Comprehensive energy system low-carbon economic optimization method and system considering fused magnesium load

Abstract

The invention discloses an integrated energy system low-carbon economic optimization method and system considering fused magnesium load. The method comprises the following steps: determining a load matching index based on an actual load value and a wind-light-storage combined output value; based on the wind and light storage installed capacity and the operation and maintenance cost coefficient, combining a pre-constructed average electricity cost model to obtain an average electricity cost; with the load matching degree and the average cost per kilowatt-hour as optimization objectives, an NSGA-II algorithm is adopted for optimization to obtain the optimal wind and light storage installed capacity; establishing a master-slave game energy transaction model based on the optimal wind and light storage installed capacity and demand response, and solving the master-slave game energy transaction model based on an improved grey wolf algorithm to obtain a comprehensive energy system low-carbon economic optimization scheme; wherein the pre-constructed average cost-per-kilowatt-hour model is constructed by taking the minimum cost-per-kilowatt-hour as an optimization target and combining constraint conditions. According to the invention, optimization solution is carried out based on the improved grey wolf algorithm to obtain the optimal scheme, and the carbon emission and carbon transaction cost of the system are reduced.

Description

Technical Field

[0001] The present invention relates to the field of optimization scheduling of integrated energy systems, and in particular to a low-carbon economic optimization method and system for an integrated energy system taking into account fused magnesium load. Background Art

[0002] With the substantial increase in renewable energy power generation, how to improve the absorption rate of renewable energy and effectively reduce carbon dioxide emissions on the demand side is the core issue at present. Taking the large-capacity industrial load such as fused magnesium production as an example, its main production load is the fused magnesium furnace, which emits a large amount of CO2 and has high electricity costs during the smelting of magnesite ore.

[0003] The existing technology mainly adopts the time-of-use electricity price method to solve the problem of electricity cost, which is not friendly to the production of industrial loads and cannot timely absorb the power generation of new energy such as wind and solar. As for the problem of CO2, the current practice of enterprises is direct emission, which is not friendly to the environment. The current mainstream carbon recovery and utilization technology is carbon capture and power-to-gas, but the implementation cost is high. Summary of the invention

[0004] In order to solve the problem that the demand-side resource consumption rate of wind and solar power and other new energy sources in the prior art integrated energy system is not high, the new energy installed capacity does not fully consider the power consumption characteristics of the load side and the construction cost of new energy, and does not fully consider the carbon trading of the integrated energy system, nor does it consider the carbon trading of the fused magnesium load, resulting in the high carbon trading cost of the integrated energy system as a whole, the present invention proposes a low-carbon economic optimization method for the integrated energy system considering the fused magnesium load, including:

[0005] Determine the load matching index based on the actual load value and the combined output value of wind, solar and energy storage;

[0006] Based on the wind, solar and storage installed capacity and the operation and maintenance cost coefficient, combined with the pre-built average cost per kilowatt-hour model, the average cost per kilowatt-hour is obtained;

[0007] Taking load matching and average cost per kilowatt-hour as optimization objectives, the NSGA-II algorithm is used to optimize the optimal wind, solar and storage installed capacity.

[0008] A master-slave game energy trading model is established based on the optimal wind, solar and storage installed capacity and demand response, and an improved grey wolf algorithm is used to solve the master-slave game energy trading model to obtain a low-carbon economic optimization plan for the integrated energy system;

[0009] Among them, the pre-built average cost per kilowatt-hour model is constructed with the minimum cost per kilowatt-hour as the optimization goal and combined with constraint conditions.

[0010] Optionally, the construction of the average cost per kilowatt-hour model includes:

[0011] Taking the minimum electricity cost as the optimization goal, construct the objective function;

[0012] Setting constraints for the objective function;

[0013] Constructing an average electricity cost model based on the objective function and the constraint conditions;

[0014] Among them, the constraints include: wind power installed capacity constraints, photovoltaic installed capacity constraints, energy storage system installed capacity constraints, capacity constraints, charging and discharging state constraints, charging and discharging power constraints, wind turbine output constraints and photovoltaic unit output constraints.

[0015] Optionally, the objective function is as shown below:

[0016] f b =min(C)

[0017]

[0018] In the formula, f b is the minimum electricity cost, C is the electricity cost, L sys is the initial investment cost of the system, O sys_r is the annual operation and maintenance cost of the system, s is the discount rate, r is the number of years of discount, R is the total number of years of discount, E wt 、E pv 、E es They are the annual power generation of wind power and photovoltaic power and the storage capacity of energy storage units.

[0019] Optionally, the optimal wind-solar-storage installed capacity obtained by optimization using the NSGA-II algorithm with load matching and average kilowatt-hour cost as optimization targets includes:

[0020] I. Take the wind, solar and storage installed capacity that meets the constraints as individuals, form a population from all individuals, initialize the number of iterations, and set the maximum number of iterations;

[0021] II. The maximum load matching degree f a Transform into minimizing the objective function and calculate the maximum load matching degree f corresponding to each individual in the population a , minimum electricity cost f b ;

[0022] III. Calculate the non-dominated sorting and crowding degree of individuals in the population to obtain the individual dominance sorting level and crowding degree;

[0023] IV. Operate the individuals in the population according to the individual dominance ranking and crowding degree to generate a sub-population, and merge the sub-population with the parent population to form a new population P i+1, and at the same time, non-dominated sorting and crowding degree calculation are performed on the sub-population to obtain the individual dominated sorting level and crowding degree of the sub-population, where the operations include: selection, crossover, and mutation;

[0024] V. For the new population P i+1 Sort and filter to determine whether the number of iterations is greater than the maximum number of iterations. If not, return to step IV, otherwise go to step VI;

[0025] VI. Output the optimal population, and use the wind-solar-storage installed capacity corresponding to the optimal population as the optimal wind-solar-storage installed capacity, and end.

[0026] Optionally, establishing a master-slave game energy trading model based on the optimal wind, solar and storage installed capacity and demand response includes:

[0027] Based on the optimal wind, solar and storage installed capacity, the working models and operation constraints of the combined heat and power unit, the carbon capture system and the power-to-gas system are established respectively;

[0028] Based on the working modes and operating constraints of the combined cooling, heating and power units, carbon capture systems and power-to-gas systems, an integrated energy system is constructed in combination with the constraints under the joint operation mode;

[0029] The carbon emissions of the combined heating, cooling and power units and the integrated energy system after adding the fused magnesium load are analyzed, and a master-slave game energy trading model is established.

[0030] Optionally, the constraints in the joint operation mode include:

[0031] Electric power balance constraints, CO2 amount constraints captured by the carbon capture system, heat balance constraints, cold balance constraints, etc.

[0032] Optionally, the carbon emissions of the combined cooling, heating and power unit and the system operating jointly after adding the fused magnesium load are analyzed to establish a master-slave game energy trading model, including:

[0033] Ⅰ. According to the coupling relationship between sources and loads, the master-slave game energy trading model is divided into two layers: the upper layer is the energy operator, and the lower layer is the load manager;

[0034] II. Constructing supply-side objective functions through energy operators with the goal of maximizing comprehensive economic benefits and minimizing carbon dioxide emissions;

[0035] III. Construct the demand-side objective function by taking the minimum electricity purchase cost as the target through the load manager;

[0036] IV. Based on the master-slave game mechanism in the energy market, a dichotomy method is used to find the balance of interests between energy operators and load managers, and a master-slave game energy trading model is established.

