An electric-car-green three market coupling and collaborative optimization method for solving double counting

Abstract

The present application belongs to the technical field of energy internet and low-carbon power system, and provides a method for solving the coupling and optimization of electric-carbon-green three markets with repeated accounting. The present application introduces the marginal emission factor of new energy units, and dynamically calculates a variable carbon-green mutual recognition coefficient. The coefficient reflects the actual emission reduction contribution of new energy output in different time periods. Based on the coefficient, the environmental rights and interests corresponding to renewable energy are mutually exclusive allocated to two paths: part of the green power certificates directly enters the green power certificate market, and the other part is converted into CCER the carbon market according to the coefficient, thereby eliminating repeated accounting from the source. Based on the mutual recognition result, the comprehensive carbon quota price and the comprehensive green power certificate price considering the supply and demand relationship are calculated respectively. Further, the two prices are coupled into the electricity market through the system marginal emission factor and the renewable energy consumption responsibility weight, forming the comprehensive electricity price in each time period, and providing a unified, real and dynamic price signal for market participants.

Description

Technical Field

[0001] This invention belongs to the field of energy internet and low-carbon power system technology, specifically involving a method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting. Background Technology

[0002] In the context of the global energy transition, the synergy and flexible resource optimization of the electricity, carbon, and green electricity certificate markets are key to achieving low-carbon goals. However, in reality, the coupling mechanism of these three markets remains unclear, and the relationship between green electricity certificates and... CCER Double accounting EV -Limitations such as insufficient energy storage integration. To address the issue of double-counting, this application proposes a variable carbon-green electricity certificate mutual recognition coefficient based on marginal emission factors, resolving the two-way interaction mechanism of the carbon-green electricity certificate market under the double-counting problem; regarding market coupling, this application constructs a comprehensive price model under the coupling of the electricity, carbon, and green electricity certificate markets, providing a unified dynamic price signal for subsequent microgrid system optimization; then, it establishes... EV -Energy storage and three-market synergistic optimization framework, integrating EV Charging demand, energy storage scheduling, and price signals from the three markets enhance the overall low-carbon and economic efficiency of the system. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method for the coupled and synergistic optimization of the electricity-carbon-green energy three-market system to resolve redundant accounting, thereby solving the problems in the prior art. The technical solution adopted by this invention is as follows: A method for coordinating and optimizing the electricity-carbon-green energy markets to address duplicate accounting includes: Step 1: Data Acquisition. Acquire basic operational data of the microgrid system, as well as transaction parameters for the electricity market, carbon market, and green electricity certificate market. Step 2: Construct a carbon-green market interaction model that considers repeated accounting, and introduce the marginal emission factor of new energy units. MCE Based on marginal emission factor MCE Dynamically calculate the variable carbon-green mutual recognition factor, used to proportionally convert a unit of green electricity certificate into [amount missing]. CCER The same entity's green electricity certificate can only be used to enter the green electricity certificate market or be converted to... CCER Participating in the carbon market allows for the acquisition of the actual number of green electricity certificates entering the green electricity certificate market and the carbon market itself. CCER Conversion quantity; Step 3: Construct a comprehensive price model for the electricity-carbon-green energy three-market system; based on the calculation results of Step 2, calculate the trading intensity and comprehensive green energy certificate price in the green energy certificate market, and the trading intensity and comprehensive carbon quota price in the carbon market; based on the comprehensive carbon quota price and comprehensive green energy certificate price, and combined with the system marginal emission factor, obtain the comprehensive electricity price; Step 4: Establish a collaborative optimization model for electric vehicle and energy storage charging and discharging; using the comprehensive electricity price from Step 3 as input, construct the electric vehicle charging and discharging model and the energy storage system charging and discharging model, and build a system optimization scheduling model; Step 5: Solve the system optimization scheduling model built in Step 4 to obtain the Pareto optimal solution set; Step 6: Based on the Pareto optimal solution set obtained in Step 5, generate a collaborative optimization scheduling strategy for the microgrid system.

[0004] Furthermore, step 2 includes: Step 2.1, construct a mechanism to promote green electricity certificates based on carbon quotas, represented as follows:

[0005] in, This refers to the actual supply of green electricity certificates entering the carbon market. This refers to the total number of green electricity certificates issued. It is a new energy unit j exist t Time period is used for CCER The amount of green electricity certificates converted; This is driven by the increase in the number of additional green electricity certificates, which is boosted by adding 1 kilogram of carbon allowance. This indicates the number of green electricity certificates that can be redeemed for a unit of renewable energy generation. It is the amount of thermal power generated per unit of carbon quota; The increase in the number of green electricity certificates is driven by carbon quotas. It refers to the amount of carbon allowances used to promote green electricity certificates; It is to promote the carbon allowance ratio of green electricity certificates. It's a free carbon credit. Indicates actual carbon emissions, Indicates the volume of carbon quota trading; Step 2.2, construct a system based on marginal emission factors. MCE Variable carbon-green mutual recognition coefficient: Marginal emission factor MCE This indicates the change in total carbon emissions from the power system caused by an increase in power system resources per unit of power:

