Park integrated energy system optimization scheduling method considering green certificate carbon transaction

Abstract

The invention provides a park integrated energy system optimization scheduling method considering green certificate carbon transaction, which comprises the steps of establishing an optimization scheduling model comprising a step green certificate transaction model, a step type carbon transaction model and a demand response model, establishing a master-slave game double-layer optimization model on the basis of the model, taking an energy operator as a leader, and taking the energy operator as the leader as a leader; and an energy supplier, an energy storage operator and a load aggregator serve as followers. And then, solving the established double-layer master-slave game model by adopting a differential evolution algorithm in combination with a Cplex solver. And finally, verifying the effectiveness of the model and the solving method by adopting example analysis. After a green certificate transaction mechanism, a stepped carbon transaction mechanism and demand response are introduced, the peak clipping and valley filling effect is obvious, reduction of electric, thermal and cold loads is realized in a reasonable range, an optimization result can form a reasonable energy purchase and sale price and supply and demand balance relationship, the carbon emission of the system is reduced, and the energy consumption of the system is reduced. The method is of great significance to energy conservation and emission reduction of a park comprehensive energy system and improvement of main body benefits.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of park comprehensive energy system optimization, and particularly relates to a park comprehensive energy system optimization scheduling method considering green certificate carbon trading. BACKGROUND

[0002] In recent years, traditional fossil energy shortage, energy security, environmental protection and other issues are increasingly serious. Future energy systems will develop towards clean, low-carbon, intelligent and comprehensive directions. The comprehensive energy system characterized by integration of "source, network, load and storage" and multi-energy complementary cooperation is concerned, which can couple various energy networks, flexibly convert electric energy, thermal energy, cold energy and gas energy, etc. to efficiently supply energy and absorb distributed renewable energy, and has important value in improving energy utilization efficiency and promoting energy sustainable development.

[0003] With the increase of mutual coupling between various energies in the comprehensive energy system, the multi-energy market has been further developed. Therefore, the single electricity market cannot meet the needs of the diversified development of China's energy. The operation and optimization of the park comprehensive energy system depend on the coordinated cooperation of multiple interest subjects, and the basic problem to be solved is how to describe the interaction between the large-scale complex system and different subjects. Therefore, the master-slave game theory is introduced in the research to solve the interest distribution problem of multiple subjects. In addition, in order to further reduce the carbon emissions of the system, the green certificate carbon trading mechanism is added to the system to limit the carbon emissions of each subject, so it has important research significance to study the optimization operation of the park comprehensive energy system, the trading mode of the multi-energy supply subject and the improvement of the economic benefit of the park comprehensive energy system. SUMMARY

[0004] The park comprehensive energy system optimization scheduling method considering green certificate carbon trading comprises the following steps:

[0005] 1) The baseline method is used to determine the free carbon emission quota and carbon emissions of the system, mainly including grid electricity purchase, combined heat and power unit, gas boiler, and the grid electricity purchase is defaulted as coal-fired unit power generation. The carbon trading cost generated by external power purchase belongs to EO, and the rest belongs to ES;

[0006] The free carbon emission quota of EO is:

[0007] The free carbon emission quota of EO is:

[0008]

[0009] In the formula, D cEO is the free carbon emission quota of EO, i.e. external power purchase; δ e is the carbon emission allocation per unit of electricity; and P is the electricity quantity purchased by EO from the external grid.

[0010] The free carbon emission quota of ES is: D cES = D GB + D cchp

[0011]

[0012] D GB and D cchp are the free carbon emission quotas of GB and CCHP, respectively; δ h is the carbon emission allocation quota per unit of heat; and are the heat supply, cooling supply and power supply of CCHP, respectively; is the heat production of GB; is the conversion coefficient of power supply to heat supply.

[0013] The carbon emission trading amount actually participating in the carbon trading market is:

[0014] D coi = D pi -D ci

[0015] wherein, i includes EO and ES, D coi is the carbon emission trading amount actually participating in the carbon trading market; D pi is the actual carbon emission amount of the system; D ci is the free carbon emission amount of the system;

[0016] The carbon trading model of the reward and punishment ladder type is:

[0017]

[0018] wherein, i∈{EO, ES}, F co2i is the carbon trading cost; λ is the growth amplitude of the carbon trading price; c is the benchmark price of carbon trading; d is the length of the carbon emission interval;

[0019] Similar to the ladder type carbon trading mechanism, the ladder type green certificate trading mechanism also combines the quota allocation system with the green certificate trading system, and the green certificate trading cost is borne by ES;

[0020] The green certificate quota index available for ES is:

[0021]

[0022] wherein, α is the green certificate quota coefficient; is the power purchase power of EO to ES; is the power consumption of ice storage air conditioning;

[0023] A green certificate represents 1 MWh of renewable energy on-grid power. Therefore, the actual number of green certificates held by the ES can be represented as:

[0024]

[0025] wherein, Pv and Pw represent the photovoltaic power generation and wind turbine power generation, respectively;

[0026] The amount of green certificates that the ES can participate in green certificate trading is:

[0027] R GCT = G d -G s

[0028] The step-type green certificate trading model is:

[0029]

[0030] wherein, F GCT is the green certificate trading cost; x GCT is the green certificate base price; v is the green certificate price growth coefficient; and ΔU is the interval length of green certificate trading;

