Virtual power plant low-carbon economic optimization scheduling method based on improved sparrow search algorithm

Abstract

According to the virtual power plant low-carbon economic optimization scheduling method based on the improved sparrow search algorithm, the virtual power plant model related to a carbon transaction mechanism is established, the improved sparrow search algorithm is used, and a self-adaptive weight updating mechanism and a multi-strategy fusion exploration method are introduced, so that the search capability and the convergence speed of the algorithm are improved; the improved sparrow search algorithm is used for solving the established model, a scheduling scheme which is lowest in cost and meets all constraint conditions is found, the defects in the prior art are overcome, higher convergence speed and higher optimization efficiency are achieved, a better scheduling scheme is obtained, and the method is high in efficiency, high in universality and ideal in effect.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to a low-carbon economic optimization scheduling method based on a sparrow search algorithm applied to a virtual power plant. Background Art

[0002] At present, with the intensification of global climate change and energy crisis, low-carbon economy has become the common pursuit of the international community. In order to help achieve the strategic goal of "carbon peak and carbon neutrality", my country is building a new power system with new energy as the main body. In the process of building a new power system, virtual power plants are one of the focuses of construction. The so-called virtual power plant is a power coordination management system that uses advanced information and communication technology and software systems to achieve the aggregation and coordinated optimization of DER such as DG, energy storage systems, controllable loads, and electric vehicles, so as to participate in the power market and power grid operation as a special power plant.

[0003] The core of the concept of virtual power plant can be summarized as "communication" and "aggregation". The key technologies of virtual power plant mainly include coordinated control technology, smart metering technology and information and communication technology. The most attractive function of virtual power plant is that it can aggregate DER to participate in the operation of electricity market and auxiliary service market, and provide management and auxiliary services for distribution network and transmission network. The solution of "virtual power plant" has great market potential in my country. It is undoubtedly a good choice for China, which is facing the contradiction between "electricity shortage and low energy efficiency".

[0004] It can be seen that virtual power plants can organically integrate different energy sources together to form a multi-energy complementary energy system, promote the solution of scheduling problems caused by the operating characteristics of different power sources, and effectively improve energy utilization efficiency.

[0005] Carbon trading is a market-based environmental policy tool. The principle is that one party to the contract obtains greenhouse gas emission reduction quotas by paying the other party. The buyer can use the purchased emission reduction quotas to mitigate the greenhouse effect and achieve its emission reduction goals. By buying and selling carbon emission rights, companies are encouraged to reduce carbon emissions, which is an effective way to achieve a low-carbon economy.

[0006] Combining virtual power plants with carbon trading is the low-carbon economic operation or planning issue of integrated energy microgrids or virtual power plants that most research institutes are currently focusing on.

[0007] However, the scheduling algorithms under the existing technology face multiple problems when dealing with the economic optimization scheduling of virtual power plants. These problems are mainly manifested in high computational complexity, slow convergence speed, and difficulty in finding the global optimal solution. Specifically:

[0008] 1. The optimization problem of a virtual power plant is essentially a complex problem with high dimensions, multiple objectives and nonlinearity;

[0009] 2. There are many variables involved in virtual power plants, including but not limited to power load, heat load, start and stop status of power generation equipment, and environmental factors;

[0010] 3. The above-mentioned virtual power plant problem results in a huge solution space, which increases the computational burden of the algorithm when searching for the optimal solution.

[0011] In summary, there is an urgent need for a new optimization scheduling method that can solve the deficiencies in the above-mentioned existing technologies and achieve faster convergence speed and higher optimization efficiency. Summary of the invention

[0012] In order to solve the multiple problems faced by the scheduling algorithms under the existing technology when dealing with the economic optimization scheduling of virtual power plants, the present invention provides a low-carbon economic optimization scheduling method for virtual power plants based on an improved sparrow search algorithm, and establishes a low-carbon economic optimization scheduling model for virtual power plants by utilizing the carbon trading mechanism. The improved sparrow search algorithm is used to introduce an adaptive weight update mechanism and a multi-strategy fusion exploration method to improve the search capability and convergence speed of the algorithm. The established low-carbon economic optimization scheduling model of the virtual power plant is solved by using the improved sparrow search algorithm to obtain a scheduling solution with the lowest cost and satisfying all constraints.

[0013] The low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention has the following specific steps:

[0014] 1) Combined with the carbon trading mechanism, a low-carbon economic optimization scheduling model for virtual power plants is established;

[0015] 2) Use the improved sparrow search algorithm to solve the low-carbon economic optimization scheduling model of the established virtual power plant to find the scheduling plan with the lowest cost and meeting all constraints. This step is specifically divided into three parts: initializing the population through Circle chaos mapping to improve the global search capability, introducing an adaptive weight factor to adjust its follower mechanism to improve the globality of the search, and using the tangent flight strategy to perturb the optimal solution.