[0037] Optionally, the improved grey wolf algorithm is used to solve the master-slave game energy trading model to obtain a low-carbon economic optimization plan for the integrated energy system, including:

[0038] Step 1: The objective function before adding the fused magnesium load is taken as the reference value, the supply-side objective function and the demand-side objective function are normalized, and the normalized supply-side objective function and the demand-side objective function are merged into a single objective function;

[0039] Step 2: The global optimal solution is regarded as prey, and the other alternative solutions are regarded as a wolf pack. α represents the head wolf, responsible for decision-making; β assists α in leading the wolf pack, and will become the new α when α is vacant; the third layer is γ, which obeys α and β and commands other bottom-level wolves;

[0040] Step 3: Taking the single objective function as the fitness function, calculating the fitness function of each individual in the wolf population and the distance between the individual and the prey, and selecting the top three individuals in the contemporary population as new α, β and γ;

[0041] Step 4: Determine whether the number of iterations has reached the maximum. If so, proceed to step 5; otherwise, add 1 to the number of iterations and return to step 3;

[0042] Step 5: Output the new α, β and γ, and combine them with the average weight to obtain the system operation cost, system CO2 emissions and demand-side electricity purchase cost. Here, the average weight is set to 1 / 3.

[0043] In another aspect, the present invention also discloses a low-carbon economic optimization system for an integrated energy system considering fused magnesium load, comprising:

[0044] An index calculation module is used to determine the load matching index based on the actual load value and the combined output value of wind, solar and energy storage;

[0045] The cost calculation module is used to obtain the average cost per kilowatt-hour based on the wind, solar and storage installed capacity and the operation and maintenance cost coefficient, combined with the pre-built average cost per kilowatt-hour model;

[0046] The optimal capacity solution module is used to optimize the load matching degree and the average cost of electricity by using the NSGA-II algorithm to obtain the optimal wind, solar and storage installed capacity;

[0047] An optimization scheme determination module is used to establish a master-slave game energy trading model based on the optimal wind, solar and storage installed capacity and demand response, and solve the master-slave game energy trading model based on the improved grey wolf algorithm to obtain a low-carbon economic optimization scheme for the integrated energy system;

[0048] Among them, the pre-built average cost per kilowatt-hour model is constructed with the minimum cost per kilowatt-hour as the optimization goal and combined with constraint conditions.

[0049] Optionally, a model building module is also included for:

[0050] Taking the minimum electricity cost as the optimization goal, construct the objective function;

[0051] Setting constraints for the objective function;

[0052] Constructing an average electricity cost model based on the objective function and the constraint conditions;

[0053] Among them, the constraints include: wind power installed capacity constraints, photovoltaic installed capacity constraints, energy storage system installed capacity constraints, capacity constraints, charging and discharging state constraints, charging and discharging power constraints, wind turbine output constraints and photovoltaic unit output constraints.

[0054] Optionally, the objective function is as shown below:

[0055] f b =min(C)

[0056]

[0057] In the formula, f b is the minimum electricity cost, C is the electricity cost, L sys is the initial investment cost of the system, O sys_r is the annual operation and maintenance cost of the system, s is the discount rate, r is the number of years of discount, R is the total number of years of discount, E wt 、E pv 、E es They are the annual power generation of wind power and photovoltaic power and the storage capacity of energy storage units.

[0058] Optionally, the optimal capacity solving module is specifically used for:

[0059] I. Take the wind, solar and storage installed capacity that meets the constraints as individuals, form a population from all individuals, initialize the number of iterations, and set the maximum number of iterations;

[0060] II. The maximum load matching degree f a Transform into minimizing the objective function and calculate the maximum load matching degree f corresponding to each individual in the population a , minimum electricity cost f b ;

[0061] III. Calculate the non-dominated sorting and crowding degree of individuals in the population to obtain the individual dominance sorting level and crowding degree;

[0062] IV. Operate the individuals in the population according to the individual dominance ranking and crowding degree to generate a sub-population, and merge the sub-population with the parent population to form a new population P i+1, and at the same time, non-dominated sorting and crowding degree calculation are performed on the sub-population to obtain the individual dominated sorting level and crowding degree of the sub-population, where the operations include: selection, crossover, and mutation;

[0063] V. For population P i+1 Sort and filter to determine whether the number of iterations is greater than the maximum number of iterations. If not, return to step IV, otherwise go to step VI;

[0064] VI. Output the optimal population, and use the wind-solar-storage installed capacity corresponding to the optimal population as the optimal wind-solar-storage installed capacity, and end.

[0065] Optionally, the optimization scheme determination module includes:

[0066] The model building submodule is used to analyze the carbon emissions of the combined cooling, heating and power units and the integrated energy system after adding the fused magnesium load, and to establish a master-slave game energy trading model;

[0067] The solution submodule is used to solve the master-slave game energy trading model based on the improved grey wolf algorithm to obtain a low-carbon economic optimization plan for the integrated energy system.

[0068] Optionally, the model building submodule is specifically used for:

[0069] Ⅰ. According to the coupling relationship between sources and loads, the master-slave game energy trading model is divided into two layers: the upper layer is the energy operator, and the lower layer is the load manager;

[0070] II. Constructing supply-side objective functions through energy operators with the goal of maximizing comprehensive economic benefits and minimizing carbon dioxide emissions;

[0071] III. Construct the demand-side objective function by taking the minimum electricity purchase cost as the target through the load manager;

[0072] IV. Based on the master-slave game mechanism in the energy market, a dichotomy method is used to find the balance of interests between energy operators and load managers, and a master-slave game energy trading model is established.

[0073] Optionally, the solution submodule is specifically used for:

[0074] Step 1: The objective function before adding the fused magnesium load is taken as the reference value, the supply-side objective function and the demand-side objective function are normalized, and the normalized supply-side objective function and the demand-side objective function are merged into a single objective function;

[0075] Step 2: The global optimal solution is regarded as prey, and the other alternative solutions are regarded as a wolf pack. α represents the head wolf, responsible for decision-making; β assists α in leading the wolf pack, and will become the new α when α is vacant; the third layer is γ, which obeys α and β and commands other bottom-level wolves;

[0076] Step 3: Taking the single objective function as the fitness function, calculating the fitness function of each individual in the wolf population and the distance between the individual and the prey, and selecting the top three individuals in the contemporary population as new α, β and γ;

[0077] Step 4: Determine whether the number of iterations has reached the maximum. If so, proceed to step 5; otherwise, add 1 to the number of iterations and return to step 3;

[0078] Step 5: Output the new α, β and γ, and combine them with the average weight to obtain the system operation cost, system CO2 emissions and demand-side electricity purchase cost. Here, the average weight is set to 1 / 3.

[0079] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0080] The memory is used to store one or more programs;

[0081] When the one or more programs are executed by the at least one processor, a low-carbon economic optimization method for an integrated energy system taking into account the fused magnesium load as described above is implemented.