[0031] 2) Considering the flexible supply characteristics of considering the comprehensive consideration of electrical load and thermal load, a demand response model is constructed;

[0032] The park comprehensive energy system load includes fixed load, transferable load and replaceable load, and the transferable load and replaceable load model is as follows:

[0033]

[0034] wherein: Pf,i(t) represents the fixed load of the i-th load at the t time period; n represents the different types of loads (n = p, c), when n = p, Pf,i(t) represents the fixed load of the i-th load at the t time period; n represents the different types of loads (n = p, c), when n = p, Pf,i(t) represents the fixed load of the i-th load at the t time period; n represents the different types of loads (n = p, c), when n = p, Pf,i(t) represents the fixed load of the i-th load at the t time period; n represents the different types of loads (n = p, c), when n = p, Pf,i(t) represents the fixed load of the i-th load at the t time period; n represents the different types of loads (n = p, c), when n = p, Pf,i(t) represents the fixed load of the i-th load at the t time period; n represents the different types of loads (n = p, c), when n = p, Pf,i(t) represents the fixed load of the i-th load at the t time period; n represents the different types of loads (n = p, c), when n = p, Pf,i(t) represents the fixed load of the i-th load at the t time period; n represents the different types of loads (n = p, c), when n = p,

[0035] The DR model can change energy demand through various demand response methods, as follows:

[0036]

[0037] In the formula: This represents the power after the i-th type of load participates in demand response during time period t; This represents the power of the i-th type of load participating in demand response during time period t.

[0038] 3) Based on the green certificate trading model, carbon trading model, and demand response model, a master-slave game-theoretic two-layer optimization model for the park's integrated energy system is constructed; the specific implementation is as follows:

[0039] Energy operator revenue model:

[0040] As the top leader, the EO sets energy purchase and sale prices, considering the output characteristics of each piece of equipment and the energy consumption characteristics of users. It guides the output of each piece of equipment and prompts users to adjust their energy demand through demand response. Its revenue comes from energy mutual assistance among other stakeholders. The objective function is...

[0041]

[0042] In the formula: the superscript t represents the time period t; and These are revenues from energy sales to load aggregators and ESOs, respectively. and These represent the interaction costs between EO and ES, ESO and the power grid, respectively; C dr The EO is responsible for providing the user demand response compensation function; The carbon trading cost is denoted by EO (External Electricity Purchase); T represents the total time period, taken as 24 hours. The above expressions are as follows.

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] In the formula: and These are the actual electrical, heating, and cooling loads on the energy consumption side; and These represent the electricity, heat, and cooling power purchased by EO from ES, respectively. and respectively, are the electricity and heat power sold by EO to ESO; and respectively, are the electricity, heat and cold power prices sold by EO; and respectively, are the electricity, heat and cold power prices bought by EO from ES; and respectively, are the electricity prices sold and bought by EO to and from the grid; p , μ c respectively, are the unit compensation coefficients of the transferable load and the replaceable load participating in demand response; respectively, are the powers of the transferable load participating in demand response in period t; respectively, are the powers of the replaceable load after participating in demand response in period t;

[0049] Energy operator buying and selling energy price constraints:

[0050] In order to ensure the interests of each subject, the buying and selling energy prices of EO satisfy the following constraint conditions:

[0051]

[0052]

[0053] In the formula: is the average electricity, heat and cold price.

[0054] In addition, EO also needs to satisfy the power interaction constraints with the external grid.

[0055]

[0056]

[0057] In the formula: is the electricity power sold and bought by EO to and from the external grid; and are 0-1 variables, indicating the electricity buying and selling flag bit of EO to and from the external grid; is the upper limit of the electricity power sold and bought.

[0058] Energy supplier revenue model:

[0059] According to the energy price formulated by EO, the energy supplier adjusts the internal equipment output by maximizing its own profit on the basis of the energy buying price given by EO, and its objective function is

[0060]

[0061] In the formula: is the energy selling revenue of ES; represents the carbon trading cost of ES; This indicates the fuel costs for CCHP and GB. Indicates the start-stop cost of GT; F GCT The cost of ES green certificate transactions.

[0062]

[0063] In the formula: a e b e c e and a h b h c h These represent the cost coefficients for CCHP and GB, respectively.

[0064]

[0065] In the formula: c Q c T For GT, the single start / stop cost coefficient; This represents the start / stop state of GT, and is an integer variable between 0 and 1.

[0066] Balance constraints of electrical, thermal, and cooling power output by ES during time period t

[0067]

[0068]

[0069]

[0070] In the formula: These are the output powers of WT and PV, respectively. and These represent the electrical, thermal, and cooling power outputs of the CCHP system, respectively. This refers to the power consumption of the ISAC. Output thermal power for GB; and These are the output cooling power and ice-melting power of the ISAC, respectively.

[0071] Energy storage operator revenue model:

[0072] ESO achieves arbitrage through "low charge, high discharge," and the equipment includes electrical energy storage devices and thermal energy storage devices. Its objective function is:

[0073]

[0074]

[0075]

[0076]

[0077] In the formula: These are the charging cost and the energy release revenue of energy storage, respectively. The operating cost of ESO is α, where α is the unit operating and maintenance cost. and These are the electricity and heat sales prices for ESO, respectively.