[0006] in, express New energy units j exist t Marginal carbon emission factor over a period of time Indicates thermal power unit i carbon emission intensity, express t Periodic new energy units j Increased unit output of thermal power units i Change in output R This indicates the total number of new energy generating units. T Indicates a time scale; New energy units j exist t The variable carbon-green mutual recognition coefficient at time t is expressed as:

[0007] Step 2.3, construct the carbon emission offsetting mechanism of green electricity certificates, expressed as:

[0008] in, Qc j,t It is a new energy unit j exist t The time period is obtained by converting green electricity certificates. CCER quantity, for t Time system CCER Conversion quantity, It is used for CCER The amount of green electricity certificates converted, Indicates new energy power units j exist t The variable carbon-green cross-recognition coefficient at any given time.

[0009] Furthermore, step 3 includes: Step 3.1, calculate the trading intensity and overall green electricity certificate price in the green electricity certificate market:

[0010] in, Indicates time t The price of a comprehensive green electricity certificate at that time This indicates its predicted liquidation price. It is its trading strength. For green electricity certificate trading volume, This indicates the number of green electricity certificates required to meet the assessment criteria. T Indicates a time scale; Step 3.2, calculate the trading intensity and composite carbon allowance price in the carbon market:

[0011] in, It is the comprehensive carbon quota price. It is the predicted liquidation price. It is the intensity of carbon emission quotas. For carbon quota trading volume, This refers to the carbon quota allocation ratio. This indicates the total permissible carbon emissions. Let t be the total CCER conversion amount of the system at time t; Step 3.3, calculate the comprehensive electricity price:

[0012] in, It is the coupled electricity price. It is the predicted liquidation price. The marginal emission factor of the system. Assign responsibility weights to renewable energy power consumption.

[0013] Furthermore, step 4 includes: Step 4.1: Construct an electric vehicle charging and discharging model; Step 4.2: Construct a charging and discharging model for the energy storage system; Step 4.3: Construct a system optimization scheduling model.

[0014] Further, in step 4.1, the electric vehicle charging and discharging model is constructed as follows:

[0015] in, and They represent time respectively t and t -1 hour electric car v The state of charge; ηch v and η dis v These represent the charge / discharge efficiency, respectively. and Indicates charging and discharging power; Indicates electric vehicles v The maximum energy capacity of the battery V This represents the total number of electric vehicles. T Indicates a time scale. Indicates a time interval; Construct an economic cost function for electric vehicles that considers battery degradation, expressed as follows:

[0016] In the formula, Indicates electric vehicles v existt The cost of time; Indicates the price of discharge. The cost of battery degradation per unit of energy throughput; For battery investment costs, For battery cycle life, DOD For the depth of discharge used in the test protocol, This indicates the charging price, which is the previous coupled electricity price. The constraints include:

[0017] In the formula, v,t and , respectively representing electric vehicles v Maximum charging and discharging power; This is an indicator of vehicle availability. for V 2 G Service participation willingness coefficient; The state of charge must satisfy the following conditions:

[0018] In the formula, and They represent electric vehicles. v The maximum and minimum permissible state of charge values; electric vehicles pass V 2 G The carbon emissions reduced by the service are:

[0019] in, and These represent the aggregate power of electric vehicle discharge and charging, respectively. and They represent the time periods respectively t Marginal carbon emission factor.

[0020] Further, step 4.2 involves constructing a charge / discharge model for the energy storage system, including: The energy state dynamics of an energy storage system conforms to a dynamic equilibrium equation:

[0021] In the formula, and They represent t Time and t 1 Hour Stored in a storage unit s Energy within; δs For storage units s Self-discharge coefficient; s and Representing storage units s The charging and discharging efficiency, and Representing storage units s At any moment t The charging and discharging power, The total number of storage units. T Indicates a time scale; The cost of lithium-ion batteries, taking into account energy costs and degradation losses, is as follows:

[0022] In the formula, For energy storage units s At any moment t The cost; For storage units s exist t The price of discharge at any given moment; Indicates operating and maintenance costs; For storage units s The degradation cost coefficient, For storage system s The cost of updating For storage units s Rated capacity, Cyclic aging caused by depth of discharge. A It is the Arrhenius constant. R This is the universal gas constant. T Absolute temperature E a For activation energy, z The reaction rate constant is... and Representing storage units s At any moment t The charging and discharging power, and Representing storage units s The charging and discharging efficiency, This represents the battery's cycle depth at time t. Indicates the charging price; Energy storage systems must meet the following constraints:

[0023]

[0024] in, and For storage system s Maximum charge and discharge power limit; Indicates energy storage system s The slope rate limit; and These represent the minimum and maximum allowable energy levels, respectively. and These represent the charging / discharging power of the storage system s at time t and time t-1, respectively. This represents the absolute value of the difference between the charging / discharging power of the storage system s at time t and time t-1; The contribution of energy storage systems is quantified as follows:

[0025] In the formula, and These represent the energy storage system in t Aggregate power over a given period of time.