[0082] On the other hand, the present application also provides a readable storage medium having an execution program stored thereon, which, when executed, implements a low-carbon economic optimization method for an integrated energy system taking into account the molten magnesium load as described above.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] The present invention provides a low-carbon economic optimization method for an integrated energy system considering the fused magnesium load, including determining a load matching index based on an actual load value and a wind, solar, and storage combined output value; obtaining an average kilowatt-hour cost based on wind, solar, and storage installed capacity and an operation and maintenance cost coefficient in combination with a pre-constructed average kilowatt-hour cost model; taking load matching and average kilowatt-hour cost as optimization targets, optimizing using the NSGA-II algorithm to obtain an optimal wind, solar, and storage installed capacity; establishing a master-slave game energy trading model based on the optimal wind, solar, and storage installed capacity and demand response, and using an improved gray wolf algorithm to solve the master-slave game energy trading model to obtain an integrated energy system low-carbon economic optimization plan; wherein the pre-constructed average kilowatt-hour cost model is constructed with the minimum kilowatt-hour cost as the optimization target and in combination with constraints. The present invention uses an improved gray wolf algorithm to optimize and solve to obtain the optimal solution, thereby reducing the carbon emissions and carbon trading costs of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1A flow chart of a low-carbon economic optimization method for an integrated energy system considering fused magnesium load according to the present invention;

[0086] Figure 2 It is the flow chart of NSGA-II algorithm of the present invention;

[0087] Figure 3 It is a structural block diagram of the comprehensive energy system of the present invention;

[0088] Figure 4 It is a schematic diagram of the structure of the master-slave game two-layer optimization model of the present invention;

[0089] Figure 5 It is a solution flow chart based on the improved grey wolf algorithm of the present invention;

[0090] Figure 6 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0091] In view of the problems existing in the existing technology, the construction of wind, solar, storage and combined cooling, heating and power (CCHP) units, carbon capture system (CCS), power to gas (P2G) system and fused magnesium load is considered to construct an integrated energy system (IES). From the perspective of energy consumption on the demand side, the goal is to reduce carbon emissions on the demand side and increase the consumption rate of new energy. However, in order to balance the interests of both supply and demand, it is necessary to combine the actual situation of the supply and demand layers to construct appropriate trading strategies and response models to ensure the low-carbon economic operation of IES.

[0092] The present invention is used to reduce carbon emissions on the demand side and improve the new energy consumption rate. First, according to the load conditions on the demand side, a load matching index of the combined output of wind, solar and storage is established, and a corresponding power generation cost model is established. Finally, the installed capacity of wind, solar and storage is determined based on the NSGA-II algorithm. Then, the operation mechanisms of the combined cooling, heat and power (CCHP), carbon capture system (CCS) and power-to-gas (P2G) system are analyzed and corresponding models are established. Secondly, the carbon emissions of the CCHP unit and the system after adding the fused magnesium load are analyzed in turn. Finally, a master-slave game energy trading model considering the integrated demand response (IDR) is established, and the improved grey wolf algorithm is used to optimize and solve multiple objectives.

[0093] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0094] Embodiment 1:

[0095] A low-carbon economic optimization method for an integrated energy system considering fused magnesium load, such as Figure 1 As shown, including:

[0096] Step S1: Determine the load matching index based on the actual load value and the wind, solar and energy storage combined output value;

[0097] Step S2: Based on the wind, solar and storage installed capacity and the operation and maintenance cost coefficient, combined with the pre-built average cost per kilowatt-hour model, the average cost per kilowatt-hour is obtained;

[0098] Step S3: Taking load matching and average cost per kWh as optimization targets, the NSGA-II algorithm is used to optimize and obtain the optimal wind, solar and storage installed capacity;

[0099] Step S4: establishing a master-slave game energy trading model based on the optimal wind, solar and storage installed capacity and demand response, and using an improved grey wolf algorithm to solve the master-slave game energy trading model to obtain a low-carbon economic optimization plan for the integrated energy system;

[0100] Among them, the pre-built average cost per kilowatt-hour model is constructed with the minimum cost per kilowatt-hour as the optimization goal and combined with constraint conditions.

[0101] The present invention proposes a low-carbon economic optimization method for an integrated energy system considering the fused magnesium load, so as to achieve the matching of the installed capacity of new energy and the demand-side load, thereby improving the load matching degree and reducing the carbon trading cost of the integrated energy system. The present invention optimizes the installed capacity of wind, solar and storage and establishes an integrated energy system model, optimizes the installed capacity of wind, solar and storage using the non-dominated genetic optimization algorithm (NSGA-II), establishes a master-slave game energy trading model considering the comprehensive demand response, and adopts an improved gray wolf algorithm, with the comprehensive benefits and carbon emissions of energy operators and the electricity purchase costs of load managers as the objective functions for optimization, thereby obtaining the best operation plan. This method can be applied to enterprises and even regions with good applicability, economy and sociality.

[0102] The present invention can be widely applied to establish an optimized operation plan for an integrated energy system in a variety of scenarios, and improve the low-carbon economic optimization strategy of the integrated energy system considering the fused magnesium load by the gray wolf algorithm and the master-slave game. Based on the demand-side load power consumption, the load matching index and average kilowatt-hour cost model of the combined output of wind, solar and storage are established, and then the installed capacity of wind, solar and storage is optimized by the algorithm. Then, a model of the integrated energy system is established, and the carbon emissions of the CCHP unit and the system after the addition of the fused magnesium load are analyzed. Finally, a master-slave game energy trading model considering the integrated demand response (IDR) is established and the improved gray wolf algorithm is used to optimize and solve the problem. The present invention can better establish the optimal control plan for various scenarios such as enterprises / regions, has universal applicability, and can effectively reduce carbon emissions and promote the consumption of new energy.

[0103] Step S1: Determine the load matching index based on the actual load value and the wind, solar and storage combined output value, including: (1) Wind, solar and storage installed capacity

[0104] The present invention takes into account the load conditions on the demand side, constructs a load matching index and average kilowatt-hour cost model for the combined output of wind, solar and storage, and on this basis, takes the load matching degree and average kilowatt-hour cost as the optimization goals, and adopts the NSGA-II algorithm for optimization to obtain the optimal wind, solar and storage installed capacity.

[0105] The schematic diagram of the process of optimizing the wind, solar and storage installed capacity using the NSGA-II algorithm is as follows: Figure 2 ,In the figure, Gen represents the evolutionary generation, and Gen_max represents the maximum evolutionary generation.

[0106] The calculation formula for the load matching index of wind, solar and storage combined output is as follows:

[0107]

[0108] Among them, H S_L is the load matching degree, μ L ,μ eq are the standard deviations of the collected load values ​​and equivalent loads, respectively.

[0109]

[0110] Where L e (t) is the equivalent load at time t, which is the difference between the actual load and the combined output of wind, solar and energy storage. is the average load, t is a certain moment, T is the total time, and the formula is as follows:

[0111]

[0112] Where L(t) is the actual load value at time t, P C (t) is the combined output of wind, solar and energy storage.

[0113] P C (t) = P WT ×η WT (t)+P PH ×η PH (t)+P ES ×η ES (t)

[0114] Where P WT , P PH , P ES is the installed capacity of wind power, photovoltaic and energy storage units, η WT (t), η PH (t) and η ES (t) is the unit output rate.

[0115] First, construct the load matching degree H S_L The objective function is S_LThe larger the value is, the stronger the tracking ability of wind, solar and storage combined output is for load. The objective function f a as follows:

[0116] f a =max(H S_L )

[0117] Secondly, the average cost per kilowatt-hour model of wind, solar and energy storage combined output is constructed. The specific steps are as follows:

[0118] Ⅰ. Considering the constraints of wind, solar and energy storage operation

[0119] 1) Installed capacity constraints of wind power, photovoltaic and energy storage systems:

[0120]

[0121] In the formula, Q wt , Q pv , Q es They are the installed capacity of wind power, photovoltaic and energy storage units. Q wt , Q pv , Q es They are the lower limits of installed capacity of wind power, photovoltaic and energy storage units, They are the upper limits of installed capacity of wind power, photovoltaic and energy storage units respectively.

[0122] 2) Due to the involvement of the electric energy storage system, its capacity constraints, charge and discharge state constraints, charge and discharge power constraints and charge state constraints must also be considered:

[0123] Charge and discharge state constraints:

[0124]

[0125] in They respectively represent the charging and discharging state quantities of the i-th energy storage device in time period t.

