Flexible load optimization method and system based on differential power carbon emission factors

Abstract

A flexible load optimization method and system based on differentiated power carbon emission factors are characterized in that a power generation side and a power utilization side are regarded as two interest subjects to establish a flexible load optimization model, the flexible load optimization model is a double-layer optimization model, and the outer layer of the double-layer optimization model takes maximization of power generation profit as a target; and the inner layer of the double-layer optimization model aims at minimizing the power consumption cost, the inner layer constraint is considered, and the carbon emission reduction potential of the user side is further mined by simultaneously considering the power generation side and the power consumption side, so that the effect of a flexible load optimization scheme is improved. According to the method, the power generation side and the power utilization side can be taken as two interest subjects to be optimized at the same time, the effect of a flexible load optimization scheme is effectively improved, and the effects of reducing the comprehensive power utilization cost and improving the power generation income can be achieved by reasonably utilizing the regulation and control capability of the time-of-use electricity price and the differentiated carbon emission factors.

Description

Technical Field

[0001] The present invention relates to a flexible load optimization method, and in particular to a flexible load optimization method and system based on differentiated electricity carbon emission factors. Background Art

[0002] Thermal power generation has always been one of the main sources of global carbon emissions. With the large-scale use of fossil fuels, carbon emissions from the power industry account for a significant proportion of global greenhouse gas emissions. In order to meet the challenges of climate change, countries have introduced policies to reduce carbon emissions. In the power industry, the carbon emission factor has become a key indicator for measuring the level of carbon emissions per unit of electricity. The accurate measurement and management of the power carbon emission factor is of great significance for evaluating the environmental impact of different sources of electricity and formulating effective emission reduction policies.

[0003] On the electricity consumption side, by adopting electricity consumption response to flexibly adjust electricity demand, it is not only possible to balance the supply and demand of the power system, but also to significantly reduce dependence on thermal power generation, thereby reducing carbon emissions. However, when implementing electricity consumption response, how to efficiently regulate the load on the electricity consumption side becomes a key challenge. Electricity loads are generally divided into important loads that cannot be interrupted and flexible loads. The former are critical facilities such as hospitals and data centers, where power supply stability is crucial; the latter include industrial, commercial, and household electricity demands that can be flexibly adjusted according to power supply conditions.

[0004] The distribution network can reduce carbon emissions on the user side through the optimization and regulation of flexible loads on the user side. The carbon reduction cost is low and the carbon reduction potential is huge. However, in the existing technology, there is no carbon asset management module on the power consumption side when studying and formulating the optimal strategy for load regulation of the distribution network, and it is impossible to fully mobilize the carbon reduction potential on the user side. Therefore, it is urgent to use the existing foundation of the new power load management system to study the carbon asset development technology on the power consumption side, build a flexible load optimization and regulation model, realize the development of carbon assets on the user side from the perspective of power grid regulation, and promote the safe, economical and low-carbon operation of the power grid.

[0005] In this context, it is particularly important to introduce differentiated electricity carbon emission factors as a new means of regulating electricity load. By optimizing the scheduling of flexible loads in time and space, the power system can not only more effectively regulate the load characteristics on the electricity side while ensuring the stable operation of important loads, but also reduce the electricity demand during peak periods.

[0006] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of the application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art that operation optimization cannot be performed from the power generation side and the power consumption side, and to provide a flexible load optimization method and system based on differentiated electricity carbon emission factors that can perform operation optimization from the power generation side and the power consumption side.

[0008] To achieve the above objectives, the technical solution of the present invention is:

[0009] In a first aspect, the present invention proposes a flexible load optimization method based on differentiated electricity carbon emission factors, the method comprising:

[0010] S1. Construct a flexible load optimization model based on differentiated electricity carbon emission factors. The flexible load optimization model is a two-layer optimization model. The outer layer of the two-layer optimization model aims to maximize power generation profits and consider outer layer constraints. The inner layer of the two-layer optimization model aims to minimize electricity costs and consider inner layer constraints.

[0011] S2. The double-layer optimization problem of the flexible load optimization model is reconstructed into a single-layer optimization problem through the KKT condition. The single-layer optimization problem takes into account the original feasible constraints, the dual feasible constraints and the complementary relaxation constraints. The reconstructed single-layer optimization problem is solved to obtain a flexible load optimization scheme that performs operation optimization on both the power generation side and the power consumption side.

