Integrated energy system optimization scheduling method based on improved spider bee optimization algorithm

Abstract

The invention relates to a comprehensive energy system optimization scheduling method based on an improved spider bee optimization algorithm, and the method comprises the steps: firstly building a comprehensive energy system optimization scheduling model considering price type demand response and alternative type demand response, so as to solve a problem that the power of a source side and a load side is not matched, and improve the operation stability and economical efficiency of a system; and then, a chaos sequence generated by Tent mapping is utilized to provide an initial solution for a spider bee optimization algorithm, so that a global optimal solution can be searched more easily. And finally, solving the comprehensive energy optimization scheduling model by using an improved spider bee optimization algorithm. Compared with a traditional intelligent optimization algorithm, the method is higher in solving speed and better in optimization result. According to the method, the total operation cost and carbon emission of the integrated energy system can be obviously reduced, and meanwhile, compared with a traditional intelligent optimization algorithm, the solving speed is higher, and the optimization result is better.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy technology, and in particular to an integrated energy system optimization scheduling method based on an improved spider bee optimization algorithm. Background Art

[0002] At present, there is an urgent need to analyze the new situation of green development and explore new ways to save energy and reduce carbon emissions. The integrated energy system combines electricity, gas, heat and other energy sources to achieve multi-energy coordinated production, distribution, utilization and storage, which can improve the overall efficiency of energy utilization and the flexibility of system operation. However, wind power has low power generation during peak load periods, but high power generation during low load periods. This power mismatch between source and load has become a difficulty in improving the low-carbon and economic nature of the integrated energy system.

[0003] Demand response can change the user's electricity consumption habits by compensating or adjusting electricity prices, so as to reduce the electricity load or cut the peak electricity consumption, thereby improving the stability and economy of power grid operation. At present, some studies on the optimization of the energy consumption side in the IES model take into account the demand response behavior of the electricity load in the system; some scholars consider analyzing the price elasticity matrix to simulate the demand response behavior and using transferable loads to optimize the energy consumption on the load side. These studies have not considered the role of different load response characteristics in improving the power mismatch between source and load. On the other hand, IES optimization scheduling is a large-scale nonlinear and non-convex problem that is difficult to solve. Current research mostly uses intelligent algorithms to solve it. The particle swarm optimization algorithm is used to solve the IES optimization scheduling problem, but there are problems such as poor convergence accuracy, dependence on the initial population, and easy to fall into the local optimum when converging too early; when the gray wolf optimization algorithm is used to solve the IES problem, the global search ability is improved, but the convergence speed still needs to be further improved; when the sparrow search algorithm is used to solve the IES optimization scheduling problem, it can have a strong global search ability and high solution efficiency, but the initial population is unevenly distributed, and there is a problem of low quality of the initial optimal solution. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and propose an optimized scheduling method for an integrated energy system based on an improved spider bee optimization algorithm. First, an optimized scheduling model for an integrated energy system considering price-based demand response and alternative demand response is established to address the power mismatch problem between the source and the load, thereby improving the stability and economy of the system operation. Then, the chaotic sequence generated by the Tent mapping is used to provide an initial solution for the spider bee optimization algorithm, which makes it easier to search for the global optimal solution. Finally, the improved spider bee optimization algorithm is used to solve the integrated energy optimization scheduling model. The present invention can significantly reduce the total cost and carbon emissions of the operation of the integrated energy system. At the same time, compared with the traditional intelligent optimization algorithm, the solution speed is faster and the optimization results are better.

[0005] The present invention solves the technical problem by adopting the following technical solutions:

[0006] The integrated energy system optimization scheduling method based on the improved spider bee optimization algorithm includes the following steps:

[0007] Step 1: Establish an integrated energy system optimization dispatch model that considers price-based demand response and substitution-based demand response;

[0008] Step 2: Use the chaotic sequence generated by Tent mapping to provide an initial solution for the spider bee optimization algorithm and improve the spider bee optimization algorithm;

[0009] Step 3: Use the spider bee optimization algorithm improved in step 2 to solve the comprehensive energy optimization scheduling model constructed in step 1 to obtain the comprehensive energy system optimization scheduling method.

