Multi-energy alliance dynamic optimization decision-making method based on electric carbon green evidence multi-market linkage

Abstract

The invention discloses a multi-energy alliance dynamic optimization decision-making method based on power, carbon and green certificate multi-market linkage, and belongs to the field of energy system optimization, and the method comprises the following steps: S1, integrating power, carbon and green certificate market incomes, and constructing an alliance total income objective function; s2, constructing an electric power spot market clearing model on the premise of considering operation safety constraints; and S3, solving the electric power spot market clearing model constructed in the step S2 by adopting a mixed integer linear programming method by taking the alliance total income objective function in the step S1 as an optimization direction, and optimizing output strategies of each power generation main body in different time periods. By adopting the multi-energy alliance dynamic optimization decision-making method based on electricity-carbon-green certificate multi-market linkage, electricity-carbon-green certificate market linkage is incorporated into a multi-energy alliance optimization system, and the resource utilization efficiency is improved through cross-market collaborative decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system optimization, and particularly to a dynamic optimization decision-making method for a multi-energy alliance based on the multi-market linkage of electricity-carbon green certificates. Background Art

[0002] The intermittency and volatility of new energy output have led to a sharp increase in the peak shaving pressure of the power grid, making the traditional power system face challenges such as frequency stability and insufficient reserve capacity. For this reason, the multi-energy complementary system has become an important way to improve the new energy consumption capacity by integrating the complementary characteristics of multiple types of power sources such as thermal power, wind power, photovoltaic, and energy storage.

[0003] Existing research mainly focuses on the collaborative optimization of single or two markets. For example, in the field of carbon trading, "Multi-objective optimal scheduling model with IGDT method of integrated energy system considering ladder-type carbon trading mechanism" proposes a ladder-type carbon price mechanism to achieve the balance between carbon emission reduction and economy through different trading intervals. "Low-carbon economic bi-level optimal dispatching of an integrated power and natural gas energy system considering carbon trading" further introduces a reward and punishment mechanism, proving that the marginal benefit of the ladder carbon price in suppressing carbon emissions is better than the unified carbon price.

[0004] In terms of green certificate trading, "Economic and low-carbon dispatching strategy for cross-regional interconnected system aiming at improving green certificate demand" proposes a green certificate pricing model based on supply and demand elasticity. "New energy cross-provincial trading model considering carbon-green certificate trading mechanism" verifies the effectiveness of the carbon-green certificate joint mechanism in reducing the curtailment rate of wind and light. In addition, "Carbon-oriented operational planning in coupled electricity and emission trading markets" optimizes carbon quota allocation through the ZSG-DEA model, and "Peer-to-peer joint 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 can be seen that the 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 the present invention is to provide a dynamic optimization decision-making method for a multi-energy alliance based on the multi-market linkage of electricity, carbon, and green certificates to solve the above technical problems.

[0009] To achieve the above purpose, the present invention provides a dynamic optimization decision-making method for a multi-energy alliance based on the multi-market linkage of electricity, carbon, and green certificates, including the following steps:

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

[0011] S2. On the premise of considering the operating security constraints, construct the clearing model of the electricity spot market;

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

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

[0014]

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

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

[0017]

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

[0019] Preferably, the operation safety constraints described 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 expression of the voltage safety constraint described in step S2 is as follows:

[0021]

[0022] In the formula: V i is the voltage amplitude of node i, V i max , V i min are the upper and lower limits of the voltage of node i, respectively;

[0023] The expression of the frequency safety constraint is as follows:

[0024] f min ≤ f ≤ f max (5);

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

[0026] The expression of the branch power flow constraint is as follows:

[0027]

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

[0029] The expression of the pumped-storage power constraint 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 are the pumping power and the generating power, respectively; P ch,max , P ch,min , P dis,max , P dis,minThey are the upper and lower limits of pumping and the upper and lower limits of power generation respectively;

[0033] The water balance constraint expression of the pumped - storage power station is as follows:

[0034]

[0035] In the formula: V t , V t-1 are the reservoir water storage at time t and time t - 1 respectively; η ch , η dis are the pumping efficiency and power generation efficiency respectively; P t ch , P t dis are the pumping power and power generation power at time t respectively; Δt is the time interval duration.

