Optimal scheduling method of multi-energy storage system considering carbon emission rights and green certificate transaction

Abstract

The application discloses a kind of considering carbon emission rights and green certificate transaction multi-energy storage system optimization scheduling method, comprising: the multi-source heterogeneous operation data of multi-energy storage system and market transaction data are collected, multi-objective optimization model is constructed, and constraint system including multi-energy flow balance constraint, equipment physical operation constraint and carbon emission rights and green certificate transaction constraint is set.By using improved-constraint method, the multi-objective optimization model is converted into a sequence of multiple single-objective optimization models, and the corresponding candidate scheduling scheme is generated by systematically adjusting the emission upper limit and risk upper limit.For different carbon emission rights and green certificate conversion mechanism, the candidate scheduling scheme is solved and compared respectively to form a candidate scheduling solution set under multiple trading mechanisms.Based on fuzzy multi-attribute decision-making method, the candidate solution set is comprehensively evaluated, and the scheduling scheme with the optimal score is selected to realize the economic, environmental and risk collaborative optimization scheduling of multi-energy storage system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy storage scheduling, and particularly relates to a multi-energy storage system optimal scheduling method considering carbon emission rights and green certificate transactions. BACKGROUND

[0002] The carbon emission rights trading market (carbon market) and the green power certificate trading market (TGC market) have become important policy tools for promoting clean and low-carbon energy transformation worldwide. Under this background, the optimal scheduling of the park-level comprehensive energy system (CES) as an important energy consumption unit is of great significance to achieving low-carbon transformation, reducing energy costs, and improving system operation efficiency. The park-level comprehensive energy system is usually composed of multiple energy devices, including combined heat and power (CHP), photovoltaic (PV), energy storage battery (BAT), electric chiller (EC), absorption chiller (ABC), heat pump (HP), etc. These devices form a complex energy network with coupled electric, thermal, and cold multi-energy flows. How to efficiently schedule these devices to achieve optimal energy allocation has become an important research direction in the current energy field.

[0003] In the prior art, CN115115114A discloses a comprehensive energy system optimal scheduling method considering carbon trading, including: step 1) establishing a carbon trading model and mechanism for the internal subjects of the comprehensive energy system; step 2) establishing output models, carbon emission models, and cost models for distributed power sources, energy storage, and other energy conversion devices; establishing an energy hub mathematical model and an internal and external interaction model for electricity-gas-heat-cold; step 3) layering the internal comprehensive energy system; step 4) establishing a mixed game structure for the comprehensive energy system; step 5) solving the interactive process; step 6) establishing a more suitable carbon price pricing mechanism for the comprehensive energy system; and step 7) verifying that the mixed game scheme can achieve stable, economic, and environmentally friendly operation of the comprehensive energy system.

[0004] However, this method has the following limitations: 1) only considers the carbon emission rights trading mechanism, lacks the introduction and modeling of the green power certificate (TGC) mechanism, resulting in fragmented market mechanisms and the inability to achieve coordinated optimization of carbon trading and green certificate trading. 2) The conversion relationship between carbon quotas and green certificates is not established, and the dynamic role of green certificates in carbon emission reduction, economic benefits, and clean energy value is not modeled, making it difficult to reflect the optimal scheduling strategy under the linkage of the two markets. 3) Lack of a multi-objective optimization framework, making it difficult to meet the multi-dimensional collaborative optimization needs of the park comprehensive energy system.

[0005] Therefore, there is an urgent need for a method of energy storage system fine modeling and comprehensive scheduling decision-making to address the problems of fragmented market mechanisms, missing conversion relationships, insufficient target dimensions, and imperfect risk control in the prior art. SUMMARY

[0006] The present application aims to provide a multi-energy storage system optimal scheduling method considering carbon emission rights and green certificate trading to overcome the defects of the prior art.

[0007] The object of the present application can be achieved by the following technical solutions:

[0008] The present application provides a multi-energy storage system optimal scheduling method considering carbon emission rights and green certificate trading, comprising the following steps:

[0009] Obtain multi-source heterogeneous operation and market data of the multi-energy storage system;

[0010] Preprocess the multi-source heterogeneous operation and market data to obtain preprocessed multi-source heterogeneous operation and market data;

[0011] According to the preprocessed multi-source heterogeneous operation and market data, a multi-objective optimization model containing economy, environmental protection and operation risk is constructed, and the optimization objective function of the optimization model is determined;

[0012] According to the multi-energy flow balance relationship, the physical operation requirements of the equipment and the carbon emission rights and green certificate trading rules, the constraint system of the multi-objective optimization model is defined;

[0013] The multi-objective optimization model is converted into a solvable single-objective optimization model sequence by using an improved constraint method, and a plurality of candidate scheduling schemes are obtained;

[0014] The candidate scheduling schemes are respectively solved and compared according to different trading mechanisms to determine the optimal trading mechanism of each candidate scheduling scheme;

[0015] Based on the candidate scheduling solution set, a fuzzy multi-attribute decision method is used for comprehensive evaluation to determine the optimal scheduling scheme.

[0016] Further, the multi-source heterogeneous operation and market data includes the operation performance parameters of key equipment, real-time electricity prices of the electricity market, natural gas prices, carbon emission right prices, green power certificate prices, time series demand data, renewable energy prediction data, carbon quota allocation standards, green certificate assessment proportion and risk preference coefficient; the key equipment includes gas turbines, energy storage devices, electric chillers, absorption chillers and heat pumps; the time series demand data includes demand information of power load, heat load and cold load at different time nodes.