[0126] Charge and discharge power constraints:

[0127]

[0128] Among them, P ch,max , P dis,max Respectively represent the maximum value of the charging power and discharging power of the energy storage device, P ch (i,t) is the charging power of the i-th energy storage device in time period t, P dis (i,t) is the discharge power of the i-th energy storage device in time period t.

[0129] State of Charge Constraints:

[0130] SOC(i,t)=Q es(i,t) / Q N

[0131]

[0132] 0.2≤SOC(i,t)≤0.95

[0133] Where SOC(i,t) is the state of charge of the i-th energy storage at time t, Q es (i,t) is the capacity of the i-th energy storage at time t, Q N is the rated capacity of the i-th energy storage, SOC(i,t-1) is the state of charge of the i-th energy storage at time t-1, Δt is the time interval, η ch is the charging efficiency, η dis is the discharge efficiency.

[0134] The capacity constraint consists of two parts, one is the maximum number of charge and discharge times constraint, and the other is the maximum energy storage capacity constraint. The capacity constraint has been involved in 1).

[0135] Charge and discharge times constraints:

[0136]

[0137] Where M and N represent the maximum number of charges and discharges within a specific time period, respectively.

[0138] 3) Output constraints of wind turbines and photovoltaic units

[0139]

[0140] in are the upper limits of wind turbine and photovoltaic output respectively, is the output of fan i at time t, is the output of PV unit i at time t.

[0141] Before step S2, the average cost per kilowatt-hour model is first introduced. The construction of the average cost per kilowatt-hour model includes:

[0142] Ⅱ. Based on the constraints in Ⅰ, the cost of the wind-solar-storage complementary system is considered, and the minimum cost per kWh is taken as the optimization goal. The objective function is f b =min(C)

[0143]

[0144] L sys =k wt Q wt +k pv Q pv +k es Q es

[0145] O sys-y =o wt-y Q wt +o pv-y Q pv +o es-y Q es

[0146] Among them, C is the cost per kilowatt-hour, L is sys is the initial investment cost of the system, O sys_r is the annual operation and maintenance cost of the system, s is the discount rate, r is the number of years of discount, R is the total number of years of discount, E wt 、E pv 、E es are the annual power generation of wind power and photovoltaic power and the storage capacity of energy storage units, Q wt , Q pv , Q es are the installed capacities of wind power, photovoltaic and energy storage units, k wt , k pv , k es are the unit construction cost coefficients of each unit, o wt-y , o pv-y , o es-y are the operation and maintenance cost coefficients of each unit in the current year.

[0147] Step S2: Based on the wind, solar and storage installed capacity and the operation and maintenance cost coefficient, combined with the pre-built average cost per kilowatt-hour model, the average cost per kilowatt-hour is obtained.

[0148] Step S3: Taking load matching and average cost per kWh as optimization targets, the optimal wind, solar and storage installed capacity is obtained by using the NSGA-II algorithm, which specifically includes:

[0149] The non-dominated genetic algorithm (NSGA-II) based on the reference point optimizes the installed capacity of wind, solar and storage. The flow chart is as follows Figure 2 As shown, the specific steps are as follows:

[0150] I. Randomly generate an initial population P that satisfies the constraints in the previous step i , the maximum load matching degree f a Transformed into minimizing the objective function and calculating the f corresponding to each individual in the population a 、f b value;

[0151] Ⅱ. Perform non-dominated sorting and crowding calculation on individuals in the population;

[0152] III. According to the individual dominance ranking and crowding degree, the individuals in the population are selected, crossed, mutated, and other operations are performed to generate a new population, and the child population is merged with the parent population to form a new population P i+1 , and perform non-dominated sorting and crowding calculation on the sub-populations at the same time;

[0153] IV. Sort and screen the population, and repeat the above steps until the algorithm iteration is completed.

[0154] Step S4: Based on the optimal wind, solar and storage installed capacity and demand response, a master-slave game energy trading model is established, and the improved grey wolf algorithm is used to solve it to obtain a low-carbon economic optimization plan for the integrated energy system, which specifically includes:

[0155] The present invention constructs a working model of the integrated energy system based on the operating mechanisms and corresponding operating constraints of wind, solar and storage units, CCHP units, CCS and P2G units, such as Figure 3 As shown, the carbon emissions of the integrated energy system and the carbon emissions of the fused magnesium load are then analyzed. Finally, the master-slave game energy trading strategy considering integrated demand response (IDR) is adopted, and the optimization solution is performed based on the improved grey wolf algorithm.

[0156] First, establish the working models and operation constraints of CCHP units, CCS and P2G systems respectively. The working models and operation constraints of wind, solar and storage units are shown in the above steps. The specific steps are as follows:

[0157] Ⅰ. Relationship between CCHP unit's power generation, heat generation, cooling capacity and natural gas consumption:

[0158] The relationship between the power generation and heat production of CCHP units and the natural gas consumption is as follows:

[0159]

[0160] Where P CCHP_e,t , P CCHP_h,t , P CCHP_c,t , P CCHP,t are the power generation, heat generation, cooling capacity of the CCHP unit at time t and the gas power consumed by the CCHP unit, η e , η h are the power generation efficiency and heat generation efficiency of the CCHP unit, η c is the cooling efficiency of the absorption chiller in the CCHP unit, is the natural gas consumption of the CCHP unit at time t, The heat output per unit of natural gas combustion.

[0161] II. Energy consumption and natural gas generation model of P2G equipment:

[0162]

[0163] in P P2G,t are the gas production power of the P2G device at time t and the power provided by the CCHP unit to the P2G device, is the amount of CO2 required by the P2G equipment to produce natural gas at time t, η reactor is the gas production efficiency of the P2G equipment, is the CO2 conversion efficiency of the P2G device.

[0164] III. Electricity consumption model of CCS equipment and electricity consumption model of CO2 capture:

[0165] P CCS,t =P CCS_cl,t +P CCS_c,t

[0166]

[0167] Where P CCS,t is the electrical power consumed by the CCS equipment, P CCS_cl,t is the fixed consumption of CCS equipment, P CCS_c,t is the energy consumption of CCS to capture CO2, γ is the conversion coefficient of the electric energy consumed by CCS to capture CO2, is the amount of CO2 captured.

[0168] IV. The constraints of CCHP units include two parts: output constraints and climbing constraints.

[0169] Output constraints:

[0170]

[0171] in They are the lower limit constraints of the CCHP unit’s electrical output, thermal output, cooling output, and grid-connected power; are the upper limits of the CCHP unit’s electrical output, thermal output, cooling output, and grid-connected power; P CCHP_e,t , P CCHP_h,t , P CCHP_c,t , P CCHP_sy,t They are the electrical output, thermal output, cooling output and grid-connected power of the CCHP unit at time t.

[0172] Hill climbing constraints:

[0173]

[0174] in are the lower and upper climbing limits of the CCHP unit, P CCHP,t , P CCHP,t-1They are the output of the CCHP unit at time t and time t-1, including electrical output, thermal output and cooling output.

[0175] Ⅴ. The output constraints and climbing constraints of P2G equipment are as follows:

[0176] Output constraints:

[0177]

[0178] Hill climbing constraints:

[0179]

[0180] in are the minimum operating power of the P2G equipment and the gas output power, are the maximum operating power of the P2G equipment and the maximum gas output power, are the upper and lower limits of the ramp rate of the P2G device, P P2G,t is the operating power of the P2G device at time t, is the gas output power of the P2G device at time t, P P2G,t-1 is the operating power of the P2G device at time t-1.

