Electricity-carbon collaborative industrial park energy optimization scheduling method and system

Abstract

The invention discloses an electricity-carbon collaborative industrial park energy optimization scheduling method and system. The method comprises the following steps: establishing an industrial park production full-link electricity consumption model and a transferable load demand response model; establishing an electricity-carbon coupling price model based on the time-of-use electricity price and the floating carbon price; an industrial park comprehensive energy double-layer optimization model with electricity-carbon coupling price transmission is constructed, an upper-layer model is an energy consumption price optimization decision model with the minimum operation cost of an industrial park comprehensive energy system as the target, and a lower-layer model is a user energy consumption optimization model with the optimal energy consumption utility as the target; and solving the industrial park comprehensive energy double-layer optimization model by adopting an improved particle swarm to obtain an optimal electricity-carbon coupling price and optimal power of each link of industrial production, and carrying out electricity-carbon collaborative industrial park energy optimization scheduling. The low-carbon target is achieved, meanwhile, the energy utilization efficiency is improved, and effective conduction of the carbon value is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial park energy optimization, and relates to an electricity-carbon coordinated industrial park energy optimization scheduling method and system. Background Art

[0002] The increasing prominence of energy and environmental issues has prompted changes in the way humans consume energy. Improving energy efficiency, reducing environmental pollution, and achieving sustainable energy development have become important issues of common concern to the world. The park's comprehensive energy system, which has the advantages of multi-energy complementarity and cascade utilization, effectively improves the economic and environmental benefits of the energy system while meeting multiple load demands by unifying the dispatch of grid electricity, natural gas energy, and distributed energy. It has become an important solution to promote the low-carbon transformation of the energy industry.

[0003] The carbon market is a market mechanism created under the low-carbon economic model with the goal of reducing greenhouse gas emissions such as carbon dioxide. Based on environmental economics theory, greenhouse gases are eligible to enter the trading market after being confirmed, and the carbon trading market came into being. As the carbon market continues to grow and develop, high-emission parks will face greater pressure, which will drive them to improve energy efficiency and achieve energy conservation and emission reduction. In addition, the carbon market will also accelerate the development and utilization of non-fossil energy, promote the development of renewable energy such as wind power, photovoltaics, and biomass, encourage more consumers to use green energy, and help build my country's new power system.

[0004] However, existing research usually characterizes carbon trading costs in the form of unified or tiered carbon prices, and analyzes the impact of the carbon market on the operation of the park's integrated energy system, which fails to achieve accurate and dynamic transmission of carbon value to bottom-level users. The single carbon price has insufficient guiding effect on bottom-level users, and it is difficult to give full play to the positive role of user interaction in park carbon reduction. Therefore, how to design an effective electricity-carbon price coupling mechanism to achieve dynamic and accurate transmission of carbon value during park optimization has become an issue worthy of in-depth study. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides an energy optimization scheduling method and system for an industrial park with electricity-carbon synergy. Through multi-dimensional and multi-level synergy, it achieves low-carbon goals while comprehensively improving the energy utilization efficiency of the park, promoting the effective transmission of carbon value, and reducing carbon emissions.

[0006] The present invention adopts the following technical solution.

[0007] The first aspect of the present invention provides an energy optimization scheduling method for an industrial park with electricity and carbon synergy, comprising:

[0008] Establish an electricity consumption model for all production links in industrial parks and a transferable load demand response model;

[0009] Establish an electricity-carbon coupling price model based on time-of-use electricity prices and floating carbon prices;

[0010] Construct a two-layer optimization model for the comprehensive energy of industrial parks based on the electricity-carbon coupling price transmission. The upper model is an energy price optimization decision model with the goal of minimizing the operating cost of the comprehensive energy system of the industrial park, and the lower model is a user energy optimization model with the goal of optimizing energy efficiency.

[0011] Based on the electricity consumption model of all production links in the industrial park and the transferable load demand response model, the improved particle swarm and CPLEX are used to solve the comprehensive energy two-layer optimization model of the industrial park to obtain the optimal electricity-carbon coupling price and the optimal power of each link of industrial production, and to carry out energy optimization scheduling of the industrial park with electricity-carbon coordination.

[0012] Preferably, the electricity consumption model for all production links of the industrial park includes an industrial production electricity consumption model and an industrial basic electricity consumption model; wherein the industrial production electricity consumption model includes total power consumption, sub-process power consumption, material storage relationship, production level constraint, production rate constraint, input rate constraint and inventory limit;

[0013] The industrial basic electricity consumption model includes the system efficiency, system power consumption, operation constraints, efficiency constraints and capacity constraints of the water storage cooling system.

[0014] Preferably, the total power consumption is:

[0015]

[0016] Where P t represents the amount of electrical energy consumed by industrial production at time t; I represents the number of sub-links in the entire production process; represents the power consumption of link i at time t;

[0017] Sub-process power consumption:

[0018]

[0019] In the formula, It represents the rated electric power consumed by link i to produce unit output at time t; It represents the production output per unit time at time t;

[0020]

[0021] In the formula, φ represents the ratio of output weight to input weight; It represents the material input rate of sub-link i-1 to link i at time t;

[0022] The material storage relationship is:

[0023]

[0024] In the formula, It represents the material storage quantity of link i at time t and t-1;

[0025] The production level constraint is:

[0026]

[0027] Where M or It represents the customer's demand for goods; T is the scheduling cycle;

[0028] The production rate constraint is:

[0029]

[0030] In the formula, Indicates the lowest and highest production rate during the entire production period;

[0031] The input rate constraint is:

[0032]

[0033] In the formula, It indicates the highest and lowest output volume per unit time flowing into the next link from the storage of sub-link products;

[0034] The stock limit is:

[0035]

[0036] In the formula, Indicates the minimum and maximum storage capacity in the memory of each link.

[0037] Preferably, the system efficiency is:

[0038]

[0039] In the formula, It indicates the total cooling capacity that can be released by the water cooling system per unit time; Indicates the rated power of the water cooling system; Indicates the cooling and storage power of the water cooling system;

[0040] The system power consumption is:

[0041]

[0042] In the formula, Represents the power consumption of the water storage cooling system per unit time; η e Indicates the conversion coefficient between the cooling capacity and power consumption of the water storage system; η dis , η cha Indicates the energy loss coefficient of the water cooling system's discharge and storage power per unit time;

[0043] The operating constraints are:

[0044]

[0045] In the formula, They represent the 0-1 state of the water cooling system during cooling or cooling in a unit time, where 0 means stop and 1 means start, and the cooling and cooling processes do not occur at the same time;

[0046] The efficiency constraint is:

[0047]

[0048] In the formula, It is the rated power, cooling and minimum storage power of the water cooling system; The rated power, cooling and maximum storage power of the water cooling system;

[0049] The capacity constraint is:

[0050]

[0051] In the formula, Indicates the minimum and maximum cold storage capacity of the cold storage tank; It is the cold storage capacity of the cold storage tank.

