A dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of carbon green certificates

Abstract

This invention discloses a dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of electricity, carbon, and green certificates, belonging to the field of energy system optimization. The method includes the following steps: S1, integrating the revenues from the electricity, carbon, and green certificate markets to construct a total revenue objective function for the alliance; S2, constructing an electricity spot market clearing model while considering operational safety constraints; S3, using the total revenue objective function of the alliance described in step S1 as the optimization direction, solving the electricity spot market clearing model constructed in step S2 using mixed-integer linear programming to optimize the output strategies of each power generation entity at different time periods. By employing this dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of electricity, carbon, and green certificates, the linkage between the electricity, carbon, and green certificate markets is incorporated into the multi-energy alliance optimization system, improving resource utilization efficiency through cross-market collaborative decision-making.

Description

Technical Field

[0001] This invention relates to the field of energy system optimization technology, and in particular to a dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of electricity carbon green certificates. Background Technology

[0002] The intermittent and fluctuating nature of renewable energy output has led to a surge in grid peak-shaving pressure, posing challenges to traditional power systems such as frequency stability and insufficient reserve capacity. To address this, multi-energy complementary systems, by integrating the complementary characteristics of various power sources including thermal power, wind power, photovoltaic power, and energy storage, have become an important way to enhance the absorption capacity of renewable energy.

[0003] Existing research primarily focuses on the synergistic optimization of single or paired markets. For example, in the field of carbon trading, the paper "Multi-objective optimal scheduling model with IDT method of integrated energy system considering ladder-type carbon trading mechanism" proposes a tiered carbon pricing mechanism to achieve a balance between carbon emission reduction and economic efficiency through differentiated trading ranges. The paper "Low-carbon economicbi-level optimal dispatching of an integrated power and natural gas energy system considering carbon trading" further introduces a reward and penalty mechanism, demonstrating that tiered carbon pricing has a better marginal benefit in curbing carbon emissions than a uniform carbon price.

[0004] Regarding green certificate trading, the paper "Economic Low-Carbon Dispatch Strategy for Cross-Regional Interconnected Systems Oriented to Enhance Green Certificate Demand" proposes a green certificate pricing model based on supply and demand elasticity. The paper "A New Energy Cross-Provincial Trading Model Considering Carbon-Green Certificate Trading Mechanism" verifies the effectiveness of the carbon-green certificate joint mechanism in reducing wind and solar curtailment rates. Furthermore, the paper "Carbon-oriented operational planning in coupled electricity and emission trading markets" optimizes carbon quota allocation through the ZSG-DEA model, and the paper "Peer-to-peerjoint electricity and carbon trading based on carbon-aware distribution locational marginal pricing" constructs a P2P carbon-electricity joint trading framework, both providing theoretical support for multi-market collaboration.

[0005] It is evident that existing research still has the following significant limitations:

[0006] 1. Insufficient market linkage: The interaction between carbon trading and green certificate trading has not been systematically analyzed, and there is a lack of dynamic optimization methods under multi-market coupling;

[0007] 2. Poor adaptability of decision-making models: Traditional optimization methods are difficult to simultaneously meet the dynamic response requirements of power system security constraints, multi-energy complementarity characteristics, and market price fluctuations. Summary of the Invention

[0008] The purpose of this invention is to provide a dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of electric carbon green certificates, thereby solving the above-mentioned technical problems.

[0009] To achieve the above objectives, this invention provides a dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of electricity carbon green certificates, comprising the following steps:

[0010] S1. Integrate the revenue from the electricity, carbon, and green certificate markets to construct the overall revenue objective function of the alliance;

[0011] S2. Construct a power spot market clearing model, taking into account operational safety constraints.

[0012] S3. Taking the total revenue objective function of the alliance described in step S1 as the optimization direction, use mixed integer linear programming to solve the electricity spot market clearing model constructed in step S2, and optimize the output strategy of each power generation entity in different time periods.