[0036] Preferably, in the voltage security constraint, V i min = 0.95 p.u., V i max = 1.05 p.u.;

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

[0038] Therefore, the multi - energy alliance dynamic optimization decision - making method based on the multi - market linkage of electricity - carbon - green certificates adopted by the present invention has the following beneficial effects:

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

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

[0041] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. Brief Description of the Drawings

[0042] Figure 1 is the flow chart of a multi - energy alliance dynamic optimization decision - making method based on the multi - market linkage of electricity - carbon - green certificates according to the present invention;

[0043] Figure 2 is the curve diagram of unit bid price and marginal clearing electricity price for simulation verification according to the present invention. Detailed Embodiments

[0044] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention more clearly understood, the following further elaborates on the embodiments of the present invention in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout.

[0045] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0046] The following elaborates in detail on the implementation manners of the present invention in conjunction with the accompanying drawings.

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

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

[0049] The expression of the total revenue objective function of the alliance body described in step S1 is as follows:

[0050]

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

[0052] S2. On the premise of considering the operation safety constraints, construct the clearing model of the electricity spot market;

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

[0054]

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

[0056] Preferably, the operation safety constraints described in step S2 include voltage safety constraints, frequency safety constraints, branch power flow constraints, pumped-storage power constraints, and pumped-storage water volume balance constraints.

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

[0058]

[0059] In the formula: V i is the voltage amplitude of node i, V i max , V i min are the upper and lower limits of the voltage of node i, respectively;

[0060] The frequency safety 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 are the upper and lower limits of the system frequency, respectively;

[0063] The branch power flow constraint expression is as follows:

[0064]

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

[0066] The pumped-storage power constraint expression 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 are the pumping power and the generating power, respectively; P ch,max , P ch,min , P dis,max , P dis,minThey are the upper and lower limits of pumping and the upper and lower limits of power generation respectively;

[0070] The water balance constraint expression of pumped - storage energy is as follows:

[0071]

[0072] In the formula: V t , V t-1 are the reservoir water storage at time t and time t - 1 respectively; η ch , η dis are the pumping efficiency and power generation efficiency respectively; P t ch , P t dis are the pumping power and power generation power at time t respectively; Δt is the time interval duration.

[0073] Preferably, in the voltage security constraint, V i min = 0.95 p.u., V i max = 1.05 p.u.;

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

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

[0076] In step S3, define the output of each power generation entity at different time periods, carbon - emission - related variables (such as carbon emissions), green - certificate - trading - related variables (such as the number of green - certificate trades), etc. as decision variables. Then input the objective function of the total revenue of the consortium, the operating safety constraint conditions, and the decision variables into a solver such as Gurobi or CPLEX, and call the solver for solution to obtain the output strategies of each power generation entity at different time periods.

[0077] Simulation verification

[0078] System configuration: Adopt the IEEE 14 - node system, and assume that a total of 8 power generation entities are connected to the IEEE 14 - node system; among them, node 2 is connected to the No. 1 thermal power plant and the No. 2 thermal power plant, node 4 is connected to the No. 1 wind power plant and the No. 2 wind power plant, node 8 is connected to the No. 1 photovoltaic power station and the No. 2 photovoltaic power station, node 11 is connected to the pumped - storage power station, and node 14 is connected to the No. 3 photovoltaic power station. Operate in a multi - energy consortium operation mode composed of the No. 1 and No. 2 thermal power plants, the No. 1 wind power plant, the No. 1 photovoltaic power station and the pumped - storage power station.

[0079] Parameter settings: The carbon emission calculation coefficients a, b, and c are 36, -0.38, and 0.0034 respectively. Parameters of the pumped storage power station: The maximum pumping power / generation power are 200 MW / 150 MW respectively, and the maximum pumping efficiency / generation efficiency are 85% / 80% respectively.