[0010] Moreover, the comprehensive energy system optimization scheduling model constructed in step 1 includes: a demand response model, an objective function and constraints.

[0011] Moreover, the demand response model includes a price-based demand response model and a substitution-based demand response model:

[0012] The price-based demand response model is:

[0013]

[0014] Where: L o,t , L m,t are the loads before and after the response at time t; E m is the elastic coefficient; c o,t , c m,t are the energy prices before and after the response at time t;

[0015] The alternative demand response model is:

[0016]

[0017] Where: ΔL e,t , ΔL h,t are the replaceable electric load and the replaced load at time t respectively; e,h is the electric heat substitution coefficient; v e , v h are the unit calorific values ​​of electrical energy and thermal energy respectively; are the energy utilization rates of electrical energy and thermal energy, respectively.

[0018] Moreover, the objective function aims to minimize the operating cost and carbon emission cost of IES during operation. The operating cost of IES includes three parts: electricity purchase cost, gas purchase cost and system operation and maintenance cost:

[0019] C l =C e +C g +C o

[0020]

[0021]

[0022]

[0023] Where: C l , C e , C g , C o They are the total IES operation cost, electricity purchase cost, gas purchase cost and system operation and maintenance cost; g b,t , g s,t , g g Respectively represent the electricity purchase and sale price and the natural gas purchase price at time t; P b,t , P s,t , P g,t K are the power purchased and sold from the superior power grid at time t, and the natural gas purchase rate; i is the operation and maintenance coefficient of equipment i; P i,t is the output power of device i at time t;

[0024] The cost of carbon emissions is:

[0025]

[0026] Where: C H is the carbon emission cost; W is the penalty coefficient; μ e , μ g They are the carbon dioxide emission coefficients per unit of electric power and natural gas respectively.

[0027] Furthermore, the constraints include equipment power constraints and electrical, thermal and cooling power balance constraints:

[0028] The equipment power constraints are:

[0029]

[0030] Where: P max , P min The upper and lower limits of the device output power; E max , E min The upper and lower limits of the EES storage capacity;

[0031] The power balance constraints for electricity, heat and cooling are:

[0032]

[0033] Where: L e , L h , L e They are the electrical, heating and cooling loads of the system respectively.

[0034] Furthermore, the step 2 comprises the following steps:

[0035] Step 2.1: Set the number of individuals in the population N, the minimum number of individuals in the population Nmin, the crossover rate CR, the trade-off rate TR, and the maximum number of iterations tmax;

[0036] Step 2.2: Use Tent chaos mapping method to generate the initial population;

[0037] Step 2.3: Set the number of iterations to 1;

[0038] Step 2.4: Generate a random number r6 between 0 and 1, and determine whether r6<TR. If r6<TR, proceed to step 2.5, otherwise proceed to step 2.6;

[0039] Step 2.5: Use hunting nesting behavior to update the position of the spider bee and proceed to step 2.7;

[0040] Step 2.6: Produce a new generation of spider bees through mating behavior, and proceed to step 2.7;

[0041] Step 2.7: Calculate the updated population fitness and update the optimal fitness;

[0042] Step 2.8: Set the number of iterations t = t + 1;

[0043] Step 2.9, determine whether the maximum number of iterations has been reached. If so, output the optimal individual and the optimal fitness. Otherwise, return to step 2.4.

[0044] The advantages and positive effects of the present invention are:

[0045] 1. The present invention first establishes an integrated energy system optimization scheduling model that takes into account price-based demand response and alternative demand response to address the problem of power mismatch between the source and the load, and improve the stability and economy of the system operation. Then, the chaotic sequence generated by the Tent mapping is used to provide an initial solution for the spider bee optimization algorithm, which can make it easier to search for the global optimal solution. Finally, the improved spider bee optimization algorithm is used to solve the integrated energy optimization scheduling model. Compared with traditional intelligent optimization algorithms, the present invention has a faster solution speed and better optimization results. The present invention can significantly reduce the total cost and carbon emissions of the operation of the integrated energy system. At the same time, compared with traditional intelligent optimization algorithms, the solution speed is faster and the optimization results are better.

[0046] 2. The present invention can achieve a good "peak shaving and valley filling" effect on the load side by adding demand responses of two different load response characteristics, namely, a price-based demand response model and an alternative demand response model, into the model.