[0026] Furthermore, in step 4.3, the system optimization scheduling model is constructed as follows:

[0027]

[0028]

[0029] in, express t Regular units g The cost of electricity generation; Indicates renewable energy units j At any moment t Operating costs; For carbon emission costs, For the revenue from green electricity certificates; V and S These represent electric vehicles and storage systems, respectively. ΔE j To reduce emissions from new energy sources, and They are respectively t Electric vehicles pass through during the period V 2 G The achieved emission reductions and the emission reductions from energy storage systems, For new energy units j exist t Efforts during specific time periods.

[0030] Furthermore, the optimization process is constrained by the system power balance:

[0031] In the formula, This indicates network loss.

[0032] Furthermore, in step 5, a multi-objective war strategy optimization algorithm is adopted. MOWSO Solve the system optimization scheduling model built in step 4.

[0033] This invention has the following beneficial effects: This invention establishes green electricity certificates based on marginal emission factors and... CCER The mutually exclusive allocation mechanism eliminates double accounting at the source. On this basis, it realizes the indirect promotion of carbon quotas to the green electricity certificate market and the dynamic offsetting path of green electricity certificates to the carbon market, and completes the coupling of the three markets to generate a unified comprehensive electricity price signal, effectively supporting the low-carbon and economically optimized scheduling of electric vehicles and energy storage. Attached Figure Description

[0034] Figure 1 A schematic diagram of a carbon-green market interaction mechanism to address double accounting; Figure 2 A schematic diagram of the market price generation mechanism; Figure 3 for MOWSO Algorithm flow diagram; Figure 4 Schematic diagram of the model solution process. Detailed Implementation

[0035] The following will be described in conjunction with embodiments of the present invention. Figures 1-4 The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0036] The purpose of this application is to provide a method for the coupled and synergistic optimization of the electricity, carbon, and green energy markets to solve the problem of double accounting; where "electricity" refers to the electricity market (electricity trading market), and "carbon" refers to the carbon market (carbon emission trading market). CET The "green" in "market" refers to the green electricity certificate market. GCT Market). This invention introduces the marginal emission factor of new energy units ( MCE A variable carbon-green mutual recognition coefficient is dynamically calculated. This coefficient reflects the actual emission reduction contribution of renewable energy output to the system at different times. Based on this coefficient, the environmental rights corresponding to renewable energy are mutually exclusively allocated to two paths: a portion of green electricity certificates directly enter the green electricity certificate market (…). GCT The other part is converted according to a coefficient. CCER Entering the carbon market ( CETThis eliminates double counting at the source. Based on the mutual recognition results obtained in step 1, the comprehensive carbon allowance price and comprehensive green electricity certificate price, taking into account supply and demand, are calculated respectively. Furthermore, these two prices are then converted using the system marginal emission factor (…). MCI The comprehensive electricity price for each time period is coupled with the renewable energy consumption responsibility weight into the electricity market, providing market participants with a unified, accurate, and dynamic price signal. An optimal scheduling model incorporating electric vehicles and multiple types of energy storage is established with the objectives of minimizing total system cost and maximizing carbon emission reduction. This model uses the comprehensive electricity price generated in step 2 as its core input to drive the scheduling of electric vehicles (…). EV ) and energy storage systems ( ESS Charge and discharge at the optimal time to maximize the overall efficiency of the system.

[0037] To achieve the above objectives, the present invention includes the following steps: Step 1: Data Acquisition. Acquire basic operational data of the microgrid system, as well as transaction parameters of the electricity market, carbon market, and green electricity certificate market. Basic operational data includes load, wind and solar power forecast data, and electric vehicle user behavior and availability information in the microgrid system. Transaction parameters include clearing prices, carbon allowances, and green electricity certificate-related parameters.