[0012] The outer objective function of the flexibility load optimization model includes:

[0013] max F E -C T -C V -C S (1);

[0014] The inner objective function of the flexibility load optimization model includes:

[0015] min F E +C N (2);

[0016] The flexibility load optimization model includes:

[0017]

[0018] In the above formula, F E For electricity charges; C T is the cost of thermal power generation; C V is the photovoltaic operation and maintenance cost; C S is the operating cost of pumped storage; C N for the cost of carbon emissions; is the power output from the upper grid to the substation k during time period t; T U is the unit time; is the electricity price in period t; is the output of the thermal power plant in period t; PR T is the unit cost of thermal power generation; is the actual output of the centralized photovoltaic system in period t; PR V is the unit operation and maintenance cost of centralized photovoltaics; and are the energy storage and discharge of the pumped storage power station in period t, and one of them must be 0 in the same period; PR S is the unit operating cost of the pumped storage power station; is the carbon emissions caused by thermal power generation in period t; PR N is the unit carbon emission cost.

[0019] The outer constraints include:

[0020]

[0021]

[0022] In the above formula, They are the output of thermal power plant, centralized photovoltaic power station and pumped storage power station at time period t, respectively. According to the operating status of the pumped storage power station, The value of can be positive or negative. When the pumped storage power station is working in the state of outputting electric energy When it is working in the input power state △P T is the maximum power variation of a thermal power plant in a single time period; P IT is the total installed capacity of thermal power plants; P IV The photovoltaic installed capacity of the centralized photovoltaic power station; is the maximum output coefficient of photovoltaic power generation in time period t; and are the input and output power of the pumped storage power station in time period t, respectively; and are the mass of water pumped from the lower reservoir to the upper reservoir and the mass of water discharged from the upper reservoir to the lower reservoir in the pumped storage power station during the period t; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs; T U is the unit time; P SM is the upper limit of the power of the pumped storage power station unit; δ M is the large M constant; is the working state variable of the pumped storage power station in period t. When it is in the state of output power, It is in the state of storing electrical energy; △M t is the amount of water transported between reservoirs of the pumped storage power station during time period t.t >0, the pumped storage power station releases water, otherwise it pumps water; △W is the upper limit of the amount of water delivered per unit time by the pumped storage power station; H t and D t are the water volume of the upper and lower reservoirs of the pumped storage power station in time period t; M U and M D are the upper and lower reservoir water caps respectively; tn is the last period of a typical day; A t is the auxiliary variable used in McCormick envelope method; ε N It is the unit carbon emission of thermal power generation.

[0023] The inner constraints include:

[0024]

[0025]

[0026] In the above formula, and are the non-interruptible load, flexible load and distributed photovoltaic output of area k in time period t, respectively; and are the spatiotemporal flexibility load and time-shiftable load of zone k in time period t, respectively; and are the total amount of spatiotemporal flexibility load and time-shiftable load respectively; and are the differentiated carbon emission factors of non-interruptible load and flexible load in time period t, respectively; is the installed capacity of transformer in area k; L and δ H are the lower and upper limits of the load factor of the substation respectively; is the photovoltaic installation capacity of distributed photovoltaic in area k; is the photovoltaic output characteristic coefficient in time period t.

[0027] The S2 includes:

[0028] The double-layer optimization problem is reconstructed into a single-layer optimization problem through the KKT condition. Equations (32) and (33) are substituted into the inner-layer objective function of the flexibility load optimization model. The variables of the inner-layer model are: and And reconstruct its constraints, the original feasible condition constraints are shown as (34) to (38);

[0029] The dual feasible condition constraints include:

[0030]

[0031] The complementary slack condition constraints include:

[0032]

[0033]

[0034] In the above formula, All are Lagrange multipliers;

[0035] Linearize the complementary slack condition constraints (43) to (48) to obtain the linearized complementary slack condition constraints:

[0036]

[0037] In the above formula, and All are binary auxiliary variables.

[0038] In a second aspect, the present invention proposes a flexible load optimization system based on differentiated electricity carbon emission factors, the system comprising a model building module and a model solving module;

[0039] The model building module is used to build a flexible load optimization model based on differentiated electricity carbon emission factors. The flexible load optimization model is a two-layer optimization model. The outer layer of the two-layer optimization model aims to maximize power generation profits and considers outer layer constraints. The inner layer of the two-layer optimization model aims to minimize electricity costs and considers inner layer constraints.

[0040] The model solving module is used to reconstruct the double-layer optimization problem of the flexible load optimization model into a single-layer optimization problem through the KKT condition. The single-layer optimization problem considers the original feasible constraints, the dual feasible constraints and the complementary relaxation constraints, and solves the reconstructed single-layer optimization problem to obtain a flexible load optimization scheme that performs operation optimization on both the power generation side and the power consumption side.

[0041] The outer objective function of the flexibility load optimization model includes:

[0042] max F E -C T -C V -C S (1);

[0043] The inner objective function of the flexibility load optimization model includes:

[0044] min F E +C N (2);

[0045] The flexibility load optimization model includes:

[0046]

[0047] In the above formula, F E For electricity charges; C T is the cost of thermal power generation; C V is the photovoltaic operation and maintenance cost; C S is the operating cost of pumped storage; C N for the cost of carbon emissions; is the power output from the upper grid to the substation k during time period t; T U is the unit time; is the electricity price in period t; is the output of the thermal power plant in period t; PR T is the unit cost of thermal power generation; is the actual output of the centralized photovoltaic system in period t; PR V is the unit operation and maintenance cost of centralized photovoltaics; and are the energy storage and discharge of the pumped storage power station in period t, and one of them must be 0 in the same period; PR S is the unit operating cost of the pumped storage power station; is the carbon emissions caused by thermal power generation in period t; PR N is the unit carbon emission cost.