[0181] VI. The operating constraints of CCS equipment include: operating power constraints and ramp constraints of CCS equipment during CO2 capture:

[0182] Operating power constraints:

[0183]

[0184] in are the upper and lower limits of the operating power of the CCS equipment, P CCS,t is the operating power of the CCS equipment at time t.

[0185] Hill climbing constraints:

[0186]

[0187] in are the upper and lower limits of the ramp rate of the CCS equipment, P CCS,t-1 is the operating power of the CCS equipment at time t-1.

[0188] Secondly, analyze the overall operation of the integrated energy system. The specific steps are as follows:

[0189] Ⅰ. The electrical power, thermal power and cooling power of the entire integrated energy system are kept balanced:

[0190]

[0191] Where P pv,t , P wd,t are the electric power output of the wind turbine and photovoltaic unit at time t, P ess_ch,t , P ess_dis,t are the charging and discharging power of the energy storage unit at time t, P buy_e,t is the purchased electricity of the integrated energy system at time t, P CCHP_e,t is the electric power output of the CCHP unit at time t, P CCHP_sy,t is the grid-connected power of the CCHP unit at time t, P load_e,t , P load_h,t , P load_c,t are the electrical load, heat load and cooling load at time t, respectively, CCS,t is the operating power of the CCS equipment at time t, P P2G,t is the power provided by CCHP unit to P2G equipment, P CCHP_h,t is the thermal output power of the CCHP unit at time t, P CCHP_c,t is the cooling output power of the CCHP unit at time t.

[0192] II. Constraints on the amount of CO2 captured by CCS equipment:

[0193]

[0194] in are the amount of CO2 captured by CCS and the amount of CO2 emitted by CCHP, respectively.

[0195] III. Analyze the carbon emissions of CCHP units in the integrated energy system and the carbon emissions after adding fused magnesium load.

[0196] Carbon emissions from CCHP units as follows:

[0197]

[0198] in is the carbon emission coefficient of CCHP unit, σ 1_h , σ 1_c They are the electricity-to-heat and electricity-to-cold conversion coefficients, respectively.

[0199] Add fused magnesium load and establish a comprehensive energy system with fused magnesium load participation. The specific steps are as follows:

[0200] Ⅰ. Analyze the load operation characteristics of the fused magnesium furnace, as follows:

[0201] The state of the fused magnesium furnace can be adjusted during operation, and its active power P Mg (t) are as follows:

[0202]

[0203] in is the active power of the fused magnesium furnace under normal operation, G Mg (t) is the number of fused magnesium furnaces running at time t; P S , P X They represent the upward and downward adjustment margins of the fused magnesium furnace at time t respectively; The state variables respectively represent the power increase and decrease of the fused magnesium furnace at time t. A value of 1 indicates that the power of the fused magnesium furnace is increased / decreased at time t, and vice versa.

[0204] The power fluctuation of the electric fused magnesium furnace group is relatively large during operation, and it is often required that the power fluctuation amplitude should not exceed the specified value. Here, it is stipulated that it should not exceed 90% to 95% of the specified value. The limitation of the power fluctuation amplitude of the electric arc furnace group during operation is expressed as follows:

[0205]

[0206] in It represents the average power of the nth electric magnesium furnace from the beginning of smelting to the t1th minute, is the upper limit of power fluctuation, P N is the maximum power allowed for the operation of the fused magnesium furnace group, p n (t) is the power of the nth fused magnesium furnace at time t, T is the total smelting time, N is the total number of fused magnesium furnaces, t k is the smelting time required for the kth batch of the electric fused magnesium furnace.

[0207] Ⅱ. Establish the power regulation constraints and regulation times constraints for the operation of the fused magnesium furnace, as follows:

[0208] Power regulation constraints:

[0209] p Mg,min ≤p Mg_e,t ≤p Mg,max

[0210] where p Mg,max and p Mg,min are the maximum and minimum operating power of the fused magnesium furnace under the premise of ensuring safe operation, p Mg_e,t It is the operating power of the electric fused magnesium furnace under the premise of ensuring safe operation.

[0211] Adjustment times constraint:

[0212]

[0213] Where T is the total melting time, M is the maximum number of adjustments. The above formula shows that there is a limit to the total number of adjustments in the entire adjustment cycle.

[0214] During the adjustment process, the fused magnesium furnace cannot be adjusted across levels. The specific constraints are as follows:

[0215]

[0216] In the formula, is the gear position of the fused magnesium furnace when it is in the upward state at time t, is the gear position of the fused magnesium furnace when it is in the upward state at time t-1, is the gear position of the fused magnesium furnace when it is in the downward state at time t, It is the gear position of the electric fused magnesium furnace when it is in the downward state at time t-1.

[0217] During adjustment, the fused magnesium furnace needs to wait for a period of time at each gear. The specific constraints are as follows:

[0218] 0≤t wait ≤t max

[0219] Where, t wait is the waiting time, t max is the maximum waiting time.

[0220] III. When magnesite production adopts the one-step method, 0.5 tons of CO2 will be emitted for every ton of ore smelted. The CO2 emission estimation model is as follows:

[0221]

[0222] in, and are the amount of CO2 emitted from one furnace of fused magnesium smelting and the weight of magnesite ore consumed in one furnace, N(0,σ 2 ) is the standard Gaussian distribution, is the fluctuation of CO2 emissions, which satisfies the Gaussian distribution.

[0223] IV. After adding fused magnesium load, the amount of CO2 captured by CCS equipment is constrained:

[0224]

[0225] in They are the amount of CO2 captured by the CCS equipment and the amount of CO2 emitted by the CCHP unit and the magnesite load.

[0226] Ⅴ. After adding the fused magnesium load, the main consideration here is the power balance of the system:

[0227] P pv,t +Pwd,t +P buy_e,t +P ess_dis,t +P CCHP_e,t =

[0228] P CCHP_sy,t +P CCS,t +P P2G,t +P ess_ch,t +P Mg_e,t +P load_e,t

[0229] Where P Mg_e,t is the total load of the fused magnesium enterprise at time t, and other parameters are the same as above.

[0230] Then, a master-slave game energy trading strategy considering integrated demand response (IDR) is adopted on the load side, such as Figure 4 As shown, the specific steps are as follows:

[0231] Ⅰ. According to the coupling relationship between source and load, it is divided into two layers: the upper layer is the energy operator and the lower layer is the load manager.

[0232] II. Energy operators manage the energy supply equipment of the entire energy system and set prices for energy including electricity, heat, cooling, gas, etc.

[0233] III. Manage different types of users on the load side through load managers and make reasonable allocations;

[0234] IV. Based on the master-slave game mechanism in the energy market, a dichotomy method is used to find the balance of interests between the two. Finally, an optimization solution method for the improved grey wolf algorithm is proposed on the supply side. The specific process is as follows:

[0235] Ⅰ. Energy operators control energy prices such as electricity, heating, cooling, and gas with the goal of maximizing comprehensive economic benefits and minimizing CO2 emissions.

[0236] 1) The comprehensive economic benefits of energy operators are maximized, that is, the comprehensive operating costs are minimized. The economic objective function is as follows:

[0237] minf1=C buy +C dev +C pen +C co2 -C sell

[0238] Among them C buy , C dev , C pen , and C sellThey are respectively the energy operator’s energy purchase cost, equipment cost, penalty cost, carbon trading cost and sales revenue, and f1 is the energy operator’s comprehensive operating cost.

[0239] Energy purchase cost model of energy operators C buy as follows:

[0240]

[0241] where p gas , They are the unit price of natural gas per unit volume, the power grid purchase price, and the power grid sales price; They are the volume of natural gas purchased, the amount of electricity purchased from the grid, and the amount of electricity sold to the grid.