[0038] Step 2: As Figure 1 Establish a carbon-green market interaction model that considers double-counting. Introduce marginal emission factors for renewable energy units to address the environmental rights associated with renewable energy generation. MCE Based on this factor, a variable carbon-green mutual recognition factor is dynamically calculated, which is used to proportionally convert a unit of green electricity certificate into [amount missing]. CCER The key point is that a single entity's green electricity certificate can only be used to enter the green electricity certificate market or be converted into... CCER Participation in the carbon market and the carbon market are mutually exclusive pathways, thus preventing double-counting of environmental rights from the outset. Furthermore, this allows us to determine the actual amount of green electricity certificates entering the green electricity certificate market and the carbon market's... CCER Conversion quantity. CCER ( ChineseCertifiedEmissionReduction ( ) refers to nationally certified voluntary emission reductions, which are greenhouse gas emission reductions generated by voluntary emission reduction projects that have been registered and certified by the relevant national authorities. 1 unit CCER The emission reduction equivalent to 1 ton of carbon dioxide is primarily used to offset the carbon emission quota gap and serves as an important supplementary tool for emission control entities to fulfill their carbon emission management obligations. CCER The maximum offset ratio is 5% of the carbon allowance.

[0039] Step 2.1, the role of carbon allowances in promoting green electricity certificates: The continued tightening of carbon quotas restricts the power generation capacity of high-carbon units. The resulting power supply gap must be filled by renewable energy. This process not only improves the actual absorption rate of renewable energy but also simultaneously increases the total number of green electricity certificates issued. This process can be represented as:

[0040] in, This refers to the actual supply of green electricity certificates entering the carbon market. This represents the total issuance of green electricity certificates, generated from renewable energy power generation. MWh One green electricity certificate corresponds to one new energy power generation. It is a new energy unit j exist t Time period is used for CCER The amount of green electricity certificates converted. This is driven by the increase in the number of additional green electricity certificates, which is boosted by adding 1 kilogram of carbon allowance. This indicates the number of green electricity certificates that can be redeemed for a unit of renewable energy generation. It is the amount of thermal power generated per unit of carbon quota; The increase in the number of green electricity certificates is driven by carbon quotas. It refers to the amount of carbon allowances used to promote green electricity certificates; It is to promote the carbon allowance ratio of green electricity certificates. It's a free carbon credit. Indicates actual carbon emissions, This indicates the volume of carbon quota trading.

[0041] Step 2.2, construct a system based on marginal emission factors. MCE Variable carbon-green mutual recognition coefficient: Marginal emission factor MCE This indicates the change in total carbon emissions from the power system caused by an increase in power system resources per unit of power:

[0042] in, Represented as new energy units j exist t Marginal carbon emission factor over a period of time Indicates thermal power unit i carbon emission intensity, express t Periodic new energy units j Increased unit output of thermal power units i Change in output. R This indicates the total number of new energy generating units. T Indicates a time scale (24 hours a day).

[0043] The carbon-green cross-recognition coefficient based on the marginal emission factor reflects the change in carbon emissions caused by changes in the output of renewable energy units at different times, and reflects the time-varying emission reduction contribution of renewable energy units. The coefficient is expressed as follows:

[0044] Step 2.3, Constructing the carbon emission offsetting mechanism for green electricity certificates: After the introduction of the carbon-green electricity certificate mutual recognition factor, the carbon emission reduction attribute of the green electricity certificate no longer uses a fixed value, but is determined by... MCE Dynamically determined. If you choose to convert green electricity certificates to... CCER The carbon emission reductions are calculated based on the mutual recognition coefficient and used to offset the carbon quota gap; once converted, these green electricity certificates are withdrawn from the carbon market.

[0045]

[0046] in, It is a new energy unit j exist t The time period is obtained by converting green electricity certificates. CCER quantity, for t Time system CCER Conversion quantity, It is used for CCER The amount of green electricity certificates converted, Indicates new energy power units j exist t The variable carbon-green cross-recognition coefficient at any given time.

[0047] CCER The maximum offset ratio is 5% of the carbon allowance, as shown in the following formula:

[0048] Step 3: As Figure 2 A comprehensive price model for the electricity, carbon, and green energy markets is constructed. Based on the calculation results from step 2, the trading intensity and comprehensive green energy certificate price in the green energy certificate market, and the trading intensity and comprehensive carbon allowance price in the carbon market are calculated respectively. Based on the comprehensive carbon allowance price and comprehensive green energy certificate price, combined with the system marginal emission factor, the comprehensive electricity price is obtained, including: Step 3.1, Calculate the trading intensity and composite green electricity certificate price in the green electricity certificate market: Based on the predicted clearing price and considering the trading intensity factor, the composite price of green electricity certificates is finally obtained.

[0049]

[0050] in, Indicates timet The comprehensive price of green electricity certificates, This indicates its predicted liquidation price. It is its trading strength. For green electricity certificate trading volume, This indicates the number of green electricity certificates required to meet the assessment criteria. T Indicates a time scale (24 hours a day).

[0051] Step 3.2, calculate the trading intensity and composite carbon allowance price in the carbon market: Carbon allowances allocated to conventional power generation units are directly related to the unit's total load and the carbon emission allowance coefficient established by policy. These two factors together constitute the core basic parameters for carbon allowance calculation.

