Optimized operation method and system considering carbon transaction between power generation side and taxi operator

Abstract

The invention discloses an optimized operation method and system considering carbon transaction between a power generation side and a taxi operator, and the method comprises the steps: firstly constructing a double-layer optimization model, then reconstructing the double-layer optimization model into a single-layer optimization problem through employing a KKT condition, and solving an optimized operation scheme of the power generation side and the taxi operator; the optimization target of the outer-layer optimization model is to maximize the income of the power generation side, and the income of the power generation side comprises the power generation and sale income of the power generation side, the power generation cost of the thermal power plant and the centralized photovoltaic power station, the operation cost of the centralized energy storage power station and the carbon emission cost of the power generation side; the optimization target of the inner-layer optimization model is to minimize the operation cost of a taxi operator consisting of an electric taxi and a fuel taxi, and the operation cost of the taxi operator comprises the electricity purchasing cost, the fuel cost and the carbon emission cost of the taxi operator. The power generation side profit can be increased, the operation cost of a taxi operator is reduced, and meanwhile the carbon emission of the whole system is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization operation, and specifically relates to an optimization operation method and system taking into account carbon trading between a power generation side and a taxi operator. Background Art

[0002] In recent years, the rapid development of electric vehicles has brought significant environmental benefits to the transportation sector. As an application example, electric taxis have the advantages of reducing carbon emissions, saving energy costs and reducing noise pollution. Compared with traditional fuel taxis, electric taxis perform better in reducing greenhouse gas emissions, especially in high-frequency urban operating environments, and can significantly reduce carbon emissions per kilometer.

[0003] At the same time, the restrictions on carbon emission quotas are gradually restricting the use of traditional thermal power generation, further promoting the transformation of the power system to clean energy. Traditional thermal power plants have high carbon emissions due to the combustion of fossil fuels, and it is difficult to maintain high-load operation under strict carbon emission quotas. With the advancement of carbon trading and carbon emission restrictions, the power generation system needs to reduce its dependence on high-emission thermal power by increasing the proportion of photovoltaic and energy storage power stations. The carbon trading mechanism enables low-carbon power suppliers to obtain income by selling carbon emission rights, further stimulating the use of clean energy.

[0004] In view of the above background, how to optimize the power generation operation mode and the operation model of taxi operators, while increasing the profit of the power generation side and reducing the operating costs of taxi operators, and reducing the carbon emissions of the entire system, is an urgent problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to address the above-mentioned problems existing in the prior art and to provide an optimized operation method and system that takes into account carbon trading between the power generation side and taxi operators, which can increase the profit of the power generation side, reduce the operating costs of taxi operators, and reduce the carbon emissions of the entire system.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention proposes an optimized operation method taking into account carbon trading between the power generation side and the taxi operator, the optimized operation method comprising:

[0008] S1. Construct a two-layer optimization model, which includes an outer optimization model and an inner optimization model. The optimization goal of the outer optimization model is to maximize the income of the power generation side consisting of thermal power plants, centralized photovoltaic power stations and centralized energy storage power stations. The income of the power generation side includes the power generation and sales income of the power generation side, the power generation cost of thermal power plants and centralized photovoltaic power stations, the operating cost of centralized energy storage power stations, and the carbon emission cost of the power generation side. The optimization goal of the inner optimization model is to minimize the operating cost of taxi operators consisting of electric taxis and fuel taxis. The operating cost of taxi operators includes the electricity purchase cost, fuel cost, and carbon emission cost of taxi operators.

[0009] S2. Use KKT conditions to reconstruct the double-layer optimization model into a single-layer optimization problem and then solve it to obtain the optimized operation plan for the power generation side and the optimized operation plan for the taxi operator.

[0010] The objective function of the outer optimization model is:

[0011] max I E -F P -F S -F C ;

[0012]

[0013] In the above formula, i E The income from electricity sales on the power generation side; F P is the power generation cost of thermal power plants and centralized photovoltaic power plants; F S is the operating cost of the centralized energy storage power station; F C is the carbon emission cost on the power generation side; is the output power of the thermal power plant in period t; is the output power of the centralized photovoltaic power station in time period t; is the power delivered from the power generation side to the charging station in the distribution network area e during time period t; are the charging energy and discharging energy of the centralized energy storage power station in time period t; PR V is the unit operating cost of a centralized photovoltaic power station; is the electricity price in period t; PR P is the unit operating cost of thermal power plants; PR RS The unit cost of charging and discharging of a centralized energy storage power station; PR C is the unit carbon emission cost; The carbon sink trading electricity consumed by the distribution network area e during period t is obtained by using carbon trading methods to exchange carbon emission quotas; C is the carbon emission caused by the power generation of the thermal power plant in period t; Tis the carbon emission quota of thermal power plants; T U As unit time.