[0242] Equipment cost model C for energy operators dev as follows:

[0243]

[0244] Where S is the type of device, s is the serial number of the device type, The unit installation and maintenance costs of the equipment, L s and I s are the service life and installation capacity of the equipment, M s is the investment value of the equipment, and ρ is the base discount rate.

[0245] The penalty cost is the penalty cost for abandoning wind and solar power. The penalty cost model C pun as follows:

[0246]

[0247] Where m and n are the penalty cost coefficients for abandoning solar and wind power, P abandon_WT,t , P abandon_PV,t are the wind power and solar power abandonment at time t respectively.

[0248] In the context of tiered carbon trading prices, carbon trading costs The mathematical model is:

[0249]

[0250] E IES,t =E IES_a -E IES_0

[0251] Where θ is the base price of carbon trading, d is the length of the carbon emission interval, λ is the price growth rate, and E IES,t is the carbon trading volume at time t, E IES_0 and E IES_aThey are the initial carbon emission quota and the actual carbon emission quota respectively.

[0252] 2) Energy operators aim to minimize carbon dioxide emissions:

[0253]

[0254] Among them, f2 is the carbon dioxide emission, They are the amount of CO2 emitted by the CCHP unit at time t, the CO2 captured by the CCS equipment, and the amount of CO2 corresponding to the amount of electricity purchased from the power grid. The mathematical model is as follows:

[0255]

[0256] Where K is the carbon emission coefficient when purchasing electricity from the power grid, is the purchased electricity at time t.

[0257] II. The load manager aims to minimize the electricity purchase cost, and its main control variables are the electricity, heating, cooling and gas loads participating in demand response;

[0258]

[0259] Among them, f3 is the electricity cost on the load side, is the energy purchase cost, C equ_main is the equipment maintenance cost, C IDR The compensation cost for loads participating in demand-side response.

[0260] Energy purchase cost The mathematical model is as follows:

[0261]

[0262] where λ e,t , h,t , g,t , c,t are the unit prices of electricity, heat, gas and cooling at time t, respectively, load_e,t , P load_h,t , P load_g,t , P load_c,t They are respectively the electricity, heat, gas and cooling consumption at time t.

[0263] The equipment maintenance cost model is as follows:

[0264] C equ_main =cS MT

[0265] Among them, C equ_main is the equipment maintenance cost, c is the equipment maintenance cost coefficient, S MTThe capacity of the device.

[0266] Compensation cost for participating in demand-side response C IDR The model is as follows:

[0267]

[0268] where λ cur , tran are the price supplement coefficients for curtailable and transferable loads, are the load that can be reduced and transferred at time t respectively.

[0269] III. Comprehensively consider the objective functions of the supply side and the demand side. There are three objective functions: minimum system operating cost, minimum system CO2 emissions, and minimum demand side electricity purchase cost. Since the objective function has inconsistent dimensions, the normalization method is adopted. In order to reflect the changes in the system before and after the addition of the fused magnesium load, the operating cost of the system before the addition of the fused magnesium load is taken as f 01 、System CO2 emissions f 02 and demand side electricity purchase cost f 03 For benchmark purposes, the normalized expression is as follows:

[0270]

[0271] Among them, F is the total cost, f 01 、f 02 、f 03 are the operating cost, system CO2 emissions and demand-side electricity purchase cost of the system before adding the fused magnesium load, f1, f2, f3 are the operating cost, system CO2 emissions and demand-side electricity purchase cost of the system after adding the fused magnesium load, κ1, κ2, κ3 are the weights of the three objective functions.

[0272] After converting the multi-objective problem into a single-objective problem, the improved grey wolf algorithm is used to optimize and solve the problem. The solution process is as follows: Figure 5 shown.

[0273] Based on the supply-side objective function and demand-side objective function in the master-slave game energy trading model, as well as the constraints, the maximum and minimum values ​​of the demand and supply sides are determined;

[0274] After determining the objective function, the objective function is solved next. Since the objective function has inconsistent dimensions, the objective function is first normalized, and the objective function before adding the fused magnesium load is selected as the benchmark value. At the same time, in order to simplify the solution process, the objective functions of the supply side and the demand side after normalization are combined into a single objective function. The results are as follows:

[0275]

[0276] where f 01 、f 02 、f 03 are the operating cost, system CO2 emissions and demand-side electricity purchase cost of the system before adding the fused magnesium load, f1, f2, f3 are the operating cost, system CO2 emissions and demand-side electricity purchase cost of the system after adding the fused magnesium load, κ1, κ2, κ3 are the weights of the three objective functions.

[0277] After the simplification process, the solution process of the gray wolf algorithm was improved. The solution process of the gray wolf algorithm simulates the hunting behavior of gray wolves, regards the global optimal solution of the wolf as prey, and regards the other alternative solutions as a wolf population. There is a strict hierarchy within the population, in which α represents the head wolf, who is the leader of the population and is responsible for decision-making; β assists α in leading the wolf pack, and when α is vacant, it will become the new α; the third layer is γ, which obeys α and β, but can command other bottom-level wolves. The bottom layer is ω, which is responsible for the balance within the entire population. In the algorithm, α represents the individual with the best fitness value. Similarly, the second-best and third-best individuals are β and γ. The optimization process is the process of determining the change in the position of the population based on the distance between α, β and γ and the prey, thereby approaching the prey. The mathematical expression is:

[0278] D=|CM p (t)-M(t)|

[0279] M(t+1)=M p (t)-AD

[0280] Where D represents the distance between the wolf and its prey. The following formula is the way the gray wolf updates its position. t is the number of iterations.

[0281] Number, M p (t) is the position of the prey of the tth generation, M(t) is the position of the gray wolf individuals of the tth generation, and A and C are coefficient vectors.

[0282] A=2pr2-p

[0283] C=2r1

[0284] p=2(1-t / N _lter )

[0285] Among them, r1, r2 are random numbers in [0,1], p is the convergence factor in [0,2] that decreases linearly with the number of iterations, N _lter Indicates the total number of iterations.

[0286] In order to improve the situation that the traditional grey wolf algorithm is prone to fall into the local optimal solution, the linear decreasing process of the convergence factor is improved, and the nonlinear change process of the convergence factor is proposed:

[0287]

[0288] Where e is the natural base.

[0289] The mathematical model of the wolf pack tracking prey is as follows:

[0290] D i =|C k M i (t)-M(t)|,i∈{α,β,γ},k∈{1,2,3}

[0291] M k =M i -A k D i ,i∈{α,β,γ},k={1,2,3}

[0292]

[0293] Where D i is the distance between individual i and prey, M i (t) is the position of the t-generation prey corresponding to individual i, M(t) is the position of the t-generation gray wolf individual, M k is the displacement of ω in the direction of individual i, M (t+1) is the final position of the gray wolf individual, A k , C k is the coefficient vector, and the above is the solution model of the gray wolf optimization algorithm. Based on the traditional gray wolf optimization algorithm, an elite individual selection strategy is proposed, that is, each time the top three individuals in the fitness function are selected from the contemporary population as new α, β and γ, without comparing with the parent generation. The improved gray wolf optimization algorithm is used to solve the objective function. The solution process is as follows Figure 5 shown.

[0294] The above solution process is to find a balance point of conflicting interests in the maximum and minimum value ranges of the demand side and the supply side. The operating parameters corresponding to the supply side value and the demand side value corresponding to the balance point are used as the comprehensive energy low-carbon economic optimization plan.