[0078] Energy storage device charge / discharge constraints:

[0079] Considering the energy loss and efficiency during charging and discharging, the energy storage state constraints of the energy storage device satisfy:

[0080]

[0081]

[0082]

[0083] In the formula: x∈{BT,HST}; η x,chr and η x,dis These refer to the charging and discharging efficiencies of energy storage and thermal devices, respectively. and They are respectively The minimum and maximum values ​​of γ; x This refers to the energy self-loss rate of energy storage devices.

[0084] Load aggregator revenue model:

[0085] Based on the selling price given by the EO, load aggregators optimize the power of their transferable and substitutable loads participating in demand response. The objective function is:

[0086]

[0087]

[0088]

[0089] In the formula: For user satisfaction function; For users' energy purchase costs; For users to participate in the demand response compensation function; i∈{e,h,c}; v e u e v h u h v c u c These are the user's preference coefficients for electricity, heat, and cooling energy consumption, respectively.

[0090] 4) The differential evolution algorithm combined with the Cplex solver is used to solve the established two-layer master-slave game model;

[0091] The upper layer uses the differential evolution algorithm for solving, and the lower layer uses the Cplex solver. The specific steps of the solution are as follows:

[0092] 4.1) Initialize the population a, and set the iteration count K = 0;

[0093] 4.2) Initialize the energy operator's purchase and sale prices for energy, and transmit these prices to lower-level followers;

[0094] 4.3) Lower-level followers call the Yalmip tool and CPLEX solver to calculate their respective gains and feed back the optimization strategy to the upper-level leader;

[0095] 4.4) The upper-level leader calculates their own benefit U1 based on the optimization strategies fed back by the lower-level followers;

[0096] 4.5) Perform crossover and mutation operations on population a to obtain a new population b.

[0097] 4.6) The Yalmip tool and CPLEX solver are called again to calculate the optimization of the payoff function for each follower, and the optimization results are sent to the upper leader. The upper leader then calculates its own payoff U2 again.

[0098] 4.7) Compare the sizes of U1 and U2. If U1 > U2, then let a = b, U1 = U2, and K = K + 1; otherwise, leave them unchanged.

[0099] 4.8) Determine if K has reached the maximum number of iterations. If it has, output the optimal result; otherwise, return to step 4.5.

[0100] 5) Use numerical examples to analyze and verify the effectiveness of the model and solution method;

[0101] 2. The method for optimizing the scheduling of a park's integrated energy system considering green certificate carbon trading as described in claim 1, characterized in that step 5) involves verifying the effectiveness of the model and solution method through numerical examples, including:

[0102] Analysis of the results of the example

[0103] (1) Analysis of EO pricing results

[0104] The EMO's electricity, heating, and cooling pricing strategies are consistently structured within predetermined price ranges, providing more favorable prices for both energy suppliers and consumers. Furthermore, the fluctuation trends of the EMO's electricity, heating, and cooling prices align with the external grid's time-of-use pricing, heating price cap, and cooling price cap, respectively, indicating that the prices set by the EMO are reasonable.

[0105] (2) Comparative analysis of different strategies

[0106] Optimized scheduling methods that consider green certificate trading mechanisms and tiered carbon trading mechanisms can effectively reduce the carbon emissions of the system and achieve low-carbon operation of the park's integrated energy system.

[0107] (3) Optimize scheduling results and supply and demand balance analysis

[0108] By considering the optimization scheduling methods of green certificate trading mechanism and tiered carbon trading mechanism, and introducing a demand response model, peak shaving and valley filling can be achieved, and the optimized scheduling results can achieve a balance of electrical power, thermal power and cold power.

[0109] The beneficial effects of this invention are:

[0110] This invention provides an optimized scheduling method for a park's integrated energy system considering green certificate carbon trading. It establishes a tiered green certificate trading model, a tiered carbon trading model, and a demand response model. Based on these models, a master-slave game-themed two-layer optimization model is established, dividing the park's integrated energy system into four entities: Energy Operator (EO), Energy Supplier (ES), Load Aggregator (LA), and Energy Storage Operator (ESO). The Energy Operator is positioned as the leader, and the Energy Supplier, LA, and Load Aggregator as followers. The behavior of each party in pursuing its own profit maximization is analyzed. Then, a differential evolution algorithm combined with a Cplex solver is used to solve the established two-layer master-slave game model. Finally, numerical examples are used to verify the effectiveness of the model and solution method. After introducing the green certificate trading mechanism, the tiered carbon trading mechanism, and demand response, the peak shaving and valley filling effects are significant, and the reduction of cooling and heating loads within a reasonable range is achieved, reducing user energy costs while significantly improving user energy satisfaction. By introducing a master-slave game mechanism, a one-master-many-slave game model is formed. At the same time, the energy purchase and sale price of the upper-level leader and the energy output and energy consumption of the lower-level followers are optimized. The optimization result can form a reasonable energy purchase and sale price and a supply and demand balance, which reduces the carbon emissions of the system and is of great significance to the energy conservation and emission reduction of the park's comprehensive energy system and the improvement of the benefits of each entity. Attached Figure Description

[0111] The present invention will now be described in further detail with reference to the accompanying drawings;

[0112] Figure 1 A framework diagram of a master-slave game-theoretic two-layer optimization model for the park's integrated energy system.