[0014] The thermal power generation constraints include:

[0015]

[0016]

[0017] In the above formula, are the binary operating state variables of the thermal power plant at time periods t and t-1 respectively; is the binary startup operation variable of the thermal power plant at time period t; is the binary shutdown operation variable of the thermal power plant at time period t; N OM 、N SM are the upper limits of the number of startup and shutdown operations of a thermal power plant in a typical day; δ M is the large M constant; PA L ,PA H They are the lower and upper limits of the output of thermal power plants respectively; is the output power of the thermal power plant in period t-1; AP L , ΔP H are the upper limits of the down-slope and up-slope rates of the thermal power plant output respectively; C MT The upper limit of the daily carbon emission quota on the power generation side;

[0018] The centralized energy storage operation constraints include:

[0019]

[0020] In the above formula, is the operating power of the centralized energy storage power station during period t; EC O is the initial capacity of centralized energy storage; EC E is the rated capacity of centralized energy storage; η L , η H are the lower and upper limits of the energy state of the centralized energy storage, respectively; tn is any period in a typical day; is the operating state variable of the centralized energy storage power station; P SM The upper limit of the operating power of the centralized energy storage power station;

[0021] The centralized photovoltaic power generation constraints include:

[0022]

[0023] In the above formula, is the maximum output of the centralized photovoltaic system in the time period;

[0024] The power balance constraints include:

[0025]

[0026] In the above formula, It is the power transmitted from the generation side to the distribution network area e during time period t.

[0027] The objective function of the inner optimization model is:

[0028] f E +F G +F D ;

[0029]

[0030] In the above formula, F E is the electricity purchase cost of the taxi operator; F G is the fuel cost of taxi operators; F D Carbon emission costs for taxi operators; V is the service volume completed by the fuel taxi in time period t; G Gasoline consumed for a single service of a fuel taxi; PR G is the unit fuel price; is the carbon emissions caused by fuel taxis in period t.

[0031] The distribution network area constraints include:

[0032]

[0033] In the above formula, is the basic load of distribution network section e in time period t; is the transformer capacity of the distribution network area e;

[0034] The taxi service constraints include:

[0035]

[0036] In the above formula, is the total taxi service demand in time period t; is the service volume completed by the electric taxi in time period t; X E The electric energy consumed by a single electric taxi service; C UG Carbon emissions generated by a single fuel taxi service; C MV A daily carbon emission quota cap for taxi operators;

[0037] The carbon trading constraints include:

[0038]

[0039] In the above formula, ε T is the trading coefficient of carbon emission and electricity; is the determination coefficient for the trough period.

[0040] In a second aspect, the present invention proposes an optimized operation system taking into account carbon trading between the power generation side and the taxi operator, the optimized operation system comprising:

[0041] A model building module, for building a two-layer optimization model, the two-layer optimization model includes an outer optimization model and an inner optimization model, the optimization goal of the outer optimization model is to maximize the income of the power generation side consisting of thermal power plants, centralized photovoltaic power stations and centralized energy storage power stations, the income of the power generation side includes the power generation and sales income of the power generation side, the power generation cost of thermal power plants and centralized photovoltaic power stations, the operating cost of centralized energy storage power stations, and the carbon emission cost of the power generation side, the optimization goal of the inner optimization model is to minimize the operating cost of taxi operators consisting of electric taxis and fuel taxis, the operating cost of taxi operators includes the electricity purchase cost, fuel cost, and carbon emission cost of taxi operators;

[0042] The simulation calculation module is used to reconstruct the double-layer optimization model into a single-layer optimization problem by adopting KKT conditions and then solve it to obtain the optimized operation plan for the power generation side and the optimized operation plan for the taxi operator.

[0043] The objective function of the outer optimization model is:

[0044] max I E -F P -F S -F C ;

[0045]

[0046] In the above formula, I E The income from electricity sales on the power generation side; F P is the power generation cost of thermal power plants and centralized photovoltaic power plants; F S is the operating cost of the centralized energy storage power station; F C is the carbon emission cost on the power generation side; is the output power of the thermal power plant in period t; is the output power of the centralized photovoltaic power station in time period t; is the power delivered from the power generation side to the charging station in the distribution network area e during time period t; are the charging energy and discharging energy of the centralized energy storage power station in time period t; PR V is the unit operating cost of a centralized photovoltaic power station; is the electricity price in period t; PR Pis the unit operating cost of thermal power plants; PR RS The unit cost of charging and discharging of a centralized energy storage power station; PR C is the unit carbon emission cost; The carbon sink trading electricity consumed by the distribution network area e during period t is obtained by using carbon trading methods to exchange carbon emission quotas; C is the carbon emission caused by the power generation of the thermal power plant in period t; T is the carbon emission quota of thermal power plants; T U As unit time.

[0047] The thermal power generation constraints include:

[0048]

[0049] In the above formula, are the binary operating state variables of the thermal power plant at time periods t and t-1 respectively; is the binary startup operation variable of the thermal power plant at time period t; is the binary shutdown operation variable of the thermal power plant at time period t; N OM 、N SM are the upper limits of the number of startup and shutdown operations of a thermal power plant in a typical day; δ M is the large M constant; PA L ,PA H They are the lower and upper limits of the output of thermal power plants respectively; is the output power of the thermal power plant in period t-1; ΔP L , ΔP H are the upper limits of the down-slope and up-slope rates of the thermal power plant output respectively; C MT The upper limit of the daily carbon emission quota on the power generation side;

[0050] The centralized energy storage operation constraints include:

[0051]

[0052] In the above formula, is the operating power of the centralized energy storage power station during period t; EC O is the initial capacity of centralized energy storage; EC E is the rated capacity of centralized energy storage; η L , η H are the lower and upper limits of the energy state of the centralized energy storage, respectively; tn is any period in a typical day; is the operating state variable of the centralized energy storage power station; P SM The upper limit of the operating power of the centralized energy storage power station;

[0053] The centralized photovoltaic power generation constraints include:

[0054]

[0055] In the above formula, is the maximum output of the centralized photovoltaic system in time period t;

[0056] The power balance constraints include:

[0057]

[0058] In the above formula, It is the power transmitted from the generation side to the distribution network area e during time period t.

