EAIGDT-based park integrated energy system optimization scheduling method containing P2G-CCS and cloud energy storage

Abstract

The invention relates to the technical field of power system operation and control, and discloses an EAIGDT-based park integrated energy system optimization scheduling method containing P2G-CCS and cloud energy storage. The method comprises the steps of constructing a PIES scheduling framework considering coupling of a multi-working-condition electrolytic cell (EL), a methane generator (MR), carbon capture and storage (CCS) and cloud energy storage (CES), and establishing a double-layer optimization model with an upper layer main body being a PIES operator and a lower layer main body being a CES operator under a reward and punishment stepped carbon price mechanism. Furthermore, an entropy weight adaptive information gap decision theory method (EAIGDT) is provided, subjective factors are eliminated and non-probability features of the subjective factors are reserved when multi-source-load uncertainty is processed, and a risk avoidance (RA) strategy and a risk seeking (RS) strategy are respectively constructed according to risk preferences of an operator. According to the EAIGDT-based park integrated energy system optimization scheduling method containing P2G-CCS and cloud energy storage provided by the invention, benefits of all subjects can be effectively considered, and PIES low-carbon economic operation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and control, and more specifically, to an optimization scheduling method for a park integrated energy system containing P2G-CCS and cloud energy storage based on EAIGDT. Background Art

[0002] The severity of energy consumption, environmental degradation and climate change continues to escalate with the progress of economy and society. P2G-CCS technology plays an important role in energy conservation and emission reduction. CCS can separate CO2 from waste gas emitted by the energy industry and is one of the key technologies for achieving low-carbonization of integrated energy systems. In areas where renewable energy is abundant but difficult to absorb, P2G uses P2G technology to convert surplus electricity such as wind power and photovoltaic power into gas fuel, solving the problem of wind and photovoltaic abandonment of renewable energy. The concept of cloud energy storage provides a method to solve the pressure of new energy absorption, but existing cloud energy storage research does not consider source and load uncertainty and does not meet the dual requirements of system robustness and economy. Among them, IGDT is an effective method to deal with source and load uncertainty in PIES. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an optimization scheduling method for a comprehensive energy system of a park containing P2G-CCS and cloud energy storage based on EAIGDT, which effectively constrains the carbon emissions of the system, reduces the cost of emission reduction, promotes the coordinated utilization of energy cascades, and achieves mutual benefit and win-win results for two different stakeholders.

[0004] Technical solution: To achieve the above-mentioned purpose, the present invention provides the following technical solution: an EAIGDT-based optimization and scheduling method for a park integrated energy system containing P2G-CCS and cloud energy storage, comprising the following steps: S1. Constructing an optimization and scheduling model for a park integrated energy system containing P2G-CCS and cloud energy storage, wherein the optimization and scheduling model for the park integrated energy system includes a multi-operating-state electrolyzer (EL), a methane generator (MR), a carbon capture and storage (CCS) and a cloud energy storage (CES) coupling model; S2. Under a reward-and-penalty ladder carbon trading, a two-layer optimization model is established in which the upper entity is the PIES operator and the lower entity is the CES operator; S3. For the source-load uncertainty problem in the scheduling model, an entropy weight adaptive information gap decision theory method (EAIGDT) is proposed. While dealing with the source-load uncertainty, the subjective factors are eliminated and its non-probabilistic characteristics are retained, and risk avoidance (RA) and risk seeking (RS) strategies are established respectively according to the risk preferences of the operator.

[0005] As a preferred technical solution of the present invention, the step S1: a coupling model of a multi-operating electrolyzer (EL), a methane generator (MR), carbon capture and storage (CCS) and a cloud energy storage (CES) is expressed as follows:

[0006] (1) Considering two-stage P2G and CCS modeling under multiple conditions:

[0007] The working state of a single EL can be roughly divided into: fluctuating operating condition, overload operating condition, low-load operating condition, standby condition and shutdown state. The specific model is as follows:

[0008]

[0009] Where: , , , , Respectively represent Time electrolyzer In fluctuating production conditions, low-load production conditions, overload production conditions, shutdown conditions and standby conditions; is the standby power of the electrolyzer; is the power consumption of electrolytic cell n at time t; is the rated operating power of the electrolyzer; is the hydrogen production power of the electrolyzer at time t.

