A low-carbon planning method for industrial parks based on robust optimization

Abstract

The present invention relates to the technical field of power systems, and specifically discloses a low-carbon planning method for a park based on robust optimization, the method comprising: constructing a free carbon emission quota trading model; constructing a deterministic low-carbon optimization planning model for a park integrated energy system taking into account multi-energy complementarity; based on the information gap decision theory, adopting a long-term uncertainty characterization method for load growth rate to quantify the long-term uncertainty of load growth rate; constructing a low-carbon robust planning model for an integrated energy system based on the information gap decision theory based on the free carbon emission quota trading model and the deterministic low-carbon optimization planning model for a park integrated energy system taking into account multi-energy complementarity; solving the model to obtain a planning scheme. The advantages of the present invention are to achieve the coordinated optimization of carbon quota allocation and multi-energy network topology, construct annual carbon emission constraints, and achieve carbon emission reduction throughout the entire life cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a low-carbon planning method for a park based on robust optimization. Background Art

[0002] Existing research has formed a relatively complete technical system in the field of integrated energy system planning in industrial parks, which mainly revolves around the two core areas of multi-energy coupling optimization and uncertainty processing. In terms of system planning architecture, a hierarchical progressive optimization framework is generally adopted, and a two-layer or multi-level collaborative model is used to achieve an organic connection between investment decisions and operation scheduling. The typical model includes upper-level equipment configuration optimization and lower-level multi-energy flow collaborative scheduling, while integrating electricity-heat-gas mixed flow constraints, energy storage device regulation and energy cascade utilization technology, which significantly improves the system's economy and energy supply reliability. For uncertainty processing, the research presents a technical path of parallel probabilistic optimization and robust optimization: the former constructs a set of typical source-load scenarios through kernel density estimation, and the latter uses interval mathematics or two-stage robust models to deal with extreme fluctuations, especially in disaster scenarios, to develop a distributed optimization method that integrates the resilience of multi-energy networks.

[0003] Existing research on collaborative park planning focuses on multi-type load demands and optimization objectives, and most uses typical scenario generation and robust optimization to address short-term uncertainty in source and load. However, for the full lifecycle planning of a park's integrated energy system, the long-term uncertainty of load growth rates significantly impacts system robustness, while unreasonable load growth rate redundancy significantly increases economic costs. However, existing technologies lack systematic modeling of the dynamic correlation between long-term load growth trends and carbon trading policies, failing to effectively coordinate the synergistic relationship between the free quota allocation mechanism and the multi-energy flow topology. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a low-carbon planning method for a park based on robust optimization.

[0005] The purpose of the present invention is achieved through the following technical solution: a low-carbon planning method for a park based on robust optimization, the method comprising:

[0006] Construct a free carbon emission quota trading model;

[0007] Constructing a deterministic low-carbon optimization planning model for a comprehensive energy system of a park considering multi-energy complementarity; the constraints of the deterministic low-carbon optimization planning model for a comprehensive energy system of a park considering multi-energy complementarity include electric bus power balance constraints, thermal bus power balance constraints, cooling bus power balance constraints, gas bus power balance constraints, hydrogen balance constraints, energy conversion equipment constraints, energy storage constraints, space constraints, electric vehicle charging station constraints, new energy power generation constraints and low-carbon planning constraints;

[0008] Based on the information gap decision theory, the long-term uncertainty characterization method of load growth rate is used to quantify the long-term uncertainty of load growth rate.

[0009] Based on the free carbon emission quota trading model and the deterministic low-carbon optimization planning model of the park's integrated energy system considering multi-energy complementarity, a low-carbon robust planning model of the integrated energy system based on information gap decision theory is constructed; the planning scheme is obtained by solving the model.