[0113] Figure 2This is a graph showing the results of electricity price optimization for energy operators in the example analysis of this invention.

[0114] Figure 3 This is a graph showing the optimization results of heat prices for energy operators in the numerical example analysis of this invention.

[0115] Figure 4 This is a graph showing the optimization results of cooling prices for energy operators in the numerical example analysis of this invention.

[0116] Figure 5 This is a graph showing the electrical load curves before and after the demand response in the numerical analysis of this invention.

[0117] Figure 6 This is a graph showing the heat load curves before and after the demand response in the numerical analysis of this invention.

[0118] Figure 7 This is a diagram showing the cooling load curves before and after the demand response in the numerical analysis of this invention.

[0119] Figure 8 This is a power balance diagram before and after demand response in the numerical analysis of this invention.

[0120] Figure 9 This is a thermal power balance diagram before and after demand response in the numerical analysis of this invention.

[0121] Figure 10 This is a diagram showing the cooling power balance before and after the demand response in the numerical analysis of this invention. Detailed Implementation

[0122] This invention considers an optimized scheduling method for the integrated energy system of a park based on green certificate carbon trading, comprising the following steps:

[0123] 1) The baseline method is used to determine the system's free carbon emission allowances and carbon emissions, mainly including electricity purchased from the grid, combined heat and power units, and...

[0124] For gas-fired boilers, electricity purchased from the grid is assumed to be generated by coal-fired power units. The carbon trading costs of purchased electricity go to the Exclusive Owner (EO), while the remainder goes to the ES (Employment Supporter).

[0125] The free carbon emission allowances for EO are:

[0126]

[0127] In the formula, D cEO EO refers to the free carbon emission allowance for purchased electricity; δ e Carbon emission allocation per unit of electricity; Electricity purchased by the EO from an external power grid;

[0128] The free carbon emission allowances for ES are: D cES =D GB +Dcchp

[0129]

[0130] D GB and D cchp Free carbon emission allowances for GB and CCHP respectively; δ h Carbon emission allocation per unit of heat; and These are the heating, cooling, and power consumption of CCHP, respectively. The heat generated by GB; This is the conversion factor for converting electricity generation into heat supply.

[0131] The actual amount of carbon emissions traded in the carbon trading market is:

[0132] D coi =D pi -D ci

[0133] In the formula, i includes EO and ES, and D coi The amount of carbon emissions traded in the actual carbon trading market; D pi D represents the actual carbon emissions of the system. ci This refers to the system's free carbon emissions;

[0134] The reward-and-penalty tiered carbon trading model is as follows:

[0135]

[0136] In the formula, i∈{EO,ES}, F co2i λ represents the carbon trading cost; λ represents the carbon trading price increase rate; c represents the benchmark price for carbon trading; and d represents the length of the carbon emission range.

[0137] Similar to the tiered carbon trading mechanism, the tiered green certificate trading mechanism also combines the quota allocation system with the green certificate trading system, with the green certificate trading costs borne by ES.

[0138] The green certificate quota available to ES is as follows:

[0139]

[0140] In the formula, α is the green certificate quota coefficient; The amount of electricity purchased by EO from ES; Power consumption of ice storage air conditioning;

[0141] One green certificate represents 1 MWh of renewable energy fed into the grid. Therefore, the actual number of green certificates held by ES can be expressed as:

[0142]

[0143] In the formula, These are photovoltaic power generation and wind turbine power generation, respectively.

[0144] The amount of green certificates that ES can participate in green certificate trading is:

[0145] R GCT =G d -G s

[0146] The tiered green certificate trading model is as follows:

[0147]

[0148] In the formula, F GCT For green certificate transaction costs; x GCT ΔU represents the base price of the green certificate; v represents the green certificate price growth coefficient; ΔU represents the trading range length of the green certificate.

[0149] 2) Considering the flexible supply characteristics of both electrical and thermal loads, a demand response model is constructed;

[0150] The load of the park's integrated energy system includes stationary load, transferable load, and substitute load. The models for transferable load and substitute load are shown below:

[0151]

[0152] In the formula: This represents the fixed load of type i during time period t; n represents the different types of load (n = p, c), when n = p, This represents the power of the transferable load of type i in time period t after it participates in demand response; This represents the power of the transferable load (load type i) participating in the demand response during time period t; when n = c, This represents the power of the i-th type of load after the alternative loads participate in the demand response during time period t; This represents the power of the substitutable loads participating in the demand response during time period t for the i-th type of load; These represent the parameters for the transfer in and transfer out of the i-th type of load during time period t, both of which are 0-1; These represent the power transferred in and out of the i-th type of load during time period t, respectively. These represent the lower and upper limits of the i-th type of load participating in the demand response, respectively.

[0153] The DR model can change energy demand through various demand response methods, as follows:

[0154]

[0155] In the formula: This represents the power after the i-th type of load participates in demand response during time period t; This represents the power of the i-th type of load participating in demand response during time period t.