[0052] Preferably, the transferable load demand response model is:

[0053]

[0054] in, represents the transferable amount of energy at time t;

[0055] Indicates the minimum and maximum values ​​for load transfer;

[0056] T is the scheduling period.

[0057] Preferably, the electricity-carbon coupling price model established based on time-of-use electricity price and floating carbon price is as follows:

[0058]

[0059] in, is the electricity-carbon coupling price of link i at time t; Time-of-use electricity price; is the floating carbon price; t is the influence coefficient of different emission ranges; The carbon price.

[0060] Preferably, the carbon price The calculation method is as follows:

[0061] First, calculate the carbon intensity e of link i at time t t i :

[0062]

[0063] Then, according to Size division Carbon emission range:

[0064]

[0065] Finally, according to Calculation of carbon emission range

[0066]

[0067] In the formula, is the electric power consumed by industrial production link i at time t; ρ i is the carbon emission factor of link i;

[0068] e min and e max are the minimum and maximum carbon intensity at time t; e ave is the average carbon intensity of all production links at time t;

[0069] α0 is the basic price of carbon trading; η is the carbon price difference between the loads in the high-carbon emission area and the low-carbon emission area.

[0070] Preferably, the objective function of the upper model is:

[0071] f1=min(f e +f wp +f k +f s +f co2 -f sell ) (twenty two)

[0072] In the formula, f e represents the cost of the generator set of the integrated energy system; f wp is the cost of wind and solar power generation; f k is the start-up and shutdown cost of the unit; s is the operation and maintenance cost of the energy storage equipment;co2 represents the carbon trading cost of the comprehensive energy system; f soll Represents the revenue of the integrated energy system from selling electricity to the grid;

[0073] Among them, the cost of the generator set of the comprehensive energy system is:

[0074]

[0075] In the formula, is the power generation of the integrated energy system at time t; is the cost coefficient of power generation of the integrated energy system at time t; T is the scheduling period;

[0076] The cost of wind and solar power generation is:

[0077]

[0078] In the formula, is the wind and solar power generation of the integrated energy system at time t; is the cost coefficient of the wind and solar power generator set at time t;

[0079] The cost of starting and stopping the unit is:

[0080]

[0081] Where U t , U t-1 is the start and stop status of the unit at time t and t-1; f on 、f off The starting and stopping costs of the unit;

[0082] The operation and maintenance cost of energy storage equipment is:

[0083]

[0084] In the formula, is the power of energy storage in the system at time t; α soc Set an operation and maintenance cost coefficient for energy storage.

[0085] The carbon trading cost of the comprehensive energy system is:

[0086]

[0087] In the formula, Carbon emissions for participating in the carbon trading mechanism; is the total carbon emission and total carbon quota of the comprehensive energy system; λ is the carbon trading price; l is the carbon emission range; is the price increase;

[0088] The income of the integrated energy system from selling energy to load users is:

[0089]

[0090] In the formula, is the amount of electricity sold to the grid by the integrated energy system at time t; is the electricity-carbon coupling price of the comprehensive energy system at time t.

[0091] Preferably, the constraints of the upper model include:

[0092] 1) Unit capacity and climbing limit:

[0093]

[0094] Among them, P e,min , P e,max P is the minimum and maximum value of the active output of the generator set; t e is the power generation of the integrated energy system at time t; B d , B u are the minimum and maximum values ​​of climbing power respectively;

[0095] 2) WP Limitations:

[0096]

[0097] in, is the wind and solar power generation of the integrated energy system at time t; P wp,min , P wp,max It is the minimum and maximum effective output of wind and solar power generation in the integrated energy system;

[0098] 3) Energy storage capacity and charge / discharge state constraints:

[0099] SOC min ≤SOC t ≤SOC max (32)

[0100] Among them, SOC min , SOC max The minimum and maximum effective output of wind and solar power generation in the integrated energy system; SOC t is the effective output of wind and solar power generation of the integrated energy system at time t;

[0101] 4) Power balance constraints:

[0102]

[0103] in, is the load of the industrial park at time t of the integrated energy system;

[0104] They are respectively the amount of electricity released by the energy storage device and the amount of charge of the thermal energy device at time t.

[0105] Preferably, the objective function of the lower model is:

[0106] f2=min(f dr +f sh +f buy ) (34)

[0107] Among them, f dr is the industrial load demand response cost considering the electricity-carbon coupling price; sh is the compensation cost after industrial load transfer, f buy The cost of industrial energy;

[0108] Among them, the industrial load demand response cost is:

[0109]

[0110] In the formula, is the cost price per unit load response quantity; is the load response quantity; is the basic living electricity consumption, T is the dispatching period;

[0111] The compensation cost after industrial load transfer is:

[0112]

[0113] In the formula, They represent the load power of the integrated energy system before and after load transfer at time t respectively; is a 0-1 variable. When α is equal to 0, it indicates that the integrated energy system has carried out load transfer, otherwise it indicates that no load transfer has been carried out; sh The compensation price after the transfer of each load in the integrated energy system;

[0114] The cost of industrial energy is:

[0115]

[0116] In the formula, Purchase electricity for industrial production; The cost of purchasing electricity.

[0117] Preferably, the constraints of the lower model include:

[0118] 1) Power balance constraints:

[0119]

[0120] in, Purchase electricity for industrial production; P t The electricity consumption in all production links; For basic living electricity consumption; is the load of the industrial park at time t of the integrated energy system; T is the scheduling period;

[0121] 2) Response balance:

[0122]

[0123] in, Represents the transferable amount of energy at time t.

[0124] Preferably, the improved particle swarm algorithm updates the speed and position according to the following two formulas:

[0125] v j (d+1)=ωv j (d)+c1r1(p j (d)-x j (d))+c2r2(g j (d)-x j (d))

[0126] x j (d+1)=x j (d)+v j (d+1)

[0127] Where: w is the dynamically adjusted inertia weight; d is the number of iterations; p j (d) is the optimal position found by the jth particle so far, g j (d) is the optimal position found by all particles in the entire group, j represents the corresponding particle number; x j (d) and v j (d) are the position and velocity of the particle respectively; c1 and c2 are linearly decreasing learning factors; r1 and r2 are random numbers between (0,1).

[0128] Preferably, the dynamic adjustment formula of the inertia weight is:

[0129]

[0130] Among them, ω max is the maximum inertia weight, ω min Minimum inertia weight, d max is the maximum number of iterations, d is the current number of iterations, σ is the inertia adjustment factor; betarnd(p, q) is a function used to generate random numbers that obey the Beta distribution, p, q are the shape parameters of the Beta distribution.

[0131] Preferably, the linearly decreasing learning factor is:

[0132]

[0133] Among them, c max is the maximum value of the learning factor, c min is the minimum value of the learning factor, d max is the maximum number of iterations, and d is the current number of iterations.