[0010] Specifically, the construction of the free carbon emission quota trading model includes calculating the carbon emission quota of the park:

[0011]

[0012] Where, and Assignment of carbon emission quota per unit of heat and electricity to the region; P e,buy,t P is the purchased power; e,CHP,t 、P h,CHP,t are the electrical and thermal power output of CHP respectively; is the electricity-heat conversion coefficient; P h,GB,t The thermal power output of the gas boiler;

[0013] Calculate the actual carbon emissions of the system:

[0014]

[0015] Where K e and K h are the carbon emission intensity per unit of electricity and per unit of heat respectively;

[0016] Calculating the cost of carbon emissions:

[0017]

[0018]

[0019] Where, To trade carbon emissions after taking into account carbon quotas; is the cost of carbon emissions; It is the benchmark price for carbon trading.

[0020] Specifically, the objective function of the deterministic low-carbon optimization planning model for the park integrated energy system considering multi-energy complementarity is:

[0021]

[0022] Where, 、 、 、 They are the investment cost, operation cost, maintenance cost and carbon trading cost of the park’s integrated energy system; For the benefit of electric energy storage; is the discount rate; To plan equipment collection; is the unit investment cost of the i-th equipment; 、 are the planned power and planned capacity of the i-th device respectively; is a Boolean variable, which is 1, indicating that the i-th device is an energy storage device; is the conversion factor of capacity investment cost and power investment cost; 、 are the unit electricity and gas purchase costs at time t; 、 are the unit wind and solar curtailment costs, respectively; A collection of all equipment in the park; is the unit operating cost of the i-th equipment; is the power of the i-th device; P e,buy,t The power of purchased electricity; To trade carbon emissions after taking into account carbon quotas; is the cost of carbon emissions; is the carbon trading benchmark price, and N is the total number of years considered in system planning; is the unit time interval; is the unit power purchase at time t; 、 is the discharge and charge power of the energy storage at time t; 、 Wind power and solar power curtailment respectively.

[0023] Specifically, the method for characterizing the long-term uncertainty of load growth rate includes dividing the entire life cycle of the system into three stages: , the load growth rate in each stage is different, and the load growth rate benchmark value set is expressed as:

[0024]

[0025] Where, is the growth rate set of electric heating and cooling loads in the tth stage, are the growth rates of electricity, heating and cooling loads respectively;

[0026] There is an information gap between the actual growth rate and the nominal value. Let the uncertainty set of the load growth rate be:

[0027]

[0028] Where: is the uncertainty range, is the uncertainty range weight coefficient; It is the weighted uncertainty range, reflecting the system's ability to avoid uncertainty risks, and is recorded as the system robustness coefficient.

[0029] Specifically, the low-carbon robust planning model of the integrated energy system based on information gap decision theory is a two-layer robust optimization framework: the upper layer considers multi-energy complementarity to construct a deterministic low-carbon optimization planning model for the park integrated energy system, and the lower layer is an uncertainty set of load growth rates. The IGDT theory is used to quantify the risk boundary of extreme scenarios, and the trade-off relationship between economy and low-carbon goals is dynamically adjusted through the robustness coefficient, ultimately forming a planning scheme with interval immunity characteristics.

[0030] The present invention has the following advantages:

[0031] The present invention builds a free carbon emission quota trading model and a carbon quota cost target to achieve coordinated optimization of carbon quota allocation and multi-energy network topology, establish annual carbon emission constraints, and achieve full life cycle carbon emission reduction;

[0032] The present invention combines a robust programming model based on information gap decision theory with typical fluctuation scenarios to achieve multi-time-scale collaborative optimization of long-term load growth trends and short-term fluctuations;

[0033] The present invention realizes multi-energy complementarity of the park's integrated energy system and improves overall operating efficiency through a deterministic low-carbon optimization framework of electricity-heat-gas-cooling-hydrogen multi-energy coupling, combined with a multi-type equipment constraint system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the planning method of the present invention. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for the purpose of explaining the present invention and are not intended to limit the present invention. That is, the embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and illustrated in the drawings herein can be arranged and designed in various different configurations.