[0047] 3. Based on the spider bee optimization algorithm, the present invention proposes an improved spider bee optimization algorithm. The algorithm first uses Tent mapping to generate a chaotic sequence, initializes the population, and then uses the spider bee optimization algorithm to iteratively solve the problem. This method has a faster convergence speed and can achieve more accurate results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a schematic diagram of the IES architecture;

[0049] Figure 2 This is a flow chart of the spider bee optimization algorithm of the present invention;

[0050] Figure 3 This is a typical day initial load, wind power and photovoltaic output power prediction curve diagram of the system in the embodiment of the present invention;

[0051] Figure 4 A comparison diagram of the electrical load of the system before and after IDR is considered in an embodiment of the present invention;

[0052] Figure 5 A comparison diagram of the heat load of the system before and after IDR is considered in an embodiment of the present invention;

[0053] Figure 6 This is a comparison diagram of the cooling load of the system before and after IDR in an embodiment of the present invention;

[0054] Figure 7 A schematic diagram of a balanced distribution of electric loads with the goal of minimizing operating costs according to an embodiment of the present invention;

[0055] Figure 8 This is a schematic diagram of heat load balance distribution with the goal of minimizing operating costs according to an embodiment of the present invention;

[0056] Fig. 9 This is a schematic diagram of cooling load balance distribution with the goal of minimizing operating costs according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is further described in detail below with reference to the accompanying drawings.

[0058] The integrated energy system optimization scheduling method based on the improved spider bee optimization algorithm includes the following steps:

[0059] Step 1: Establish an integrated energy system optimization scheduling model that considers price-based demand response and alternative demand response.

[0060] Integrated Energy System (IES) combines electricity, gas, heat and other energy sources to achieve multi-energy coordinated production, distribution, utilization and storage, which can improve the overall efficiency of energy utilization and the flexibility of system operation, and is one of the important ways to achieve "dual carbon". Current research lacks consideration of different load response characteristics.

[0061] like Figure 1 As shown, the IES energy supply side includes the upper power grid, natural gas grid, photovoltaic (PV) and wind power generation (WT); the conversion equipment includes gas turbines (GT), waste heat boilers (WHB), gas boilers (GB), electric boilers (EB), electric refrigerators (EC) and absorption refrigerators (AC); the energy storage equipment is electric energy storage (EES).

[0062] Among them, the gas turbine (GT) model is

[0063]

[0064] Where: P GT,e , P GT,g , P GT,h are GT power generation, gas consumption, and heat generation respectively; η GT,e , η GT,h They are GT power generation efficiency and heat generation efficiency respectively.

[0065] The waste heat boiler (WHB) model is

[0066]

[0067] Where: P WHB is the WHB output power; η WHB is the WHB heating efficiency.

[0068] The gas boiler (GB) model is

[0069] P GB,h =η GB,h P GB,g (1.3)

[0070] Where: P GB,h , P GB,g GB is the heat generation power and gas consumption power respectively; η GB,h is the GB heat generation efficiency.

[0071] The Electric Boiler (EB) model is

[0072] P EB.h =η EB,h P EB,e(1.4)

[0073] Where: P EB.h , P EB,e are EB heat generation power and electrical power consumption respectively; η EB,h is the heat generation efficiency of EB.

[0074] The Electric Chiller (EC) model is

[0075] P EC.c =η EC,c P EC,e (1.5)

[0076] Where: P EC.c , P EC,e are the EC output cooling power and input electrical power respectively; η EC,c is the EC cooling coefficient.

[0077] The absorption chiller (AC) model is

[0078] P AC,c =η AC,c P AC,h (1.6)

[0079] Where: P AC,c , P AC,h They are AC cooling power and input heating power respectively.

[0080] The electrical energy storage (EES) model is

[0081]

[0082] Where: E EES,t represents the amount of electricity stored in EES at time t; P ch,e (t), P dis,e (t) represents the EES charging and discharging power at time t; η ch,e , η dis,e are EES charging and discharging efficiency, respectively.

[0083] According to the above IES architecture, the integrated energy system optimization scheduling model constructed includes: demand response model, objective function and constraints.

