost of renewable energy generator sets is constructed based on the unit operation and maintenance cost and the unit start-up and shutdown cost, as shown in formula (18):

[0091]

[0092] Where: P j,t is the output of renewable energy generator j in period t; is the maintenance cost of unit j; is the startup cost of unit j; is the shutdown cost of unit j. Based on the marginal cost of renewable energy generation, the operation and maintenance cost of the renewable energy generator set, i.e., the maintenance cost of unit j, is obtained as shown in formula (19):

[0093]

[0094] Where: P j,t is the output of renewable energy generator j in period t; a j ,b j ,c j is the cost coefficient of renewable energy generator set j, and the specific value is obtained by fitting the marginal cost curve of renewable energy power generation.

[0095] Furthermore, the constraints constructed with comprehensive energy system parameters include:

[0096] Construct the power balance constraint of the coupled system according to the load demand and the number of load nodes;

[0097] constructing a spinning reserve constraint based on the spinning reserve factor;

[0098] Construct the unit output constraint according to the maximum output power and minimum output power of the unit;

[0099] Establish the unit climbing constraint according to the unit's climbing rate and descending rate;

[0100] Construct the unit startup and shutdown cost constraints based on the unit's single startup cost and single shutdown cost;

[0101] Construct power flow safety constraints based on the power flow constraints of each branch.

[0102] The power balance constraint of the coupled system is constructed according to the load demand and the number of load nodes as shown in formula (20):

[0103]

[0104] Among them, P d,t is the load demand of node d in period t; N L is the number of load nodes.

[0105] The spinning reserve constraint is constructed according to the spinning reserve coefficient as shown in formula (21):

[0106]

[0107] Among them, u i,t is the start and stop status of unit i in time period t; ρ is the spinning reserve coefficient; N is the total number of units.

[0108] The unit output constraint is constructed according to the maximum output power and the minimum output power of the unit as shown in formula (22):

[0109] u i,t P i,min ≤P i ≤u i,t P i,max (twenty two)

[0110] Among them, P i,max ,,P i,min They are the maximum output power and the minimum output power of the unit respectively.

[0111] The unit climbing constraint is constructed according to the unit's climbing rate and climbing rate as shown in formula (23):

[0112] -R d ≤P i,t -P i,t-1 ≤R u (twenty three)

[0113] Since when the unit starts with minimum output P min If the ramp rate is greater than R, all the shut-down units will be unable to start, so the unit ramp constraint is as shown in formula (24):

[0114]

[0115] Among them, R d is the ramp down rate of the unit, R u is the ramp-up rate of the unit.

[0116] The unit startup and shutdown cost constraints are constructed based on the unit single startup cost and single shutdown cost as shown in formula (25):

[0117]

[0118] Among them, H i ,J i are the single startup / shutdown costs of unit i respectively.

[0119] The power flow safety constraints constructed based on the power flow constraints of each branch include:

[0120] The generator output power transfer distribution factor matrix G is introduced to describe the power flow of each branch and construct the DC power flow model, as shown in formula (26):

[0121]

[0122] Among them, P l,max ,P l,min is the bidirectional power flow constraint of line l; G is the transfer distribution factor matrix of power flow; G l-e , G l-f , G l-i , G l-j is the impact of the injected power of nodes e and f on line l; e,t is the output of the machine at node e at time t, P d,t is the load demand of node d at time t.

[0123] As the second embodiment of the present application, a specific embodiment is provided for illustration. Figure 4 As shown, in this embodiment, the improved IEEE30 node is used for example analysis, in which the photovoltaic unit and the wind turbine unit replace the 8th, 11th, and 13th units in the original system respectively. Referring to the 2025 power grid planning data of a certain province in China, the IES renewable energy installed capacity accounts for 35%, the unit configuration parameters are shown in Table A1 and Table A2, and the predicted values ​​of wind turbine units, photovoltaic units, and IES loads are shown in Figure 5 The spinning reserve rate of the receiving system is 5%. Considering that the GCT mechanism is in the development stage, the CET price is set to 50 yuan / t, the price growth rate is 0.25, and the carbon emission interval length is 500t; the renewable energy quota ratio is 0.35, the green certificate price is 50 yuan / book, and the penalty fee is 150 yuan / MW·h; the confidence level is 0.9, and the risk value costs are 500 yuan / MW and 150 yuan / MW respectively.