[0036] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely intended to represent selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0037] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0038] The present invention will be further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0039] like Figure 1 As shown, a low-carbon planning method for a park based on robust optimization includes:

[0040] Construct a free carbon emission quota trading model;

[0041] Calculating the carbon emission cost of the integrated energy system requires considering the carbon emissions from post-trading carbon quotas. Carbon emission quotas are provided to the system using a free allocation and baseline method. The system's carbon emission sources are primarily external electricity purchases, gas turbines, and gas boilers. The park's carbon emission quota can be expressed as follows:

[0042] (1)

[0043] Where, and Assignment of carbon emission quota per unit of heat and electricity to the region; P e,buy,t P is the purchased power; e,CHP,t 、P h,CHP,t are the electrical and thermal power output of CHP respectively; is the electricity-heat conversion coefficient; P h,GB,t The thermal power output of the gas boiler;

[0044] The actual carbon emission model of the system is similar to the carbon allocation model and is calculated using the factor method. The difference is that the regional unit electricity carbon emission allocation is replaced by the thermal power carbon emission coefficient:

[0045] (2)

[0046] Where K e and K h are the carbon emission intensity per unit of electricity and per unit of heat respectively;

[0047] The traded carbon emissions after taking into account the carbon quota are the difference between the actual carbon emissions and the carbon emission quota. At the same time, the carbon emission cost is calculated using a fixed carbon trading price:

[0048] (3)

[0049] (4)

[0050] Where, To trade carbon emissions after taking into account carbon quotas; is the cost of carbon emissions; It is the benchmark price for carbon trading.

[0051] Constructing a deterministic low-carbon optimization planning model for a comprehensive energy system of a park considering multi-energy complementarity; the constraints of the deterministic low-carbon optimization planning model for a comprehensive energy system of a park considering multi-energy complementarity include electric bus power balance constraints, thermal bus power balance constraints, cooling bus power balance constraints, gas bus power balance constraints, hydrogen balance constraints, energy conversion equipment constraints, energy storage constraints, space constraints, electric vehicle charging station constraints, new energy power generation constraints and low-carbon planning constraints;

[0052] In order to take into account the coordinated planning of multiple types of energy storage and energy-consuming equipment within the park, the investment cost, operation and maintenance cost, and carbon trading cost of the park's integrated energy system are taken as the park's comprehensive cost. At the same time, the peak-shaving and valley-filling benefits of electric energy storage are considered to construct the park's collaborative planning objective function. The objective function of the low-carbon optimization planning model is:

[0053] (5)

[0054] Where, 、 、 、 They are the investment cost, operation cost, maintenance cost and carbon trading cost of the park’s integrated energy system; For the benefit of electric energy storage; is the discount rate; To plan equipment collection; is the unit investment cost of the i-th equipment; 、 are the planned power and planned capacity of the i-th device respectively; is a Boolean variable, which is 1, indicating that the i-th device is an energy storage device; is the conversion factor of capacity investment cost and power investment cost; 、 are the unit electricity and gas purchase costs at time t; 、 are the unit wind and solar curtailment costs, respectively; A collection of all equipment in the park; is the unit operating cost of the i-th equipment; is the power of the i-th device; P e,buy,t The power of purchased electricity; To trade carbon emissions after taking into account carbon quotas; is the cost of carbon emissions; is the carbon trading benchmark price, and N is the total number of years considered in system planning; is the unit time interval; is the unit power purchase at time t; 、 is the discharge and charge power of the energy storage at time t; 、 Wind power and solar power curtailment respectively.

[0055] The power balance constraint of the electric bus is:

[0056] (6)

[0057] (7)

[0058] Where, 、 Contribute to the scenery; The power of purchased electricity; 、 、 、 are the electric power of hydrogen fuel cell, gas turbine, electrolyzer and electric refrigerator at time t respectively; represents the electric load power during period t; and Respectively represent the discharge and charging power of the electric vehicle; and Respectively represent the discharge and charge power of the energy storage; It is the power limit for purchasing electricity.