[0080] The unit price quotation and the marginal clearing price are as Figure 2 shown. It can be seen that the marginal clearing price of the alliance fluctuates between 30 and 48 yuan. The high-price periods are mainly concentrated from daytime to evening (11:00, 12:00, 14:00, 21:00), while the low-price periods mostly appear at night or during low-demand periods (1:00, 5:00, 9:00, 18:00). When the output of renewable energy (wind power, photovoltaic) is sufficient, it can effectively suppress the electricity price. For example, at 7:00, the output of wind and light is relatively high, and the electricity price drops to 32 yuan. However, at 19:00 and 21:00, the output of photovoltaic drops to zero, and the electricity price rises to 44 - 48 yuan. The pumped storage units mainly discharge during peak periods to meet the demand, and its high-output period (11:00) usually corresponds to a relatively high clearing price. Generally speaking, the output of photovoltaic during the day has an inhibitory effect on the electricity price, the electricity price rises during the midday peak, and the electricity price at night depends on thermal power and pumped storage for support, showing obvious price fluctuation characteristics.

[0081] After using the multi-energy alliance dynamic optimization decision-making method based on the linkage of electricity-carbon green certificates of the present invention, the new energy consumption rate can be increased by 18%, and the wind and light abandonment rates can be decreased by 22%; the total revenue of the alliance can be increased by 9.7%, the proportion of carbon trading revenue reaches 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 the present invention.

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

Claims

1. A multi - energy alliance dynamic optimization decision - making method based on the multi - market linkage of electric carbon green certificates, characterized in that: It includes the following steps: S1. Integrate the revenues of the electricity, carbon, and green certificate markets to construct the total revenue objective function of the consortium; S2. On the premise of considering the operation safety constraints, construct the clearing model of the electricity spot market; S3. Taking the total revenue objective function of the consortium described in step S1 as the optimization direction, use the mixed-integer linear programming method to solve the clearing model of the electricity spot market constructed in step S2, and optimize the output strategies of each power generation entity in different time periods.

2. The multi-energy alliance dynamic optimization decision-making method based on the multi-market linkage of electric carbon green certificates according to claim 1, wherein: The expression of the total revenue objective function of the consortium described in step S1 is as follows: Where, F all is the total revenue of the consortium; F g , F gre are the trading revenues of the consortium participating in the electricity market, carbon market and green certificate market respectively.

3. The multi-energy alliance dynamic optimization decision-making method based on the multi-market linkage of electric carbon green certificates according to claim 2, characterized in that: The expression of the clearing model of the electricity spot market described in step S2 is as follows: Wherein: and are the clearing prices of the power generation entity m and the load n in the t period, respectively; and are the clearing powers of the power generation entity m and the load n in the t period, respectively; M is the number of power generation entities; N is the number of loads.

4. The multi-energy alliance dynamic optimization decision-making method based on the multi-market linkage of e-carbon green certificates according to claim 3, characterized in that: The operation safety constraints described in step S2 include voltage safety constraints, frequency safety constraints, branch power flow constraints, pumped-storage power constraints, and pumped-storage water volume balance constraints.

5. The multi-energy alliance dynamic optimization decision-making method based on the multi-market linkage of electric carbon green certificates according to claim 4, characterized in that: The expression of the voltage safety constraint described in step S2 is as follows: Where: V i is the voltage magnitude of node i, V i max , V i min are the upper and lower limits of the voltage of node i, respectively; The expression of the frequency safety constraint is as follows: f min ≤ f ≤ f max (5); where: f is the system frequency; f max , f min are the upper and lower limits of the system frequency, respectively; The expression of the branch power flow constraint is as follows: In the formula: is the upper limit of the branch power flow; P ij is the power flow of branch ij; The expression of the pumped-storage power constraint is as follows: P ch,min ≤P ch ≤P ch,max (7); P dis,min ≤P dis ≤P dis,max (8); Where: P ch , P dis are the pumping power and the generating power respectively; P ch,max , P ch,min , P dis,max , P dis,min are the upper and lower limits of pumping and the upper and lower limits of generating power respectively; The expression of the pumped-storage water volume balance constraint is as follows: Where: V t , V t-1 are the reservoir water storage at time t and time t-1 respectively; η ch , η dis are the pumping efficiency and power generation efficiency respectively; P t ch , P t dis are the pumping power and power generation power at time t respectively; Δt is the time interval duration.

6. The multi-energy alliance dynamic optimization decision-making method based on the multi-market linkage of electric carbon green certificates according to claim 5, characterized in that: In the voltage security constraint, V i min = 0.95 p.u., V i max = 1.05 p.u.; In the frequency security constraint, f min = 49.5 Hz, f max = 50.5 Hz.

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