[0048] The outer constraints include:

[0049]

[0050]

[0051] In the above formula, They are the output of thermal power plant, centralized photovoltaic power station and pumped storage power station at time period t, respectively. According to the operating status of the pumped storage power station, The value of can be positive or negative. When the pumped storage power station is working in the state of outputting electric energy When it is working in the input power state △P T is the maximum power variation of a thermal power plant in a single time period; P IT is the total installed capacity of thermal power plants; P IV The photovoltaic installed capacity of the centralized photovoltaic power station; is the maximum output coefficient of photovoltaic power generation in time period t; and are the input and output power of the pumped storage power station in time period t, respectively; and are the mass of water pumped from the lower reservoir to the upper reservoir and the mass of water discharged from the upper reservoir to the lower reservoir in the pumped storage power station during the period t; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs; T U is the unit time; P SM is the upper limit of the power of the pumped storage power station unit; δ M is the large M constant; is the working state variable of the pumped storage power station in period t. When it is in the state of output power, It is in the state of storing electrical energy; △M t is the amount of water transported between reservoirs of the pumped storage power station during time period t. t >0, the pumped storage power station releases water, otherwise it pumps water; △W is the upper limit of the amount of water delivered per unit time by the pumped storage power station; H t and D t are the water volume of the upper and lower reservoirs of the pumped storage power station in time period t; M U and M D are the upper and lower reservoir water caps respectively; tn is the last period of a typical day; A t is the auxiliary variable used in McCormick envelope method; ε N It is the unit carbon emission of thermal power generation.

[0052] The inner constraints include:

[0053]

[0054]

[0055] In the above formula, and are the non-interruptible load, flexible load and distributed photovoltaic output of area k in time period t, respectively; and are the spatiotemporal flexibility load and time-shiftable load of zone k in time period t, respectively; and are the total amount of spatiotemporal flexibility load and time-shiftable load respectively; and are the differentiated carbon emission factors of non-interruptible load and flexible load in time period t, respectively; is the installed capacity of transformer in area k; L and δ H are the lower and upper limits of the load factor of the substation respectively; is the photovoltaic installation capacity of distributed photovoltaic in area k; is the photovoltaic output characteristic coefficient in time period t.

[0056] The specific operation steps of the model solving module include:

[0057] The double-layer optimization problem is reconstructed into a single-layer optimization problem through the KKT condition. Equations (32) and (33) are substituted into the inner-layer objective function of the flexibility load optimization model. The variables of the inner-layer model are: and And reconstruct its constraints, the original feasible condition constraints are shown as (34) to (38);

[0058] The dual feasible condition constraints include:

[0059]

[0060] The complementary slack condition constraints include:

[0061]

[0062] In the above formula, All are Lagrange multipliers;

[0063] Linearize the complementary slack condition constraints (43) to (48) to obtain the linearized complementary slack condition constraints:

[0064]

[0065] In the above formula, and All are binary auxiliary variables.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 1. In a flexible load optimization method based on differentiated electricity carbon emission factors of the present invention, a flexible load optimization model is established by treating the power generation side and the power consumption side as two interest entities. The flexible load optimization model is a two-layer optimization model. The outer layer of the two-layer optimization model aims to maximize the power generation profit and considers the outer layer constraints. The inner layer of the two-layer optimization model aims to minimize the electricity cost and considers the inner layer constraints. By considering the power generation side and the power consumption side at the same time, the carbon emission reduction potential on the user side is further explored, and the effect of the flexible load optimization scheme is improved. Therefore, this design can effectively improve the effect of the flexible load optimization scheme by treating the power generation side and the power consumption side as two interest entities and optimizing them simultaneously.

[0068] 2. In the flexible load optimization method based on differentiated electric carbon emission factors of the present invention, on the power generation side, the operation optimization of thermal power plants, centralized photovoltaic power plants and pumped storage power stations is considered, and on the power consumption side, the optimization of non-interruptible loads, flexible loads and distributed photovoltaics is considered, and the effect of reducing the comprehensive electricity cost while improving the power generation income is achieved by rationally utilizing the control capabilities of time-of-use electricity prices and differentiated carbon emission factors. Therefore, this design can achieve the effect of reducing the comprehensive electricity cost while improving the power generation income by rationally utilizing the control capabilities of time-of-use electricity prices and differentiated carbon emission factors.