[0156] 3) Based on the green certificate trading model, carbon trading model, and demand response model, a master-slave game-theoretic two-layer optimization model for the park's integrated energy system is constructed; the specific implementation is as follows:

[0157] The master-slave game two-level optimization model of the park's integrated energy system is as follows: Figure 1 As shown. It includes four main entities: energy operators, energy suppliers, load aggregators, and energy storage operators. The objective functions for each entity are as follows:

[0158] As a bridge between energy supply and demand, the Energy Provider (EO) connects the energy supply side, the energy storage side, and the load side, realizing integrated operation of source, grid, load, and storage. The EO simultaneously considers the supply and demand relationship among source, load, and storage when setting energy purchase and sale prices. It purchases energy produced by the Energy Provider (ES) and sells it to the Provider (LA) and ESO, profiting from the transaction. As the leader, the EO sets energy purchase and sale prices with the goal of maximizing net profit. When the electricity purchased by the ESO from the ES cannot meet user demand, it must purchase electricity from the external grid and bear the carbon emission costs of the purchased electricity. Compared with the grid, the EO offers a more flexible pricing strategy, with a higher purchase price than the grid's on-grid price and a lower sales price than the grid's time-of-use price, better incentivizing the participation of all stakeholders and load regulation.

[0159] Energy operator revenue model:

[0160] As the top leader, the EO sets energy purchase and sale prices, considering the output characteristics of each piece of equipment and the energy consumption characteristics of users. It guides the output of each piece of equipment and prompts users to adjust their energy demand through demand response. Its revenue comes from energy mutual assistance among other stakeholders. The objective function is...

[0161]

[0162] In the formula: the superscript t represents the time period t; and These are revenues from energy sales to load aggregators and ESOs, respectively. and These represent the interaction costs between EO and ES, ESO and the power grid, respectively; C dr The EO is responsible for providing the user demand response compensation function; The carbon trading cost is denoted by EO (External Electricity Purchase); T represents the total time period, taken as 24 hours. The above expressions are as follows.

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] In the formula: and These are the actual electrical, heating, and cooling loads on the energy consumption side; and These represent the electricity, heat, and cooling power purchased by EO from ES, respectively. and These represent the electricity and heat sales volume from EO to ESO, respectively. and These are the prices of electricity, heat, and cooling energy sold by EO, respectively. and These are the prices that the EO purchases from the ES for electricity, heat, and cooling energy, respectively. and These represent the electricity sales and purchase prices from the grid by the EO, respectively; μ p μ c These represent the unit compensation coefficients for transferable loads and substitutable loads participating in demand response, respectively. These represent the power of transferable loads participating in demand response during time period t; These represent the power generated by the alternative loads participating in the demand response during time period t.

[0169] Energy operators' energy purchase and sale price constraints:

[0170] To ensure the interests of all stakeholders, the purchase and sale prices of EO energy must meet the following constraints:

[0171]

[0172]

[0173] In the formula: This represents the average selling price of electricity, heat, and cooling.

[0174] In addition, the EO must also meet the power constraints for interaction with the external power grid.

[0175]

[0176]

[0177] In the formula: This refers to the power that the EO sells or purchases from the external power grid. The sum is a 0-1 variable, representing the flag bit of EO purchasing or selling electricity to the external power grid; This refers to the upper limit of the power capacity for electricity sales and purchases.

[0178] The Energy Storage (ES) system, acting as the source, uses natural gas as auxiliary fuel and provides electricity, heat, and cooling energy to the system through CCHP units, renewable energy power generation equipment, and ice storage air conditioning. Based on the energy purchase price set by the Energy Management Authority (EO), and considering the carbon emissions generated during CCHP and GB operations, the output of each unit is optimized with the objective function of maximizing energy sales revenue and minimizing fuel and carbon trading costs.

[0179] Energy supplier revenue model:

[0180] Based on the energy price set by the EO, energy suppliers adjust the output of their internal equipment to maximize their own profits, according to the energy purchase price given by the EO. The objective function is as follows:

[0181]

[0182] In the formula: For the revenue from the sale of ES energy; This represents the carbon trading cost of ESC; This indicates the fuel costs for CCHP and GB. Indicates the start-stop cost of GT; F GCT The cost of ES green certificate transactions.

[0183]

[0184] In the formula: a e b e c e and a h b h c h These represent the cost coefficients for CCHP and GB, respectively.

[0185]

[0186] In the formula: c Q c T For GT, the single start / stop cost coefficient; This represents the start / stop state of GT, and is an integer variable between 0 and 1.

[0187] Balance constraints of electrical, thermal, and cooling power output by ES during time period t

[0188]

[0189]

[0190]

[0191] In the formula: These are the output powers of WT and PV, respectively. and These represent the electrical, thermal, and cooling power outputs of the CCHP system, respectively. This refers to the power consumption of the ISAC. Output thermal power for GB; and These are the output cooling power and ice-melting power of the ISAC, respectively.

[0192] ESOs purchase and store energy at lower prices during periods of low energy demand and sell it at higher prices during periods of high demand. Based on price information, they optimize their charging and discharging power between ESOs and LAs by charging low and discharging high, thereby generating profits.

[0193] Energy storage operator revenue model:

[0194]

[0195]

[0196]

[0197]

[0198] In the formula: These are the charging cost and the energy release revenue of energy storage, respectively. The operating cost of ESO is α, where α is the unit operating and maintenance cost. and These are the electricity and heat sales prices for ESO, respectively.