[0084] (1) Demand response model

[0085] Demand response models include price-based demand response models and substitution-based demand response models:

[0086] The price-based demand response model is:

[0087]

[0088] Where: L o,t , L m,t are the loads before and after the response at time t; E m is the elastic coefficient; c o,t , c m,t are the energy prices before and after the response at time t respectively.

[0089] The alternative demand response model is:

[0090]

[0091] Where: ΔL e,t , ΔL h,t are the replaceable electric load and the replaced load at time t respectively; e,h is the electric heat substitution coefficient; v e , v h are the unit calorific values ​​of electrical energy and thermal energy respectively; are the energy utilization rates of electrical energy and thermal energy, respectively.

[0092] (2) Objective function

[0093] The objective function is to minimize the operating cost and carbon emission cost of IES during operation. The operating cost of IES includes three parts: electricity purchase cost, gas purchase cost and system operation and maintenance cost:

[0094] C l =C e +C g +C o (1.10)

[0095]

[0096]

[0097]

[0098] Where: C l , C e , C g , C o They are the total IES operation cost, electricity purchase cost, gas purchase cost and system operation and maintenance cost; g b,t , g s,t , g g Respectively represent the electricity purchase and sale price and the natural gas purchase price at time t; P b,t , P s,t , P g,t K are the power purchased and sold from the superior power grid at time t, and the natural gas purchase rate;i is the operation and maintenance coefficient of equipment i; P i,t is the output power of device i at time t.

[0099] The cost of carbon emissions is:

[0100]

[0101] Where: C H is the carbon emission cost; W is the penalty coefficient; μ e , μ g They are the carbon dioxide emission coefficients per unit of electric power and natural gas respectively.

[0102] (3) Constraints

[0103] Constraints include equipment power constraints and electrical, thermal, and cooling power balance constraints:

[0104] The equipment power constraint is

[0105]

[0106] Where: P max , P min The upper and lower limits of the device output power; E max , E min The upper and lower limits of EES storage capacity.

[0107] The power balance constraints for electricity, heat and cooling are:

[0108]

[0109] Where: L e , L h , L e They are the electrical, heating and cooling loads of the system respectively.

[0110] Step 2: Use the chaotic sequence generated by Tent mapping to provide an initial solution for the spider bee optimization algorithm and improve the spider bee optimization algorithm.

[0111] The Spider Wasp Optimization (SWO) algorithm simulates the hunting, nesting and mating behaviors of female spider wasps in nature. It includes four operations, simulating the search for spiders, tracking and moving away from fallen spiders, nesting on paralyzed spiders, and mating behaviors to lay eggs of female spider wasps. The algorithm has a variety of unique update strategies and is applicable to a wide range of optimization problems with different search requirements. The fitness function value of random numbers generated by chaotic mapping is significantly improved. Replacing the conventional uniformly distributed random number generator with chaotic mapping can get better results, especially when there are many local solutions in the search space, it is easier to search for the global optimal solution. The use of chaotic sequences for population initialization, selection, crossover and mutation operations will affect the entire process of the algorithm, and often achieve better results than pseudo-random numbers.

[0112] like Figure 2 As shown, this step includes the following steps:

[0113] Step 2.1: Set the number of individuals in the population N, the minimum number of individuals in the population Nmin, the crossover rate CR, the trade-off rate TR, and the maximum number of iterations tmax;

[0114] Step 2.2: Use Tent chaos mapping method to generate the initial population;

[0115] Step 2.3: Set the number of iterations to 1;

[0116] Step 2.4: Generate a random number r6 between 0 and 1, and determine whether r6<TR. If r6<TR, proceed to step 2.5, otherwise proceed to step 2.6;

[0117] Step 2.5: Use hunting nesting behavior to update the position of the spider bee and proceed to step 2.7;

[0118] Step 2.6: Produce a new generation of spider bees through mating behavior, and proceed to step 2.7;

[0119] Step 2.7: Calculate the updated population fitness and update the optimal fitness;

[0120] Step 2.8: Set the number of iterations t = t + 1;

[0121] Step 2.9, determine whether the maximum number of iterations has been reached. If so, output the optimal individual and the optimal fitness. Otherwise, return to step 2.4.