[0134] The second aspect of the present invention provides an energy optimization and dispatching system for an industrial park with electricity and carbon synergy, comprising:

[0135] The model building module is used to establish an electricity consumption model for all production links in the industrial park and a transferable load demand response model; to establish an electricity-carbon coupling price model based on time-of-use electricity prices and floating carbon prices; to build a two-layer optimization model for the comprehensive energy of the industrial park transmitted by the electricity-carbon coupling price, in which the upper model is an energy price optimization decision model with the goal of minimizing the operating cost of the comprehensive energy system of the industrial park, and the lower model is a user energy optimization model with the goal of optimizing energy efficiency;

[0136] The optimization scheduling module is used to solve the comprehensive energy two-layer optimization model of the industrial park based on the improved particle swarm and CPLEX, obtain the optimal electricity-carbon coupling price and the optimal power of each link of industrial production, and perform energy optimization scheduling of the industrial park with electricity-carbon coordination.

[0137] Compared with the prior art, the beneficial effects of the present invention include at least:

[0138] The present invention comprehensively considers the energy consumption and carbon emissions of each production link, analyzes from a low-carbon perspective, divides the system into low, medium and high carbon emission zones according to the carbon intensity, establishes a time-dependent electricity-carbon coupled electricity price model for different carbon emission zones, and constructs a two-layer optimization model of the comprehensive energy of the industrial park based on the electricity-carbon coupled price transmission, wherein the upper model is an energy price optimization decision-making model with the goal of minimizing the operating cost of the comprehensive energy system of the industrial park, and the lower model is an energy optimization model for users with the goal of optimizing energy efficiency. While achieving the low-carbon goal, it improves energy utilization efficiency, promotes the effective transmission of carbon value, and encourages park users to actively manage low-carbon energy consumption, thereby enhancing the overall environmental protection and economic benefits of the park, and can effectively solve the problem of reduced scheduling effect due to the existing independent consideration of system economy or low-carbon and single demand response.

[0139] The present invention comprehensively optimizes the inertia weight and learning factor during the iteration of speed and position update in the particle swarm algorithm. The inertia weight is relatively large in the early stage, and it decreases nonlinearly with the increase of the number of iterations. The overall value distribution of the inertia weight ω is adjusted using the beta distribution, and an inertia adjustment factor σ is added to control the degree of deviation of the inertia weight, so that the adjustment of the inertia weight ω is more reasonable. The learning factor takes into account the social information and cognitive ability between particles, and gradually decreases with the increase of the number of iterations, so that the particles can obtain the optimal solution at a faster convergence speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0140] Figure 1 is a flow chart of the method of the present invention;

[0141] Figure 2 The electric-carbon coupling time-sharing price in the embodiment of the present invention;

[0142] Figure 3 This is a graph of electric load before and after demand response in scenario 1 in an embodiment of the present invention;

[0143] Figure 4 This is a heat load curve diagram before and after demand response in scenario 2 in an embodiment of the present invention. DETAILED DESCRIPTION

[0144] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0145] Embodiment 1 of the present invention provides an industrial park energy optimization scheduling method based on electricity-carbon synergy, such as Figure 1 As shown, the method comprises the following steps:

[0146] Step 1: Establish an electricity consumption model for all production links in the industrial park and a transferable load demand response model;

[0147] Further preferably, an electricity consumption model and a demand response model are established for all production links of the industrial park, mainly including industrial production electricity and industrial basic electricity and time-shiftable loads in the industrial park:

[0148] (1) Electricity consumption for industrial production

[0149] 1) Total power consumption

[0150]

[0151] Where P trepresents the amount of electricity consumed in production at time t; I represents the number of sub-links in the entire production process; P t i Indicates the power consumption of each link.

[0152] 2) Sub-process consumption

[0153]

[0154] In the formula, It represents the rated electric power consumed by link i to produce unit output at time t, in MW·h / ton; It represents the production output per unit time at time t, that is, the input rate of the product to the warehouse, with the unit of ton / h, which represents the production rate (the input storage rate of the intermediate product).

[0155] 3) Input-output ratio

[0156] The input-output ratio represents the relationship between the output and input weight of each subprocess i (each intermediate product storage), expressed as:

[0157]

[0158] In the formula, φ represents the ratio of output weight to input weight; It represents the material input rate of sub-link i-1 to link i at time t.

[0159] 4) Material storage relationship

[0160]

[0161] In the formula, Represents the material storage quantity of link i at time t.

[0162] 5) Production level constraints

[0163]

[0164] Where M or It indicates the customer demand for goods, which satisfies the balance between production and demand during the entire production period.

[0165] 6) Production rate limit

[0166]

[0167] In the formula, It indicates the lowest rate and high value during the entire production period, satisfying the constraints on the production rate.

[0168] 7) Input restrictions

[0169]

[0170] In the formula, Indicates the range of output flows from the storage of sub-stage products to the next stage.

[0171] 8) Inventory Limit

[0172]

[0173] In the formula, It indicates the minimum and maximum storage capacity in the memory of each link, satisfying the constraints on the memory capacity in each production link.

[0174] (2) Basic industrial electricity consumption

[0175] Under the time-of-use electricity price or energy storage preferential policy, the water storage cooling air conditioning system can use electricity to cool the water storage tank when the electricity price is low, convert the electricity into low-temperature water to store electricity; when the electricity price is high, release the chilled water for refrigeration. The water storage cooling air conditioning system can provide cooling capacity through a refrigerator or a cold storage tank. The energy storage technology has significant economic benefits and the ability to reduce peaks and fill valleys. Considering the current situation of time-of-use electricity prices and demand response, the industrial basic electricity consumption model of the present invention is mainly modeled for the water storage cooling model.

[0176] The industrial basic electricity consumption model includes the system efficiency, system power consumption, operation constraints, efficiency constraints and capacity constraints of the water storage cooling system, as follows:

[0177] 1) System efficiency

[0178]

[0179] In the formula, It indicates the total cooling capacity that can be released by the water cooling system per unit time; Indicates the rated power of the cooling system; Indicates the cooling and storage power of the cooling system.

[0180] 2) System power consumption

[0181]

[0182] In the formula, Represents the power consumption of the cooling system per unit time; η e Indicates the conversion coefficient between the cooling capacity and power consumption of the cooling system; η dis , η cha It indicates the energy loss coefficient of the cooling system's discharge and storage power per unit time.

[0183] 3) Operational constraints

[0184]

[0185] In the formula, They represent the 0-1 state of the cooling system during cooling or cold storage in a unit of time, where 0 represents stop and 1 represents start, and the cooling and cold storage processes will not occur at the same time.

[0186] 4) Efficiency constraints

[0187]

[0188] In the formula, the cooling, cooling and cold storage powers of the cooling system during working hours are within the limit constraints.