[0199] Energy storage device charge / discharge constraints:

[0200] Considering the energy loss and efficiency during charging and discharging, the energy storage state constraints of the energy storage device satisfy:

[0201]

[0202]

[0203]

[0204] In the formula: x∈{BT,HST}; η x,c h r and η x,dis These refer to the charging and discharging efficiencies of energy storage and thermal devices, respectively. and They are respectively The minimum and maximum values ​​of γ; xThis refers to the energy self-loss rate of energy storage devices.

[0205] LA brings together a group of users with demand response capabilities, representing them in market transactions and subjecting them to regulation. By introducing a certain proportion of adjustable load, and comprehensively considering energy purchase costs, energy comfort, and demand response compensation, it adjusts energy demand to maximize the overall benefits on the user side. The adjusted actual energy demand will, in turn, affect the benefits of each stakeholder.

[0206] Based on the selling price given by the EO, load aggregators optimize the power of their transferable and substitutable loads participating in demand response. The load aggregator's revenue model is as follows:

[0207]

[0208]

[0209]

[0210] In the formula: For user satisfaction function; For users' energy purchase costs; For users to participate in the demand response compensation function; i∈{e,h,c}; v e u e v h u h v c u c These are the user's preference coefficients for electricity, heat, and cooling energy consumption, respectively.

[0211] 4) The differential evolution algorithm combined with the Cplex solver is used to solve the established two-layer master-slave game model;

[0212] The upper layer uses the differential evolution algorithm for solving, and the lower layer uses the Cplex solver. The specific steps of the solution are as follows:

[0213] 4.1) Initialize the population a, and set the iteration count K = 0;

[0214] 4.2) Initialize the energy operator's purchase and sale prices for energy, and transmit these prices to lower-level followers;

[0215] 4.3) Lower-level followers call the Yalmip tool and CPLEX solver to calculate their respective gains and feed back the optimization strategy to the upper-level leader;

[0216] 4.4) The upper-level leader calculates their own benefit U1 based on the optimization strategies fed back by the lower-level followers;

[0217] 4.5) Perform crossover and mutation operations on population a to obtain a new population b.

[0218] 4.6) The Yalmip tool and CPLEX solver are called again to calculate the optimization of the payoff function for each follower, and the optimization results are sent to the upper leader. The upper leader then calculates its own payoff U2 again.

[0219] 4.7) Compare the sizes of U1 and U2. If U1 > U2, then let a = b, U1 = U2, and K = K + 1; otherwise, leave them unchanged.

[0220] 4.8) Determine if K has reached the maximum number of iterations. If it has, output the optimal result; otherwise, return to step 4.5.

[0221] 5) Use numerical examples to analyze and verify the effectiveness of the model and solution method;

[0222] Case Analysis

[0223] Energy supply equipment includes wind turbines, photovoltaic systems, gas-fired boilers, and gas turbines; energy conversion equipment includes ice storage air conditioners, absorption chillers, and waste heat boilers; energy storage devices include batteries and thermal storage tanks; and the load side includes electrical, thermal, and cooling loads.

[0224] (1) Analysis of EO pricing results

[0225] like Figure 2 , 3 As shown in Figure 4, the EO's electricity, heating, and cooling price strategies are always contained within the established electricity, heating, and cooling prices, providing more favorable prices for both energy suppliers and consumers. Furthermore, the fluctuation trends of the EO's electricity, heating, and cooling prices are consistent with the external grid's time-of-use electricity price, heating price ceiling, and cooling price ceiling, respectively, indicating that the prices set by the EO are reasonable.

[0226] (2) Comparative analysis of different strategies

[0227] This invention establishes the following two scenarios for comparison:

[0228] Scenario 1: Optimization and scheduling model of the park's integrated energy system without considering green certificate trading mechanism and tiered carbon trading mechanism;

[0229] Scenario 2: An optimized scheduling model for the integrated energy system of the industrial park, considering both green certificate trading and tiered carbon trading mechanisms;

[0230] The carbon emission results for the two scenarios are shown in Table 1. It can be seen that the total carbon emission of scenario 2 is reduced by 8.98% compared with scenario 1. Therefore, the optimized scheduling method considering the green certificate trading mechanism and the tiered carbon trading mechanism can effectively reduce the carbon emission of the system and achieve low-carbon operation of the park's integrated energy system.

[0231] Table 1 Carbon Emissions under Different Scenarios

[0232]

[0233] (3) Optimize scheduling results and supply and demand balance analysis

[0234] Consider the load curve after optimization of the demand response optimization scheduling model, for example... Figure 5 , 6 As shown in Figure 7, taking electricity load as an example, when only considering price-based IDR, during the off-peak period from 23:00 to 07:00, users are willing to transfer their peak-period portable load to this period, thereby maximizing their overall benefits. During the normal electricity load periods from 08:00 to 10:00 and from 14:00 to 17:00, users make small changes to their electricity consumption strategies, considering both user satisfaction and energy purchase costs. During the peak electricity load periods from 11:00 to 13:00 and from 18:00 to 22:00, users disregard user satisfaction at this time and actively transfer a large amount of portable load to the off-peak period, reducing their energy purchase costs.