[0124] Table A1 IES unit cost parameters

[0125]

[0126] Table A2 IES unit configuration parameters

[0127]

[0128] The following five scenarios are set up for simulation verification: Scenario I: basic scenario, i.e., traditional day-ahead optimization scheduling; Scenario II: considering the CET mechanism on the basis of Scenario I; Scenario III: considering the GCT mechanism on the basis of Scenario I; Scenario IV: considering both CET and GCT mechanisms on the basis of Scenario I; Scenario V: considering the role of risk value on the basis of Scenario IV.

[0129] The scheduling results of each scenario are shown in Table 1 and Table 2. It can be seen from the table that compared with scenario I, the IES operating costs of scenarios II-V increased by 38887, 335204, 354255, and 335804 yuan respectively. This is because after considering the CET and GCT mechanisms, IES tends to choose to use low-carbon units and renewable energy units with higher power generation costs. At the same time, the total IES costs of scenarios II-V increased by 93700, 343500, 406600, and 659700 respectively. Compared with scenario I, the carbon emissions under scenarios II-IV decreased by 4.9459t, 14.8962t, and 19.2587t, that is, 8.52%, 25.67%, and 33.19%; the carbon emissions under scenario V increased by 3.3406t compared with scenario IV, that is, 8.62%. Compared with scenario II, which only considers CET, scenario IV's CET cost is reduced by 30,767 yuan, and the operating cost is increased by 11.69%; compared with scenario III, which only considers GCT, scenario IV's GCT cost is reduced by 14,998 yuan, and the IES operating cost is increased by 0.57%; compared with scenarios I-IV, scenario IV considers both CET and GCT mechanisms. At this time, IES will increase the output of wind and solar units due to the market mechanism, while reducing the output of fossil energy units, so the overall low-carbon performance is the best; compared with scenario IV, scenario V's CET cost is reduced by 16,787 yuan, the GCT cost is increased by 14,998 yuan, and the IES operating cost is reduced by 0.55%. This is because after the introduction of risk value, due to the randomness of wind and solar output, IES will reduce wind and solar consumption. After the introduction of risk value, the IES operating cost increases, but the total cost increase is low. Therefore, the day-ahead optimization scheduling strategy for the integrated energy system output by the optimization scheduling model constructed by the day-ahead optimization scheduling method for the integrated energy system of this application can promote IES energy conservation and emission reduction, reduce carbon emissions, and at the same time reduce the energy waste caused by the abandonment of wind and solar power due to the access of renewable energy, thereby improving energy utilization.

[0130] Table 1 Analysis of day-ahead scheduling cost

[0131] Scenario Operating cost / (yuan) CET cost / (yuan) GCT cost / (yuan) Risk value / (yuan) Total cost / (yuan) Ⅰ 2994000 — — — 2994000 Ⅱ 2997887 89813 — — 3087700 Ⅲ 3329204 — 8296 — 3337500 Ⅳ 3348255 59046 -6701 — 3400600 Ⅴ 3329804 42259 8297 273340 3653700

[0132] Table 2 Comparison of scheduling results before

[0133] Scenario Green electricity share / (%) Cost per kilowatt-hour (yuan / KW·h) Carbon emissions / (t) Ⅰ 8.813 0.5035 58.0238 Ⅱ 14.801 0.5193 53.0779 Ⅲ 32.21 0.5613 43.1276 Ⅳ 37.254 0.5719 38.7651 Ⅴ 32.21 0.6144 42.1057

[0134] Figure 6The output of fossil energy and renewable energy is summarized for the whole period under different scenarios. It can be seen from the figure that before 07:00, the output of the units in the five scenarios is relatively small, because the predicted power of renewable energy is small at this time, and the load is low at this time. The cost of starting and stopping the coal-fired unit is huge, so the output of the coal-fired unit is higher at this time. Compared with scenario I, in scenarios II, IV, and V, the output of thermal power unit 2 decreases and the output of thermal power unit 3 increases. This is because the comprehensive unit variable output cost of coal-fired units changes after considering the CET mechanism. During the peak load period from 07:00 to 16:00, the output of the units changes in the five scenarios. Comparing Scenario I and Scenario II, the output of wind turbines increased by 64.70% year-on-year; comparing Scenario I and Scenario III, the output of wind turbines increased by 97.17% year-on-year; comparing Scenario II and Scenario IV, the output of wind turbines increased by 88.20% year-on-year; comparing Scenario III and Scenario IV, the output of wind turbines increased by 43.20% year-on-year; comparing Scenario IV and Scenario V, the output of wind turbines increased by 43.20% year-on-year. In the period of 16:00-24:00, the wind power generation forecast reached the maximum value of the day, and due to the reduction of photovoltaic power generation forecast, the output of renewable energy units was all completed by wind power generation. Comparing Scenario IV and Scenario V, although the total output of wind turbines did not change, due to the existence of risk value, IES tends to predict wind turbines with smaller errors. It can be seen that the day-ahead optimization scheduling strategy for the integrated energy system output by the optimization scheduling model constructed by the day-ahead optimization scheduling method for the integrated energy system of this application can improve the level of renewable energy consumption in IES, and the output of coal-fired units will also decrease accordingly, and it tends to use low-carbon units with lower carbon emissions, thereby reducing carbon emissions while improving energy utilization.