[0059] The thermal bus power balance constraint is:

[0060] (8)

[0061] Where, 、 、 、 are the thermal power of the gas turbine, gas boiler, hydrogen fuel cell, and absorption chiller at time t respectively; represents the heat load power during period t; and They represent the heat release and heat charging power of the heat storage at time t respectively.

[0062] The cold bus power balance constraint is:

[0063] (9)

[0064] Where, 、 are the cooling powers of electric refrigerator and absorption refrigerator respectively; 、 Respectively represent the charging and discharging power of water storage cooling; 、 Respectively represent the charging and discharging power of ice storage; Indicates the cooling load power.

[0065] The power balance constraint of the gas bus is:

[0066] (10)

[0067] (11)

[0068] Where, is the gas purchasing power at time t; 、 、 Indicates the output or required gas power of methane reactor, gas boiler, gas turbine; is the gas load; and Respectively represent the charging and discharging power; It is the gas purchasing power limit.

[0069] The hydrogen balance constraint is:

[0070] (12)

[0071] Where, is the hydrogen production power of the electrolyzer; 、 are the hydrogen power required by the methane reactor and hydrogen fuel cell respectively; and They represent the hydrogen storage and discharge and hydrogen charging power respectively.

[0072] The energy conversion device constraints are:

[0073] At the energy input end of the Park Integrated Energy System (PIES), wind and solar power generators, along with upstream power and gas grids, form a stable energy supply. Diesel generators (DE) are used to respond to emergencies. During the energy conversion process, equipment such as combined heat and power (CHP), electric refrigeration (ER), and gas boilers (GB) play a crucial role. In particular, the combined cooling, heating, and power (CCHP) system, through the coordinated operation of CHP and absorption chillers (AC), achieves progressive energy utilization, further optimizing energy efficiency. P2G also considers two key phases of operation: power-to-hydrogen and power-to-gas. The two-stage P2G technology can improve the efficiency and environmental friendliness of energy conversion and utilization, and mainly includes electrolyzers (EL), hydrogen fuel cells (HFC) and methane reactors (MR). In the energy storage link, the park has considered a variety of energy storage equipment, including electric storage systems (ES), heat storage systems (HS), gas storage systems (GS), hydrogen storage systems (H2S), ice storage systems (IS) and water storage systems (WS), to improve the flexibility and reliability of the park's overall energy management. According to the relationship between the energy input and output between PIES systems, the input power, output power and conversion efficiency of each energy conversion device are recorded as follows: 、 、 Its output characteristics and constraints are shown in the following formula:

[0074] (13)

[0075] Where, represents a collection of electrolyzers, methane reactors, hydrogen fuel cells, boilers, gas turbines, electric refrigeration, and absorption refrigeration equipment; Indicates the upper limit of the output power of each energy conversion device, is the input power of the i-th energy conversion device, is the output power of the i-th energy conversion device; is the conversion efficiency of the i-th energy conversion device.

[0076] The energy storage constraint is:

[0077] (14)

[0078] Where, , It is a combination of electricity storage, heat storage, gas storage, hydrogen storage, water storage and ice storage; 、 are the charging and discharging powers of the i-th energy storage device in period t, respectively; 、 are the maximum power of single charging and discharging of the i-th energy storage device respectively; 、 are the state parameters of charging and discharging of the i-th energy storage device in period t, which are binary variables; 、 They represent the charging and discharging efficiency of the energy storage device respectively; 、 Respectively represent the upper and lower limits of the capacity of the i-th energy storage device; is the capacity of the i-th energy storage device in period t, is the capacity of the i-th energy storage device in period t-1, The unit time interval.

[0079] The spatial constraints are:

[0080] (15)

[0081] Where: 、 The area occupied per unit power of ice storage and water storage; 、 The total power capacity of ice storage and water storage; This is the maximum space limit for the cold storage device.