[0013] Preferably, the objective function expression for the total revenue of the alliance constructed in step S1 is as follows:

[0014]

[0015] In the formula, F all For the total revenue of the alliance; F g , F gre These represent the trading revenues of the consortium participating in the electricity market, carbon market, and green certificate market, respectively.

[0016] Preferably, the electricity spot market clearing model expression described in step S2 is as follows:

[0017]

[0018] In the formula: and These are the clearing prices of power generation entity m and load n during time period t, respectively. and These represent the clearing power of power generation entity m and the clearing power of load n during time period t, respectively; M is the number of power generation entities; and N is the number of loads.

[0019] Preferably, the operational safety constraints mentioned in step S2 include voltage safety constraints, frequency safety constraints, branch power flow constraints, pumped storage power constraints, and pumped storage water balance constraints.

[0020] Preferably, the voltage safety constraint expression in step S2 is as follows:

[0021]

[0022] In the formula: V i Let V be the voltage magnitude at node i. i max V i min These are the upper and lower limits of the voltage at node i, respectively;

[0023] The frequency security constraint expression is as follows:

[0024] f min ≤f≤f max (5);

[0025] In the formula: f is the system frequency; f max ,f min These are the upper and lower limits of the system frequency, respectively.

[0026] The branch flow constraint expressions are as follows:

[0027]

[0028] In the formula: P is the upper limit of the branch power flow; ij For the power flow of branch ij;

[0029] The power constraint expression for pumped storage is as follows:

[0030] P ch,min ≤P ch ≤P ch,max (7);

[0031] P dis,min ≤P dis ≤P dis,max (8);

[0032] In the formula: P ch P dis These are pumping power and power generation power, respectively; P ch,max P ch,min P dis,max P dis,minThese are the upper and lower limits of pumping power and power generation power, respectively.

[0033] The water balance constraint expression for pumped storage is as follows:

[0034]

[0035] In the formula: V t V t-1 The reservoir storage volumes for time periods t and t-1 are respectively; η ch ,η dis These are pumping efficiency and power generation efficiency, respectively; P t ch P t dis Δt represents the pumping power and power generation power during time period t, respectively; Δt is the duration of the time period.

[0036] Preferably, in voltage safety constraints, V i min =0.95pu, V i max =1.05 pu;

[0037] In frequency security constraints, f min =49.5Hz, f max =50.5Hz.

[0038] Therefore, the present invention employs the above-mentioned dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of electric carbon green certificates, which has the following beneficial effects:

[0039] 1. Incorporate the electricity-carbon-green certificate market linkage into the multi-energy alliance optimization system to improve resource utilization efficiency through cross-market collaborative decision-making;

[0040] 2. A multi-objective optimization model based on MILP (Mixed Integer Linear Programming) is proposed, which can take into account both system security and economy, thereby solving the problems of low computational efficiency and poor dynamic adaptability of traditional methods.

[0041] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0042] Figure 1 The flowchart below illustrates a dynamic optimization decision-making method for a multi-energy alliance based on multi-market linkage of electricity carbon green certificates, as described in this invention.

[0043] Figure 2 This is a simulation verification graph of the unit price and marginal clearing price curve described in this invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the 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 effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0045] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as a process, method, system, product, or server that includes a series of steps or units, not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device.

[0046] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0047] like Figure 1 As shown, a dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of electricity carbon green certificates includes the following steps:

[0048] S1. Integrate the revenue from the electricity, carbon, and green certificate markets to construct the overall revenue objective function of the alliance;

[0049] The objective function for the total revenue of the alliance constructed in step S1 is expressed as follows:

[0050]

[0051] In the formula, F all For the total revenue of the alliance; F g , F gre These represent the trading revenues of the consortium participating in the electricity market, carbon market, and green certificate market, respectively.

[0052] S2. Construct a power spot market clearing model, taking into account operational safety constraints.