[0010] Since the startup process under standby conditions will cause the loss of output hydrogen energy, the relationship between EL power consumption and hydrogen production power can be expressed as:

[0011]

[0012] Where: For the nth EL Hydrogen production efficiency at each moment; is the electricity-to-hydrogen conversion efficiency of EL; is the penalty coefficient of hydrogen production power during standby start; binary variable When it is 1, it means that EL switches from the standby state to the working state, which satisfies the following constraints:

[0013]

[0014] (2) HFC model

[0015]

[0016] Where: is the hydrogen consumption power at time t; and are the electricity generation power and heat generation power at time t respectively; The energy conversion efficiency of the fuel cell; and are the upper and lower limits of hydrogen power respectively; , is the maximum and minimum climbing power; and The upper and lower limits of the adjustable range of the fuel cell's electric-to-heat ratio.

[0017] (3) MR model

[0018]

[0019] Where: Enter the hydrogen energy of MR for period t; is the power of natural gas output by MR during period t; is the conversion efficiency of MR; , are the upper and lower limits of hydrogen energy input to MR, respectively; is the calculation coefficient of CO2; , They are the upper and lower limits of MR climbing respectively.

[0020] (4) CCS model:

[0021]

[0022] Where: The power consumed by the CCS device to capture CO2 at time t and are the basic energy consumption and operating energy consumption of CCS at time t respectively; The power consumption per unit CO2 captured; and are the amount of CO2 consumed by methanogenesis and the amount of CO2 stored at time t, respectively; is the amount of CO2 emitted by the gas unit at time t; is the carbon capture rate of CCS.

[0023] (5) Cloud energy storage model:

[0024]

[0025] Where: is the storage capacity of cloud energy storage in period t; Charging power for cloud energy storage; is the discharge power of cloud energy storage; is the upper limit of energy storage capacity; is the lower limit of energy storage capacity; The energy storage status of the cloud energy storage; is the discharge state of cloud energy storage; and are storage and discharge efficiency respectively; The upper limit of charging power for cloud energy storage; is the upper limit of the cloud energy storage discharge power; It is the initial period capacity of cloud energy storage in a scheduling cycle; It is the capacity of cloud energy storage at the end of a scheduling cycle.

[0026] As a preferred technical solution of the present invention, the step S2: reward-penalty ladder carbon trading, its expression is as follows:

[0027] 1) Under the carbon trading mechanism, the carbon quota obtained by the unit according to the baseline method for:

[0028]

[0029] Where: , They are the carbon quota for power supply and heat supply of the units respectively.

[0030] 2) The calculation formula for the carbon quota of the power supply of the unit is:

[0031]

[0032] Where: It is the base value of power supply for the unit; is the unit cooling correction factor, It is the power supply correction factor of the unit; It is the unit output correction factor.

[0033] 3) In addition, the calculation formula for the unit heating carbon quota is:

[0034]

[0035] In the formula, It is the heating reference value of the unit.

[0036]

[0037] Where: is the actual total carbon emissions; and Actual carbon emissions for each unit of electricity and natural gas purchased from the grid; and is the unit carbon emission factor.

[0038] 4) Carbon trading costs:

[0039]

[0040] Where: is the cost of PIES carbon trading; is the carbon emission trading price; μ and λ are the reward and punishment coefficients of different step-type carbon emission intervals; h is the length of the value interval of different carbon emissions.

[0041] As a preferred technical solution of the present invention, the PIES operator target cost described in step S2 includes energy purchase cost, wind and solar power abandonment penalty cost, carbon trading cost, carbon storage cost and CES power interaction cost, and the specific expression is as follows:

[0042]

[0043] Where: Operating costs for PIES operators; The cost of purchasing energy; Penalty costs for curtailing wind and solar power; For carbon trading costs; Carbon sequestration costs; For the cost of interacting with CES power; is the electricity price in period t; is the purchased power at time t; is the gas purchasing power at time t; The penalty cost coefficient for curtailing wind and solar power; is the wind power abandoned at time t; is the abandoned optical power at time t; is the price of electricity purchased from CES during period t; is the charging power of CES at time t; is the price of electricity sold to CES during period t; is the discharge power of CES at time t; The profit coefficient for CES services; is the amount of carbon dioxide captured at time t.