[0017] Further, the preprocessing of the multi-source heterogeneous operation and market data specifically includes: cleaning processing of the multi-source heterogeneous operation and market data; interpolation filling of missing values in the data; identification and elimination of abnormal values; minimum-maximum standardization processing of the data.

[0018] Further, the optimization model optimizes the objective function, which is formulated as:

[0019]

[0020] wherein, represents the economic objective function; represents the environmental objective function; represents the risk objective function.

[0021] Further, the economic objective function aims to minimize the total life cycle cost of the system, which is formulated as:

[0022]

[0023] wherein, represents the investment cost of key equipment, which is calculated by the annuity method, considering the equipment life cycle and the time value of money; represents the operation and maintenance cost of key equipment, including fixed maintenance cost and variable operation cost; represents the fuel cost, which is calculated based on the natural gas price and the consumption of gas turbines and gas boilers; represents the cost of purchased electricity, which is based on the real-time electricity price in the electricity market, and is differentiated between green electricity and gray electricity; represents the carbon trading cost, which is calculated based on the difference between the carbon emission right price and the actual carbon emissions and carbon quota of the system; represents the green certificate trading cost, which is determined based on the green electricity certificate price, the renewable energy generation of the system, and the green certificate assessment ratio; represents the electricity sales revenue; represents the carbon trading revenue; represents the green certificate trading revenue;

[0024] The environmental objective function aims to minimize the total carbon emissions of the system, which is calculated by the life cycle assessment method, and is formulated as:

[0025]

[0026] wherein, represents the direct emissions, which are generated by the consumption of natural gas in gas turbines and gas boilers; represents the indirect emissions, which are generated by the consumption of gray electricity; represents the carbon offset, which is achieved through green certificate trading;

[0027] The risk objective function aims to minimize the operation risk of the system, which is calculated by the conditional value at risk method, and is formulated as:

[0028]

[0029] wherein, represents the at-risk value; represents the excess loss under the scenario ; represents the occurrence probability of the scenario ; represents the confidence level; represents the total number of operating scenarios.

[0030] Further, the constraint system of the multi-objective optimization model comprises multi-energy flow balance constraints, device operation physical constraints, market transaction mechanism constraints and operation risk control constraints; the multi-energy flow balance constraints comprise power balance constraints, heat balance constraints and cold balance constraints; the device operation physical constraints comprise gas turbine operation constraints, energy storage system operation constraints and renewable energy constraints; the market transaction mechanism constraints comprise carbon transaction mechanism constraints and green certificate transaction mechanism constraints.

[0031] Further, the power balance constraints are represented as:

[0032]

[0033] wherein, , , , , respectively represent the power generation of the gas turbine, the power generation of the photovoltaic, the discharging power of the energy storage system, the green power purchased from the power grid, and the gray power purchased from the power grid; , , , , respectively represent the power load demand, the power consumption of the electric refrigerator, the power consumption of the heat pump, the charging power of the energy storage system, and the power sold to the power grid; represents that the variable value corresponds to the season and the hour ;

[0034] The heat balance constraints are represented as:

[0035]

[0036] wherein, , , respectively represent the heat generation power of the gas turbine, the gas boiler and the heat pump; , respectively represent the heat load demand and the heat consumption of the absorption refrigerator;

[0037] The cold balance constraints are represented as:

[0038]

[0039] wherein, , respectively represent the refrigeration power of the electric chiller and the absorption chiller; represents the cooling load demand;

[0040] The gas turbine operation constraints are represented as:

[0041]

[0042] wherein, represents the rated capacity of the gas turbine; represents the minimum technical output ratio of the gas turbine; , respectively represent the upper limit of the gas turbine power ramp rate up and down;

[0043] The energy storage system operation constraints are represented as:

[0044]

[0045]

[0046] wherein, represents the rated capacity of the energy storage battery; , respectively represent the minimum and maximum state of charge of the energy storage; represents the state of charge of the energy storage battery; , respectively represent the charging and discharging efficiency;

[0047] The renewable energy constraints are represented as:

[0048]

[0049] wherein, represents the theoretical maximum output of the photovoltaic system; represents the total area of the photovoltaic panels; represents the overall efficiency of the photovoltaic system;

[0050] The carbon trading mechanism constraints are represented as:

[0051]

[0052]

[0053] wherein, , respectively represent the carbon emission factor of the gas and the ash electricity. Indicates natural gas consumption; Indicates the power of purchased gray electricity; This represents the system's actual total carbon emissions; This represents the carbon trading costs incurred by the system due to exceeding carbon quotas; Indicates the price of carbon emission rights; This represents the total amount of carbon emission allowances obtained by the system;

[0054] The constraints of the green certificate trading mechanism are expressed as follows:

[0055]

[0056]

[0057] in, This indicates the amount of green certificates the system needs to process. Indicates the green certificate assessment ratio; This indicates the actual number of green certificates purchased;

[0058] The operational risk management constraints are expressed as follows:

[0059]

[0060]

[0061] in, Indicates the value at risk; Representing a scene Excess losses; Representing a scene The probability of occurrence; For the scene The system's hourly operating costs are as follows. Confidence level; This is the upper limit of the conditional risk value.