[0069] 3. In the flexible load optimization method based on differentiated electricity carbon emission factors of the present invention, the double-layer optimization problem of the flexible load optimization model is reconstructed into a single-layer optimization problem through the KKT condition, and its constraints are reconstructed, so that a more efficient single-layer optimization algorithm and solver can be used to solve it, thereby improving the solution efficiency. Therefore, this design can reconstruct the double-layer optimization problem of the flexible load optimization model into a single-layer optimization problem through the KKT condition, effectively improving the solution efficiency.

[0070] 4. In the flexible load optimization method based on differentiated electricity carbon emission factors of the present invention, the flexible load includes spatiotemporal flexible load and time-shiftable load, which respectively represent loads that can be flexible in time and space and loads that are flexible only in time. Loads that are flexible in time and space include electric vehicle charging loads, etc., and loads that are flexible only in time include temperature control loads, etc. By optimizing the scheduling of flexible loads in time and space, the power system can not only more effectively regulate the load characteristics on the power consumption side while ensuring the stable operation of important loads, but also reduce the power demand during peak periods. Therefore, this design can effectively reduce the power demand during peak periods by optimizing the scheduling of flexible loads in time and space. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a flow chart of the method of the present invention.

[0072] Figure 2 It is a structural diagram of the system described in the present invention.

[0073] Figure 3 It is a schematic diagram of the interactive topological structure of power generation and power consumption in the present invention.

[0074] Figure 4 This is the industrial and commercial time-of-use electricity price diagram in Example 1.

[0075] Figure 5 This is a photovoltaic output characteristic diagram in Example 1.

[0076] Figure 6This is a characteristic diagram of the load characteristic curve of Strategy 1 in Example 1.

[0077] Figure 7 This is a characteristic diagram of the load characteristic curve of Strategy 2 in Example 1.

[0078] Figure 8 It is a structural diagram of the device described in Example 3. DETAILED DESCRIPTION

[0079] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0080] Embodiment 1:

[0081] See also Figure 1 and Figure 3 , a flexible load optimization method based on differentiated electricity carbon emission factors, the optimization method comprising:

[0082] S1. Construct a flexible load optimization model based on differentiated electricity carbon emission factors. The flexible load optimization model is a two-layer optimization model. The outer layer of the two-layer optimization model aims to maximize power generation profits and consider outer layer constraints. The inner layer of the two-layer optimization model aims to minimize electricity costs and consider inner layer constraints.

[0083] S2. The double-layer optimization problem of the flexible load optimization model is reconstructed into a single-layer optimization problem through the KKT condition. The single-layer optimization problem takes into account the original feasible constraints, the dual feasible constraints and the complementary relaxation constraints. The reconstructed single-layer optimization problem is solved to obtain a flexible load optimization scheme that performs operation optimization on both the power generation side and the power consumption side.

[0084] The outer objective function of the flexibility load optimization model includes:

[0085] max F E -C T -C V -C S (1);

[0086] The inner objective function of the flexibility load optimization model includes:

[0087] min F E +C N (2);

[0088] The flexibility load optimization model includes:

[0089]

[0090] In the above formula, F E For electricity charges; C Tis the cost of thermal power generation; C V is the photovoltaic operation and maintenance cost; C S is the operating cost of pumped storage; C N for the cost of carbon emissions; is the power output from the upper grid to the substation k during time period t; T U is the unit time; is the electricity price in period t; is the output of the thermal power plant in period t; PR T is the unit cost of thermal power generation; is the actual output of the centralized photovoltaic system in period t; PR V is the unit operation and maintenance cost of centralized photovoltaics; and are the energy storage and discharge of the pumped storage power station in period t, and one of them must be 0 in the same period; PR S is the unit operating cost of the pumped storage power station; is the carbon emissions caused by thermal power generation in period t; PR N is the unit carbon emission cost.

[0091] The outer constraints include:

[0092]

[0093]

[0094] In the above formula, They are the output of thermal power plant, centralized photovoltaic power station and pumped storage power station at time period t, respectively. According to the operating status of the pumped storage power station, The value of can be positive or negative. When the pumped storage power station is working in the state of outputting electric energy When it is working in the input power state △P T is the maximum power variation of a thermal power plant in a single time period; P IT is the total installed capacity of thermal power plants; P IV The photovoltaic installed capacity of the centralized photovoltaic power station; is the maximum output coefficient of photovoltaic power generation in time period t; and are the input and output power of the pumped storage power station in time period t, respectively; and are the mass of water pumped from the lower reservoir to the upper reservoir and the mass of water discharged from the upper reservoir to the lower reservoir in the pumped storage power station during the period t; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs; T U is the unit time; P SM is the upper limit of the power of the pumped storage power station unit; δ Mis the large M constant; is the working state variable of the pumped storage power station in period t. When it is in the state of output power, It is in the state of storing electrical energy; △M t is the amount of water transported between reservoirs of the pumped storage power station during time period t. t >0, the pumped storage power station releases water, otherwise it pumps water; △W is the upper limit of the amount of water delivered per unit time by the pumped storage power station; H t and D t are the water volume of the upper and lower reservoirs of the pumped storage power station in time period t; M U and M D are the upper and lower reservoir water caps respectively; tn is the last period of a typical day; A t is the auxiliary variable used in McCormick envelope method; ε N It is the unit carbon emission of thermal power generation.