[0082] Electric vehicle charging station constraints consider the typical charging and discharging capabilities of electric vehicle clusters within the constraints of electric vehicle charging piles, thereby improving planning efficiency. The aforementioned electric vehicle operation model can be used to establish a model of all electric vehicle clusters within a region, treating them as a single entity. The charging and discharging capacity model of an electric vehicle cluster can be derived by summing the charging and discharging capacity models of individual electric vehicles. The operating constraints of electric vehicle charging stations primarily consider the constraints on their charging and discharging power, which are as follows:

[0083] (16)

[0084] Where: The limit of discharge capacity ratio; The charging capacity ratio limit; charging station capacity for electric vehicles; and Represent the discharging and charging power of electric vehicles respectively.

[0085] The constraints on renewable energy generation are:

[0086] (17)

[0087] Where: 、 Represent the upper limits of wind power and photovoltaic output power respectively; 、 Contribute to the scenery.

[0088] The low-carbon planning constraints are:

[0089] To ensure the low-carbon nature of the park planning, it is set that every year after the carbon peak, the traded carbon emissions must decrease annually at a fixed rate of v%. The constraints are as follows:

[0090]

[0091]

[0092] In the formula, x means that the carbon peak will be achieved in the xth year of the plan, which is the 6th year; y means the year after the carbon peak.

[0093] Based on the information gap decision theory, the long-term uncertainty characterization method of load growth rate is used to quantify the long-term uncertainty of load growth rate; due to the grid-connected power limitation, the uncertainty of load growth rate is an important parameter affecting robustness. As a non-probabilistic uncertainty modeling method, Information Gap Decision Theory (IGDT) shows unique advantages in dealing with the uncertainty of load growth rate: it quantifies the potential deviation range of parameters through "information gap", getting rid of the dependence on probability distribution assumptions, and is particularly suitable for long-term load forecasting scenarios where multiple factors such as policy guidance and economic fluctuations are intertwined; the robust IGDT is based on a two-layer optimization framework. While optimizing decision variables such as equipment capacity, it obtains the maximum allowable deviation threshold of the load growth rate, thereby avoiding the risk of insufficient energy supply in the park due to unexpected load growth; the entire life cycle of the system is divided into three stages , the load growth rate in each stage is different, and the load growth rate benchmark value set is expressed as:

[0094] (20)

[0095] Where, is the growth rate set of electric heating and cooling loads in the tth stage, are the growth rates of electricity, heating and cooling loads respectively;

[0096] There is an information gap between the actual growth rate and the nominal value. Let the uncertainty set of the load growth rate be:

[0097]

[0098] Where: is the uncertainty range, is the uncertainty range weight coefficient; is the weighted uncertainty range, reflecting the system's ability to avoid uncertainty risks, and is recorded as the system robustness coefficient. is the uncertain load growth rate.

[0099] Based on the free carbon emission quota trading model and the deterministic low-carbon optimization planning model of the park's integrated energy system considering multi-energy complementarity, a low-carbon robust planning model of the integrated energy system based on information gap decision theory is constructed; the planning scheme is obtained by solving the model.

[0100] The low-carbon robust planning model for the integrated energy system based on information gap decision theory is a two-layer robust optimization framework: the upper layer considers multi-energy complementarity to construct a deterministic low-carbon optimization planning model for the integrated energy system of the park; the lower layer couples the uncertainty set of load growth rates, uses the IGDT theory to quantify the risk boundary of extreme scenarios, and dynamically adjusts the trade-off between economic efficiency and low-carbon goals through the robustness coefficient, ultimately forming a planning scheme with interval immunity characteristics. Its core decision model can be characterized as follows:

[0101]

[0102] Where, Costs calculated for a park planning model that considers long-term uncertainty; The minimum cost value calculated by the deterministic park planning model is: is the uncertainty load growth rate; is the acceptable cost increase factor; is the weight of the uncertainty range.

[0103] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make many possible changes and modifications to the technical solution of the present invention using the above technical content, or modify it into an equivalent embodiment with equivalent changes. Therefore, any changes, modifications, equivalent changes, and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the present technical solution.