[0062] Furthermore, the scheduling scheme includes:

[0063] Start-up and shutdown status and output sequence of key equipment within the system;

[0064] The charging and discharging schedule of the energy storage system includes the charging power and discharging power of each energy storage unit at different time points;

[0065] Electricity purchase and sale strategies, including the power timing of purchasing green electricity, grey electricity, and selling electricity to the grid;

[0066] Carbon emissions trading schemes, including carbon allowance usage and excess purchase schemes;

[0067] Green electricity certificate trading scheme, including the amount of green certificates purchased;

[0068] TGC—carbon quota conversion configuration, including weight setting and conversion ratio under different transaction mechanisms.

[0069] Further, the improved - constraint method converts the multi-objective optimization model into a solvable single-objective optimization model sequence, to obtain a corresponding number of candidate scheduling schemes, specifically including:

[0070] The economic objective function in the multi-objective optimization model is taken as the main objective, and the environmental protection and risk objective functions are converted into constraint conditions, and combined with the constraint system of the multi-objective optimization model, to construct a single-objective optimization problem sequence, with the formula being:

[0071]

[0072]

[0073] wherein, , represent the upper limits of emission and risk constraints of the i-th single-objective optimization problem; represents the total number of single-objective problem sequences; represents the constraint system in the multi-objective optimization model, including multi-energy flow balance constraints, device operation physical constraints, market transaction mechanism constraints, and operation risk control constraints, represents the total number of constraint functions; is a decision variable vector, including device start-stop state, energy storage charging and discharging power, electricity purchase and sale amount, and carbon quota / green certificate transaction amount; The single-objective optimization problem is converted into a mixed integer linear programming form:

[0074]

[0075] wherein,

[0076] is a constraint coefficient matrix, is a constraint right-end vector; The single-objective optimization problem sequence is solved one by one through a commercial solver to obtain a corresponding candidate scheduling solution set.

[0077] Further, based on the candidate scheduling solution set, a fuzzy multi-attribute decision method is used for comprehensive evaluation to determine the optimal scheduling scheme, specifically including:

[0078] The candidate scheduling solution set

[0079] is ranked according to the comprehensive evaluation results. The membership degree function of each scheduling scheme to the economy target function, the environmental protection target function and the risk target function is calculated:

[0080]

[0081]

[0082]

[0083] Wherein, The economy index membership degree, the environmental protection index membership degree and the risk index membership degree are respectively represented by, , , The economy target function value, the environmental protection target function value and the risk target function value of the scheduling scheme are respectively represented by, , The maximum and minimum values of the economy target function in the candidate scheme set are respectively represented by, , The maximum and minimum values of the environmental protection target function in the candidate scheme set are respectively represented by, , The maximum and minimum values of the risk target function in the candidate scheme set are respectively represented by,

[0084] According to the preference of the decision maker, the weight coefficients of the index membership degrees are set, the weight coefficients and the index membership degrees are weighted and calculated to obtain the comprehensive satisfaction degree of each scheduling scheme, and the scheme with the highest comprehensive satisfaction degree is selected as the optimal scheduling scheme.

[0085] Compared with the prior art, the present application has the following advantages:

[0086] (1) In the comprehensive energy system scheduling of the present application, the prior art generally only processes a single market mechanism, for example, only carbon trading or only green certificate trading, and the two are independently operated, so that a linkage adjustment mechanism cannot be formed between carbon emission reduction and renewable energy utilization rate, the market incentive signals are inconsistent, and it is difficult to support the system to realize collaborative low-carbon operation. The present application introduces the carbon emission right trading mechanism and the green certificate trading mechanism into the scheduling model at the same time, and constructs a unified multi-energy flow balance and market transaction constraint system, realizes the unified modeling of carbon emission amount, renewable energy utilization amount, green certificate demand amount and transaction cost, thereby solving the problem of insufficient low-carbon scheduling optimization caused by market fragmentation, realizing collaborative optimization under the double constraints of carbon cost and green electricity incentive, and making the system low-carbon operation more continuous, controllable and quantifiable.

[0087] ​(2) The prior art does not build a complete three-target framework of economy, environmental protection and operation risk in the optimization of the comprehensive energy system, most models still stay at the single-target or double-target optimization level, cannot comprehensively weigh from multiple dimensions, and lead to the results being biased towards economy or low carbon, which is not practical enough. The present application adopts a three-target optimization function system including economy, environmental protection and operation risk, and through explicit modeling of investment cost, fuel cost, carbon emission, green certificate offset and CVaR operation risk, the scheduling results comprehensively cover multiple dimensions of cost, emission and risk, thereby solving the problem that a single target cannot meet the actual park demand, and realizing comprehensive improvement of three-dimensional balance of economy, low carbon and safety.

[0088] (3) The prior art lacks fine description of the multi-energy flow coupling relationship, most models only establish a single energy network or ignore the energy conversion coupling relationship between devices, leading to the optimization results being out of sync with the real system. The present application accurately describes the energy supply and demand matching of the multi-energy coupling system at each time node by building power, heat and cold energy flow balance constraints, and combining with the physical operation constraints of gas turbines, energy storage systems, photovoltaic, electric chillers, absorption chillers and heat pumps, thereby solving the problems of rough energy flow description and lack of engineering feasibility of the scheduling results of traditional models, realizing full coupling optimization of device output and energy flow balance, and making the scheduling scheme closer to the real system operation.