[0135] Figure 7 The impact of CET price on IES operating cost. As shown in the figure: when CET price increases from 50 to 600 yuan / t, the total cost of the system increases rapidly, and the carbon emissions of the system decrease accordingly. Due to the higher CET price, IES limits the output of units with higher carbon emission levels, which in turn increases the output of low-carbon units and renewable energy units. When CET price increases from 200 yuan / t to 250 yuan / t, carbon emissions decrease rapidly, and the rising trend of IES total cost remains unchanged. Due to the CET price constraint, IES tends to output low-carbon units, resulting in a reduction in carbon emissions. In the process of increasing from 500 yuan / t to 550 yuan / t, carbon emissions decrease the fastest, and the rising trend of IES total cost accelerates. Since the CET price reaches the critical value, the higher CET price encourages the system to limit the output of fossil energy units. The comprehensive energy system optimizes the scheduling strategy to select the output of renewable energy units with lower carbon emissions.

[0136] Figure 8The figure shows the impact of green certificates and renewable energy consumption coefficient on IES operating costs. It can be seen from the figure that when the price of green certificates increases from 50 yuan to 100 yuan and the renewable energy consumption coefficient increases from 0.35 to 0.6, the total cost of IES gradually increases. When the renewable energy consumption coefficient is 0.35, with the increase of green certificate prices, the total cost of IES increases at the slowest rate. In this case, the total cost of IES is not very sensitive to the price of green certificates. This is because the renewable energy consumption coefficient is low at this time, and IES tends to output more fossil energy units. When the renewable energy consumption coefficient is 0.6, the total cost of IES increases the fastest. In this case, the total cost of IES is more sensitive to the price of green certificates. This is because in order to meet the renewable energy consumption, the choice is to increase the output of unstable renewable energy units, and more fossil energy units are required to provide sufficient spinning reserves, which ultimately leads to an increase in the total cost of IES.

[0137] This application is based on the CET and GCT mechanisms to establish a low-carbon economic dispatch model that takes into account the carbon-green certificate trading mechanism in IES. Combined with the properties of CET and GCT, based on the uncertainty of renewable energy output, the dispatch results under different scenarios are simulated, and the impact of different CET prices and green certificate prices on IES operation is analyzed. The CET and GCT mechanisms change the output of different units of IES in a market-oriented manner. In order to reduce the total cost of the system, the consumption of renewable energy is given priority. After considering the CET and GCT mechanisms, the IES cost per kilowatt-hour increased by 0.0684 yuan / KW·h during the dispatch cycle, which improved energy utilization and practical feasibility; carbon emissions were reduced by 33.19%, with good low-carbon performance. The high proportion of renewable energy units connected increases the difficulty of day-ahead dispatch. The CVaR model provides sufficient spinning reserves to cope with the operational risks brought by wind and solar. After considering the risk cost, the IES cost per kilowatt-hour increased by 0.0425 yuan / KW·h during the dispatch cycle, which meets the reliability of power supply and has better economy.

[0138] The specific implementation method described above is a preferred implementation method of the comprehensive energy system optimization scheduling method of the present application, and is not intended to limit the specific implementation scope of the present application. The scope of the present application includes but is not limited to the specific implementation method. All equivalent changes made in accordance with the shape and structure of the present application are within the protection scope of the present application.