[0059] The objective function of the inner optimization model is:

[0060] min F E +F G +F D ;

[0061]

[0062] In the above formula, F E is the electricity purchase cost of the taxi operator; F G is the fuel cost of taxi operators; F D Carbon emission costs for taxi operators; V is the service volume completed by the fuel taxi in time period t; G Gasoline consumed for a single service of a fuel taxi; PR G is the unit fuel price; is the carbon emissions caused by fuel taxis in period t.

[0063] The distribution network area constraints include:

[0064]

[0065] In the above formula, is the basic load of distribution network section e in time period t; is the transformer capacity of the distribution network area e;

[0066] The taxi service constraints include:

[0067]

[0068] In the above formula, is the total taxi service demand in time period t; is the service volume completed by the electric taxi in time period t; X E The electric energy consumed by a single electric taxi service; C UGCarbon emissions generated by a single fuel taxi service; C MV A daily carbon emission quota cap for taxi operators;

[0069] The carbon trading constraints include:

[0070]

[0071] In the above formula, ε T is the trading coefficient of carbon emission and electricity; is the determination coefficient for the trough period.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] The optimized operation method taking into account the carbon trading between the power generation side and the taxi operator described in the present invention first constructs a double-layer optimization model, and then uses the KKT condition to reconstruct the double-layer optimization model into a single-layer optimization problem and then solves it to obtain the optimized operation plan of the power generation side and the optimized operation plan of the taxi operator; in the double-layer optimization model, the optimization goal of the outer optimization model is to maximize the income of the power generation side composed of thermal power plants, centralized photovoltaic power stations and centralized energy storage power stations. The income of the power generation side includes the power generation and sales income of the power generation side, the power generation cost of the thermal power plant and the centralized photovoltaic power station, the operation cost of the centralized energy storage power station, and the carbon emission cost of the power generation side. The optimization goal of the inner optimization model is to minimize the operation cost of the taxi operator composed of electric taxis and fuel taxis. The operation cost of the taxi operator includes the power purchase cost, fuel cost, and carbon emission cost of the taxi operator. The optimized operation plan of the power generation side and the optimized operation plan of the taxi operator obtained by solving the double-layer optimization model can reduce the carbon emissions of the entire system while increasing the profit of the power generation side and reducing the operation cost of the taxi operator. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The present invention is a flowchart of the optimization operation method.

[0075] Figure 2 Schematic diagram of the optimization object of the optimization operation method described in the present invention.

[0076] Figure 3 Taxi service demand curve used for simulation calculation

[0077] Figure 4 The time-of-use electricity price used in the simulation calculation.

[0078] Figure 5 This is the photovoltaic output characteristic curve used in simulation calculations.

[0079] Figure 6 It is a structural block diagram of the optimization operation system described in the present invention. DETAILED DESCRIPTION

[0080] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0081] like Figure 2 As shown, the optimization object faced by the present invention takes into account the two interest subjects of the power generation side and the taxi operator, and establishes a two-layer optimization model with the charging fee of electric taxis and the carbon emission rights trading as the main interactive mechanism. The power generation side considers the power structure mainly composed of thermal power plants, centralized photovoltaic power stations and centralized energy storage power stations, and the taxi operator level considers the two main aggregation elements of electric taxis and traditional fuel taxis. During the operation process, taxi operators can reduce operating costs and carbon emissions by optimizing the number of electric taxis, optimizing service task allocation and vehicle charging plans, and can use the remaining carbon emission quotas as carbon sinks to exchange for valley charging power with the power generation side. The power generation side can improve the overall profit by optimizing the power generation operation mode and using the carbon emission quotas obtained by the valley power supply. For the system composed of the power generation side and the taxi operator, the optimization operation method described in the present invention can reduce the carbon emissions of the entire system while improving the profit of the power generation side and reducing the operating costs of the taxi operator.

[0082] Embodiment 1:

[0083] See also Figure 1 , an optimized operation method taking into account carbon trading between the power generation side and the taxi operator, specifically follows the following steps:

[0084] S1. Constructing a two-layer optimization model

[0085] The two-layer optimization model includes an outer optimization model and an inner optimization model. The optimization goal of the outer optimization model is to maximize the income of the power generation side consisting of a thermal power plant, a centralized photovoltaic power station and a centralized energy storage power station. The income of the power generation side includes the power generation and sales income of the power generation side, the power generation cost of the thermal power plant and the centralized photovoltaic power station, the operation cost of the centralized energy storage power station, and the carbon emission cost of the power generation side. The objective function of the outer optimization model is specifically:

[0086] max I E -F P -F S -F C ;

[0087]