[0295] Example 2

[0296] The present invention proposes a low-carbon economic optimization method for an integrated energy system considering the fused magnesium load, taking into account the load on the demand side of the integrated energy system, so as to improve the matching degree between the output of new energy and the demand side and reduce the average cost of electricity, and optimize the installed capacity of new energy on this basis, and then consider the carbon trading cost of the system and the carbon trading cost of the system after adding the fused magnesium load, construct an energy trading model considering the master-slave game of IDR and adopt the improved gray wolf algorithm to solve it, so as to obtain the optimal solution, and the detailed steps are as follows:

[0297] Step 1: Establish load matching indicators for wind, solar and energy storage combined output

[0298] Step 2: Build the average cost per kilowatt-hour of wind, solar and energy storage combined output;

[0299] Step 3: Based on the historical load, the NSGA-II algorithm is used to optimize the installed capacity of wind, solar and storage;

[0300] Step 4: Analyze the low-carbon economic issues of combined cooling, heating and power (CCHP) units, carbon capture systems (CCS) and power-to-gas (P2G) systems;

[0301] Step 5: Establish a comprehensive energy system, including wind, solar and storage, CCHP-CCS-P2G units and various loads;

[0302] Step 6: Analyze the carbon emissions of CCHP units in the integrated energy system;

[0303] Step 7: Establish a comprehensive energy system that incorporates fused magnesium load;

[0304] Step 8: On the load side, a master-slave game energy trading strategy considering integrated demand response (IDR) is adopted;

[0305] Step 9: On the supply side, an optimization method based on the improved grey wolf algorithm is proposed.

[0306] The present invention relates to the field of integrated energy system management. Based on two indicators, namely, the matching degree between the output of new energy and the load on the demand side and the average cost per kilowatt-hour of the output of new energy, the NSGA-II algorithm is adopted to optimize the installed capacity of new energy. The carbon trading cost of the integrated energy system and the carbon trading cost of the system after adding the fused magnesium load are considered. An energy trading model is constructed by considering the IDR, and an improved grey wolf algorithm is adopted to optimize and solve the optimal solution, thereby reducing the carbon emissions and carbon trading costs of the system. A low-carbon economic optimization strategy for an integrated energy system considering the fused magnesium load based on the improved grey wolf algorithm and master-slave game is the key point and protection point of the present invention.

[0307] Example 3

[0308] The present invention based on the same inventive concept also provides a low-carbon economic optimization system for an integrated energy system considering fused magnesium load, comprising:

[0309] An index calculation module is used to determine the load matching index based on the actual load value and the combined output value of wind, solar and energy storage;

[0310] The cost calculation module is used to obtain the average cost per kilowatt-hour based on the wind, solar and storage installed capacity and the operation and maintenance cost coefficient, combined with the pre-built average cost per kilowatt-hour model;

[0311] The optimal capacity solution module is used to optimize the load matching degree and the average cost of electricity by using the NSGA-II algorithm to obtain the optimal wind, solar and storage installed capacity;

[0312] An optimization scheme determination module is used to establish a master-slave game energy trading model based on the optimal wind, solar and storage installed capacity and demand response, and adopt an improved grey wolf algorithm to solve it to obtain a low-carbon economic optimization scheme for the integrated energy system;

[0313] Among them, the pre-built average cost per kilowatt-hour model is constructed with the minimum cost per kilowatt-hour as the optimization goal and combined with constraint conditions.

[0314] Optionally, a model building module is also included for:

[0315] Taking the minimum electricity cost as the optimization goal, construct the objective function;

[0316] Setting constraints for the objective function;

[0317] Constructing an average electricity cost model based on the objective function and the constraint conditions;

[0318] Among them, the constraints include: wind power installed capacity constraints, photovoltaic installed capacity constraints, energy storage system installed capacity constraints, capacity constraints, charging and discharging state constraints, charging and discharging power constraints, wind turbine output constraints and photovoltaic unit output constraints.

[0319] Example 4

[0320] like Figure 6 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0321] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits.

[0322] (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of a low-carbon economic optimization method for an integrated energy system considering the fused magnesium load in the above-mentioned embodiment.

[0323] Example 5

[0324] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a low-carbon economic optimization method for an integrated energy system considering the fused magnesium load in the above embodiment.

[0325] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0326] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0327] 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.

[0328] 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.

[0329] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A low-carbon economic optimization method for an integrated energy system considering fused magnesium load, characterized in that: include: Determine the load matching index based on the actual load value and the combined output value of wind, solar and energy storage; Based on the wind, solar and storage installed capacity and the operation and maintenance cost coefficient, combined with the pre-built average cost per kilowatt-hour model, the average cost per kilowatt-hour is obtained; Taking load matching and average cost per kilowatt-hour as optimization objectives, the NSGA-II algorithm is used to optimize the optimal wind, solar and storage installed capacity. A master-slave game energy trading model is established based on the optimal wind, solar and storage installed capacity and demand response, and an improved grey wolf algorithm is used to solve the master-slave game energy trading model to obtain a low-carbon economic optimization plan for the integrated energy system; Among them, the pre-built average cost per kilowatt-hour model is constructed with the minimum cost per kilowatt-hour as the optimization goal and combined with constraint conditions.

2. The method according to claim 1, characterized in that The construction of the average cost per kilowatt-hour model includes: Taking the minimum electricity cost as the optimization goal, construct the objective function; Setting constraints for the objective function; Constructing an average electricity cost model based on the objective function and the constraint conditions; Among them, the constraints include: wind power installed capacity constraints, photovoltaic installed capacity constraints, energy storage system installed capacity constraints, capacity constraints, charging and discharging state constraints, charging and discharging power constraints, wind turbine output constraints and photovoltaic unit output constraints.

3. The method according to claim 2, characterized in that The objective function is shown in the following formula: f b =min(C) In the formula, f b is the minimum electricity cost, C is the electricity cost, L sys is the initial investment cost of the system, O sys_r is the annual operation and maintenance cost of the system, s is the discount rate, r is the number of years of discount, R is the total number of years of discount, E wt 、E pv 、E es They are the annual power generation of wind power and photovoltaic power and the storage capacity of energy storage units.

4. The method according to claim 1, characterized in that The optimal wind, solar and storage installed capacity obtained by optimizing the load matching degree and the average cost of electricity using the NSGA-II algorithm includes: I. Take the wind, solar and storage installed capacity that meets the constraints as individuals, form a population from all individuals, initialize the number of iterations, and set the maximum number of iterations; II. The maximum load matching degree f a Transform into minimizing the objective function and calculate the maximum load matching degree f corresponding to each individual in the population a , minimum electricity cost f b ; III. Calculate the non-dominated sorting and crowding degree of individuals in the population to obtain the individual dominance sorting level and crowding degree; IV. Operate the individuals in the population according to the individual dominance ranking and crowding degree to generate a sub-population, and merge the sub-population with the parent population to form a new population P i+1 , and at the same time, non-dominated sorting and crowding degree calculation are performed on the sub-population to obtain the individual dominated sorting level and crowding degree of the sub-population, where the operations include: selection, crossover, and mutation; V. For population P i+1 Sort and filter to determine whether the number of iterations is greater than the maximum number of iterations. If not, return to step IV, otherwise go to step VI; VI. Output the optimal population, and use the wind-solar-storage installed capacity corresponding to the optimal population as the optimal wind-solar-storage installed capacity, and end.

5. The method according to claim 1, characterized in that The master-slave game energy trading model is established based on the optimal wind, solar and storage installed capacity and demand response, including: Based on the optimal wind, solar and storage installed capacity, the working models and operation constraints of the combined heat and power unit, the carbon capture system and the power-to-gas system are established respectively; Construct an integrated energy system based on the working modes and operating constraints of wind, solar and storage units, combined cooling, heat and power units, carbon capture systems and power-to-gas systems; The carbon emissions of the combined heating, cooling and power units and the integrated energy system after adding the fused magnesium load are analyzed, and a master-slave game energy trading model is established.