[0052]

[0053] in, It is the comprehensive carbon quota price. It is the predicted liquidation price. It is the intensity of carbon emission quotas. For carbon quota trading volume, This refers to the carbon quota allocation ratio. This indicates the total permissible carbon emissions. Let t be the total CCER conversion amount of the system at time t.

[0054] Step 3.3, calculate the comprehensive electricity price: Electricity trading not only ensures a balance in energy supply, but also helps to develop the most economical and low-carbon trading strategies by comparing prices and energy properties from different sellers.

[0055]

[0056] in, It is the coupled electricity price. It is the predicted liquidation price. The marginal emission factor of the system. Assign responsibility weights to renewable energy power consumption.

[0057] Step 4: Establish a collaborative optimization model for electric vehicle and energy storage charging and discharging; using the comprehensive electricity price from Step 3 as input, construct the electric vehicle charging and discharging model and the energy storage system charging and discharging model, and build a system optimization scheduling model, including: Step 4.1, Construct an electric vehicle charging and discharging model: Within the framework of three-market coupling, optimized scheduling of electric vehicle operations is achieved. Electric vehicle state of charge (SOC) SOC The temporal evolution follows the principle of energy conservation.

[0058]

[0059] In the above formula, and They represent time respectively t and t -1 hour electric car v of SOC . and These represent the charge / discharge efficiency, respectively. and Indicates charging and discharging power; Indicates electric vehicles v The maximum energy capacity of the battery. SOC That is, the state of charge ( State of Charge ), is a core indicator for quantifying the remaining power level of electric vehicle power batteries. SOC The remaining charge of an electric vehicle battery is represented by the ratio of the battery's current remaining usable charge to its rated maximum energy capacity. It is usually presented as a percentage and is the core basis for judging the charging and discharging capabilities of an electric vehicle and formulating charging and discharging scheduling strategies. V This represents the total number of electric vehicles. T Indicates a time scale (24 hours a day). Indicates a time interval.

[0060] The economic cost function of an electric vehicle considering battery degradation is expressed as follows.

[0061]

[0062] In the formula, Indicates electric vehicles v exist t The cost of time. Indicates the price of discharge. This represents the battery degradation cost per unit of energy throughput. For battery investment costs, For battery cycle life, DOD For the depth of discharge used in the test protocol, This indicates the charging price, which is the previous coupled electricity price. The charging and discharging power of electric vehicles is limited by the capacity of charging infrastructure and vehicle availability. Furthermore, simultaneous charging and discharging is not permitted; therefore, the following constraints must be met:

[0063] In the formula, and They represent electric vehicles. v Maximum charging and discharging power; A v,tThis is an indicator of vehicle availability. for V 2 G Participation willingness coefficient. V 2 G ( Vehicle - to - Grid Electricity is a technology that enables bidirectional energy flow between electric vehicles and the power grid, allowing vehicles to both charge and feed energy back to the grid.

[0064] To prevent electric vehicles from overcharging or over-discharging, its SOC The following conditions must be met:

[0065] In the formula, and They represent electric vehicles. v Maximum and minimum allowed SOC value.

[0066] electric vehicles pass V 2 G The carbon emissions reduced by the services are as follows:

[0067] in, and These represent the aggregate power of electric vehicle discharge and charging, respectively. and They represent the time periods respectively t Marginal carbon emission factor.

[0068] Step 4.2, construct the energy storage system charge and discharge model: Within a triple-market coupling framework, the system achieves collaborative optimization. Energy storage system. ESS ( EnergyStorageSystem energy state () SoE The dynamics conform to the dynamic equilibrium equations:

[0069] In the formula, and They represent t Time and t 1 Hour Stored in a storage unit s The energy within. For storage units s Self-discharge coefficient; and Representing storage units s The charging and discharging efficiency, and Representing storage units s At any moment t The charging and discharging power. SoE ( State of Energy ) refers to the energy state, used to quantify energy storage systems ( ESS The core state parameters of the remaining available energy level are related to the electric vehicle's power battery. SOC The physical meanings of (charged state) are of the same origin. SoE It represents the remaining energy status of an energy storage unit. The value is the ratio of the current remaining available storage energy of the energy storage unit to the rated maximum energy capacity of the energy storage unit. It is a dimensionless value and is used to determine the rechargeable and dischargeable capabilities of energy storage at different times. It is the core basis for formulating energy storage charging and discharging scheduling strategies. The total number of storage units. T Indicates a time scale (24 hours a day).