Claims

1. A low-carbon planning method for a park based on robust optimization, characterized by: The method includes: Construct a free carbon emission quota trading model; Constructing a deterministic low-carbon optimization planning model for a comprehensive energy system of a park considering multi-energy complementarity; the constraints of the deterministic low-carbon optimization planning model for a comprehensive energy system of a park considering multi-energy complementarity include electric bus power balance constraints, thermal bus power balance constraints, cooling bus power balance constraints, gas bus power balance constraints, hydrogen balance constraints, energy conversion equipment constraints, energy storage constraints, space constraints, electric vehicle charging station constraints, new energy power generation constraints and low-carbon planning constraints; Based on the information gap decision theory, the long-term uncertainty characterization method of load growth rate is used to quantify the long-term uncertainty of load growth rate. Based on the free carbon emission quota trading model and the consideration of multi-energy complementarity, a deterministic low-carbon optimization planning model for the integrated energy system of the park is constructed. A low-carbon robust planning model for the integrated energy system based on information gap decision theory is constructed; the model is solved to obtain a planning scheme; The low-carbon robust planning model of the integrated energy system based on information gap decision theory is a two-layer robust optimization framework: the upper layer considers multi-energy complementarity to construct a deterministic low-carbon optimization planning model for the park integrated energy system; the lower layer couples the uncertainty set of load growth rates, adopts the IGDT theory to quantify the risk boundary of extreme scenarios, and dynamically adjusts the trade-off between economy and low-carbon goals through the robustness coefficient, ultimately forming a planning scheme with interval immunity characteristics.

2. The method for low-carbon planning of a park based on robust optimization according to claim 1, characterized in that: The construction of the free carbon emission quota trading model includes calculating the carbon emission quota of the park: ； Where, and Allocate quotas for regional carbon emissions per unit of heat and electricity; The power of purchased electricity; 、 are the electrical and thermal power output of CHP respectively; is the electricity-heat conversion coefficient; The thermal power output of the gas boiler; Calculate the actual carbon emissions of the system: ； Where, and are the carbon emission intensity per unit of electricity and per unit of heat respectively; Calculating the cost of carbon emissions: ； ； Where, To trade carbon emissions after taking into account carbon quotas; is the cost of carbon emissions; It is the benchmark price for carbon trading.

3. The method for low-carbon planning of a park based on robust optimization according to claim 2, characterized in that: The objective function of the deterministic low-carbon optimization planning model for the park's integrated energy system considering multi-energy complementarity is: ； Where, 、 、 、 They are the investment cost, operation cost, maintenance cost and carbon trading cost of the park’s integrated energy system; For the benefit of electric energy storage; is the discount rate; To plan equipment collection; For the Unit investment cost of each piece of equipment; 、 Respectively The planned power and capacity of each device; is a Boolean variable, which is 1 indicating The device is an energy storage device; is the conversion factor of capacity investment cost and power investment cost; 、 They are Unit electricity and gas purchase costs at the moment; 、 are the unit wind and solar curtailment costs respectively; A collection of all equipment in the park; For the Unit operating cost of a device; For the The power of the device; The power of purchased electricity; To trade carbon emissions after taking into account carbon quotas; is the cost of carbon emissions; is the benchmark price for carbon trading. N The total number of years considered for system planning; is the unit time interval; The unit power purchased at the time; 、 They are The discharge and charging power of the energy storage at all times; 、 Wind power and solar power curtailment respectively.

4. The method for low-carbon planning of a park based on robust optimization according to claim 1, characterized in that: The method for characterizing the long-term uncertainty of load growth rate includes dividing the system life cycle into three stages: , the load growth rate in each stage is different, and the load growth rate benchmark value set is expressed as: ； Where, For the The growth rate of electric heating and cooling load in each stage is as follows: are the growth rates of electricity, heating and cooling loads respectively; There is an information gap between the actual growth rate and the nominal value. Let the uncertainty set of the load growth rate be: ； Where, is the uncertainty range, is the uncertainty range weight coefficient; It is the weighted uncertainty range, reflecting the system's ability to avoid uncertainty risks, and is recorded as the system robustness coefficient.

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