[0044] As a preferred technical solution of the present invention, the constraints of the PIES operator described in step S2 include electric energy balance constraints, thermal energy balance constraints, natural gas energy balance constraints and hydrogen energy balance constraints, and the expressions are:

[0045] Power balance:

[0046]

[0047] Thermal balance:

[0048]

[0049] Natural gas energy balance:

[0050]

[0051] Hydrogen energy balance:

[0052]

[0053] Where: is the purchased power at time t; is the power generation capacity of the cogeneration unit at that moment; is the output power of the wind turbine during period t; is the output power of the photovoltaic unit during period t; is the power generation of the hydrogen fuel cell at time t; is the electric load in period t; is the charging power of CES at time t; is the discharge power of CES at time t; The power limit for purchasing electricity from the grid; The upper limit of wind power output; is the upper limit of photovoltaic output power; is the power generation of CHP at time t. is the heat output of the gas boiler at time t; is the heat load during period t; is the charging power of thermal energy at time t; is the discharge power of thermal energy at time t; is the gas purchasing power at time t; is the gas load in period t; is the natural gas consumption power of the gas boiler at time t; is the natural gas consumption power of CHP at time t; The power limit for selling electricity from the grid; Charging hydrogen energy storage at time t; is the hydrogen energy storage discharge power at time t.

[0054] As a preferred technical solution of the present invention, the target cost of the CES operator described in step S2 includes the loss cost of energy storage charging and discharging, the power interaction cost with PIES, and the cloud energy storage service income. The specific expression is as follows:

[0055]

[0056] Where: The loss cost of energy storage charging and discharging; Cost of interacting with PIES power; Revenue from cloud energy storage services.

[0057] As a preferred technical solution of the present invention, the entropy weight adaptive information gap decision theory method (EAIGDT) proposed in step S3 for dealing with the source-load uncertainty problem and the specific expressions of the risk avoidance (RA) and risk seeking (RS) strategies are as follows:

[0058]

[0059] Where: is the evaluation index; and are positive indicators and their ratios; is information entropy; is the weight of each indicator; is the proportion of uncertain variables to the total uncertainty. The uncertainty index is represented by the predicted value of each uncertainty variable, and the proportion of the corresponding uncertainty variable in the total uncertainty is obtained by calculating the corresponding entropy weight.

[0060] (1) The risk aversion IGDT model based on entropy weight is as follows:

[0061]

[0062] Where: δ is the robust deviation coefficient of the objective function; is the objective function under the deterministic model.

[0063] (2) The risk pursuit IGDT model based on entropy weight is as follows:

[0064]

[0065] Where: is the chance deviation coefficient of the objective function.

[0066] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the optimization method for cooperative operation of multiple microgrids taking into account flexible dual response of supply and demand are implemented.

[0067] The present invention also provides a computer-readable storage medium, which stores a program or instruction, and the program or instruction is a step for a computer to execute the method for optimizing and scheduling a campus integrated energy system based on EAIGDT containing P2G-CCS and cloud energy storage.

[0068] Compared with the prior art, the beneficial effects achieved by the present invention are: the present invention provides an optimization scheduling method for a comprehensive energy system of a park containing P2G-CCS and cloud energy storage based on EAIGDT. The coordinated operation of P2G-CCS and CES is of great significance to the low-carbon economic operation and energy cascade utilization of the system. Among them, the proposed multi-operating condition EL model adjusts the output in real time according to the load, which effectively improves the flexibility of electric-hydrogen coupling; through the reward-and-punishment type of step-by-step carbon trading mechanism, it can better constrain the system's carbon emissions, reduce emission reduction costs, and promote the coordinated utilization of energy cascades. At the same time, the EAIGDT method proposed in this method effectively eliminates the subjective factors in the IGDT method and retains the non-probabilistic characteristics of the IGDT method. EAIGDT is used to effectively describe the uncertainty of source-load predictions, which helps scheduling managers to formulate scheduling plans under RA and RS strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Schematic diagram of the specific structure of PIES in the present invention;

[0070] Figure 2 It is a schematic diagram of a double-layer optimization model of the present invention; DETAILED DESCRIPTION

[0071] The present invention will be described in detail below with reference to the accompanying drawings. Specific embodiments

[0072] Figure 1 The specific structure of PIES for considering the coordination of P2G - CCS and CES includes the supply, utilization, conversion and storage of energy. Energy supply refers to wind power, photovoltaic, external power grid and natural gas grid. Energy utilization refers to regional loads such as electricity, heat, gas and CCS. Energy conversion refers to CHP, GB and two-stage P2G. Energy storage refers to CES, thermal energy storage and hydrogen energy storage devices. The two-stage P2G consists of EL, MR and HFC. Among them, the captured CO2 is transported to MR by CCS, and MR converts it into CH4 through Sabatier reaction to supply gas cogeneration units and gas loads. Due to the introduction of CES structure, distributed batteries are no longer built inside PIES. PIES interacts with the grid and CES through the power bus.