[0095] The inner constraints include:

[0096]

[0097]

[0098] In the above formula, and are the non-interruptible load, flexible load and distributed photovoltaic output of area k in time period t, respectively; and are the spatiotemporal flexibility load and time-shiftable load of zone k in time period t, respectively; and are the total amount of spatiotemporal flexibility load and time-shiftable load respectively; and are the differentiated carbon emission factors of non-interruptible load and flexible load in time period t, respectively; is the installed capacity of transformer in area k; L and δ H are the lower and upper limits of the load factor of the substation respectively; is the photovoltaic installation capacity of distributed photovoltaic in area k; is the photovoltaic output characteristic coefficient in time period t.

[0099] The S2 includes:

[0100] The double-layer optimization problem is reconstructed into a single-layer optimization problem through the KKT condition. Equations (32) and (33) are substituted into the inner-layer objective function of the flexibility load optimization model. The variables of the inner-layer model are: and And reconstruct its constraints, the original feasible condition constraints are shown as (34) to (38);

[0101] The dual feasible condition constraints include:

[0102]

[0103] The complementary slack condition constraints include:

[0104]

[0105] In the above formula, All are Lagrange multipliers;

[0106] Linearize the complementary slack condition constraints (43) to (48) to obtain the linearized complementary slack condition constraints:

[0107]

[0108] In the above formula, and are binary auxiliary variables. Figures 4 to 7 In order to verify the flexible load optimization strategy based on differentiated electricity carbon emission factors proposed in this invention, the optimization strategy based on fixed carbon emission factors (Strategy 2) is used to compare with Strategy 1 proposed in this invention. Strategy 2 does not set differentiated carbon emission factors for different types of loads, but uses fixed carbon emission factors. The economic indicators of the two strategies are compared in Table 1. The industrial and commercial time-of-use electricity prices and photovoltaic output characteristics used in the example are as follows: Figure 4 and Figure 5 As shown;

[0109] Parameters: The unit time is 1 hour; the unit operating cost of the thermal power generating unit is 0.6 yuan per kilowatt-hour; the unit operation and maintenance cost of the centralized photovoltaic is 0.3 yuan per kilowatt-hour; the unit operating cost of the pumped storage power station is 0.25 yuan per kilowatt-hour; the total installed capacity of the thermal power generating unit is 100 megawatts; the maximum power change of the thermal power plant in a single time period is 20 megawatts; the capacity of the centralized photovoltaic is 50 megawatts; the upper limit of the power of the pumped storage power station unit is 40 megawatts; the upper limit of the water transport per unit time of the pumped storage power station is 1×10 8 kg; the upper and lower reservoirs have a water volume limit of 2×10 8 kg and 3×10 8 kilograms; the carbon emissions caused by unit power generation of thermal power generating units are 1.2 kilograms of carbon dioxide per kilowatt-hour; the unit carbon neutrality cost is 400 yuan per ton of carbon dioxide, and the unit carbon neutrality cost is the comprehensive cost of offsetting the carbon dioxide emissions generated by itself through afforestation, energy conservation and emission reduction, etc.

[0110] Table 1 Economic comparison

[0111]

[0112] See also Figure 6 and Figure 7 Comparing Strategy 1 with Strategy 2, it can be seen that the daily electricity bill, daily power generation cost and daily carbon emission cost when Strategy 1 is adopted are reduced by 3.05%, 19.73% and 25.45% respectively compared with those when Strategy 2 is adopted. For the power generation side, the daily power generation profit when Strategy 1 is adopted is increased by 50.74% compared with that when Strategy 2 is adopted; for the power consumption side, the daily comprehensive electricity cost when Strategy 1 is adopted is reduced by 22.09% compared with that when Strategy 2 is adopted. The above results show that the flexible load optimization strategy based on differentiated electricity carbon emission factors proposed in the present invention has achieved the effect of reducing the comprehensive electricity cost on the power consumption side while increasing the power generation income on the power generation side.

[0113] Embodiment 2:

[0114] See also Figure 2 , a flexible load optimization system based on differentiated electricity carbon emission factors, the system comprising a model building module and a model solving module;

[0115] The model building module is used to build a flexible load optimization model based on differentiated electricity carbon emission factors. The flexible load optimization model is a two-layer optimization model. The outer layer of the two-layer optimization model aims to maximize power generation profits and considers outer layer constraints. The inner layer of the two-layer optimization model aims to minimize electricity costs and considers inner layer constraints.