[0089] (4) In the modeling of carbon trading rules, the prior art mostly adopts a static emission calculation method, ignores the cost impact of carbon emission quota and carbon over-emission part, and does not consider the indirect emissions caused by gray electricity purchase, so that the carbon cost cannot be fully associated with the scheduling behavior. The present application unifies the accounting of gas consumption, gray electricity purchase and carbon quota usage by introducing carbon emission calculation formula, carbon trading cost formula and carbon quota constraint, thereby solving the problem of disconnection between emission data and scheduling behavior, realizing direct linkage between the scheduling scheme and carbon cost, making carbon emission explicit and cost-based, and promoting low-carbon operation. In the modeling of green certificate trading, the prior art usually only adopts a simple proportional constraint, and does not have a green certificate demand calculation formula, which does not support the linkage optimization of green certificate purchase and renewable energy output, making the green certificate trading disconnected with the system operation. The present application links photovoltaic power generation, green power purchase and green certificate demand by introducing a green certificate demand calculation formula and a green certificate purchase constraint, so that the green certificate trading and renewable energy utilization form a consistency constraint, thereby solving the problem that the green certificate incentive cannot be effectively passed to the operation level, and realizing the coordinated improvement of green certificate and renewable energy output.

[0090] (5) The prior art generally adopts the weighted summation method or the single-point search method in multi-objective optimization solving, which is difficult to generate uniformly distributed Pareto solution set, and has weak expression ability for emission and risk preference. The present application adopts an improved - constraint method, taking economy as the main target, and converting environmental protection and risk into gradually tightened - constraint, constructing a systematic single-target optimization sequence, thereby solving the problems of weak convergence and uneven distribution of solution set of traditional solving methods, realizing high-quality Pareto solution generation of decision-maker controllable preference, and providing sufficient solution set support for subsequent scheduling scheme screening. BRIEF DESCRIPTION OF DRAWINGS

[0091] Figure 1 A flow chart of the multi-energy storage system optimization scheduling method of the embodiment of the present application is shown in the figure.

[0092] Figure 2 A multi-energy storage system optimization scheduling system model diagram of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0093] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0094] Embodiment 1:

[0095] The present application proposes a multi-energy storage complementary system optimization scheduling method considering carbon emission rights and green certificate trading conversion. By constructing a multi-objective optimization framework including CA and TGC market trading, multi-enterprise internal coordination, three TGC value conversion mechanisms (physical, economic, and dynamic hybrid), and CVaR risk constraints, the coordinated optimization of the park-level integrated energy system in the three dimensions of economy, environmental protection, and risk is realized.

[0096] The embodiment specifically provides a multi-energy storage system optimization scheduling method considering carbon emission rights and green certificate trading, as shown in the figure, comprising the following steps: Figure 1

[0097] Step S1: Obtain multi-source heterogeneous operation and market data of the multi-energy storage system;

[0098] The multi-source heterogeneous operation and market data include operating performance parameters of key equipment, real-time electricity prices of the electricity market, natural gas prices, carbon emission right prices, green power certificate prices, time-series demand data, renewable energy prediction data, carbon quota allocation standards, green certificate assessment proportion, and risk preference coefficients. The key equipment includes gas turbines, energy storage devices, electric chillers, absorption chillers, and heat pumps. The time-series demand data includes demand information of power load, heat load, and cold load at different time nodes.

[0099] ​Step S2: Preprocess the multi-source heterogeneous operation and market data to obtain preprocessed multi-source heterogeneous operation and market data;

[0100] Data cleaning techniques are used to preprocess the raw data, interpolation is used to fill in missing values, statistical methods are used to identify and remove outliers, and the min-max standardization method is used to normalize the data to ensure the integrity, accuracy and consistency of the data, providing a reliable data foundation for subsequent optimization modeling.

[0101] Step S3: Based on the preprocessed multi-source heterogeneous operation and market data, construct a multi-objective optimization model that includes economic efficiency, environmental protection and operational risks, and determine the optimization objective function of the optimization model;

[0102] An optimization model incorporating three objectives—economic efficiency, environmental friendliness, and risk—is established, forming a complete mathematical description framework. The constructed three-objective optimization framework focuses on minimizing the system's total annual cost, total carbon emissions, and operational risk, resulting in the following optimization objective function:

[0103]

[0104] in, This represents the economic objective function; Represent the environmental protection objective function; This represents the risk objective function.

[0105] Construction of the economic objective function:

[0106] With the goal of minimizing the total cost over the entire system lifecycle, a complete economic objective function is constructed:

[0107]

[0108] Among them, equipment investment costs The annuity method is used for calculation, taking into account the equipment's lifespan and the time value of money; operating and maintenance costs. This includes fixed maintenance costs and variable operating costs, which are directly related to the equipment's operating status; fuel costs. Calculations based on natural gas prices and consumption, considering the thermoelectric characteristics of gas turbines and gas-fired boilers; purchased electricity costs. Differentiate between green electricity and grey electricity, and adopt a real-time electricity pricing mechanism; carbon trading costs. Calculated based on the difference between actual carbon emissions and quotas in the system, a tiered pricing mechanism is adopted; green certificate transaction costs Based on green certificate market demand and price determination; electricity sales revenue Carbon trading revenue Green certificate trading income This represents the system's potential revenue from market transactions.