[0189] 5) Capacity constraints

[0190]

[0191] Where: Indicates the minimum and maximum cold storage capacity of the cold storage tank, and limits the cold storage tank capacity within the minimum and maximum capacity range.

[0192] (3) Time-shiftable load

[0193] Transferable load demand response model:

[0194]

[0195] in, Indicates the minimum and maximum values ​​for load transfer;

[0196] It represents the transferable amount of energy at time t. The total transferable amount in one scheduling cycle is 0.

[0197] The above-mentioned electricity consumption model for all production links in the industrial park and the transferable load demand response model are the basis of the two-layer optimization model. The electricity consumption model for all production links in the industrial park provides an accurate electricity data model for the two-layer optimization model, so that the optimization model comprehensively considers the electricity demand and electricity characteristics of the park. The electricity consumption data of the electricity consumption model affects the calculation of the objective function of the operating cost and energy efficiency of the two-layer optimization model.

[0198] The transferable load demand response model allows part of the load to be transferred between different time periods, increasing the flexibility of electricity consumption in the park and providing more scheduling options for the two-layer optimization model. In the upper layer of the two-layer optimization model, we can consider how to reduce operating costs by adjusting the transfer strategy of the transferable load. In the lower layer, we can formulate detailed transfer plans and scheduling strategies for specific transferable loads to improve energy efficiency.

[0199] Step 2: Establish an electricity-carbon coupling price model based on time-of-use electricity prices and floating carbon prices;

[0200] Further preferably, an electricity-carbon coupling price model is constructed based on electricity prices and carbon prices in different time periods, as follows:

[0201] The system can increase electricity consumption during periods of low carbon intensity and reduce consumption during periods of high carbon intensity. This strategic load scheduling can reduce the carbon emission value per unit of electricity within a cycle, thereby achieving energy conservation and emission reduction. The traditional energy system considers electricity supply and demand and cost, and its pricing mechanism is relatively fixed, and often does not fully consider carbon emissions in the process of electricity production and use. Such strategies are usually simple and direct to implement, and lack incentives for energy efficiency and environmental protection. Therefore, from a low-carbon perspective, as the carbon intensity of each link increases, the carbon emission value associated with its unit electricity consumption also increases. According to the carbon intensity of the system, low, medium and high carbon emission zones are divided, and a carbon-coupled electricity price model for different carbon emission zones is established.

[0202] Calculate the carbon intensity of each link at the current time t and divide it into low, medium and high carbon emission intervals. The specific method is as follows:

[0203]

[0204]

[0205] in, represents the carbon intensity of link i at time t; is the electric power consumed by industrial production link i at time t; ρ i is the carbon emission factor of each link; min and e max are the minimum and maximum carbon intensity at time t; e ave is the average carbon intensity of all production links at time t.

[0206] The low, medium and high time-sharing carbon prices are set based on the peak, valley and average time-sharing prices. The specific carbon prices are as follows:

[0207]

[0208] Among them, η is the carbon price difference between the loads in the high emission area and the low emission area; α0 is the basic price of carbon trading; the carbon price of each link is Adjust according to different emission ranges.

[0209] The carbon price trend is characterized by a dynamic carbon emission factor, and an electricity-carbon coupling price model considering a floating carbon price is proposed, where the electricity price is the time-of-use electricity price in the spot market, and the carbon price is a floating carbon price based on the carbon emission range, which is composed of:

[0210]

[0211] in, is the electricity-carbon coupling price of link i at time t; Time-of-use electricity price; is the floating carbon price; t is the influence coefficient of different emission ranges.

[0212] Step 3: Construct a two-layer optimization model for the comprehensive energy of industrial parks with electricity-carbon coupling price transmission, in which the upper model is an energy price optimization decision model with the goal of minimizing the operating cost of the comprehensive energy system of the industrial park, and the lower model is a user energy optimization model with the goal of optimizing energy efficiency;

[0213] Further optimization, in order to construct a two-level optimization model of comprehensive energy in industrial parks with electricity-carbon coupling price as the core, two levels of optimization strategies are designed:

[0214] Upper model: A park energy price optimization decision model that aims to minimize system operating costs. This model minimizes the park's operating costs by optimizing the electricity-carbon coupling price. This optimization process takes into account factors such as system power generation costs, start-up and shutdown costs, and carbon trading costs.

[0215] Lower model: With load-side electricity and carbon coupling prices as pricing signals, multiple load types in DR are considered. The load volume of the response node is fed into the upper scheduling model, and these steps are executed iteratively. The scheduling model is designed to minimize the combined cost of user electricity purchase and DR. Through this two-layer optimization framework, the upper model provides the park energy operator with an optimized energy pricing strategy, while the lower model guides users on how to optimize energy use at a given price, thereby achieving a win-win situation for the overall benefits of the park and the energy efficiency of users.

[0216] The day-ahead optimal dispatch is an energy price optimization decision-making model that aims to minimize the operating cost of the integrated energy system of the industrial park. The electricity-carbon coupling price is used as the information medium for transmission in the dispatch. Its form, i.e., the objective function of the upper model, is:

[0217]

[0218] In the formula, f e represents the system generator set cost; f wp is the cost of wind and solar power generation; f k is the start-up and shutdown cost of the unit; s is the operation and maintenance cost of the energy storage equipment; co2 represents the system carbon trading cost; f soll Represents the system's revenue from selling electricity to the grid.

[0219] 1) The cost of the system generator set is:

[0220]

[0221] in, is the power generation of the system at time t; is the cost coefficient of power generation of the system at time t.

[0222] 2) System operation and maintenance costs are:

[0223]

[0224] in, is the wind and solar power generation of the system at time t; is the cost coefficient of the wind-solar generator set at time t.

[0225] 3) The cost of starting and stopping the unit is:

[0226]

[0227] Among them, U t , U t-1 is the start / stop status of the unit at time t and t-1, and its value is 0 or 1; f on 、f off is the start-up and stop cost of the unit.

[0228] 4) The operating cost of energy storage equipment is:

[0229]

[0230] Among them, P t soc is the power of energy storage in the system at time t; α soc Set an operation and maintenance cost coefficient for energy storage.

[0231] 5) System carbon trading costs are:

[0232]

[0233] in, is the total carbon emissions and total carbon quota of the system; is the carbon emissions participating in the carbon trading mechanism; λ is the carbon trading price; l is the carbon emission range; is the price increase rate.

[0234] It can be understood that the carbon trading price λ of the present invention is the actual price formed by the actual market or bilateral negotiation transactions, while the carbon trading basic price α0 is the price in the middle emission zone based on the basic price given by daily market transactions, and the floating carbon price This is to realize the concept of building an electricity-carbon coupled pricing model.

[0235] 6) The income from the system selling electricity to the grid is:

[0236]

[0237] in, is the amount of electricity sold by the system to the grid at time t; is the electricity-carbon coupling price of the system at time t, and its value is the electricity-carbon coupling price of each link in the system The average of the sum.