[0116] The model solving module is used to reconstruct the double-layer optimization problem of the flexible load optimization model into a single-layer optimization problem through the KKT condition. The single-layer optimization problem considers the original feasible constraints, the dual feasible constraints and the complementary relaxation constraints, and solves the reconstructed single-layer optimization problem to obtain a flexible load optimization scheme that performs operation optimization on both the power generation side and the power consumption side.

[0117] The outer objective function of the flexibility load optimization model includes:

[0118] max F E -C T -C V -C S (1);

[0119] The inner objective function of the flexibility load optimization model includes:

[0120] min F E +C N (2);

[0121] The flexibility load optimization model includes:

[0122]

[0123] In the above formula, F E For electricity charges; C T is the cost of thermal power generation; C V is the photovoltaic operation and maintenance cost; C S is the operating cost of pumped storage; C N for the cost of carbon emissions; is the power output from the upper grid to the substation k during time period t; T U is the unit time; is the electricity price in period t; is the output of the thermal power plant in period t; PR T is the unit cost of thermal power generation; is the actual output of the centralized photovoltaic system in period t; PR V is the unit operation and maintenance cost of centralized photovoltaics; and are the energy storage and discharge of the pumped storage power station in period t, and one of them must be 0 in the same period; PR S is the unit operating cost of the pumped storage power station; is the carbon emissions caused by thermal power generation in period t; PR N is the unit carbon emission cost.

[0124] The outer constraints include:

[0125]

[0126]

[0127] In the above formula, They are the output of thermal power plant, centralized photovoltaic power station and pumped storage power station at time period t, respectively. According to the operating status of the pumped storage power station, The value of can be positive or negative. When the pumped storage power station is working in the state of outputting electric energy When it is working in the input power state △P T is the maximum power variation of a thermal power plant in a single time period; P IT is the total installed capacity of thermal power plants; P IV The photovoltaic installed capacity of the centralized photovoltaic power station; is the maximum output coefficient of photovoltaic power generation in time period t; and are the input and output power of the pumped storage power station in time period t, respectively; and are the mass of water pumped from the lower reservoir to the upper reservoir and the mass of water discharged from the upper reservoir to the lower reservoir in the pumped storage power station during the period t; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs; T U is the unit time; P SM is the upper limit of the power of the pumped storage power station unit; δ M is the large M constant; is the working state variable of the pumped storage power station in period t. When it is in the state of output power, It is in the state of storing electrical energy; △M t is the amount of water transported between reservoirs of the pumped storage power station during time period t. t >0, the pumped storage power station releases water, otherwise it pumps water; △W is the upper limit of the amount of water delivered per unit time by the pumped storage power station; H t and D t are the water volume of the upper and lower reservoirs of the pumped storage power station in time period t; M U and M D are the upper and lower reservoir water caps respectively; tn is the last period of a typical day; A t is the auxiliary variable used in McCormick envelope method; ε N It is the unit carbon emission of thermal power generation.

[0128] The inner constraints include:

[0129]

[0130] In the above formula, and are the non-interruptible load, flexible load and distributed photovoltaic output of area k in time period t, respectively; and are the spatiotemporal flexibility load and time-shiftable load of zone k in time period t, respectively; and are the total amount of spatiotemporal flexibility load and time-shiftable load respectively; and are the differentiated carbon emission factors of non-interruptible load and flexible load in time period t, respectively; is the installed capacity of transformer in area k; L and δ H are the lower and upper limits of the load factor of the substation respectively; is the photovoltaic installation capacity of distributed photovoltaic in area k; is the photovoltaic output characteristic coefficient in time period t.

[0131] The specific operation steps of the model solving module include:

[0132] The double-layer optimization problem is reconstructed into a single-layer optimization problem through the KKT condition. Equations (32) and (33) are substituted into the inner-layer objective function of the flexibility load optimization model. The variables of the inner-layer model are: and And reconstruct its constraints, the original feasible condition constraints are shown as (34) to (38);

[0133] The dual feasible condition constraints include:

[0134]

[0135] The complementary slack condition constraints include:

[0136]

[0137] In the above formula, All are Lagrange multipliers;

[0138] Linearize the complementary slack condition constraints (43) to (48) to obtain the linearized complementary slack condition constraints:

[0139]

[0140]

[0141] In the above formula, and All are binary auxiliary variables.

[0142] Embodiment 3:

[0143] See also Figure 8 , a flexible load optimization device based on differentiated electricity carbon emission factors, the device comprising a processor and a memory;

[0144] The memory is used to store computer program code and transmit the computer program code to the processor;

[0145] The processor is used to execute the flexible load optimization method based on differentiated electricity carbon emission factors described in Example 1 according to the instructions in the computer program code.

[0146] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the flexible load optimization method based on differentiated electricity carbon emission factors described in Example 1.