[0122] Step 3: Use the spider bee optimization algorithm improved in step 2 to solve the comprehensive energy optimization scheduling model constructed in step 1 to obtain the comprehensive energy system optimization scheduling method.

[0123] According to the above-mentioned integrated energy system optimization scheduling method based on the improved spider bee optimization algorithm, the present invention takes a certain industrial park as the IES research object, takes 24 hours a day as the optimization operation cycle, and the unit operation time is 1 hour. The parameters of each device in the park are shown in Table 1 below, and Table 2 is the electricity purchase price in different time periods. The electricity sales price is 0.5 yuan / kWh, and the natural gas price is 2.55 yuan / m 3 The system typical day initial load, wind power and photovoltaic output power forecast curves are as follows: Figure 3 shown.

[0124] Table 1 Equipment parameters

[0125]

[0126]

[0127] Table 2 Time-of-use electricity purchase price

[0128]

[0129] The comparison curves of system electricity, heating and cooling loads before and after considering comprehensive demand response are as follows Figure 4 , Figure 5 and Figure 6 As shown. From the original load curve, it can be seen that there is an obvious peak-valley distribution. After considering the comprehensive demand response of the system, the peak-valley distribution of electricity, heat and cooling loads is more gentle, among which the peak-valley difference of electricity load is reduced by 22.1%; the peak-valley difference of heat load is reduced by 19.3%; the peak-valley difference of cooling load is reduced by 17.9%. From the results, considering the comprehensive demand response can achieve the effect of "peak shaving and valley filling" on the load side, and improve the stability and economy of the integrated energy system.

[0130] In order to verify the effectiveness of the improved spider bee algorithm, the particle swarm algorithm, gray wolf optimization algorithm, sparrow search algorithm, spider bee optimization algorithm and the proposed improved spider bee optimization algorithm were used to calculate the objective functions of minimum system operation cost and minimum carbon emission cost.

[0131] The improved spider bee optimization algorithm used in the embodiment of the present invention comprises the following steps:

[0132] Step 0: Set the number of individuals in the population N, the minimum number of individuals in the population Nmin, the crossover rate CR, the trade-off rate TR, and the maximum number of iterations tmax.

[0133] Step 1: Use the Tent chaos mapping method to generate the initial population. In this algorithm, each spider bee (female) represents a solution of the current generation and can be encoded in a D-dimensional vector by the following expression.

[0134]

[0135] We can specify a pre-specified initial upper bound and the initial lower bound N vectors are randomly generated between as follows:

[0136]

[0137] Among them SW Pop It is the initial population of Spider Bees.

[0138] Step 1.1: Randomly generate a D-dimensional vector Each component of a vector is between 0 and 1.

[0139] Step 1.2: Use Tent mapping to generate N vectors. The process of generating chaotic sequence by Tent mapping is as follows:

[0140]

[0141] Step 1.3: Generate the initial solution of the population according to the chaotic sequence:

[0142]

[0143] Where t represents the t-th generation population of spider bees, and i represents the i-th individual (i=1, 2,...N).

[0144] Step 2: Calculate the fitness of each spider bee Find the individual with the best fitness

[0145] Step 3: Determine whether the iteration limit is reached. If not, go to step 4; if the limit is reached, go to step 12.

[0146] Step 4: Generate a random number r6 between 0 and 1. If r6<TR, go to step 5; otherwise go to step 8.

[0147] Step 5: Generate a random number k. If i<N×k, go to step 6; otherwise, go to step 7.

[0148] Step 6: Generate a random number p, r3, r4 between 0 and 1. If p<k and r3<r4, go to step 6.1; if p<k and r3≥r4, go to step 6.2; if p≥k and r3<r4, go to step 6.3; if p≥k and r3≥r4, go to step 6.4.

[0149] Step 6.1: Use a fixed step size method to explore the search space, simulate the female spider bee looking for a suitable spider to feed the larvae, and use the following formula to update the position of the spider bee:

[0150]

[0151] Where a and b are two indices randomly selected from the population to determine the search direction; μ1 is used to determine the fixed step length, which is obtained by the following formula:

[0152] μ1=|rn|×r1(1.22)

[0153] Step 6.2: Use the variable step size method to explore the search space, simulating the female spider bee searching the entire area surrounded by the spider's exact location when it loses the spider's trail, and use the following formula to update the spider bee's position:

[0154]

[0155] Where c is an individual indicator randomly selected from the population; l is a random number between -2 and 1.