Claims

1. A day-ahead optimization dispatching method for an integrated energy system, characterized by: The steps include: S1: Construct CET cost calculation model and GCT cost calculation model based on comprehensive energy system parameters; S2: Construct a risk value model based on the output power of renewable energy; S3: With the goal of minimizing the comprehensive operating cost of the integrated energy system, the optimal scheduling model is constructed using the risk value model, the CET cost calculation model, and the GCT cost calculation model, and the constraints are constructed using the parameters of the integrated energy system; S4: Obtain the actual renewable energy output and integrated energy system parameters, use the optimization scheduling model to output the day-ahead optimization scheduling strategy for the integrated energy system and execute it; The optimization scheduling model is: min(C T +C R +C CET +C GCT +C ren +C CVaR ); Among them, C T is the operating cost of fossil energy generators; C R is the operating cost of renewable energy generators; C CET G is the cost of the system participating in carbon trading; GCT is the transaction cost of GCT; C ren Penalize costs for curtailing renewable energy; The value-at-risk model is: Among them, C CVaR,j is the risk value of each renewable energy unit; N R is the number of installed renewable energy capacity; T is the dispatch period; i is the renewable energy unit i; t is the time period; The risk value of each renewable energy unit C CVaR，j for: Among them, C pun Penalty fees for renewable energy curtailment units; C down The price coefficient for calling the spinning reserve of thermal power units; To correspond to the risk value of wind and solar power curtailment; is the risk value corresponding to load shedding; The penalty cost for renewable energy curtailment is: Among them, C ren Penalty cost for curtailing renewable energy; P j,t,max is the predicted output of renewable energy unit j in period t; P j,t is the actual output of renewable energy unit j during period t.

2. The method for optimizing the day-ahead scheduling of the integrated energy system according to claim 1, characterized in that: The CET cost calculation model is: ΔE=EE ΔE=E p -E q ; Among them, C CET Costs for the system to participate in carbon trading; is the carbon market trading base price; l is the length of the carbon emission interval; is the price growth rate; E q is the system carbon quota; E p is the system carbon emissions; ΔE is the difference between the carbon emissions of the carbon emission control unit and the carbon quota.

3. The method for optimizing the day-ahead scheduling of the integrated energy system according to claim 1, characterized in that: The GCT cost calculation model is: G GCT =C green (Q gain -Q RPS ); Among them, G GCT is the transaction cost of GCT; C green is the transaction price of green certificates; Q gain Number of green certificates obtained for IES; Q RPS The number of green certificates required to meet RPS.

4. The method for optimizing the day-ahead scheduling of the integrated energy system according to claim 1, characterized in that: The risk value model based on renewable energy output includes: Determine the relationship between the output power of renewable energy and the maximum power of comprehensive energy consumption, and construct a risk value model.

5. The method for optimizing the day-ahead scheduling of the integrated energy system according to claim 1, characterized in that: The operating cost of the fossil energy generating set, i.e. the coal-fired set, is: Among them, P i,t is the output of fossil energy generator set i in period t; is the coal consumption cost of unit i; is the startup cost of unit i; is the shutdown cost of unit i; T is the scheduling period; t is the time; N T is the installed capacity of fossil energy.

6. The method for optimizing the day-ahead scheduling of the integrated energy system according to claim 1, characterized in that: The operating cost of the renewable energy generator set is: Among them, P j,t is the output of renewable energy generator j in period t; is the maintenance cost of unit j; is the startup cost of unit j; is the shutdown cost of unit j; N R is the installed capacity of renewable energy; T is the scheduling period; t is the time.

7. The method for optimizing the day-ahead scheduling of the integrated energy system according to claim 1, characterized in that: The constraint conditions constructed by comprehensive energy system parameters include: Construct the power balance constraint of the coupled system according to the load demand and the number of load nodes; constructing a spinning reserve constraint based on the spinning reserve factor; Construct the unit output constraint according to the maximum output power and minimum output power of the unit; Establish the unit climbing constraint according to the unit's climbing rate and descending rate; Construct the unit startup and shutdown cost constraints based on the unit's single startup cost and single shutdown cost; Construct power flow safety constraints based on the power flow constraints of each branch.

8. The method for optimizing the day-ahead scheduling of the integrated energy system according to claim 7, characterized in that: The power balance constraint of the coupling system constructed according to the load demand and the number of load nodes is: Among them, u i,t is the start and stop status of unit i in period t; ρ is the spinning reserve coefficient; N is the total number of units; P i,max , is the maximum output power of the unit, P i,min is the minimum output power of the unit; P d,t is the load demand of node d in period t; P i,t is the output of fossil energy generator i in period t.

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