[0109] Environmental objective function construction:

[0110] The total amount of system carbon emissions is minimized by using a full life cycle accounting method:

[0111]

[0112] Among them, direct emissions from natural gas combustion, indirect emissions from purchased grid gray electricity, carbon offset achieved through green certificate trading. The carbon emissions of each energy source use the latest emission factor database to ensure accuracy.

[0113] Risk objective function construction:

[0114] The system operation risk is minimized by using the conditional value at risk method:

[0115]

[0116] Among them is the value at risk, indicating the maximum possible loss at the confidence level , is the excess loss under scenario c, 0.95 is taken to control the operation risk under the extremely adverse scenario; is the occurrence probability of scenario c, determined by historical data analysis and expert evaluation.

[0117] Step S4: According to the multi-energy flow balance relationship, the physical operation requirements of equipment, and the carbon emission rights and green certificate trading rules, the constraint system of the multi-objective optimization model is defined;

[0118] The constraint system of the multi-objective optimization model includes multi-energy flow balance constraints, equipment operation physical constraints, market trading mechanism constraints, and operation risk control constraints; the multi-energy flow balance constraints include power balance constraints, heat balance constraints, and cold balance constraints; the equipment operation physical constraints include gas turbine operation constraints, energy storage system operation constraints, and renewable energy constraints; the market trading mechanism constraints include carbon trading mechanism constraints and green certificate trading mechanism constraints.

[0119] The power balance constraint is expressed as:

[0120]

[0121] Among them, , , , , Pgen, PV, Pdis, Pgreen, Pgrey , , , , Pload, Pchiller, Pheatpump, Pcharge, Pgrid Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0122] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0123] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0124] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0125] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0126] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0127] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0128] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0129] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0130] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0131] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0132] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0133] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey

[0134] Pgen, PV, Pdis, Pgreen, Pgrey Pgen, PV, Pdis, Pgreen, Pgrey , SoCmin and SoCmax represent the minimum and maximum state of charge, respectively; SoC represents the state of charge of the energy storage battery; , Charging and discharging efficiency are represented by ηch and ηd, respectively;

[0135] Renewable energy constraints are represented as:

[0136]

[0137] where, Ppvsys represents the theoretical maximum output of the photovoltaic system; A represents the total area of the photovoltaic panel; ηpvsys represents the comprehensive efficiency of the photovoltaic system;

[0138] Carbon trading mechanism constraints are represented as:

[0139]

[0140]

[0141] where, , Carbon emission factors for gas and ash electricity are represented by fCG and fCA, respectively; CNG represents the natural gas consumption; Pash represents the purchased ash electricity power; Ctot represents the total actual carbon emissions of the system; Ctrade represents the carbon trading cost generated by the system due to exceeding the carbon quota; Pc represents the carbon emission right price; Q represents the total amount of carbon emission quotas obtained by the system;

[0142] Green certificate trading mechanism constraints are represented as:

[0143]

[0144]

[0145] where, Qgreen represents the green certificate demand amount that the system needs to complete; R represents the green certificate assessment ratio; Qgreen represents the actual number of green certificates purchased.

[0146] Operational risk management constraints are represented as:

[0147]

[0148]

[0149] where, represents the at-risk value; represents the excess loss under the scenario ; represents the occurrence probability of the scenario ; represents the system hourly operating cost under the scenario ; represents the confidence level; represents the upper limit of the conditional value at risk.

[0150] Step S5: the multi-objective optimization model is converted into a series of solvable single-objective optimization models by using the improved constraint method, to obtain a plurality of candidate scheduling schemes, specifically including:

[0151] The economic objective function in the multi-objective optimization model is taken as the main objective, and the environmental protection and risk objective functions are converted into constraint conditions, and a sequence of single-objective optimization problems is constructed in combination with the constraint system of the multi-objective optimization model, and the formula is:

[0152]

[0153]

[0154] wherein, , respectively represent the emission and risk constraint upper limit of the i-th single-objective optimization problem; represents the total number of the sequence of single-objective problems; represents the constraint system in the multi-objective optimization model, including the multi-energy flow balance constraint, the device operation physical constraint, the market transaction mechanism constraint and the operation risk control constraint, represents the total number of constraint functions; is a decision variable vector, including device start-stop state, energy storage charging and discharging power, electricity purchase and sale amount and carbon quota / green certificate transaction amount; The single-objective optimization problem is converted into a mixed integer linear programming form;

[0155] The sequence of single-objective optimization problems is solved one by one through a commercial solver to obtain a set of corresponding candidate scheduling schemes.

[0156] Step S5 generates a uniformly distributed Pareto solution set in the feasible region by systematically adjusting the value combination of the emission upper limit and the risk upper limit, adopts an adaptive step adjustment strategy to ensure that the distribution density of the solution is accurately matched with the decision maker's preference, and provides complete device capacity configuration and operation strategy schemes for subsequent decision analysis.

[0157]

[0158] ​Step S6: Solve and compare the candidate scheduling schemes according to different transaction mechanisms to determine the optimal transaction mechanism for each candidate scheduling scheme, specifically including:

[0159] In terms of trading mechanism modeling, this embodiment focuses on the optimization problem under three different weight configurations. The physical trading mechanism fully follows the principle of environmental benefits, and TGCs and carbon quotas are strictly converted into equivalent values ​​according to physical emission factors; the economic trading mechanism is market-price oriented, and the conversion relationship is determined according to the price ratio of TGCs and carbon quotas; the hybrid trading mechanism seeks the best balance between environmental protection and economic benefits through configurable weight coefficients. This multi-weight mechanism design enables the model to adapt to the diverse needs of different decision-making scenarios.