[0238] The constraints of the upper model include:

[0239] 1) Unit capacity and climbing limit:

[0240]

[0241] Among them, P e,min , P e,max B is the minimum and maximum value of the active output of the generator set; d , B u are the minimum and maximum values ​​of the climbing power respectively.

[0242] 2) WP Limitations:

[0243]

[0244] Among them, P wp,min , P wp,max It is the minimum and maximum effective output of wind and solar power generation in the system.

[0245] 3) Energy storage capacity and charge / discharge state constraints:

[0246] SOC min ≤SOC t ≤SOC max (32)

[0247] Among them, SOC min , SOC max It is the minimum and maximum effective output of wind and solar power generation in the system.

[0248] 4) Power balance constraints:

[0249]

[0250] in, is the industrial park load of the system at time t.

[0251] The objective function of the lower model is:

[0252] f2=min(f dr +f sh +f buy ) (34)

[0253] Among them, f dr is the industrial load demand response cost; f cut Compensation costs for industrial load reduction, f buy Industrial energy costs.

[0254] Industrial load demand response costs:

[0255]

[0256] Among them, P t It is the electricity consumption of all production links; P t c For basic living electricity consumption; The price of electric-carbon coupling.

[0257] 2) Industrial load reduction compensation costs:

[0258]

[0259] Among them, f sh represents the compensation cost after load transfer; α sh is the compensation price after the transfer of each load in the integrated energy system, where the transfer of each load meets the conditions of the transferable load demand response model; is a 0-1 variable. When , it indicates that the integrated energy system has carried out load transfer; They respectively represent the load power of the integrated energy system before and after load transfer at time t.

[0260] Industrial energy purchase costs:

[0261]

[0262] in, Purchase electricity for industrial production; The cost of purchasing electricity.

[0263] The constraints of the underlying model are:

[0264] 1) Power balance constraints:

[0265]

[0266] Among them, the electricity purchase amount is consistent with the load usage and its adjustment amount within a scheduling cycle.

[0267] 2) Response balance:

[0268]

[0269] in, Represents the transferable amount of energy. The total transferable amount in one scheduling cycle is 0.

[0270] Step 4: Based on the model established in steps 1 and 2, the improved particle swarm and CPLEX are used to solve the industrial park comprehensive energy two-layer optimization model to obtain the optimal electricity-carbon coupling price and the optimal power of each link in industrial production, and perform energy optimization scheduling of the industrial park with electricity-carbon coordination.

[0271] Further optimization, based on the improved particle swarm and CPLEX solution model, the optimal electricity-carbon coupling price and the optimal power output of each link of industrial production are obtained to complete the energy optimization of the industrial park, as follows:

[0272] The improved particle swarm algorithm is used to globally search for the optimal electricity-carbon coupling price. The position and speed of particles are adjusted iteratively to find the electricity-carbon coupling price that minimizes the operating costs of the park.

[0273] As an accurate solving tool, CPLEX is used to solve the user energy consumption behavior curve in the lower-level model to ensure that the user's energy consumption cost and demand response cost are minimized under the electricity-carbon coupling price.

[0274] By improving the combination of particle swarm and CPLEX, the model will output the optimal electricity-carbon coupling price and the optimized operation results of the park's integrated energy system after reaching the preset number of iterations or convergence conditions. These results can not only minimize the overall operating costs of the park, but also effectively convey the value of the carbon market and promote the active participation of park users in energy conservation and carbon reduction.

[0275] The steps for solving the optimal electricity-carbon coupling price and the optimization results of the park's integrated energy system are as follows:

[0276] (1) Set the number of particle swarms and the number of iterations.

[0277] (2) Initialize the parameters of the improved particle swarm algorithm. Under the constraints of the electricity consumption model of the entire production process of the industrial park and the transferable load demand response model, solve the upper model, obtain the initial electricity-carbon coupling price of the industrial park based on the optimization results of the upper model, and send the price to the lower model. In this process, the model formulas (1)-(16) established in step 1 are treated as equality and inequality constraints in the upper optimization model.

[0278] (3) The lower-level model uses CPLEX to solve the user’s optimal energy consumption behavior curve based on the electricity-carbon coupling price and passes the result back to the upper-level model.

[0279] (4) The upper-level model iterates and optimizes again based on the received energy consumption curve to obtain new parameters such as the electricity-carbon coupling price and operating cost. The new electricity-carbon coupling price is then sent to the lower-level model, and the operating cost parameters are retained locally for subsequent iterative calls.

[0280] (5) Determine whether the preset number of iterations has been reached. If so, output the optimal result, including the optimal electricity-carbon coupling price (i.e. The optimal value of) and the optimal power of each link in industrial production (i.e. optimal value of ).

[0281] In specific implementation, the upper model generates the initial electricity-carbon coupling price based on the optimal system energy cost. The lower layer obtains the time-of-use electricity price from the electricity-carbon coupling price, generates the optimal energy consumption curve with the goal of optimizing energy efficiency, and outputs various energy consumption parameters. The upper model receives the optimal energy consumption curve of the lower layer and solves the upper model according to the optimal energy consumption curve. The system operation cost under the current electricity-carbon coupling price is obtained. Among them, the total load value between the upper and lower models is equal:

[0282]

[0283] Further preferably, the particle swarm optimization (PSO) is an optimization method based on swarm intelligence, which originates from the study of bird flock foraging behavior. By simulating the cooperation and information sharing mechanism of bird flocks, the optimal solution is searched in multidimensional space. Its core idea is information sharing and collaborative search. In the initial stage, the algorithm randomly generates a certain number of particles, each with a random position and speed. Subsequently, the algorithm enters an iterative process, and each particle flies in the search space, updating the speed and position according to the following two formulas:

[0284]

[0285] x j (d+1)=x j (d)+v j (d+1)

[0286] Where: w is the inertia weight; d is the number of iterations; p j (d) is the optimal position found by the jth particle so far, g j (d) is the optimal position found by all particles in the entire group, j represents the corresponding particle number; x j (d) and v j(d) are the position and velocity of the particle respectively; c1 and c2 are learning factors; r1 and r2 are random numbers between (0,1).

[0287] The improved particle swarm algorithm optimizes its inertia weight and learning factor parameters. The particle swarm algorithm searches for the optimal solution in multi-dimensional space by simulating the collaboration and information sharing mechanism of bird flocks.

[0288] During the search process of the PSO algorithm, particles continue to approach the global optimal solution, and have strong global search capabilities. During the iteration process, particles quickly approach the optimal solution by sharing information, and converge quickly. Compared with other optimization algorithms, the particle swarm algorithm has fewer parameters and is easy to implement, but it also causes its optimization performance to be greatly affected by parameters. In the process of iterative calculation, the inertia weight w is an adjustable parameter of the algorithm, which mainly affects the search ability of the particles. If the value is too large, the global search ability of the algorithm will improve, but it will also lead to inaccurate optimal solutions; if the value is too small, it is easy to fall into the local optimum, affecting the optimization results. Therefore, it is necessary to select a suitable weight value so that the optimization algorithm can take into account both search ability and optimal solution.