[0235] Taking power balance as an example, the optimized scheduling results of electricity, heat, and cooling energy in the park's integrated energy system after considering green certificate carbon trading are as follows: Figure 8 , 9 As shown in Figure 10. Considering environmental protection, EO prioritizes the consumption of renewable energy PV and WT. When wind and solar power generation cannot meet the electricity load demand, gas turbines and batteries generate electricity to meet the load demand. When wind, solar and gas turbine power generation is sufficient, it is used to store ice for cold storage air conditioning or to charge batteries to achieve power balance.

Claims

1. A park integrated energy system optimization scheduling method considering green certificate carbon trading, characterized in that The method comprises the following steps: 1) The free carbon emission quota and the carbon emission of the system are determined by using a benchmark line method, mainly including grid electricity purchase, combined heat and power unit and gas boiler, and the grid electricity purchase is defaulted as coal-fired unit power generation. The carbon trading cost generated by the purchased electricity is attributed to the EO, and the remaining part is attributed to the ES; The free carbon emission quota of the EO is: The free carbon emission quota of the ES is: In the formula, D cEO is the free carbon emission quota of the EO, i.e., the purchased electricity; δ e is the carbon emission allocation per unit of electricity; is the electricity purchased by the EO from the external power grid; The carbon emission transaction amount actually participating in the carbon trading market is: D cES = D GB + D cchp D GB and D cchp respectively, the free carbon emission quota of GB and CCHP; δ h the unit heat carbon emission allocation quota; and respectively, the heat supply, cooling supply and power supply of CCHP; the heat production of GB; the conversion coefficient of power generation converted into heat supply. The carbon trading model of the reward and punishment ladder type is: D coi = D pi - D ci In the formula, i includes EO and ES, D coi The carbon emission trading amount for actual participation in the carbon trading market;D pi The actual carbon emission amount of the system;D ci The free carbon emission amount of the system; Similar to the ladder type carbon trading mechanism, the ladder type green certificate trading mechanism also combines the quota allocation system with the green certificate trading system, and the green certificate trading cost is borne by the ES; where i ∈ {EO, ES}, F co2i is the carbon trading cost; λ is the carbon trading price growth rate; c is the base price of carbon trading; d is the length of the carbon emission interval; The green certificate quota index available for the ES is: One green certificate represents 1 MWh of renewable energy on-grid power. Therefore, the actual number of green certificates held by the ES can be represented as: In the formula, a is a green certificate quota coefficient; is the power purchased by the EO from the ES; is the power consumed by the ice storage air conditioner; The green certificate amount available for the ES to participate in the green certificate trading is: In the formula, respectively, are the photovoltaic power generation and the wind turbine generator power generation; The green certificate trading model of the ladder type is: R GCT = G d - G s 2) A demand response model is constructed by comprehensively considering the flexible supply characteristics of the electric load and the thermal load; In the formula, F GCT is the green certificate transaction cost; x GCT is the green certificate base price; v is the green certificate price growth coefficient; ΔU is the interval length of the green certificate transaction; The load of the park comprehensive energy system includes fixed load, transferable load and replaceable load, and the models of the transferable load and the replaceable load are as follows: The DR model can change the energy demand through various demand response modes, as follows In the formula: Pi,n(t) represents the fixed load of the ith load at the t period; n represents different types of loads (n = p, c), when n = p, Pi,n(t) represents the power of the transferable load of the ith load at the t period after participating in the demand response; Pi,n(t) represents the power of the transferable load of the ith load at the t period after participating in the demand response; when n = c, Pi,n(t) represents the power of the replaceable load of the ith load at the t period after participating in the demand response; Pi,n(t) represents the power of the replaceable load of the ith load at the t period after participating in the demand response; Pi,n(t) represents the transfer-in and transfer-out parameters of the ith load at the t period, both of which are 0-1; Pi,n(t) represents the transfer-in and transfer-out power of the ith load at the t period; Pi,n(t) represents the lower limit value and the upper limit value of the ith load participating in the demand response. 3) Based on the green certificate trading model, the carbon trading model and the demand response model, a master-slave game double-layer optimization model of the park comprehensive energy system is constructed, and the specific implementation is as follows: In the formula: represents the power after the i-th load participates in demand response at the t time period; represents the power of the i-th load participating in demand response at the t time period. The energy operator revenue model: The EO, as the upper leader, formulates the energy purchase and sale price, considers the output characteristics of each device and the energy use characteristics of the user, guides the output of each device and promotes the user to adjust the energy use demand through demand response, and the revenue of the EO is derived from the energy interconnection between other interest subjects, and the objective function is The energy operator purchase and sale energy price constraint: where superscript t denotes the tth time period; and are the energy sale revenues from selling energy to load aggregators and ESOs, respectively; and are the interaction costs of the EO with the ES, ESO and the grid, respectively; C dr is the user demand response compensation function for the EO; is the carbon trading cost of the electricity purchased by the EO; T is the total time period, which is 24 h. The above equations are expressed as follows. wherein: and are the actual electrical, thermal and cooling loads on the demand side, respectively; and are the electrical, thermal and cooling power purchased by the EO from the ES, respectively; and are the electrical