[0053] The electricity spot market clearing model expression described in step S2 is as follows:

[0054]

[0055] In the formula: and These are the clearing prices of power generation entity m and load n during time period t, respectively. and These represent the clearing power of power generation entity m and the clearing power of load n during time period t, respectively; M is the number of power generation entities; and N is the number of loads.

[0056] Preferably, the operational safety constraints mentioned in step S2 include voltage safety constraints, frequency safety constraints, branch power flow constraints, pumped storage power constraints, and pumped storage water balance constraints.

[0057] The voltage safety constraint expression mentioned in step S2 is as follows:

[0058]

[0059] In the formula: V i Let V be the voltage magnitude at node i. i max V i min These are the upper and lower limits of the voltage at node i, respectively;

[0060] The frequency security constraint expression is as follows:

[0061] f min ≤f≤f max (5);

[0062] In the formula: f is the system frequency; f max ,f min These are the upper and lower limits of the system frequency, respectively.

[0063] The branch flow constraint expressions are as follows:

[0064]

[0065] In the formula: P is the upper limit of the branch power flow; ij For the power flow of branch ij;

[0066] The power constraint expression for pumped storage is as follows:

[0067] P ch,min ≤P ch ≤P ch,max (7);

[0068] P dis,min ≤P dis ≤P dis,max (8);

[0069] In the formula: P ch P dis These are pumping power and power generation power, respectively; P ch,max P ch,min P dis,max P dis,minThese are the upper and lower limits of pumping power and power generation power, respectively.

[0070] The water balance constraint expression for pumped storage is as follows:

[0071]

[0072] In the formula: V t V t-1 The reservoir storage volumes for time periods t and t-1 are respectively; η ch ,η dis These are pumping efficiency and power generation efficiency, respectively; P t ch P t dis Δt represents the pumping power and power generation power during time period t, respectively; Δt is the duration of the time period.

[0073] Preferably, in voltage safety constraints, V i min =0.95pu, V i max =1.05 pu;

[0074] In frequency security constraints, f min =49.5Hz, f max =50.5Hz.

[0075] S3. Taking the total revenue objective function of the alliance described in step S1 as the optimization direction, use mixed integer linear programming to solve the electricity spot market clearing model constructed in step S2, and optimize the output strategy of each power generation entity in different time periods.

[0076] In step S3, the power output of the power generation entities at different time periods, carbon emission-related variables (such as carbon emissions), and green certificate trading-related variables (such as the number of green certificates traded) are defined as decision variables. Then, the objective function of the alliance's total revenue, operational safety constraints, and decision variables are input into a solver such as Gurobi or CPLEX, and the solver is called to obtain the power output strategies of each power generation entity at different time periods.

[0077] Simulation verification

[0078] System Configuration: An IEEE 14-node system is adopted, assuming a total of 8 power generation entities connected to the IEEE 14-node system. Node 2 connects to Thermal Power Plant 1 and Thermal Power Plant 2; Node 4 connects to Wind Power Plant 1 and Wind Power Plant 2; Node 8 connects to Photovoltaic Power Plant 1 and Photovoltaic Power Plant 2; Node 11 connects to a pumped-storage power station; and Node 14 connects to Photovoltaic Power Plant 3. The system operates in a multi-energy alliance mode, consisting of Thermal Power Plants 1 and 2, Wind Power Plant 1, Photovoltaic Power Plant 1, and the pumped-storage power station.

[0079] Parameter settings: Carbon emission calculation coefficients a, b, and c are 36, -0.38, and 0.0034, respectively. Pumped storage power station parameters: Maximum pumping power / power generation is 200MW / 150MW, and maximum pumping efficiency / power generation efficiency is 85% / 80%.