[0088] In the above formula, i E The income from electricity sales on the power generation side; F P is the power generation cost of thermal power plants and centralized photovoltaic power plants; FS is the operating cost of the centralized energy storage power station; F C is the carbon emission cost on the power generation side; is the output power of the thermal power plant in period t; is the output power of the centralized photovoltaic power station in time period t; is the power delivered from the power generation side to the charging station in the distribution network area e during time period t; are the charging energy and discharging energy of the centralized energy storage power station in time period t respectively; P RV is the unit operating cost of a centralized photovoltaic power station; is the electricity price in period t; PR P is the unit operating cost of thermal power plants; PR RS The unit cost of charging and discharging of a centralized energy storage power station; PR C is the unit carbon emission cost; The carbon sink trading electricity consumed by the distribution network area e during period t is obtained by using carbon trading methods to exchange carbon emission quotas; C is the carbon emission caused by the power generation of the thermal power plant in period t; T is the carbon emission quota of thermal power plants; T U is the unit time;

[0089] The constraints of the outer optimization model include thermal power generation constraints, centralized photovoltaic power generation constraints, centralized energy storage operation constraints, and power balance constraints; wherein the thermal power generation constraints include:

[0090]

[0091]

[0092] In the above formula, is the binary operating state variable of the thermal power plant at time period t and t-1. When When , it indicates that the thermal power plant is in shutdown state; is the binary operating state variable of the thermal power plant at time period t-1; is the binary startup operation variable of the thermal power plant at time period t, When When it is on, it means that the thermal power plant does not perform the startup operation; is the binary shutdown operation variable of the thermal power plant at time period t, When When N OM 、N SMare the upper limits of the number of startup and shutdown operations of a thermal power plant in a typical day; δ M is the large M constant; PA L ,PA H They are the lower and upper limits of the output of thermal power plants respectively; is the output power of the thermal power plant in period t-1; ΔP L , ΔP H are the upper limits of the down-slope and up-slope rates of the thermal power plant output respectively; C MT The upper limit of the daily carbon emission quota on the power generation side;

[0093] The centralized energy storage operation constraints include:

[0094]

[0095] In the above formula, is the operating power of the centralized energy storage power station during period t. It indicates that the centralized energy storage power station is in the discharging state. When the centralized energy storage power station is in the energy storage state; O is the initial capacity of centralized energy storage; EC E is the rated capacity of centralized energy storage; η L , η H are the lower and upper limits of the energy state of the centralized energy storage, respectively; tn is any period in a typical day; is the operating state variable of the centralized energy storage power station. When the centralized energy storage power station is operating in the discharge state, When P is SM The upper limit of the operating power of the centralized energy storage power station;

[0096] The centralized photovoltaic power generation constraints include:

[0097]

[0098] In the above formula, is the maximum output of the centralized photovoltaic system in time period t;

[0099] The power balance constraints include:

[0100]

[0101] In the above formula, is the power transmitted from the generation side to the distribution network area e during time period t;

[0102] Since there are bilinear terms in the outer optimization model To facilitate the solution, the McCormick envelope method is used to relax the relevant constraints. The following are the constraints newly introduced by the McCormick envelope method:

[0103]

[0104] In the above formula, Y t Auxiliary variables introduced to utilize McCormick envelope method;

[0105] The optimization goal of the inner optimization model is to minimize the operating cost of the taxi operator consisting of electric taxis and fuel taxis. The operating cost of the taxi operator includes the electricity purchase cost, fuel cost, and carbon emission cost of the taxi operator. The objective function of the inner optimization model is specifically:

[0106] f E +F G +F D ;

[0107]

[0108]

[0109] In the above formula, F E is the electricity purchase cost of the taxi operator; F G is the fuel cost of taxi operators; F D Carbon emission costs for taxi operators; V is the service volume completed by the fuel taxi in time period t; G Gasoline consumed for a single service of a fuel taxi; PR G is the unit fuel price; is the carbon emissions caused by fuel taxis in period t;

[0110] The constraints of the inner optimization model include distribution network area constraints, taxi service constraints, and carbon trading constraints; wherein the distribution network area constraints include:

[0111]

[0112] In the above formula, is the basic load of distribution network section e in time period t; is the transformer capacity of the distribution network area e;

[0113] The taxi service constraints include total service constraints, electric taxi energy demand constraints, and fuel taxi carbon emission constraints. The total service constraints are:

[0114]

[0115] The energy demand constraint of electric taxi is:

[0116]

[0117] The carbon emission constraints for fuel taxis are:

[0118]

[0119] In the above formula, is the total taxi service demand in time period t; is the service volume completed by the electric taxi in time period t; X E The electric energy consumed by a single electric taxi service; C UG Carbon emissions generated by a single fuel taxi service; C MV A daily carbon emission quota cap for taxi operators;

[0120] The carbon trading constraints limit the amount of electricity used for carbon trading in any period to not exceed the actual power generation of the centralized photovoltaic power station in that period, and limit the amount of electricity used for carbon trading to be used only during off-peak periods; the carbon trading constraints include:

[0121]

[0122]

[0123] In the above formula, ε T is the trading coefficient of carbon emission and electricity; is the binary determination coefficient of the trough period. When t is the off-peak period, When means time period t is not a low-peak electricity consumption period;