[0147] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A flexible load optimization method based on differentiated electricity carbon emission factors, characterized by: The optimization method comprises: S1. Construct a flexible load optimization model based on differentiated electricity carbon emission factors. The flexible load optimization model is a two-layer optimization model. The outer layer of the two-layer optimization model aims to maximize power generation profits and consider outer layer constraints. The inner layer of the two-layer optimization model aims to minimize electricity costs and consider inner layer constraints. S2. The double-layer optimization problem of the flexible load optimization model is reconstructed into a single-layer optimization problem through the KKT condition. The single-layer optimization problem takes into account the original feasible constraints, the dual feasible constraints and the complementary relaxation constraints. The reconstructed single-layer optimization problem is solved to obtain a flexible load optimization scheme that performs operation optimization on both the power generation side and the power consumption side.

2. The method for optimizing flexible loads based on differentiated electricity carbon emission factors according to claim 1, characterized in that: The outer objective function of the flexibility load optimization model includes: max F E -C T -C V -C S (1); The inner objective function of the flexibility load optimization model includes: my F E +C N (2); The flexibility load optimization model includes: In the above formula, F E For electricity charges; C T is the cost of thermal power generation; C V is the photovoltaic operation and maintenance cost; C S is the operating cost of pumped storage; C N for the cost of carbon emissions; is the power output from the upper grid to the substation k during time period t; T U is the unit time; is the electricity price in period t; is the output of the thermal power plant in period t; PR T is the unit cost of thermal power generation; is the actual output of the centralized photovoltaic system in period t; PR V is the unit operation and maintenance cost of centralized photovoltaics; and are the energy storage and discharge of the pumped storage power station in period t, and one of them must be 0 in the same period; PR S is the unit operating cost of the pumped storage power station; is the carbon emissions caused by thermal power generation in period t; PR N is the unit carbon emission cost.

3. The method for optimizing flexible loads based on differentiated electricity carbon emission factors according to claim 2, characterized in that: The outer constraints include: In the above formula, They are the output of thermal power plant, centralized photovoltaic power station and pumped storage power station at time period t, respectively. According to the operating status of the pumped storage power station, The value of can be positive or negative. When the pumped storage power station is working in the state of outputting electric energy When it is working in the input power state △P T is the maximum power variation of a thermal power plant in a single time period; P IT is the total installed capacity of thermal power plants; P IV The photovoltaic installed capacity of the centralized photovoltaic power station; is the maximum output coefficient of photovoltaic power generation in time period t; and are the input and output power of the pumped storage power station in time period t, respectively; and are the mass of water pumped from the lower reservoir to the upper reservoir and the mass of water discharged from the upper reservoir to the lower reservoir in the pumped storage power station during the period t; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs; T U is the unit time; P SM is the upper limit of the power of the pumped storage power station unit; δ M is the large M constant; is the working state variable of the pumped storage power station in period t. When it is in the state of output power, It is in the state of storing electrical energy; △M t is the amount of water transported between reservoirs of the pumped storage power station during time period t. t >0, the pumped storage power station releases water, otherwise it pumps water; △W is the upper limit of the amount of water delivered per unit time by the pumped storage power station; H t and D t are the water volume of the upper and lower reservoirs of the pumped storage power station in time period t; M U and M D are the upper and lower reservoir water caps respectively; tn is the last period of a typical day; A t is the auxiliary variable used in McCormick envelope method; ε N It is the unit carbon emission of thermal power generation.

4. The method for optimizing flexible loads based on differentiated electricity carbon emission factors according to claim 3 is characterized by: The inner constraints include: In the above formula, and are the non-interruptible load, flexible load and distributed photovoltaic output of area k in time period t, respectively; and are the spatiotemporal flexibility load and time-shiftable load of zone k in time period t, respectively; and are the total amount of spatiotemporal flexibility load and time-shiftable load respectively; and are the differentiated carbon emission factors of non-interruptible load and flexible load in time period t, respectively; is the installed capacity of transformer in area k; L and δ H are the lower and upper limits of the load factor of the substation respectively; is the photovoltaic installation capacity of distributed photovoltaic in area k; is the photovoltaic output characteristic coefficient in time period t.

5. The method for optimizing flexible loads based on differentiated electricity carbon emission factors according to claim 4 is characterized by: The S2 includes: The double-layer optimization problem is reconstructed into a single-layer optimization problem through the KKT condition. Equations (32) and (33) are substituted into the inner-layer objective function of the flexibility load optimization model. The variables of the inner-layer model are: and And reconstruct its constraints, the original feasible condition constraints are shown as (34) to (38); The dual feasible condition constraints include: The complementary slack condition constraints include: In the above formula, All are Lagrange multipliers; Linearize the complementary slack condition constraints (43) to (48) to obtain the linearized complementary slack condition constraints: In the above formula, and All are binary auxiliary variables.