[0160] For each candidate scheduling scheme obtained in step S5 According to its time period t Power purchase capacity Gas turbine output power and renewable energy output The actual carbon emissions of this plan can be calculated using the following formula:

[0161]

[0162]

[0163] in, Indicates the time period t The actual carbon emissions of the candidate scheduling scheme; Indicates the entire scheduling cycle Total carbon emissions within the region; Indicates the total duration of the scheduling cycle; , , , , , Preset weighting coefficients;

[0164] The same candidate scheme was substituted into three different carbon emission rights and green certificate conversion mechanisms to calculate the equivalent carbon offset.

[0165] Firstly, under the physical trading mechanism, the number of green certificates is determined based on the physical output of renewable energy:

[0166]

[0167]

[0168] in, Indicates time period t The number of green certificates obtained under the physical transaction mechanism; is the physical conversion factor of green certificate, i.e. how many green certificates can be converted from per kilowatt-hour of renewable energy power generation; represents the carbon offset amount through green certificate under the physical equivalence mechanism; is the carbon emission offset amount per unit green certificate.

[0169] Secondly, under the economic transaction mechanism, the conversion is carried out according to the ratio of green certificate to carbon market price:

[0170]

[0171]

[0172] wherein, represents the time period t is the price of green certificate market; represents the time period t is the price of carbon emission market; is the carbon emission conversion factor under the economic equivalence mechanism; represents the carbon offset amount through green certificate under the economic transaction mechanism.

[0173] Thirdly, under the hybrid transaction mechanism, the dynamic conversion factor is formed by considering the weight of physical mechanism and economic mechanism:

[0174]

[0175]

[0176]

[0177] wherein, represents the time period t is the conversion factor under the hybrid transaction mechanism of the time period is the weight coefficient; represents the time period t is the green certificate quantity calculated under the hybrid transaction mechanism; represents the carbon emission amount after carbon quota offset through the hybrid transaction mechanism;

[0178] Based on the carbon offset amount obtained according to the three mechanisms, the corresponding carbon transaction cost is calculated:

[0179]

[0180]

[0181]

[0182] wherein, , , respectively represent the carbon trading cost under the physical trading mechanism, the economic trading mechanism and the hybrid trading mechanism; is the unit price of the carbon emission right market; represents the carbon emission quota of the system.

[0183] Among all the candidate scheduling schemes, the economic objective function value of each scheme under the physical, economic and hybrid trading mechanisms is calculated respectively, and the trading mechanism with the minimum economic objective function value is selected.

[0184] Step S7: Based on the candidate scheduling solution set, a fuzzy multi-attribute decision method is used for comprehensive evaluation to determine the optimal scheduling scheme. On the basis of obtaining the Pareto solution set, the improved fuzzy multi-attribute decision method is used for optimal solution screening. The satisfaction degree of each target is quantitatively evaluated by constructing an exponential membership function, and the performance in the three dimensions of economy, environmental protection and risk is comprehensively considered. The geometric weighted average method is used to calculate the comprehensive satisfaction index, and the weight coefficient is flexibly adjusted according to the risk preference and policy requirements of the decision maker, so as to identify the most valuable compromise scheme from the numerous non-dominated solutions, and ensure that the optimal solution finally selected is balanced and optimal in multiple aspects such as technology, economy and environment. Based on the optimal solution obtained by screening, the model outputs a complete implementation scheme system. The systematic output content includes the optimal capacity configuration scheme of each device, the annual hourly operation strategy, the market trading plan and the risk assessment report. Specifically, the capacity configuration scheme specifies the construction scale of key devices such as gas turbine, energy storage system and photovoltaic panel area; the operation strategy provides the power allocation scheme of electricity, heat and cold under a typical day; the trading plan specifies the best trading time and quantity of carbon quota and green certificate in detail; and the risk assessment report quantitatively analyzes the potential risks of system operation and gives the corresponding control measures.

[0185] Embodiment 2:

[0186] The embodiment provides a multi-energy energy storage system optimal scheduling system considering carbon emission right and green certificate trading, as shown in Figure 2 , which comprises:

[0187] A data acquisition module is configured to acquire basic operation data of the park integrated energy system, including electric, heat and cold load data, renewable energy output data, gas price, electricity price, carbon price and green certificate price data, and carbon emission factor, device performance data and random scene data.

[0188] A multi-energy flow modeling module is configured to build a multi-energy flow coupling model of electricity, heat and cold, form electricity balance constraints, heat balance constraints and cold balance constraints, and establish operation physical constraints of devices such as gas turbine, energy storage, electric refrigerator, absorption refrigerator, heat pump and renewable energy.

[0189] A market transaction mechanism modeling module is configured to establish carbon emission right transaction constraints and green certificate transaction constraints, and support modeling of physical conversion mechanisms, economic conversion mechanisms and hybrid weight conversion mechanisms.

[0190] A risk assessment and risk control module is configured to generate operation risk indicators based on random scenarios, and construct conditional value at risk (CVaR) constraints to form an operation risk control constraint system.

[0191] A multi-objective optimization modeling module is configured to construct a multi-objective optimization model including economic cost targets, carbon emission targets and operation risk targets, and integrate multi-energy flow balance constraints, device operation physical constraints, market transaction mechanism constraints and operation risk control constraints.