[0124] S2. Use KKT conditions to reconstruct the double-layer optimization model into a single-layer optimization problem and then solve it to obtain the optimized operation plan for the power generation side and the optimized operation plan for the taxi operator;

[0125] The variables of the inner optimization model are and The KKT condition is used to reconstruct the two-layer optimization model into a single-layer optimization problem. Specifically, the dual feasible conditions and complementary relaxation conditions are introduced into the constraints of the inner optimization model. The dual feasible conditions include:

[0126]

[0127] In the above formula, All are Lagrange multipliers;

[0128] The complementary relaxation conditions include:

[0129]

[0130]

[0131] By linearizing the complementary relaxation conditions, the constraints of the reconstructed inner optimization model include:

[0132]

[0133] In the above formula, is the large M constant used in the reconstruction phase; All are binary auxiliary variables.

[0134] Performance Verification:

[0135] In order to verify the optimized operation method proposed in the present invention that takes into account carbon trading between the power generation side and the taxi operator (referred to as Method 1), an optimized operation method that does not consider carbon trading (referred to as Method 2) is used for comparison with Method 1.

[0136] The taxi service demand curve, time-of-use electricity price and photovoltaic output characteristic curve used in the simulation example are as follows: Figure 3 , Figure 4 , Figure 5 Parameter settings: The unit operating cost of a centralized photovoltaic power station is 0.3 yuan per kilowatt-hour; the unit operating cost of a thermal power plant is 0.6 yuan per kilowatt-hour; the unit cost of charging and discharging of a centralized energy storage power station is 0.5 yuan per kilowatt-hour; the unit carbon emission cost is 400 yuan per ton of carbon dioxide; the unit time is 1 hour; the daily carbon emission quota for carbon trading is 300 tons; the carbon trading coefficient is 400 kilowatt-hours per ton of carbon dioxide; the electric energy consumed by a single service of an electric taxi is 1.5 kilowatt-hours; the gasoline consumed by a single service of a fuel taxi is 1 liter; the unit price of fuel is 10 yuan per liter; the unit operating cost of a thermal power generator is 0 .4 yuan per kilowatt-hour; the lower and upper limits of the output of thermal power generating units are 30% and 100% of the maximum power of the unit respectively; the upper limits of the down-climbing and up-climbing rates of the output of thermal power generating units are 5 MW and 10 MW respectively; the carbon emissions caused by the unit power generation of thermal power generating units are 1.2 kg of carbon dioxide per kilowatt-hour; the initial power of the centralized energy storage power station is 10 MWh; the rated capacity of the centralized energy storage power station is 20 MWh; the lower and upper limits of the energy state of the centralized energy storage power station are 0.1 and 0.9; the carbon emissions generated by a single service of a fuel taxi are 2 kg; the upper limit of the daily carbon emission quota for taxi operators is 600 tons.

[0137] The comparison of economic indicators of the power generation side when using the two methods is shown in Table 1, and the comparison of economic indicators of taxi operators is shown in Table 2:

[0138] Table 1 Comparison of economic indicators on the power generation side

[0139] method Electricity sales revenue (yuan) Operating cost (yuan) Carbon emissions from power generation (yuan) Profit (Yuan) Method 1 <![CDATA[2.24×10 6 ]]> <![CDATA[1.09×10 6 ]]> <![CDATA[4.51×10 5 ]]> <![CDATA[6.99×10 5 ]]> Method 2 <![CDATA[2.22×10 6 ]]> <![CDATA[1.12×10 6 ]]> <![CDATA[5.11×10 5 ]]> <![CDATA[6.09×10 5 ]]>

[0140] Table 2 Comparison of economic indicators of taxi operators

[0141]

[0142] As can be seen from Table 1, at the level of economic indicators on the power generation side, when using method 1, compared with method 2, the electricity sales revenue increased by 0.89%, the operating cost and the carbon emission cost on the power generation side decreased by 2.75% and 13.30% respectively, and the profit increased by 12.88%. As can be seen from Table 2, at the level of economic indicators of taxi operators, when using method 1, compared with method 2, the electricity purchase cost increased by 0.89%, the fuel cost and the carbon emission cost of taxi operators decreased by 43.01% and 3.21% respectively, and the comprehensive operating cost decreased by 8.97%. The above results show that the optimized operation method proposed in the present invention, which takes into account the carbon trading between the power generation side and the taxi operator, has achieved the effect of reducing the carbon emissions of the entire system while increasing the profit of the power generation side and reducing the operating costs of the taxi operators.