[0156] Step 6.3: Simulate the process of spider bees catching spiders and the spiders escaping. There are usually two trends: one is that the spider bee catches and traps the spider; the second is that the spider avoids the spider bee, and the distance between them increases with the number of iterations. Use the following formula to update the position of the spider bee:

[0157]

[0158] where a is an individual index randomly selected from the population; t and t max Represent the current and maximum number of iterations respectively; represents a randomly generated vector between 0 and 1; r6 is a random number between 0 and 1.

[0159] Step 6.4: Simulate the spider escaping from the spider bee. At this time, the distance between the spider bee and the spider gradually increases. Use the following formula to simulate this behavior:

[0160]

[0161] in is a vector generated between -k and k according to a normal distribution; k is generated according to the following formula to gradually increase the distance between the spider bee and the spider.

[0162]

[0163] Step 7: Generate random numbers r3, r4 between 0 and 1. If r3<r4, go to step 7.1; otherwise go to step 7.2.

[0164] Step 7.1: The simulated spider wasp pulls the spiders towards the area containing the most fit spiders, considering this to be the best place to build a nest so that the paralyzed spider can be placed in its abdomen to lay eggs. This formula is described as follows:

[0165]

[0166] in It is the best solution so far.

[0167] Step 7.2: Simulate the spider wasp building a nest at the location of a spider that is randomly selected from the population. To avoid building two nests at the same location, the equation uses an extra step. The formula is designed as follows:

[0168]

[0169] Where r3 is a random number between 0 and 1; γ is the number generated by Levy flight; a, b, c are individual indicators randomly selected from the population; is a binary vector that determines when to apply a step size to avoid building two nests in the same location.

[0170] Step 8: Simulate spider wasps to cross over and generate the next generation. A key feature of spider wasps is their ability to determine sex, which depends on the size of the host where the eggs are laid. In the introduced method, each spider wasp represents a possible solution in the current generation, and the new spider wasps represent the newly generated potential solutions in the current generation. New female spider wasps are generated according to the following formula:

[0171]

[0172] Crossover means in SW i t and The uniform crossover operator applied between SW i t and Represent male and female spider bees respectively. Male spider bees are generated by the following formula:

[0173]

[0174] Where β and β1 are two numbers randomly generated according to normal distribution, and e is an exponential constant and It is generated according to the following formula:

[0175]

[0176]

[0177] Step 9: Calculate the fitness of the updated solution Update the optimal fitness and the corresponding solution

[0178] Step 10: Update the number of iterations to t=t+1.

[0179] Step 11: To speed up convergence, some bees in the population will be terminated during the iterative run to provide more function evaluations for other bees and reduce population diversity. The following formula is used to update the number of individuals in the population, and then go to step 3.

[0180] N=N min +(NN min )×k(1.35)

[0181] Step 12: Output the optimal solution And the optimal fitness

[0182] The comparison of the solution results of different algorithms is shown in Table 3.

[0183] Table 3 Comparison of optimization scheduling results of different algorithms

[0184]

[0185]

[0186] From the comparison of the target optimal values ​​in Table 3, it is not difficult to see that the improved spider bee algorithm can always obtain a better optimal solution in solving the problem of optimizing the scheduling of the integrated energy system. Taking the lowest system operating cost as the goal, the average distribution of electricity, heat and cooling loads obtained by using the improved spider bee optimization algorithm to solve the IES optimization scheduling problem is as follows Figure 7 , Figure 8 and Fig. 9 shown.

[0187] analyze Figure 7 , Figure 8 and Fig. 9 The results of the medium load distribution show that: in order to optimize costs, wind power generation and photovoltaic power generation are used to provide electricity first, because renewable energy does not produce carbon emissions and does not require power generation costs; when renewable energy is not enough to provide enough electricity, the system will use gas boilers to generate electricity or purchase electricity from the upper power grid; when electricity prices are at a low point, the system will choose to purchase electricity, charge the electric energy storage, and sell electricity when the electricity price is at a peak, thereby reducing the total operating cost of the system. For thermal loads, the system gives priority to the heat energy of waste heat boilers, and then uses gas boilers and electric boilers as a supplement to thermal energy.