[0192] A constraint solving module is configured to use an improved constraint method to take economic targets as main targets, convert carbon emission and operation risk targets into constraints, generate multiple groups of feasible single-objective optimization models and obtain a Pareto candidate scheduling solution set.

[0193] An optimization solver module is configured to solve the single-objective optimization models, convert the models into mixed integer programming forms, and calculate device outputs, energy flow distributions and transaction amounts corresponding to each candidate scheduling scheme by using a solver.

[0194] A comprehensive evaluation and decision module is configured to comprehensively evaluate the candidate scheduling schemes based on a fuzzy multi-attribute decision method, and determine a final optimal scheduling scheme.

[0195] A scheduling instruction output module is configured to generate operation instructions of electric, thermal, cold devices and energy storage units corresponding to the optimal scheduling scheme, and send the operation instructions to an integrated energy system execution layer for implementation.

[0196] If the above functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or parts of the present application that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0197] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the scheduling of multi-energy storage systems considering carbon emission rights and green certificate trading, characterized in that, Includes the following steps: Acquire multi-source heterogeneous operation and market data for multi-energy storage systems; The multi-source heterogeneous operation and market data are preprocessed to obtain preprocessed multi-source heterogeneous operation and market data. Based on the preprocessed multi-source heterogeneous operation and market data, a multi-objective optimization model that includes economic efficiency, environmental protection and operational risks is constructed, and the optimization objective function of the optimization model is determined. The objective function of the optimization model is given by the following formula: in, This represents the economic objective function; Represent the environmental protection objective function; Represents the risk objective function; The economic objective function aims to minimize the total cost over the entire lifecycle of the system, and its formula is as follows: in, This represents the investment cost of key equipment, calculated using the annuity method, taking into account the equipment's lifespan and the time value of money. This indicates the operating and maintenance costs of key equipment, including fixed maintenance costs and variable operating costs; This represents fuel cost, calculated based on natural gas prices and the consumption of gas turbines and gas boilers. It indicates the cost of purchased electricity, based on real-time electricity market prices, and distinguishes between green electricity and grey electricity; The carbon trading cost is calculated based on the difference between the carbon emission rights price and the actual carbon emissions and carbon quotas in the system. The cost of green certificate transactions is determined based on the price of green electricity certificates, the amount of renewable energy generated by the system, and the green certificate assessment ratio. Represents revenue from electricity sales; Indicates carbon trading revenue; Indicates the proceeds from green certificate transactions; The environmental protection objective function aims to minimize the total carbon emissions of the system and adopts a life-cycle accounting method. The formula is as follows: in, This represents direct emissions, generated by the consumption of natural gas in gas turbines and gas boilers; This indicates indirect emissions, generated from the consumption of purchased gray electricity; This represents the amount of carbon offset, achieved through green certificate trading; The aforementioned risk objective function aims to minimize the system's operational risk and employs the conditional value at risk (VaR) approach, with the following formula: in, Indicates the value at risk; Representing a scene Excess losses; Representing a scene The probability of occurrence; Indicates the confidence level; This represents the total number of running scenarios; Based on the multi-energy flow balance relationship, equipment physical operation requirements, and carbon emission rights and green certificate trading rules, the constraint system of the multi-objective optimization model is defined. Adopting improved - The constraint method transforms the multi-objective optimization model into a solvable single-objective optimization model sequence, thereby obtaining several corresponding candidate scheduling schemes; The candidate scheduling schemes are solved and compared according to different transaction mechanisms to determine the optimal transaction mechanism for each candidate scheduling scheme. Based on the candidate scheduling schemes, a fuzzy multi-attribute decision-making method is used for comprehensive evaluation to determine the optimal scheduling scheme, specifically including: For the candidate scheduling scheme For each scheduling scheme, calculate its membership function with respect to the economic objective function, the environmental objective function, and the risk objective function: in, These represent the membership degrees of economic indicators, environmental indicators, and risk indicators, respectively. , , These represent the scheduling schemes respectively. The economic objective function value, the environmental objective function value, and the risk objective function value; , Let represent the maximum and minimum values ​​of the economic objective function in the candidate solution set, respectively; , Let represent the maximum and minimum values ​​of the environmental objective function in the candidate solution set, respectively; , Let represent the maximum and minimum values ​​of the risk objective function in the candidate solution set, respectively; Based on the decision-maker's preferences, weight coefficients are set for the membership degree of each indicator. The weight coefficients are then weighted and calculated with the membership degree of each indicator to obtain the overall satisfaction of each scheduling scheme. The scheme with the highest overall satisfaction is selected as the optimal scheduling scheme.

2. The method for optimizing the scheduling of a multi-energy storage system considering carbon emission rights and green certificate trading as described in claim 1, characterized in that, The multi-source heterogeneous operation and market data includes the operating performance parameters of key equipment, real-time electricity prices in the electricity market, natural gas prices, carbon emission rights prices, green electricity certificate prices, time-series demand data, renewable energy forecast data, carbon quota allocation standards, green certificate assessment ratios, and risk preference coefficients; the key equipment includes gas turbines, energy storage devices, electric chillers, absorption chillers, and heat pumps; the time-series demand data includes demand information for electricity load, heat load, and cooling load at different time points.