[0143] Embodiment 1:

[0144] See also Figure 6 , taking into account the optimized operation system of carbon trading between the power generation side and the taxi operator, including a model construction module and a simulation calculation module, the model construction module is used to construct a double-layer optimization model, the double-layer optimization model includes an outer optimization model and an inner optimization model, the optimization goal of the outer optimization model is to maximize the income of the power generation side composed of a thermal power plant, a centralized photovoltaic power station and a centralized energy storage power station, the income of the power generation side includes the power generation and sales income of the power generation side, the power generation cost of the thermal power plant and the centralized photovoltaic power station, the operation cost of the centralized energy storage power station, and the carbon emission cost of the power generation side; the objective function is specifically:

[0145] max I E -F P -F S -F C ;

[0146]

[0147] In the above formula, I E The income from electricity sales on the power generation side; F P is the power generation cost of thermal power plants and centralized photovoltaic power plants; F S is the operating cost of the centralized energy storage power station; F C is the carbon emission cost on the power generation side; is the output power of the thermal power plant in period t; is the output power of the centralized photovoltaic power station in time period t; is the power delivered from the power generation side to the charging station in the distribution network area e during time period t; are the charging energy and discharging energy of the centralized energy storage power station in time period t; PR V is the unit operating cost of a centralized photovoltaic power station; is the electricity price in period t; PR P is the unit operating cost of thermal power plants; PR RS The unit cost of charging and discharging of a centralized energy storage power station; PR C is the unit carbon emission cost; The carbon sink trading electricity consumed by the distribution network area e during period t is obtained by using carbon trading methods to exchange carbon emission quotas; C is the carbon emission caused by the power generation of the thermal power plant in period t; T is the carbon emission quota of thermal power plants; T U is the unit time;

[0148] The constraints of the outer optimization model include thermal power generation constraints, centralized photovoltaic power generation constraints, centralized energy storage operation constraints, and power balance constraints; wherein the thermal power generation constraints include:

[0149]

[0150]

[0151] In the above formula, is the binary operating state variable of the thermal power plant at time period t and t-1. When When , it indicates that the thermal power plant is in shutdown state; is the binary operating state variable of the thermal power plant at time period t-1; is the binary startup operation variable of the thermal power plant at time period t, When When it is on, it means that the thermal power plant does not perform the startup operation; is the binary shutdown operation variable of the thermal power plant at time period t, When When N OM、 N SM are the upper limits of the number of startup and shutdown operations of a thermal power plant in a typical day; δ M is the large M constant; PA L ,PA H They are the lower and upper limits of the output of thermal power plants respectively; is the output power of the thermal power plant in period t-1; ΔP L , ΔP H are the upper limits of the down-slope and up-slope rates of the thermal power plant output respectively; C MT The upper limit of the daily carbon emission quota on the power generation side;

[0152] The centralized energy storage operation constraints include:

[0153]

[0154] In the above formula, is the operating power of the centralized energy storage power station during period t. It indicates that the centralized energy storage power station is in the discharging state. When the centralized energy storage power station is in the energy storage state; O is the initial capacity of centralized energy storage; EC E is the rated capacity of centralized energy storage; η L , η H are the lower and upper limits of the energy state of the centralized energy storage, respectively; tn is any period in a typical day; is the operating state variable of the centralized energy storage power station. When the centralized energy storage power station is operating in the discharge state, When P is SM The upper limit of the operating power of the centralized energy storage power station;

[0155] The centralized photovoltaic power generation constraints include:

[0156]

[0157] In the above formula, is the maximum output of the centralized photovoltaic system in time period t;

[0158] The power balance constraints include:

[0159]

[0160] In the above formula, is the power transmitted from the generation side to the distribution network area e during time period t;

[0161] Since there are bilinear terms in the outer optimization model To facilitate the solution, the McCormick envelope method is used to relax the relevant constraints. The following are the constraints newly introduced by the McCormick envelope method:

[0162]

[0163] In the above formula, Y t Auxiliary variables introduced to utilize McCormick envelope method;

[0164] The optimization goal of the inner optimization model is to minimize the operating cost of the taxi operator consisting of electric taxis and fuel taxis. The operating cost of the taxi operator includes the electricity purchase cost, fuel cost, and carbon emission cost of the taxi operator. The specific objective function is:

[0165] f E +F G +F D ;

[0166]

[0167] In the above formula, F E is the electricity purchase cost of the taxi operator; F G is the fuel cost of taxi operators; F D Carbon emission costs for taxi operators; V is the service volume completed by the fuel taxi in time period t; G Gasoline consumed for a single service of a fuel taxi; PR G is the unit fuel price; is the carbon emissions caused by fuel taxis in period t;

[0168] The constraints of the inner optimization model include distribution network area constraints, taxi service constraints, and carbon trading constraints; wherein the distribution network area constraints include:

[0169]

[0170] In the above formula, is the basic load of distribution network section e in time period t; is the transformer capacity of the distribution network area e;

[0171] The taxi service constraints include total service constraints, electric taxi energy demand constraints, and fuel taxi carbon emission constraints. The total service constraints are:

[0172]

[0173] The energy demand constraint of electric taxi is:

[0174]

[0175] The carbon emission constraints for fuel taxis are:

[0176]

[0177] In the above formula, is the total taxi service demand in time period t; is the service volume completed by the electric taxi in time period t; X E The electric energy consumed by a single electric taxi service; C UG Carbon emissions generated by a single fuel taxi service; C MV A daily carbon emission quota cap for taxi operators;

[0178] The carbon trading constraints limit the amount of electricity used for carbon trading in any period to not exceed the actual power generation of the centralized photovoltaic power station in that period, and limit the amount of electricity used for carbon trading to be used only during off-peak periods; the carbon trading constraints include:

[0179]

[0180] In the above formula, ε T is the trading coefficient of carbon emission and electricity; is the binary determination coefficient of the trough period. When t is the off-peak period, When means time period t is not a low-peak electricity consumption period;