[0080] Unit pricing and marginal clearing price, such as Figure 2 As shown, the marginal clearing price of the consortium fluctuates between 30 and 48 yuan. High prices are mainly concentrated during the daytime to evening (11:00, 12:00, 14:00, 21:00), while low prices mostly occur at night or during demand troughs (1:00, 5:00, 9:00, 18:00). Sufficient renewable energy (wind and solar) output effectively suppresses electricity prices; for example, at 7:00, wind and solar output is high, and the price drops to 32 yuan. However, at 19:00 and 21:00, solar output drops to zero, and the price rebounds to 44-48 yuan. Pumped storage units mainly discharge during peak hours to meet demand, and their high-output period (11:00) usually corresponds to a higher clearing price. Overall, daytime solar output has a suppressive effect on electricity prices, prices rebound during the midday peak, and nighttime prices rely on thermal power and pumped storage for support, exhibiting a clear price fluctuation characteristic.

[0081] After using the multi-energy alliance dynamic optimization decision-making method based on multi-market linkage of electricity carbon green certificates described in this invention, the renewable energy consumption rate can be increased by 18%, the wind and solar curtailment rate can be reduced by 22%, the total revenue of the alliance can be increased by 9.7%, the carbon trading revenue accounts for 21%, and the MILP algorithm can complete the solution within 24 hours, meeting the dynamic response requirements of the power system, thus proving the effectiveness of this invention.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of electricity carbon green certificates, characterized in that: Includes the following steps: S1. Integrate the revenue from the electricity, carbon, and green certificate markets to construct the overall revenue objective function of the alliance; S2. Construct a power spot market clearing model, taking into account operational safety constraints. The electricity spot market clearing model expression described in step S2 is as follows: In the formula: and These are the clearing prices of power generation entity m and load n during time period t, respectively. and These represent the clearing power of power generation entity m and the clearing power of load n during time period t, respectively; M is the number of power generation entities; N is the number of loads; The operational safety constraints mentioned in step S2 include voltage safety constraints, frequency safety constraints, branch power flow constraints, pumped storage power constraints, and pumped storage water balance constraints. The voltage safety constraint expression mentioned in step S2 is as follows: In the formula: V i Let V be the voltage magnitude at node i. i max V i min These are the upper and lower limits of the voltage at node i, respectively; The frequency security constraint expression is as follows: f min ≤f≤f max (5); In the formula: f is the system frequency; f max ,f min These are the upper and lower limits of the system frequency, respectively. The branch flow constraint expressions are as follows: In the formula: P is the upper limit of the branch power flow; ij For the power flow of branch ij; The power constraint expression for pumped storage is as follows: P ch,min ≤P ch ≤P ch,max (7); P dis,min ≤P dis ≤P dis,max (8); In the formula: P ch P dis These are pumping power and power generation power, respectively; P ch,max P ch,min P dis,max P dis,min These are the upper and lower limits of pumping power and power generation power, respectively. The water balance constraint expression for pumped storage is as follows: In the formula: V t V t-1 The reservoir storage volumes for time periods t and t-1 are respectively; η ch ,η dis These are pumping efficiency and power generation efficiency, respectively; P t ch P t dis These represent the pumping power and power generation power during time period t, respectively; Δt is the duration of the time period. S3. Taking the total revenue objective function of the alliance described in step S1 as the optimization direction, use mixed integer linear programming to solve the electricity spot market clearing model constructed in step S2, and optimize the output strategy of each power generation entity in different time periods.

2. The dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of carbon emission certificates as described in claim 1, characterized in that: The objective function for the total revenue of the alliance constructed in step S1 is expressed as follows: In the formula, F all For the total revenue of the alliance; F g , F gre These represent the trading revenues of the consortium participating in the electricity market, carbon market, and green certificate market, respectively.

3. The dynamic optimization decision-making method for multi-energy alliances based on multi-market linkage of carbon emission certificates as described in claim 1, characterized in that: In voltage safety constraints, V i min =0.95pu, V i max =1.05 pu; In frequency security constraints, f min =49.5Hz, f max =50.5Hz.

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