[0073] Figure 2The diagram is a schematic diagram of the two-layer optimization model. The process is described as follows. The optimization of the upper layer is to minimize the operating costs of PIES operators, optimize the output of equipment and the interaction with external power. The lower layer aims to minimize the operating costs of CES operators and optimize the charging and discharging power of CES. The upper layer first uses historical data and equipment models for scheduling, and then transmits the operating results to the lower layer. The lower layer model returns the CES charging and discharging results to the upper layer, and finally achieves the optimal scheduling of the entire system through information coupling and repeated iterations.

Claims

1. An optimization scheduling method for a comprehensive energy system in a park containing P2G-CCS and cloud energy storage based on EAIGDT, characterized in that: The method comprises the following steps: S1. Construct an optimal scheduling model for a comprehensive energy system in a park including P2G-CCS and cloud energy storage, wherein the optimal scheduling model for a comprehensive energy system in a park includes a coupling model of a multi-operating electrolyzer (EL), a methane generator (MR), carbon capture and storage (CCS) and cloud energy storage (CES); S2. A two-tier optimization model was established under the reward-penalty ladder carbon trading, with the upper-tier subject being the PIES operator and the lower-tier subject being the CES operator; S3. For the source-load uncertainty problem in the scheduling model, an entropy-weighted adaptive information gap decision theory method (EAIGDT) is proposed. While dealing with source-load uncertainty, it eliminates subjective factors and retains its non-probabilistic characteristics, and establishes risk-avoiding (RA) and risk-seeking (RS) strategies according to the risk preferences of operators.

2. The method for optimizing and scheduling a comprehensive energy system in a park containing P2G-CCS and cloud energy storage based on EAIGDT according to claim 1 is characterized in that: The multi-operating electrolyzer (EL), methane generator (MR), carbon capture and storage (CCS) and cloud energy storage (CES) coupling model is: (1) Considering two-stage P2G and CCS modeling under multiple working conditions: The working state of a single EL can be roughly divided into: fluctuating operating condition, overload operating condition, low-load operating condition, standby condition and shutdown state. The specific model is as follows: Where: , , , , Respectively represent Time electrolyzer In fluctuating production conditions, low-load production conditions, overload production conditions, shutdown conditions and standby conditions; is the standby power of the electrolyzer; is the power consumption of electrolytic cell n at time t; is the rated operating power of the electrolyzer; is the hydrogen production power of the electrolyzer at time t. Since the startup process under standby conditions will cause the loss of output hydrogen energy, the relationship between EL power consumption and hydrogen production power can be expressed as: Where: For the nth EL Hydrogen production efficiency at each moment; is the electricity-to-hydrogen conversion efficiency of EL; is the penalty coefficient of hydrogen production power during standby start; binary variable When it is 1, it means that EL switches from the standby state to the working state, which satisfies the following constraints: (2) HFC model Where: is the hydrogen consumption power at time t; and are the electricity generation power and heat generation power at time t respectively; The energy conversion efficiency of the fuel cell; and are the upper and lower limits of hydrogen power respectively; , is the maximum and minimum climbing power; and The upper and lower limits of the adjustable range of the fuel cell's electric-to-heat ratio. (3) MR model Where: Enter the hydrogen energy of MR for period t; is the power of natural gas output by MR during period t; is the conversion efficiency of MR; , are the upper and lower limits of hydrogen energy input to MR, respectively; is the calculation coefficient of CO2; , They are the upper and lower limits of MR climbing respectively. (4) CCS model: Where: The power consumed by the CCS device to capture CO2 at time t and are the basic energy consumption and operating energy consumption of CCS at time t respectively; The power consumption per unit CO2 captured; and are the amount of CO2 consumed by methanogenesis and the amount of CO2 stored at time t, respectively; is the amount of CO2 emitted by the gas unit at time t; is the carbon capture rate of CCS. (5) Cloud energy storage model: Where: is the storage capacity of cloud energy storage in period t; Charging power for cloud energy storage; is the discharge power of cloud energy storage; The upper limit of energy storage capacity; is the lower limit of energy storage capacity; The energy storage status of the cloud energy storage; is the discharge state of cloud energy storage; and are storage and discharge efficiency respectively; The upper limit of charging power for cloud energy storage; is the upper limit of the cloud energy storage discharge power; It is the initial period capacity of cloud energy storage in a scheduling cycle; It is the capacity of cloud energy storage at the end of a scheduling cycle.