[0188] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manner. Any other implementation manners derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. An integrated energy system optimization scheduling method based on an improved spider bee optimization algorithm is characterized by: The following steps are involved: Step 1: Establish an integrated energy system optimization dispatch model that considers price-based demand response and substitution-based demand response; Step 2: Use the chaotic sequence generated by Tent mapping to provide an initial solution for the spider bee optimization algorithm and improve the spider bee optimization algorithm; Step 3: Use the spider bee optimization algorithm improved in step 2 to solve the comprehensive energy optimization scheduling model constructed in step 1 to obtain the comprehensive energy system optimization scheduling method.

2. The integrated energy system optimization scheduling method based on the improved spider bee optimization algorithm according to claim 1 is characterized in that: The integrated energy system optimization scheduling model constructed in step 1 includes: a demand response model, an objective function and constraints.

3. The integrated energy system optimization scheduling method based on the improved spider bee optimization algorithm according to claim 2 is characterized in that: The demand response model includes a price-based demand response model and a substitution-based demand response model: The price-based demand response model is: Where: L o,t , L m,t are the loads before and after the response at time t; E m is the elastic coefficient; c o,t , c m,t are the energy prices before and after the response at time t; The alternative demand response model is: Where: ΔL e,t , ΔL h,t are the replaceable electric load and the replaced load at time t respectively; e,h is the electric heat substitution coefficient; v e , v h are the unit calorific values ​​of electrical energy and thermal energy respectively; are the energy utilization rates of electrical energy and thermal energy, respectively.

4. The integrated energy system optimization scheduling method based on the improved spider bee optimization algorithm according to claim 2 is characterized in that: The objective function aims to minimize the operating cost and carbon emission cost of IES during operation. The operating cost of IES includes three parts: electricity purchase cost, gas purchase cost and system operation and maintenance cost: C l =C e +C g +C o Where: C l , C e , C g , C o They are the total IES operation cost, electricity purchase cost, gas purchase cost and system operation and maintenance cost; g b,t , g s,t , g g Respectively represent the electricity purchase and sale price and the natural gas purchase price at time t; P b,t , P s,t , P g,t K are the power purchased and sold from the superior power grid at time t, and the natural gas purchase rate; i is the operation and maintenance coefficient of equipment i; P i,t is the output power of device i at time t; The cost of carbon emissions is: Where: C H is the carbon emission cost; W is the penalty coefficient; μ e , μ g They are the carbon dioxide emission coefficients per unit of electric power and natural gas respectively.

5. The integrated energy system optimization scheduling method based on the improved spider bee optimization algorithm according to claim 2 is characterized in that: The constraints include equipment power constraints and electrical, thermal and cooling power balance constraints: The equipment power constraints are: Where: P max , P min The upper and lower limits of the device output power; E max , E min The upper and lower limits of the EES storage capacity; The power balance constraints for electricity, heat and cooling are: Where: L e , L h , L e They are the electrical, heating and cooling loads of the system respectively.

6. The integrated energy system optimization scheduling method based on the improved spider bee optimization algorithm according to claim 1 is characterized by: The step 2 comprises the following steps: Step 2.1: Set the number of individuals in the population N, the minimum number of individuals in the population Nmin, the crossover rate CR, the trade-off rate TR, and the maximum number of iterations tmax; Step 2.2: Use Tent chaos mapping method to generate the initial population; Step 2.3: Set the number of iterations to 1; Step 2.4: Generate a random number r6 between 0 and 1, and determine whether r6<TR. If r6<TR, proceed to step 2.5, otherwise proceed to step 2.6; Step 2.5: Use hunting nesting behavior to update the position of the spider bee and proceed to step 2.7; Step 2.6: Produce a new generation of spider bees through mating behavior, and proceed to step 2.7; Step 2.7: Calculate the updated population fitness and update the optimal fitness; Step 2.8: Set the number of iterations t = t + 1; Step 2.9, determine whether the maximum number of iterations has been reached. If so, output the optimal individual and the optimal fitness. Otherwise, return to step 2.4.

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