[0070] The operating cost of an energy storage system comprehensively considers both energy costs and degradation losses. The structural design of flow batteries fundamentally differentiates them from their degradation mechanisms, significantly reducing degradation costs in most cases. Therefore, the degradation cost of flow battery energy storage is negligible. The degradation cost of lithium-ion batteries, comprehensively considering both energy costs and degradation losses, is expressed as follows:

[0071] In the formula, Indicates energy storage unit s At any moment t The cost; λES s, t Represents storage unit s exist t The price of discharge at any given moment. This indicates the cost of operation and maintenance. For storage units s The degradation cost coefficient, For storage system s The cost of updating For storage units s Rated capacity, Cyclic aging caused by depth of discharge. A It is the Arrhenius constant. R This is the universal gas constant. T Absolute temperature E a For activation energy, z is the reaction rate constant. and Representing storage units s At any moment t The charging and discharging power, and Representing storage units s The charging and discharging efficiency, This represents the battery's cycle depth at time t. This indicates the charging price, which is the previous coupled electricity price.

[0072] The ramp-up capability of an energy storage system is limited by its mechanical and electrical characteristics. Furthermore, its charging and discharging operations are power-limited and cannot be performed simultaneously.

[0073]

[0074] In the formula, and For storage system s Maximum charge and discharge power limit; Indicates energy storage system s The slope rate limit.

[0075] To ensure the safe operation and extended lifespan of the storage system, and to guarantee the cyclical nature of daily operation and prevent cumulative energy deviations, the following constraints must be met.

[0076]

[0077] in, and These represent the minimum and maximum allowable energy levels, respectively. and These represent the charging / discharging power of the storage system s at time t and time t-1, respectively. This represents the absolute value of the difference between the charging / discharging power of the storage system s at time t and time t-1.

[0078] The contribution of energy storage systems to overall system carbon emission reduction is quantified as follows:

[0079] In the formula, and These represent the energy storage system in t Aggregate power over a given period of time.

[0080] Step 4.3, Construct the system optimization scheduling model: The system optimization scheduling model integrates electric vehicle charging and discharging, and multiple types of... ESS Operation and three-market coupling mechanism. Maximizing the overall system benefit is achieved through multi-objective optimization, namely minimizing the total system cost and maximizing carbon emission reduction, as shown in the following equation:

[0081]

[0082]

[0083] in, express t Regular units g The cost of electricity generation; Indicates renewable energy units j At any moment t Operating costs; For carbon emission costs, For the revenue from green electricity certificates; V and S These represent electric vehicles and storage systems, respectively. ΔE j To reduce emissions from new energy sources, and They are respectively t Electric vehicles pass through during the period V 2 G The achieved emission reductions and the emission reductions from energy storage systems, For new energy units j exist t Efforts during specific time periods.

[0084] Joint optimization is constrained by system power balance:

[0085] In the formula, P loss t This indicates network loss.

[0086] Step 5: Employ a multi-objective war strategy optimization algorithm MOWSO (Multi-Objective War Strategy Optimization Algorithm Solving the system optimization scheduling model built in step 4 yields the Pareto optimal solution set, which specifically includes: like Figure 3 , Figure 4 Multi-objective war strategy optimization algorithm ( MOWSO The core lies in the selection rules for the "King" and "Commander" and the hierarchical search of "Soldier-King-Commander", which mainly includes the following stages: (1) Soldier position update: To select the king from the army, a fuzzy membership function is introduced, expressed as:

[0087] In the formula: It is the first l Soldiers at the Pareto Front i The j The value of the objective function, and These represent the Pareto fronts of the th... j The maximum and minimum values ​​of each target.

[0088] For the l Soldiers at the Pareto Front i Its membership degree It can be represented as:

[0089] In the formula: Q It is the dimension of the objective function.

[0090] The soldier with the highest loyalty in each generation's Pareto Front is selected as the King in the next update, and a random soldier from the Pareto Front is selected as the Commander, as shown in the following expression:

[0091] In the formula: Indicates the first l -The soldier with the highest affiliation in the Pareto Front of Generation 1. Representative at the l Soldiers leading the search i The commander, Indicates from the first l -1 A soldier randomly selected from the Pareto Front.

[0092] The soldiers' attack and defense strategies are as follows:

[0093] The first formula represents the attack strategy, in which... and Indicates the first i The first in the army t +1 and the first t A soldier, The first expression represents the weighting factor of the soldier, and the second expression represents the defense strategy. For each soldier in the army, a random number between 0 and 1 is generated before taking action. ρ Compare it with action guidance factors The size. When ≥ If the situation is as described above, the soldiers will adopt an offensive strategy for this operation; otherwise, they will adopt a defensive strategy.

[0094] (2) Weight update: The rank and weight of a soldier after promotion can be calculated using the following formula:

[0095] In the formula: He was a soldier.i Military rank It represents the maximum number of iterations.

[0096] (3) Resettlement strategies for vulnerable soldiers: The strategy of repositioning the weaker soldiers in each generation of the army to the center of the Pareto front can be represented as:

[0097] In the formula, This represents the soldiers with the average membership value in the Pareto front. Interpretation for King Pareto.