[0289] The inertia weight of the improved algorithm is dynamically adjusted, and the learning factor decreases linearly. The calculation formula is:

[0290]

[0291] ω max is the maximum value of inertia weight, take 0.9, ω min The minimum inertia weight is 0.4, c max The maximum value of the learning factor is 1.5, c min The minimum value of the learning factor is 0.5, d max is the maximum number of iterations, d is the current number of iterations, σ is the inertia adjustment factor, which is taken as 0.08; betarnd is a random number generator in MATLAB that can generate random numbers that conform to the Beta distribution, increase the global search capability of the algorithm in the later iteration of the algorithm, and reduce the possibility of the algorithm falling into the local final. The shape parameters of the Beta distribution are p=1, q=4, and the generated random number set will follow the Beta distribution with parameters p and q.

[0292] The present invention comprehensively optimizes the dynamic inertia weight and learning factor in the particle swarm algorithm. The inertia weight is relatively large in the early stage, and it decreases nonlinearly with the increase of the number of iterations. The overall value distribution of the inertia weight ω is adjusted using the beta distribution, and an inertia adjustment factor σ is added to control the degree of deviation of the inertia weight, so that the adjustment of the inertia weight ω is more reasonable. The learning factor takes into account the social information and cognitive ability between particles, and gradually decreases with the increase of the number of iterations, so that the particles can obtain the optimal solution at a faster convergence speed.

[0293] The specific implementation of the above scheme involves industrial users and park operators in the park. First, the energy supply side of the park's integrated energy system builds an electricity-carbon coupling price model for electricity prices and carbon prices in different periods of time, and transmits it to the energy consumption side of the park. Based on this electricity-carbon coupling price, industrial users in the park combine energy consumption analysis and carbon emissions data of all production links to build an energy consumption model with the goal of maximizing total utility benefits, and report energy demand. Subsequently, the park operator builds an electricity-carbon coupling price model with the goal of minimizing the overall operating cost of the park based on the energy consumption curve and production energy consumption data uploaded by the user, and optimizes the price and sends it to the user again. Through multiple negotiations between the energy supply and demand parties based on energy prices, the optimal electricity-carbon coupling price and energy demand are finally determined.

[0294] This example selects data from an industrial park as the research object, takes 24 hours as a scheduling cycle, and performs optimization scheduling analysis with each scheduling interval of 1 hour. Taking into account the carbon prices in the European carbon market and the domestic carbon market, the average carbon price is selected as 0.1 yuan / kg, the initial carbon emission quota of the park is 0kg, and the time-of-use electricity price is shown in Table 1.

[0295] Table 1 Time-of-use electricity price

[0296] Time / t 0-6 o'clock 7-8 o'clock 9-11 11-18 o'clock 19-21 hours 22-23 hours Price / Yuan / kWh 0.35 0.68 1.09 0.68 1.09 0.68

[0297] Based on the above-mentioned time-of-use electricity price and time-of-use carbon price, an electricity-carbon coupling price model is constructed to determine the electricity-carbon coupling price in each period. The time-of-use electricity price, time-of-use carbon price, time-of-use electricity-carbon coupling price and general electricity-carbon coupling price within the industrial park energy system dispatch cycle are as follows: Figure 2 Compared with scenario 2, scenario 1 has relatively low prices in low-carbon periods (0, 2-9, 12, 14-15, 17-22), while prices rise in high-carbon periods (1, 10-11, 13, 16, 23). Scenario 1: floating carbon price proposed by the present invention; scenario 2: traditional electricity carbon price.

[0298] In the case of system electricity-carbon coupling prices, the integrated energy system of the industrial park is optimized for different scenarios. After optimization, the park operation costs, energy purchase costs and carbon emissions for scenarios 1 and 2 are shown in Table 2. Table 2 System operation scheduling results for each scenario

[0299] Scenario Running costs Energy purchase cost Carbon emissions 1 38832.56 33784.33 20236.87 2 43898.61 35996.86 23139.19

[0300] As shown in Table 2, in the comparison of the results of the scenarios considering the floating carbon price and the traditional carbon price, the operating cost decreased by 11.54%, the energy purchase cost decreased by 6.15%, and the carbon emissions decreased by 12.55%. In general, the electricity-carbon coupling price in scenario 1 is consistent with the trend of the dynamic carbon emission factor, with a more obvious price peak-to-valley difference, which can well guide users to reduce or transfer loads during high-carbon periods, and while achieving accurate transmission of carbon market costs in each period on the user side, it also promotes a certain degree of reduction in the carbon emission costs of the park.

[0301] After optimization, the electrical curves of scenes 1 and 2 are as follows: Figure 3 and 4 As shown. In terms of electric load, compared with scenario 2, scenario 1 has a more obvious load transfer trend, and the electric load is transferred when the electricity-carbon coupling price increases. It can be seen that the higher electricity-carbon coupling price in scenario 1 has a stronger guiding ability on the user side. Embodiment 2 of the present invention provides an energy optimization and scheduling system for industrial parks with electricity-carbon coordination, including:

[0302] The model building module is used to establish an electricity consumption model for all production links in the industrial park and a transferable load demand response model; to establish an electricity-carbon coupling price model based on time-of-use electricity prices and floating carbon prices; to build a two-layer optimization model for the comprehensive energy of the industrial park transmitted by the electricity-carbon coupling price, in which the upper model is an energy price optimization decision model with the goal of minimizing the operating cost of the comprehensive energy system of the industrial park, and the lower model is a user energy optimization model with the goal of optimizing energy efficiency;

[0303] The optimization scheduling module is used to solve the comprehensive energy two-layer optimization model of the industrial park based on the improved particle swarm and CPLEX, obtain the optimal electricity-carbon coupling price and the optimal power of each link of industrial production, and perform energy optimization scheduling of the industrial park with electricity-carbon coordination.

[0304] The optimization scheduling module includes the following units:

[0305] 1) Electricity-carbon coupling price calculation unit: Construct the initial electricity-carbon coupling price based on the grid time-of-use electricity price, the park carbon price and the carbon emission factor. With the goal of minimizing the park operation cost and the user's energy purchase cost, optimize the electricity-carbon coupling price and solve the optimal electricity-carbon coupling price through multiple iterations.

[0306] 2) Upper and lower model solving unit: used to optimize the energy price decision model.

[0307] Under the conditions of output constraints and energy storage constraints, a model is established with the goal of minimizing the park operation cost. At the same time, the user energy optimization model aims to minimize the cost of purchasing energy and demand response (DR) under the constraints of load adjustability and energy consumption. The upper and lower optimization models are solved collaboratively to obtain the park operation cost and user energy consumption curve.