3. The method for optimizing and dispatching a comprehensive energy system in a park containing P2G-CCS and cloud energy storage based on EAIGDT according to claim 1 is characterized in that: The specific incentive and punishment type of carbon trading is as follows: 1) Under the carbon trading mechanism, the carbon quota obtained by the unit according to the baseline method for: Where: , They are the carbon quota for power supply and heat supply of the units respectively. 2) The calculation formula for the carbon quota of the power supply of the unit is: Where: It is the power supply reference value of the unit; is the unit cooling correction factor, It is the power supply correction factor of the unit; It is the unit output correction factor. 3) In addition, the calculation formula for the unit heating carbon quota is: In the formula, It is the heating reference value of the unit. Where: is the actual total carbon emissions; and Actual carbon emissions for each unit of electricity and natural gas purchased from the grid; and is the unit carbon emission factor. 4) Carbon trading costs: Where: is the cost of PIES carbon trading; is the carbon emission trading price; μ and λ are the reward and punishment coefficients of different step-type carbon emission intervals; h is the length of the value interval of different carbon emissions.

4. The method for optimizing and dispatching a comprehensive energy system in a park containing P2G-CCS and cloud energy storage based on EAIGDT according to claim 1 is characterized in that: The objective function of the upper cost model is specifically: Where: Operating costs for PIES operators; The cost of purchasing energy; Penalty costs for curtailing wind and solar power; For carbon trading costs; carbon sequestration costs; For the cost of interacting with CES power; is the electricity price in period t; is the purchased power at time t; is the gas purchasing power at time t; The penalty cost coefficient for curtailing wind and solar power; is the wind power abandoned at time t; is the abandoned optical power at time t; is the price of electricity purchased from CES during period t; is the charging power of CES at time t; is the price of electricity sold to CES during period t; is the discharge power of CES at time t; The profit coefficient for CES services; is the amount of carbon dioxide captured at time t.

5. The method for optimizing and dispatching a comprehensive energy system in a park containing P2G-CCS and cloud energy storage based on EAIGDT according to claim 1 is characterized in that: The constraints of the upper cost model include electric energy balance constraints, thermal energy balance constraints, natural gas energy balance constraints and hydrogen energy balance constraints, and the expressions are: Power balance: Thermal balance: Natural gas energy balance: Hydrogen energy balance: Where: is the purchased power at time t; is the power generation capacity of the cogeneration unit at that moment; is the output power of the wind turbine during period t; is the output power of the photovoltaic unit during period t; is the power generation of the hydrogen fuel cell at time t; is the electric load in period t; is the charging power of CES at time t; is the discharge power of CES at time t; The power limit for purchasing electricity from the grid; The upper limit of wind power output; is the upper limit of photovoltaic output power; is the power generation of CHP at time t. is the heat output of the gas boiler at time t; is the heat load during period t; is the charging power of thermal energy at time t; is the discharge power of thermal energy at time t; is the gas purchasing power at time t; is the gas load in period t; is the natural gas consumption power of the gas boiler at time t; is the natural gas power consumption of CHP at time t; The power limit for selling electricity from the grid; Charging the hydrogen energy storage at time t; is the hydrogen energy storage discharge power at time t.

6. The method for optimizing and dispatching a comprehensive energy system in a park containing P2G-CCS and cloud energy storage based on EAIGDT according to claim 1 is characterized in that: The lower layer CES operator objective function is specifically: Where: The loss cost of energy storage charging and discharging; Cost of interacting with PIES power; Revenue from cloud energy storage services.

7. The method for optimizing and dispatching a comprehensive energy system in a park containing P2G-CCS and cloud energy storage based on EAIGDT according to claim 1 is characterized in that: The entropy weight adaptive information gap decision theory method is specifically: Where: is the evaluation index; and are positive indicators and their ratios; is information entropy; is the weight of each indicator; is the proportion of uncertain variables to the total uncertainty. The uncertainty index is represented by the predicted value of each uncertainty variable, and the proportion of the corresponding uncertainty variable in the total uncertainty is obtained by calculating the corresponding entropy weight. (1) The risk aversion IGDT model based on entropy weight is as follows: Where: δ is the robust deviation coefficient of the objective function; is the objective function under the deterministic model. (2) The risk pursuit IGDT model based on entropy weight is as follows: Where: is the chance deviation coefficient of the objective function.

8. A computer-readable storage medium, characterized in that: It includes one or more programs for execution by one or more processors of an electronic device, and the one or more programs include instructions for executing the optimization scheduling method of a campus integrated energy system containing P2G-CCS and cloud energy storage based on EAIGDT as described in any one of claims 1-7.

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