[0098] MOWSO The algorithm steps are as follows: Step 5.1: Parameter initialization, including army size, etc.; Step 5.2: Randomly generate initial soldiers and calculate their fitness; Step 5.3: Determine the dominant relationship and select the Pareto front; Step 5.4: Sort the soldiers and select the king; Step 5.5: Algorithm iteration. Before starting the search, a commander is randomly selected, and the strategy for updating soldier positions is determined based on the random number.

[0099] Step 5.6: Update soldier positions, adjust ranks and weights.

[0100] Step 5.7: Update the Pareto front, place weaker soldiers, and continue until the algorithm iteration ends. Step 6: Based on the Pareto optimal solution set obtained in Step 5, generate a collaborative optimization scheduling strategy for the microgrid system.

[0101] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, alterations, or substitutions made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting, characterized in that, include: Step 1: Data Acquisition. Acquire basic operational data of the microgrid system, as well as transaction parameters for the electricity market, carbon market, and green electricity certificate market. Step 2: Construct a carbon-green market interaction model that considers repeated accounting, and introduce the marginal emission factor of new energy units. MCE Based on marginal emission factor MCE Dynamically calculate the variable carbon-green mutual recognition factor, used to proportionally convert a unit of green electricity certificate into [amount missing]. CCER ; The same entity's green electricity certificates can only be used to enter the green electricity certificate market or be converted to [other methods]. CCER Participating in the carbon market allows for the acquisition of the actual number of green electricity certificates entering the green electricity certificate market and the carbon market itself. CCER Conversion quantity; Step 3: Construct a comprehensive price model for the electricity-carbon-green energy three-market system; based on the calculation results of Step 2, calculate the trading intensity and comprehensive green energy certificate price in the green electricity certificate market, and the trading intensity and comprehensive carbon quota price in the carbon market, respectively; The comprehensive electricity price is obtained by combining the comprehensive carbon quota price and the comprehensive green electricity certificate price with the system marginal emission factor; Step 4: Establish a collaborative optimization model for electric vehicle and energy storage charging and discharging; using the comprehensive electricity price from Step 3 as input, construct the electric vehicle charging and discharging model and the energy storage system charging and discharging model, and build a system optimization scheduling model; Step 5: Solve the system optimization scheduling model built in Step 4 to obtain the Pareto optimal solution set; Step 6: Based on the Pareto optimal solution set obtained in Step 5, generate a collaborative optimization scheduling strategy for the microgrid system.

2. The method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting as described in claim 1, characterized in that, Step 2 includes: Step 2.1, construct a mechanism to promote green electricity certificates based on carbon quotas, represented as follows: in, This refers to the actual supply of green electricity certificates entering the carbon market. This refers to the total number of green electricity certificates issued. It is a new energy unit j exist t Time period used CCER The amount of green electricity certificates converted; This is driven by the increase in the number of additional green electricity certificates, which is boosted by adding 1 kilogram of carbon allowance. This indicates the number of green electricity certificates that can be redeemed for a unit of renewable energy generation. It is the amount of thermal power generated per unit of carbon quota; The increase in the number of green electricity certificates is driven by carbon quotas. It refers to the amount of carbon allowances used to promote green electricity certificates; It is to promote the carbon allowance ratio of green electricity certificates. It's a free carbon credit. Indicates actual carbon emissions, Indicates the volume of carbon quota trading; Step 2.2, construct a system based on marginal emission factors. MCE Variable carbon-green mutual recognition coefficient: Marginal emission factor MCE This indicates the change in total carbon emissions from the power system caused by an increase in power system resources per unit of power: in, express New energy units j exist t Marginal carbon emission factor over a period of time Indicates thermal power unit i carbon emission intensity, express t Periodic new energy units j Increased unit output of thermal power units i Change in output R This indicates the total number of new energy generating units. T Indicates a time scale; New energy units j exist t The variable carbon-green mutual recognition coefficient at time t is expressed as: Step 2.3, construct the carbon emission offsetting mechanism of green electricity certificates, expressed as: in, Qc j,t It is a new energy unit j exist t The time period is obtained by converting green electricity certificates. CCER quantity, for t Time system CCER Conversion quantity It is used for CCER The amount of green electricity certificates converted, Indicates new energy power units j exist t The variable carbon-green cross-recognition coefficient at any given time.