[0308] 3) Iterative control unit: used to call the electricity-carbon coupling price calculation unit and the upper and lower layer solving units to iteratively calculate the electricity-carbon coupling price of the park energy operator and the user energy consumption curve. When the set maximum number of iterations is met, or the park operation cost and user utility are optimal, the control unit outputs the park operation cost and user energy consumption curve considering electricity-carbon coupling, and outputs the park energy transaction price.

[0309] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0310] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0311] Compared with the prior art, the beneficial effects of the present invention include at least:

[0312] The present invention comprehensively considers the energy consumption and carbon emissions of each production link, analyzes from a low-carbon perspective, divides the system into low, medium and high carbon emission zones according to the carbon intensity, establishes a time-dependent electricity-carbon coupled electricity price model for different carbon emission zones, and constructs a two-layer optimization model of the comprehensive energy of the industrial park based on the electricity-carbon coupled price transmission, wherein the upper model is an energy price optimization decision-making model with the goal of minimizing the operating cost of the comprehensive energy system of the industrial park, and the lower model is an energy optimization model for users with the goal of optimizing energy efficiency. While achieving the low-carbon goal, it improves energy utilization efficiency, promotes the effective transmission of carbon value, and encourages park users to actively manage low-carbon energy consumption, thereby enhancing the overall environmental protection and economic benefits of the park, and can effectively solve the problem of reduced scheduling effect due to the existing independent consideration of system economy or low-carbon and single demand response.

[0313] The present invention achieves low-carbon goals through multi-dimensional and multi-level synergy, while improving energy utilization efficiency and promoting the effective transmission of carbon value.

[0314] The present invention comprehensively optimizes the inertia weight and learning factor during the iteration of speed and position update in the particle swarm algorithm. The inertia weight is relatively large in the early stage, and it decreases nonlinearly with the increase of the number of iterations. The overall value distribution of the inertia weight ω is adjusted using the beta distribution, and an inertia adjustment factor σ is added to control the degree of deviation of the inertia weight, so that the adjustment of the inertia weight ω is more reasonable. The learning factor takes into account the social information and cognitive ability between particles, and gradually decreases with the increase of the number of iterations, so that the particles can obtain the optimal solution at a faster convergence speed.

[0315] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0316] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0317] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0318] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0319] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An energy optimization scheduling method for industrial parks with electricity and carbon synergy, characterized in that: include: Establish an electricity consumption model for all production links in industrial parks and a transferable load demand response model; Establish an electricity-carbon coupling price model based on time-of-use electricity prices and floating carbon prices; Construct a two-layer optimization model for the comprehensive energy of industrial parks based on the electricity-carbon coupling price transmission. The upper model is an energy price optimization decision model with the goal of minimizing the operating cost of the comprehensive energy system of the industrial park, and the lower model is a user energy optimization model with the goal of optimizing energy efficiency. Based on the electricity consumption model of all production links in the industrial park and the transferable load demand response model, the improved particle swarm is used to solve the comprehensive energy two-layer optimization model of the industrial park to obtain the optimal electricity-carbon coupling price and the optimal power of each link of industrial production, and to carry out energy optimization scheduling of the industrial park with electricity-carbon coordination.

2. The method for optimizing the energy dispatching of an industrial park based on electricity and carbon synergy according to claim 1 is characterized by: The electricity consumption model for all production links of the industrial park includes an industrial production electricity consumption model and an industrial basic electricity consumption model; wherein the industrial production electricity consumption model includes total electricity consumption, sub-process electricity consumption, material storage relationship, production level constraint, production rate constraint, input rate constraint and inventory limit; The industrial basic electricity consumption model includes the system efficiency, system power consumption, operation constraints, efficiency constraints and capacity constraints of the water storage cooling system.

3. The method for optimizing the energy dispatching of an industrial park based on electricity and carbon synergy according to claim 2 is characterized by: The total power consumption is: Where P t represents the amount of electricity consumed by industrial production at time t; I represents the number of sub-links in the entire production process; P t i represents the power consumption of link i at time t; Sub-process power consumption: In the formula, It represents the rated electric power consumed by link i to produce unit output at time t; It represents the production output per unit time at time t; In the formula, φ represents the ratio of output weight to input weight; It represents the material input rate of sub-link i-1 to link i at time t; The material storage relationship is: In the formula, It represents the material storage quantity of link i at time t and t-1; The production level constraint is: Where M or It represents the customer's demand for goods; T is the scheduling cycle; The production rate constraint is: In the formula, Indicates the lowest and highest production rate during the entire production period; The input rate constraint is: In the formula, It indicates the highest and lowest output volume per unit time flowing into the next link from the storage of sub-link products; The stock limit is: In the formula, Indicates the minimum and maximum storage capacity in the memory of each link.

4. The method for optimizing the energy dispatching of an industrial park based on electricity and carbon synergy according to claim 2 is characterized by: The system efficiency is: In the formula, It indicates the total cooling capacity that can be released by the water cooling system per unit time; Indicates the rated power of the water cooling system; Indicates the cooling and storage power of the water cooling system; The system power consumption is: Where P t c Represents the power consumption of the water storage cooling system per unit time; η e Indicates the conversion coefficient between the cooling capacity and power consumption of the water storage system; η dis , η cha Indicates the energy loss coefficient of the water cooling system's discharge and storage power per unit time; The operating constraints are: In the formula, They represent the 0-1 state of the water cooling system during cooling or cooling in a unit time, where 0 means stop and 1 means start, and the cooling and cooling processes do not occur at the same time; The efficiency constraint is: In the formula, It is the rated power, cooling and minimum storage power of the water cooling system; The rated power, cooling and maximum storage power of the water cooling system; The capacity constraint is: In the formula, Indicates the minimum and maximum cold storage capacity of the cold storage tank; It is the cold storage capacity of the cold storage tank.

5. The method for optimizing energy scheduling in an industrial park with electricity and carbon synergy according to claim 1 is characterized by: The transferable load demand response model is: in, represents the transferable amount of energy at time t; Indicates the minimum and maximum values ​​for load transfer; T is the scheduling period.

6. The method for optimizing the energy dispatching of an industrial park based on electricity and carbon synergy according to claim 1 is characterized by: The electricity-carbon coupling price model established based on time-of-use electricity prices and floating carbon prices is as follows: in, is the electricity-carbon coupling price of link i at time t; Time-of-use electricity price; is the floating carbon price; t is the influence coefficient of different emission ranges; The carbon price.

7. The method for optimizing energy dispatching in an industrial park with electricity and carbon synergy according to claim 6 is characterized by: Carbon price The calculation method is as follows: First, calculate the carbon intensity of link i at time t Then, according to Size division Carbon emission range: Finally, according to Calculation of carbon emission range Where P t i is the electric power consumed by industrial production link i at time t; ρ i is the carbon emission factor of link i; e min and e max are the minimum and maximum carbon intensity at time t; e ave is the average carbon intensity of all production links at time t; α0 is the basic price of carbon trading; η is the carbon price difference between loads in high carbon emission zones and low carbon emission zones.