6. A flexible load optimization system based on differentiated electricity carbon emission factors, characterized by: The system includes a model building module and a model solving module; The model building module is used to build a flexible load optimization model based on differentiated electricity carbon emission factors. The flexible load optimization model is a two-layer optimization model. The outer layer of the two-layer optimization model aims to maximize power generation profits and considers outer layer constraints. The inner layer of the two-layer optimization model aims to minimize electricity costs and considers inner layer constraints. The model solving module is used to reconstruct the double-layer optimization problem of the flexible load optimization model into a single-layer optimization problem through the KKT condition. The single-layer optimization problem considers the original feasible constraints, the dual feasible constraints and the complementary relaxation constraints, and solves the reconstructed single-layer optimization problem to obtain a flexible load optimization scheme that performs operation optimization on both the power generation side and the power consumption side.

7. The flexible load optimization system based on differentiated electricity carbon emission factors according to claim 6 is characterized by: The outer objective function of the flexibility load optimization model includes: max F E -C T -C V -C S (1); The inner objective function of the flexibility load optimization model includes: my F E +C N (2); The flexibility load optimization model includes: In the above formula, F E For electricity charges; C T is the cost of thermal power generation; C V is the photovoltaic operation and maintenance cost; C S is the operating cost of pumped storage; C N for the cost of carbon emissions; is the power output from the upper grid to the substation k during time period t; T U is the unit time; is the electricity price in period t; is the output of the thermal power plant in period t; PR T is the unit cost of thermal power generation; is the actual output of the centralized photovoltaic system in period t; PR V is the unit operation and maintenance cost of centralized photovoltaics; and are the energy storage and discharge of the pumped storage power station in period t, and one of them must be 0 in the same period; PR S is the unit operating cost of the pumped storage power station; is the carbon emissions caused by thermal power generation in period t; PR N is the unit carbon emission cost.

8. The flexible load optimization system based on differentiated electricity carbon emission factors according to claim 7 is characterized by: The outer constraints include: In the above formula, They are the output of thermal power plant, centralized photovoltaic power station and pumped storage power station at time period t, respectively. According to the operating status of the pumped storage power station, The value of can be positive or negative. When the pumped storage power station is working in the state of outputting electric energy When it is working in the input power state △P T is the maximum power variation of a thermal power plant in a single time period; P IT is the total installed capacity of thermal power plants; P IV The photovoltaic installed capacity of the centralized photovoltaic power station; is the maximum output coefficient of photovoltaic power generation in time period t; and are the input and output power of the pumped storage power station in time period t, respectively; and are the mass of water pumped from the lower reservoir to the upper reservoir and the mass of water discharged from the upper reservoir to the lower reservoir in the pumped storage power station during the period t; g is the acceleration of gravity; h is the height difference between the upper and lower reservoirs; T U is the unit time; P SM is the upper limit of the power of the pumped storage power station unit; δ M is the large M constant; is the working state variable of the pumped storage power station in period t. When it is in the state of output power, It is in the state of storing electrical energy; △M t is the amount of water transported between reservoirs of the pumped storage power station during time period t. t >0, the pumped storage power station releases water, otherwise it pumps water; △W is the upper limit of the amount of water delivered per unit time by the pumped storage power station; H t and D t are the water volume of the upper and lower reservoirs of the pumped storage power station in time period t; M U and M D are the upper and lower reservoir water caps respectively; tn is the last period of a typical day; A t is the auxiliary variable used in McCormick envelope method; ε N It is the unit carbon emission of thermal power generation.

9. The flexible load optimization system based on differentiated electricity carbon emission factors according to claim 8 is characterized by: The inner constraints include: In the above formula, and are the non-interruptible load, flexible load and distributed photovoltaic output of area k in time period t, respectively; and are the spatiotemporal flexibility load and time-shiftable load of zone k in time period t, respectively; and are the total amount of spatiotemporal flexibility load and time-shiftable load respectively; and are the differentiated carbon emission factors of non-interruptible load and flexible load in time period t, respectively; is the installed capacity of transformer in area k; L and δ H are the lower and upper limits of the load factor of the substation respectively; is the photovoltaic installation capacity of distributed photovoltaic in area k; is the photovoltaic output characteristic coefficient in time period t.

10. The flexible load optimization system based on differentiated electricity carbon emission factors according to claim 9, characterized in that: The specific operation steps of the model solving module include: The double-layer optimization problem is reconstructed into a single-layer optimization problem through the KKT condition. Equations (32) and (33) are substituted into the inner-layer objective function of the flexibility load optimization model. The variables of the inner-layer model are: and And reconstruct its constraints, the original feasible condition constraints are shown as (34) to (38); The dual feasible condition constraints include: The complementary slack condition constraints include: In the above formula, All are Lagrange multipliers; Linearize the complementary slack condition constraints (43) to (48) to obtain the linearized complementary slack condition constraints: In the above formula, and All are binary auxiliary variables.

Images