and thermal power sold by the EO to the ESO, respectively; and are the prices of the electrical, thermal and cooling energy sold by the EO, respectively; and are the prices of the electrical, thermal and cooling energy purchased by the EO from the ES, respectively; and are the prices of the electrical energy sold and purchased by the EO to / from the grid, respectively; μ p , μ c are the unit compensation coefficients of the transferable and replaceable loads participating in the demand response, respectively; are the powers of the transferable load participating in the demand response at time period t, respectively; are the powers of the replaceable load after participating in the demand response at time period t, respectively; In order to ensure the interests of each subject, the energy purchase and sale price of the EO meets the following constraint conditions: In addition, the EO also needs to meet the interactive power constraint with the external grid. In the formula: is the average price of electricity, heat, and cold sold. The energy supplier revenue model: wherein: Pout is the power sold to the external grid by the EO; and is a 0-1 variable indicating the EO's power purchase / sale flag to the external grid; Pmax is the power upper limit for power purchase / sale. The ES adjusts the internal device output by maximizing its own profit based on the energy purchase price given by the EO, and the objective function is The ES output electric, heat and cold power balance constraint at t period wherein: is the revenue from the sale of ES; is the carbon trading cost of ES; is the fuel cost of CCHP and GB; is the start-stop cost of GT;F GCT is the green certificate trading cost of ES. wherein: a e , b e , c e and a h , b h , c h represent the cost coefficients of CCHP and GB, respectively. where: c Q , c T is the GT's single start, stop cost coefficient; is the GT's start, stop state, a 0-1 integer variable. The energy storage operator revenue model: In the formula: respectively, the output power of WT, PV; and respectively, the electric, heat, and cold power output by the CCHP system; is the power consumption of ISAC; is the heat output power of GB; and respectively, the output cold power and ice melting power of ISAC. The ESO realizes arbitrage through "low charging and high discharging", and the device includes electric energy storage device and thermal energy storage device, and the objective function is: The energy storage device charging and discharging energy constraint: In the formula: respectively, the charging cost and discharging benefit of energy storage; is the operation and maintenance cost of the ESO, and a is the unit operation and maintenance cost; and respectively, the electricity and heat selling prices of the ESO. Considering the energy loss and efficiency of charging and discharging, the energy storage state constraint of the energy storage device meets: The load aggregator revenue model: wherein: x∈{BT,HST};η x,chr and η x,dis are the charging and discharging efficiencies of the electrical and thermal storage devices, respectively; and are the minimum and maximum values of the electrical and thermal storage capacities, respectively; γ x is the energy self-loss rate of the energy storage device. The load aggregator optimizes the power of the transferable load and the replaceable load participating in demand response based on the sale price given by the EO. The objective function is 4) The double-layer master-slave game model established is solved by using the differential evolution algorithm combined with the Cplex solver; In the formula: is a user satisfaction function; is a user's cost of purchasing energy; is a user's participation in demand response compensation function; i∈{e,h,c}; v e , u e , v h , u h , v c , u c are respectively the preference coefficients of the user consuming electric, heat and cold energy. The upper layer is solved by using the differential evolution algorithm, and the lower layer is solved by using the Cplex solver, and the specific steps of the solution are as follows: 4.1) Initialize the population a, and set the iteration number K=0; 4.2) Initialize the energy purchase and sale price of the energy operator, and pass the energy purchase and sale price to the lower layer follower; ​ 4.3) The lower follower calls the Yalmip tool and the CPLEX solver to calculate the respective benefits, and feeds the optimization strategy back to the upper leader; 4.4) The upper leader calculates its own benefit U1 according to the optimization strategy fed back by the lower follower; 4.5) Cross and mutate the population a to get a new population b 4.6) Again call the Yalmip tool and the CPLEX solver to optimize and solve the follower's respective benefit function, and send the optimization result to the upper leader, which again calculates its own benefit U2; 4.7) Compare the size of U1 and U2, if U1> U2, then a = b, U1 = U2, K = K + 1; otherwise, keep unchanged; 4.8) Determine whether K reaches the maximum number of iterations, if so, output the optimal result, if not, return to step 4.5) 5) Use example analysis to verify the effectiveness of the model and solution method.

2. The method of claim 1, wherein the method further comprises: determining a carbon price based on the carbon price data; and determining a carbon price adjustment value based on the carbon price and the carbon price adjustment data. The content of step 5) includes: Example result analysis (1) Analysis of EO pricing results The EO electricity price, heat price and cold price strategy is always between the formulated electricity price, heat price and cold price, providing more optimal prices for the energy supply end and the energy consumption end. In addition, the fluctuation trend of the EMO's electricity sales price, heat sales price and cold sales price is consistent with the external power grid time-of-use electricity price, heat price upper limit and cold price upper limit, respectively, indicating that the EO's prices are reasonable. (2) Comparative analysis of different strategies The optimization scheduling method considering green certificate trading mechanism and step-type carbon trading mechanism can effectively reduce the carbon emissions of the system and achieve low-carbon operation of the park comprehensive energy system. (3) Analysis of optimization scheduling results and supply and demand balance The optimization scheduling method considering green certificate trading mechanism and step-type carbon trading mechanism, combined with the demand response model, can achieve peak clipping and valley filling, and the optimization scheduling results can achieve balance of electric power, heat power and cold power.

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