6. The method according to claim 5, characterized in that The operation constraints include: Electric power balance constraints, CO2 amount constraints captured by the carbon capture system, and cooling / heating load constraints.

7. The method according to claim 5, characterized in that The carbon emissions of the combined cooling, heating and power units and the integrated energy system after adding the fused magnesium load are analyzed, and a master-slave game energy trading model is established, including: Ⅰ. According to the coupling relationship between sources and loads, the master-slave game energy trading model is divided into two layers: the upper layer is the energy operator, and the lower layer is the load manager; II. Constructing supply-side objective functions through energy operators with the goal of maximizing comprehensive economic benefits and minimizing carbon dioxide emissions; III. Construct the demand-side objective function by taking the minimum electricity purchase cost as the target through the load manager; IV. Based on the master-slave game mechanism in the energy market, a dichotomy method is used to find the balance of interests between energy operators and load managers, and a master-slave game energy trading model is established.

8. The method according to claim 7, characterized in that The improved grey wolf algorithm is used to solve the master-slave game energy trading model to obtain a low-carbon economic optimization plan for the integrated energy system, including: Step 1: The objective function before adding the fused magnesium load is taken as the reference value, the supply-side objective function and the demand-side objective function are normalized, and the normalized supply-side objective function and the demand-side objective function are merged into a single objective function; Step 2: The global optimal solution is regarded as prey, and the other alternative solutions are regarded as a wolf pack. α represents the head wolf, responsible for decision-making; β assists α in leading the wolf pack, and will become the new α when α is vacant; the third layer is γ, which obeys α and β and commands other bottom-level wolves; Step 3: Taking the single objective function as the fitness function, calculating the fitness function of each individual in the wolf population and the distance between the individual and the prey, and selecting the top three individuals in the contemporary population as new α, β and γ; Step 4: Determine whether the number of iterations has reached the maximum. If so, proceed to step 5; otherwise, add 1 to the number of iterations and return to step 3; Step 5: Output the new α, β and γ, and combine them with the average weight to obtain the system operating cost, system CO2 emissions and demand-side electricity purchase cost.

9. A low-carbon economic optimization system for an integrated energy system considering fused magnesium load, characterized in that: include: An index calculation module is used to determine the load matching index based on the actual load value and the combined output value of wind, solar and energy storage; The cost calculation module is used to obtain the average cost per kilowatt-hour based on the wind, solar and storage installed capacity and the operation and maintenance cost coefficient, combined with the pre-built average cost per kilowatt-hour model; The optimal capacity solution module is used to optimize the load matching degree and the average cost of electricity by using the NSGA-II algorithm to obtain the optimal wind, solar and storage installed capacity; An optimization scheme determination module is used to establish a master-slave game energy trading model based on the optimal wind, solar and storage installed capacity and demand response, and use an improved grey wolf algorithm to solve the master-slave game energy trading model to obtain a low-carbon economic optimization scheme for the integrated energy system; Among them, the pre-built average cost per kilowatt-hour model is constructed with the minimum cost per kilowatt-hour as the optimization goal and combined with constraint conditions.

10. The system according to claim 9, characterized in that Also included are model building modules for: Taking the minimum electricity cost as the optimization goal, construct the objective function; Setting constraints for the objective function; Constructing an average electricity cost model based on the objective function and the constraint conditions; Among them, the constraints include: wind power installed capacity constraints, photovoltaic installed capacity constraints, energy storage system installed capacity constraints, capacity constraints, charging and discharging state constraints, charging and discharging power constraints, wind turbine output constraints and photovoltaic unit output constraints.

11. The system according to claim 10, characterized in that The objective function is shown in the following formula: f b =min(C) In the formula, f b is the minimum electricity cost, C is the electricity cost, L sys is the initial investment cost of the system, O sys_r is the annual operation and maintenance cost of the system, s is the discount rate, r is the number of years of discount, R is the total number of years of discount, E wt 、E pv 、E es They are the annual power generation of wind power and photovoltaic power and the storage capacity of energy storage units.

12. The system according to claim 9, characterized in that The optimal capacity solving module is specifically used for: I. Take the wind, solar and storage installed capacity that meets the constraints as individuals, form a population from all individuals, initialize the number of iterations, and set the maximum number of iterations; II. The maximum load matching degree f a Transform into minimizing the objective function and calculate the maximum load matching degree f corresponding to each individual in the population a , minimum electricity cost f b ; III. Calculate the non-dominated sorting and crowding degree of individuals in the population to obtain the individual dominance sorting level and crowding degree; IV. Operate the individuals in the population according to the individual dominance ranking and crowding degree to generate a sub-population, and merge the sub-population with the parent population to form a new population P i+1 , and at the same time, non-dominated sorting and crowding degree calculation are performed on the sub-population to obtain the individual dominated sorting level and crowding degree of the sub-population, where the operations include: selection, crossover, and mutation; V. For population P i+1 Sort and filter to determine whether the number of iterations is greater than the maximum number of iterations. If not, return to step IV, otherwise go to step VI; VI. Output the optimal population, and use the wind-solar-storage installed capacity corresponding to the optimal population as the optimal wind-solar-storage installed capacity, and end.

13. The system of claim 9, wherein: The optimization scheme determination module includes: The model building submodule is used to analyze the carbon emissions of the combined cooling, heating and power units and the integrated energy system after adding the fused magnesium load, and to establish a master-slave game energy trading model; The solving submodule is used to solve the master-slave game energy trading model by using the improved grey wolf algorithm to obtain a low-carbon economic optimization plan for the integrated energy system.

14. The system of claim 13, wherein: The model building submodule is specifically used for: Ⅰ. According to the coupling relationship between sources and loads, the master-slave game energy trading model is divided into two layers: the upper layer is the energy operator, and the lower layer is the load manager; II. Constructing supply-side objective functions through energy operators with the goal of maximizing comprehensive economic benefits and minimizing carbon dioxide emissions; III. Construct the demand-side objective function by taking the minimum electricity purchase cost as the target through the load manager; IV. Based on the master-slave game mechanism in the energy market, a dichotomy method is used to find the balance of interests between energy operators and load managers, and a master-slave game energy trading model is established.

15. The system of claim 13, wherein: The solution submodule is specifically used for: Step 1: The objective function before adding the fused magnesium load is taken as the reference value, the supply-side objective function and the demand-side objective function are normalized, and the normalized supply-side objective function and the demand-side objective function are merged into a single objective function; Step 2: The global optimal solution is regarded as prey, and the other alternative solutions are regarded as a wolf pack. α represents the head wolf, responsible for decision-making; β assists α in leading the wolf pack, and will become the new α when α is vacant; the third layer is γ, which obeys α and β and commands other bottom-level wolves; Step 3: Taking the single objective function as the fitness function, calculating the fitness function of each individual in the wolf population and the distance between the individual and the prey, and selecting the top three individuals in the contemporary population as new α, β and γ; Step 4: Determine whether the number of iterations has reached the maximum. If so, proceed to step 5; otherwise, add 1 to the number of iterations and return to step 3; Step 5: Output the new α, β and γ, and combine them with the average weight to obtain the system operating cost, system CO2 emissions and demand-side electricity purchase cost.

16. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a low-carbon economic optimization method for an integrated energy system taking into account fused magnesium load as described in any one of claims 1 to 8 is implemented.

17. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a low-carbon economic optimization method for an integrated energy system taking into account the molten magnesium load as described in any one of claims 1 to 8 is implemented.

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