[0181] The simulation calculation module is used to reconstruct the double-layer optimization model into a single-layer optimization problem by using KKT conditions and then solve it to obtain the optimized operation plan for the power generation side and the optimized operation plan for the taxi operator; since the variables of the inner optimization model are and The KKT condition is used to reconstruct the two-layer optimization model into a single-layer optimization problem. Specifically, the dual feasible conditions and complementary relaxation conditions are introduced into the constraints of the inner optimization model. The dual feasible conditions include:

[0182]

[0183] In the above formula, for;

[0184] The complementary relaxation conditions include:

[0185]

[0186]

[0187] By linearizing the complementary relaxation conditions, the constraints of the reconstructed inner optimization model include:

[0188] In the above formula, is the large M constant used in the reconstruction phase; All are binary auxiliary variables.

[0189] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0190] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0191] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0192] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0193] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An optimized operation method taking into account carbon trading between power generation side and taxi operators, characterized by: The optimization operation method comprises: S1. Construct a two-layer optimization model, which includes an outer optimization model and an inner optimization model. The optimization goal of the outer optimization model is to maximize the income of the power generation side consisting of thermal power plants, centralized photovoltaic power stations and centralized energy storage power stations. The income of the power generation side includes the power generation and sales income of the power generation side, the power generation cost of thermal power plants and centralized photovoltaic power stations, the operating cost of centralized energy storage power stations, and the carbon emission cost of the power generation side. The optimization goal of the inner optimization model is to minimize the operating cost of taxi operators consisting of electric taxis and fuel taxis. The operating cost of taxi operators includes the electricity purchase cost, fuel cost, and carbon emission cost of taxi operators. S2. Use KKT conditions to reconstruct the double-layer optimization model into a single-layer optimization problem and then solve it to obtain the optimized operation plan for the power generation side and the optimized operation plan for the taxi operator.

2. The optimized operation method taking into account carbon trading between power generation side and taxi operators according to claim 1 is characterized by: The objective function of the outer optimization model is: max I E -F P -F S -F C ; In the above formula, I E The income from electricity sales on the power generation side; F P is the power generation cost of thermal power plants and centralized photovoltaic power plants; F S is the operating cost of the centralized energy storage power station; F C is the carbon emission cost on the power generation side; is the output power of the thermal power plant in period t; is the output power of the centralized photovoltaic power station in time period t; is the power delivered from the power generation side to the charging station in the distribution network area e during time period t; are the charging energy and discharging energy of the centralized energy storage power station in time period t; PR V is the unit operating cost of a centralized photovoltaic power station; is the electricity price in period t; PR P is the unit operating cost of the thermal power plant; PR RS The unit cost of charging and discharging operations for centralized energy storage power stations; PR C is the unit carbon emission cost; The carbon sink trading electricity consumed by the distribution network area e during period t is obtained by using carbon trading methods to exchange carbon emission quotas; C is the carbon emission caused by the power generation of the thermal power plant in period t; T is the carbon emission quota of thermal power plants; T U As unit time.

3. The optimized operation method taking into account carbon trading between power generation side and taxi operators according to claim 2 is characterized by: The thermal power generation constraints include: In the above formula, are the binary operating state variables of the thermal power plant at time periods t and t-1 respectively; is the binary startup operation variable of the thermal power plant at time period t; is the binary shutdown operation variable of the thermal power plant at time period t; N OM 、N SM are the upper limits of the number of startup and shutdown operations of a thermal power plant in a typical day; δ M is the large M constant; PA L ,PA H They are the lower and upper limits of the output of thermal power plants respectively; is the output power of the thermal power plant in period t-1; ΔP L , ΔP H are the upper limits of the down-slope and up-slope rates of the thermal power plant output respectively; C MT The upper limit of the daily carbon emission quota on the power generation side; The centralized energy storage operation constraints include: In the above formula, is the operating power of the centralized energy storage power station during period t; EC O is the initial capacity of centralized energy storage; EC E is the rated capacity of centralized energy storage; η L , η H are the lower and upper limits of the energy state of the centralized energy storage, respectively; tn is any period in a typical day; is the operating state variable of the centralized energy storage power station; P SM The upper limit of the operating power of the centralized energy storage power station; The centralized photovoltaic power generation constraints include: In the above formula, is the maximum output of the centralized photovoltaic system in time period t; The power balance constraints include: In the above formula, It is the power transmitted from the generation side to the distribution network area e during time period t.

4. The optimized operation method taking into account carbon trading between power generation side and taxi operators according to claim 3 is characterized by: The objective function of the inner optimization model is: min F E +F G +F D ; In the above formula, F E is the electricity purchase cost of the taxi operator; F G is the fuel cost of taxi operators; F D Carbon emission costs for taxi operators; V is the service volume completed by the fuel taxi in time period t; G Gasoline consumed for a single service of a fuel taxi; PR G is the unit fuel price; is the carbon emissions caused by fuel taxis in period t.