3. The method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting as described in claim 2, characterized in that, Step 3 includes: Step 3.1, calculate the trading intensity and overall green electricity certificate price in the green electricity certificate market: in, Indicates time t The price of a comprehensive green electricity certificate at that time This indicates its predicted liquidation price. It is its trading strength. For green electricity certificate trading volume, This indicates the number of green electricity certificates required to meet the assessment criteria. T Indicates a time scale; Step 3.2, calculate the trading intensity and composite carbon allowance price in the carbon market: in, It is the comprehensive carbon quota price. It is the predicted liquidation price. It is the intensity of carbon emission quotas. For carbon quota trading volume, This refers to the carbon quota allocation ratio. This indicates the total permissible carbon emissions. Let t be the total CCER conversion amount of the system at time t; Step 3.3, calculate the comprehensive electricity price: in, It is the coupled electricity price. It is the predicted liquidation price. The marginal emission factor of the system. Assign responsibility weights to renewable energy power consumption.

4. The method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting as described in claim 1, characterized in that, Step 4 includes: Step 4.1: Construct an electric vehicle charging and discharging model; Step 4.2: Construct a charging and discharging model for the energy storage system; Step 4.3: Construct a system optimization scheduling model.

5. The method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting as described in claim 4, characterized in that, Step 4.1, construct the electric vehicle charging and discharging model as follows: in, and They represent time respectively t and t -1 hour electric car v The state of charge; ηch v and ηdis v These represent the charge / discharge efficiency, respectively. and Indicates charging and discharging power; Indicates electric vehicles v The maximum energy capacity of the battery V This represents the total number of electric vehicles. T Indicates a time scale. Indicates a time interval; Construct an economic cost function for electric vehicles that considers battery degradation, expressed as follows: In the formula, Indicates electric vehicles v exist t The cost of time; Indicates the price of discharge. The cost of battery degradation per unit of energy throughput; For battery investment costs, For battery cycle life, DOD For the depth of discharge used in the test protocol, This indicates the charging price, which is the previous coupled electricity price. The constraints include: In the formula, v,t and , respectively representing electric vehicles v Maximum charging and discharging power; This is an indicator of vehicle availability. for V 2 G Service participation willingness coefficient; The state of charge must satisfy the following conditions: In the formula, and They represent electric vehicles. v The maximum and minimum permissible state of charge values; electric vehicles pass V 2 G The carbon emissions reduced by the service are: in, and These represent the aggregate power of electric vehicle discharge and charging, respectively. and They represent the time periods respectively t Marginal carbon emission factor.

6. The method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting as described in claim 5, characterized in that, Step 4.2, construct the energy storage system charge and discharge model, including: The energy state dynamics of an energy storage system conforms to a dynamic equilibrium equation: In the formula, and They represent t Time and t 1 Hour Stored in a storage unit s Energy within; δ s For storage units s Self-discharge coefficient; s and Representing storage units s The charging and discharging efficiency, and Representing storage units s At any moment t The charging and discharging power, The total number of storage units. T Indicates a time scale; The cost of lithium-ion batteries, taking into account energy costs and degradation losses, is as follows: In the formula, For energy storage units s At any moment t The cost; For storage units s exist t The price of discharge at any given moment; Indicates operating and maintenance costs; For storage units s The degradation cost coefficient, For storage system s The cost of updating For storage units s Rated capacity, Cyclic aging caused by depth of discharge. A It is the Arrhenius constant. R This is the universal gas constant. T Absolute temperature E a For activation energy, z The reaction rate constant is... and Representing storage units s At any moment t The charging and discharging power, and Representing storage units s The charging and discharging efficiency, This represents the battery's cycle depth at time t. Indicates the charging price; Energy storage systems must meet the following constraints: in, and For storage system s Maximum charge and discharge power limit; Indicates energy storage system s The slope rate limit; and These represent the minimum and maximum allowable energy levels, respectively. and These represent the charging / discharging power of the storage system s at time t and time t-1, respectively. This represents the absolute value of the difference between the charging / discharging power of the storage system s at time t and time t-1; The contribution of energy storage systems is quantified as follows: In the formula, and These represent the energy storage system in t Aggregate power over a given period of time.

7. The method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting as described in claim 6, characterized in that, Step 4.3, construct the system optimization scheduling model as follows: in, express t Conventional units g The cost of electricity generation; Indicates renewable energy units j At any moment t Operating costs; For carbon emission costs, For the revenue from green electricity certificates; V and S These represent electric vehicles and storage systems, respectively. ΔE j To reduce emissions from new energy sources, and They are respectively t Electric vehicles pass through during the period V 2 G The achieved emission reductions and the emission reductions from energy storage systems, For new energy units j exist t The output during a specific time period.

8. The method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting as described in claim 7, characterized in that, The optimization process is constrained by the system power balance: In the formula, This indicates network loss.

9. A method for coordinating and optimizing the electricity-carbon-green three-markets to solve the problem of duplicate accounting, as described in any one of claims 1-8, characterized in that, In step 5, a multi-objective war strategy optimization algorithm is adopted. MOWSO Solve the system optimization scheduling model built in step 4.

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