3. The method for optimizing the scheduling of a multi-energy storage system considering carbon emission rights and green certificate trading as described in claim 1, characterized in that, The preprocessing of the multi-source heterogeneous operation and market data specifically includes: cleaning the multi-source heterogeneous operation and market data; interpolating and filling missing values ​​in the data; identifying and removing outliers; and performing min-max standardization on the data.

4. The method for optimizing the scheduling of a multi-energy storage system considering carbon emission rights and green certificate trading as described in claim 1, characterized in that, The constraint system of the multi-objective optimization model includes multi-energy flow balance constraints, equipment operation physical constraints, market trading mechanism constraints, and operation risk management constraints; the multi-energy flow balance constraints include power balance constraints, thermal balance constraints, and cooling balance constraints; the equipment operation physical constraints include gas turbine operation constraints, energy storage system operation constraints, and renewable energy constraints; the market trading mechanism constraints include carbon trading mechanism constraints and green certificate trading mechanism constraints.

5. The method for optimizing the scheduling of a multi-energy storage system considering carbon emission rights and green certificate trading as described in claim 4, characterized in that, The power balance constraint is expressed as: in, , , , , These represent the power generation capacity of the gas turbine, the power generation capacity of the photovoltaic system, the discharge capacity of the energy storage system, the green electricity purchased from the grid, and the gray electricity purchased from the grid, respectively. , , , , These represent the electricity load demand, the power consumption of the electric chiller, the power consumption of the heat pump, the charging power of the energy storage system, and the power sold to the grid, respectively. This indicates that the variable's value corresponds to the season. and hours ; The thermodynamic balance constraint is expressed as: in, , , These represent the heat production capacity of the gas turbine, gas boiler, and heat pump, respectively. , These represent the heat load demand and the heat consumption power of the absorption chiller, respectively. The cooling balance constraint is expressed as: in, , These represent the cooling power of the electric chiller and the absorption chiller, respectively. Indicates cooling load demand; The gas turbine operating constraints are expressed as follows: in, Indicates the rated capacity of the gas turbine; This indicates the minimum technical output ratio of the gas turbine; , These represent the upper and lower ramp rates of gas turbine power, respectively. The operating constraints of the energy storage system are expressed as follows: in, Indicates the rated capacity of the energy storage battery; , These represent the minimum and maximum states of charge of energy storage, respectively; Indicates the state of charge of the energy storage battery; , These represent the charging and discharging efficiencies, respectively. The renewable energy constraint is expressed as: in, This represents the theoretical maximum output of the photovoltaic system; Indicates the total area of ​​the photovoltaic panels; Indicates the overall efficiency of the photovoltaic system; The constraints of the carbon trading mechanism are expressed as follows: in, , These represent the carbon emission factors for natural gas and ash electricity, respectively. Indicates natural gas consumption; Indicates the power of purchased gray electricity; This represents the system's actual total carbon emissions; This represents the carbon trading costs incurred by the system due to exceeding carbon quotas; Indicates the price of carbon emission rights; This represents the total amount of carbon emission allowances obtained by the system; The constraints of the green certificate trading mechanism are expressed as follows: in, This indicates the amount of green certificates the system needs to process. Indicates the green certificate assessment ratio; This indicates the actual number of green certificates purchased; The operational risk management constraints are expressed as follows: in, Indicates the value at risk; Representing a scene Excess losses; Representing a scene The probability of occurrence; For the scene The system's hourly operating costs are as follows. Confidence level; This is the upper limit of the conditional risk value.

6. The method for optimizing the scheduling of a multi-energy storage system considering carbon emission rights and green certificate trading as described in claim 1, characterized in that, The scheduling scheme includes: Start-up and shutdown status and output sequence of key equipment within the system; The charging and discharging schedule of the energy storage system includes the charging power and discharging power of each energy storage unit at different time points; Electricity purchase and sale strategies, including the power timing of purchasing green electricity, grey electricity, and selling electricity to the grid; Carbon emissions trading schemes, including carbon allowance usage and excess purchase schemes; Green electricity certificate trading scheme, including the amount of green certificates purchased; TGC—Carbon Quota Conversion Configuration, including weight settings and conversion ratios under different trading mechanisms.

7. The method for optimizing the scheduling of a multi-energy storage system considering carbon emission rights and green certificate trading as described in claim 1, characterized in that, The improved - The constraint method transforms the multi-objective optimization model into a sequence of solvable single-objective optimization models, obtaining several corresponding candidate scheduling schemes, specifically including: The economic objective function in the multi-objective optimization model As the primary objective, environmental protection and risk are considered in the objective function. Transformed into constraints, and combined with the constraint system of the multi-objective optimization model, a sequence of single-objective optimization problems is constructed, as shown in the formula: in, , They represent the first Upper limits of emission and risk constraints for a single-objective optimization problem; This represents the total number of sequences of single-objective problems; This represents the constraint system in a multi-objective optimization model, including multi-energy flow balance constraints, physical constraints on equipment operation, market transaction mechanism constraints, and operational risk management constraints. This indicates the total number of constraint functions; The decision variable vector includes equipment start / stop status, energy storage charging and discharging power, electricity purchased and sold, and carbon quota / green certificate trading volume; The single-objective optimization problem is transformed into a mixed-integer linear programming form: in, The constraint coefficient matrix, To constrain the right-hand vector; The sequence of single-objective optimization problems is solved one by one using a commercial solver to obtain a set of corresponding candidate scheduling schemes.

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