5. The optimized operation method taking into account carbon trading between the power generation side and the taxi operator according to claim 4 is characterized by: The distribution network area constraints include: In the above formula, is the basic load of distribution network section e in time period t; is the transformer capacity of the distribution network area e; The taxi service constraints include: In the above formula, is the total taxi service demand in time period t; is the service volume completed by the electric taxi in time period t; X E The electric energy consumed by a single electric taxi service; C UG Carbon emissions generated by a single fuel taxi service; C MV A daily carbon emission quota cap for taxi operators; The carbon trading constraints include: In the above formula, ε T is the trading coefficient of carbon emission and electricity; is the determination coefficient for the trough period.

6. An optimized operation system taking into account carbon trading between power generation and taxi operators, characterized by: The optimized operation system comprises: A model building module, for building a two-layer optimization model, the two-layer optimization model includes an outer optimization model and an inner optimization model, the optimization goal of the outer optimization model is to maximize the income of the power generation side consisting of thermal power plants, centralized photovoltaic power stations and centralized energy storage power stations, the income of the power generation side includes the power generation and sales income of the power generation side, the power generation cost of thermal power plants and centralized photovoltaic power stations, the operating cost of centralized energy storage power stations, and the carbon emission cost of the power generation side, the optimization goal of the inner optimization model is to minimize the operating cost of taxi operators consisting of electric taxis and fuel taxis, the operating cost of taxi operators includes the electricity purchase cost, fuel cost, and carbon emission cost of taxi operators; The simulation calculation module is used to reconstruct the double-layer optimization model into a single-layer optimization problem by adopting KKT conditions and then solve it to obtain the optimized operation plan for the power generation side and the optimized operation plan for the taxi operator.

7. The optimized operation system taking into account carbon trading between power generation side and taxi operators according to claim 6 is characterized by: The objective function of the outer optimization model is: max I E -F P -F S -F C ; In the above formula, I E The income from electricity sales on the power generation side; F P is the power generation cost of thermal power plants and centralized photovoltaic power plants; F S is the operating cost of the centralized energy storage power station; F C is the carbon emission cost on the power generation side; is the output power of the thermal power plant in period t; is the output power of the centralized photovoltaic power station in time period t; is the power delivered from the power generation side to the charging station in the distribution network area e during time period t; are the charging energy and discharging energy of the centralized energy storage power station in time period t; PR V is the unit operating cost of a centralized photovoltaic power station; is the electricity price in period t; PR P is the unit operating cost of the thermal power plant; PR RS The unit cost of charging and discharging operations for centralized energy storage power stations; PR C is the unit carbon emission cost; The carbon sink trading electricity consumed by the distribution network area e during period t is obtained by using carbon trading methods to exchange carbon emission quotas; C is the carbon emission caused by the power generation of the thermal power plant in period t; T is the carbon emission quota of thermal power plants; T U As unit time.

8. The optimized operation system taking into account carbon trading between power generation side and taxi operators according to claim 7 is characterized by: The thermal power generation constraints include: In the above formula, are the binary operating state variables of the thermal power plant at time periods t and t-1 respectively; is the binary startup operation variable of the thermal power plant at time period t; is the binary shutdown operation variable of the thermal power plant at time period t; N OM 、N SM are the upper limits of the number of startup and shutdown operations of a thermal power plant in a typical day; δ M is the large M constant; PA L ,PA H They are the lower and upper limits of the output of thermal power plants respectively; is the output power of the thermal power plant in period t-1; ΔP L , ΔP H are the upper limits of the down-slope and up-slope rates of the thermal power plant output respectively; C MT The upper limit of the daily carbon emission quota on the power generation side; The centralized energy storage operation constraints include: In the above formula, is the operating power of the centralized energy storage power station during period t; EC O is the initial capacity of centralized energy storage; EC E is the rated capacity of centralized energy storage; η L , η H are the lower and upper limits of the energy state of the centralized energy storage, respectively; tn is any period in a typical day; is the operating state variable of the centralized energy storage power station; P SM The upper limit of the operating power of the centralized energy storage power station; The centralized photovoltaic power generation constraints include: In the above formula, is the maximum output of the centralized photovoltaic system in time period t; The power balance constraints include: In the above formula, It is the power transmitted from the generation side to the distribution network area e during time period t.

9. The optimized operation system taking into account carbon trading between power generation side and taxi operators according to claim 8 is characterized by: The objective function of the inner optimization model is: min F E +F G +F D ; In the above formula, F E is the electricity purchase cost of the taxi operator; F G is the fuel cost of taxi operators; F D Carbon emission costs for taxi operators; V is the service volume completed by the fuel taxi in time period t; G Gasoline consumed for a single service of a fuel taxi; PR G is the unit fuel price; is the carbon emissions caused by fuel taxis in period t.

10. The optimized operation system taking into account carbon trading between power generation side and taxi operators according to claim 9 is characterized by: The distribution network area constraints include: In the above formula, is the basic load of distribution network section e in time period t; is the transformer capacity of the distribution network area e; The taxi service constraints include: In the above formula, is the total taxi service demand in time period t; is the service volume completed by the electric taxi in time period t; X E The electric energy consumed by a single electric taxi service; C UG Carbon emissions generated by a single fuel taxi service; C MV A daily carbon emission quota cap for taxi operators; The carbon trading constraints include: In the above formula, ε T is the trading coefficient of carbon emission and electricity; is the determination coefficient for the trough period.

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