8. The method for optimizing energy dispatching in an industrial park with electricity and carbon synergy according to claim 1 is characterized by: The objective function of the upper model is: f1=min(f e +f wp +f k +f s +f co2 -f sell ) (22) In the formula, f e represents the cost of the generator set of the integrated energy system; f wp is the cost of wind and solar power generation; f k is the start-up and shutdown cost of the unit; s is the operation and maintenance cost of the energy storage equipment; co2 represents the carbon trading cost of the comprehensive energy system; f soll Represents the revenue of the integrated energy system from selling electricity to the grid; Among them, the cost of the generator set of the comprehensive energy system is: Where P t e is the power generation of the integrated energy system at time t; is the cost coefficient of power generation of the integrated energy system at time t; T is the scheduling period; The cost of wind and solar power generation is: Where P t wp is the wind and solar power generation of the integrated energy system at time t; is the cost coefficient of the wind and solar power generator set at time t; The start-up and shutdown costs of the unit are: Where U t , U t-1 is the start and stop status of the unit at time t and t-1; f on 、f off The starting and stopping costs of the unit; The operation and maintenance cost of energy storage equipment is: Where P t soc is the power of energy storage in the system at time t; α soc Set an operation and maintenance cost coefficient for energy storage. The carbon trading cost of the comprehensive energy system is: In the formula, Carbon emissions for participating in the carbon trading mechanism; is the total carbon emission and total carbon quota of the comprehensive energy system; λ is the carbon trading price; l is the carbon emission range; is the price increase; The income of the integrated energy system from selling energy to load users is: Where P t sell is the amount of electricity sold to the grid by the integrated energy system at time t; is the electricity-carbon coupling price of the comprehensive energy system at time t.

9. The method for optimizing the energy dispatching of an industrial park based on electricity and carbon synergy according to claim 1 is characterized by: The constraints of the upper model include: 1) Unit capacity and climbing limit: Among them, P e,min , P e,max P is the minimum and maximum value of the active output of the generator set; t e is the power generation of the integrated energy system at time t; B d , B u are the minimum and maximum values ​​of climbing power respectively; 2) WP Limitations: P wp,min ≤P t wp ≤P wp,max (31) Among them, P t wp is the wind and solar power generation of the integrated energy system at time t; P wp,min , P wp,max It is the minimum and maximum effective output of wind and solar power generation in the integrated energy system; 3) Energy storage capacity and charge / discharge state constraints: SOC min ≤SOC t ≤SOC max (32) Among them, SOC min , SOC max The minimum and maximum effective output of wind and solar power generation in the integrated energy system; SOC t is the effective output of wind and solar power generation of the integrated energy system at time t; 4) Power balance constraints: P t e +P t wp +P t dis =P t load +P t cha (33) Among them, P t load is the load of the industrial park at time t of the integrated energy system; P t dis , P t cha They are respectively the amount of electricity released by the energy storage device and the amount of charge of the thermal energy device at time t.

10. The method for optimizing energy dispatching in an industrial park with electricity and carbon synergy according to claim 1 is characterized by: The objective function of the lower model is: f2=min(f dr +f sh +f buy ) (34) Among them, f dr is the industrial load demand response cost considering the electricity-carbon coupling price; sh is the compensation cost after industrial load transfer, f buy The cost of industrial energy; Among them, the industrial load demand response cost is: In the formula, is the cost price of unit load response; P t dr is the load response; P t c is the basic living electricity consumption, T is the dispatching period; The compensation cost after industrial load transfer is: Where P t sh , P t sh ′ respectively represents the load power of the integrated energy system before and after load transfer at time t; is a 0-1 variable. When α is equal to 0, it indicates that the integrated energy system has carried out load transfer, otherwise it indicates that no load transfer has been carried out; sh The compensation price after the transfer of each load in the integrated energy system; The cost of industrial energy is: Where P t buy Purchase electricity for industrial production; The cost of purchasing electricity.

11. The method for optimizing energy dispatching in an industrial park with electricity and carbon synergy according to claim 1 is characterized by: The constraints of the lower model include: 1) Power balance constraints: Among them, P t buy Purchase electricity for industrial production; P t It is the electricity consumption of all production links; P t c For basic living electricity consumption; P t load is the load of the industrial park at time t of the integrated energy system; T is the scheduling period; 2) Response balance: in, Represents the transferable amount of energy at time t.

12. The method for optimizing energy dispatching in an industrial park with electricity and carbon synergy according to claim 1, characterized in that: The improved particle swarm algorithm updates the speed and position according to the following two formulas: v j (d+1)=ωv j (d)+c1r1(p j (d)-x j (d))+c2r2(g j (d)-x j (d)) x j (d+1)=x j (d)+v j (d+1) Where: w is the dynamically adjusted inertia weight; p j (d) is the optimal position found by the jth particle so far, g j (d) is the optimal position found by the current positions of all particles in the entire group; x j (d) and v j (d) are the position and velocity of the jth particle at the dth iteration; c1 and c2 are linearly decreasing learning factors; r1 and r2 are random numbers between (0,1).

13. The method for optimizing energy dispatching in an industrial park with electricity and carbon synergy according to claim 12, characterized in that: The dynamic adjustment formula of the inertia weight is: Among them, ω max is the maximum inertia weight, ω min Minimum inertia weight, d max is the maximum number of iterations, d is the current number of iterations, σ is the inertia adjustment factor; betarnd(p, q) is a function used to generate random numbers that obey the Beta distribution, p, q are the shape parameters of the Beta distribution.

14. The method for optimizing energy dispatching in an industrial park with electricity and carbon synergy according to claim 12, characterized in that: The linearly decreasing learning factor is: Among them, c max is the maximum value of the learning factor, c min is the minimum value of the learning factor, d max is the maximum number of iterations, and d is the current number of iterations.

15. An energy optimization and dispatching system for industrial parks with electricity and carbon synergy, using the method described in any one of claims 1 to 14, characterized in that: The optimization scheduling system comprises: The model building module is used to establish an electricity consumption model for all production links in the industrial park and a transferable load demand response model; to establish an electricity-carbon coupling price model based on time-of-use electricity prices and floating carbon prices; to build a two-layer optimization model for the comprehensive energy of the industrial park transmitted by the electricity-carbon coupling price, in which the upper model is an energy price optimization decision model with the goal of minimizing the operating cost of the comprehensive energy system of the industrial park, and the lower model is a user energy optimization model with the goal of optimizing energy efficiency; The optimization scheduling module is used to solve the industrial park comprehensive energy two-layer optimization model based on the improved particle swarm, obtain the optimal electricity-carbon coupling price and the optimal power of each link of industrial production, and perform energy optimization scheduling of the industrial park with electricity-carbon coordination.

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