[0016] According to the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention, it is characterized in that the low-carbon economic optimization scheduling model of a virtual power plant is established in step 1), which is specifically divided into a carbon emission quota model, a carbon emission cost model and a carbon trading cost model, as follows:

[0017] 2.1) For the established virtual power plant, the carbon emission sources are gas turbines, referred to as MT and gas boilers, referred to as GB, and the allocated carbon emission quotas are:

[0018]

[0019] Where:

[0020] θ is the regional carbon emission allocation coefficient per unit electricity;

[0021] is the heat and electricity power output of the micro-turbine at time t;

[0022] is the wind power and photovoltaic power at time t;

[0023] λ is the conversion factor of electricity;

[0024] is the thermal power output of the gas boiler at time t;

[0025] According to the current development status of my country's carbon trading market, the carbon emission quota of the virtual power plant in this step is determined based on a certain amount of carbon emissions initially allocated free of charge to the virtual power plant. Among them, the National Development and Reform Commission issued a document stipulating the "China Regional Power Grid Baseline Emission Factor", based on which the regional unit electricity carbon emission allocation coefficient can be determined, and the carbon emission quota is determined by this allocation coefficient.

[0026] 2.2) Establish a model for carbon emission costs:

[0027] The carbon emissions in this model and virtual power plant are positively correlated with the output of micro-turbines and gas boilers. The carbon emissions at time t are:

[0028]

[0029] In the formula, β MT , β GB is the carbon emission coefficient of micro turbines and gas boilers;

[0030] 2.3) Establish a model for carbon trading costs:

[0031] According to the trading rules of the virtual power plant - if the emission exceeds the quota, additional emission rights must be purchased, and if the emission is lower than the quota, the excess emission rights can be sold. The carbon trading cost at time t is:

[0032] C t =μ(E r,t -E p,t )

[0033] In the formula, μ is the price in the carbon trading market, and the carbon price is the average of the typical daily transaction prices in the national carbon market;

[0034] Combining the above models, a low-carbon economic optimization scheduling model for virtual power plants was established.

[0035] According to the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention, it is characterized in that the low-carbon economic optimization scheduling model of the virtual power plant takes the economic optimum under the safe operation of the virtual power plant as the goal, and constructs an economic scheduling model of a virtual power plant participating in the carbon trading market. In the model, the objective function of the virtual power plant is to maximize the comprehensive benefit, that is, to minimize the comprehensive operating cost, and its formula is:

[0036]

[0037] In the formula, C buy The cost of purchasing energy;

[0038] C t For carbon trading costs;

[0039] C op For operation and maintenance costs;

[0040] F min To minimize the overall operating cost.

[0041] According to the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention, it is characterized in that the objective function of the virtual power plant is to maximize the comprehensive benefit, that is, in the formula with the minimum comprehensive operating cost, T is a scheduling cycle, and the energy purchase cost C buy , Operation and maintenance cost C op The specific formula is as follows:

[0042]

[0043] In the formula, is the electric power purchased and sold from the grid at time t;

[0044] is the price of electricity purchased and sold from the power grid at time t;

[0045] is the amount of natural gas purchased from the gas grid at time t;

[0046] is the price of natural gas purchased from the gas grid at time t;

[0047] C h , C pv , C wt , C ES They are the operation and maintenance costs of thermal power units, photovoltaic power generation units, wind power units and energy storage equipment respectively.

[0048] According to the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention, it is characterized in that in the step 1) of establishing a low-carbon economic optimization scheduling model of a virtual power plant, constraints need to be satisfied, and the constraints are: wind power photovoltaic power constraints, energy storage system constraints, thermal power unit power balance, electric power constraints and gas network balance constraints, which are specifically as follows:

[0049] 5.1) Wind and photovoltaic power constraints:

[0050] 0≤P wt ≤P wtmax

[0051] 0≤P pv ≤P pvmax

[0052] Where P wt , P pv are the power of wind power and photovoltaic power respectively;

[0053] 5.2) Energy storage system constraints:

[0054] S ES,t0 -S ES,t =0

[0055] S ES,min ≤S ES,t ≤S ES,max

[0056]

[0057] In the formula, S ES,t0 is the initial charge state of the energy storage;

[0058] S ES,t is the state of charge of the energy storage at time t;

[0059] η c , η d are the efficiencies of energy storage in charging and discharging states, respectively;

[0060] E is the rated capacity of energy storage;

[0061] 5.3) Thermal power unit power balance constraints:

[0062]

[0063] In the formula, are the heat generation power of the heat pump and other thermal energy equipment at time t respectively;

[0064] is the heat load at time t;

[0065] 5.4) Electrical balance constraints:

[0066]

[0067] In the formula, is the power consumption of the heat pump at time t;

[0068] is the power generation of the micro-turbine at time t;

[0069] P ES,t,c , P ES,t,d are the charging and discharging power of energy storage at time t respectively;

[0070] is the electrical load at time t;

[0071] 5.5) The gas grid balance constraint is:

[0072]

[0073] In the formula, is the gas volume purchased by the micro-turbine from the gas grid at time t;

[0074] is the gas volume purchased by the gas boiler from the gas grid at time t;

[0075] is the gas purchase volume from the gas grid at time t.

[0076] The low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm according to the present invention is characterized in that the population is initialized by Circle chaos mapping in step 2) to improve the global search capability, which is specifically:

[0077] In this step, since the sparrow search algorithm has the problem of uneven initialization component layout during initialization, the Circle chaos map is combined with the sparrow search algorithm to improve the global search ability of the algorithm. The optimized function is:

[0078]

[0079] Where n is the dimension of the solution;

[0080] d1, d2, and d3 are constants, and their values ​​can be adjusted to change the effect of chaotic mapping.

[0081] The improved sparrow search algorithm is the preferred solution of the low-carbon economic optimization scheduling method of the virtual power plant based on the improved sparrow search algorithm of the present invention. The improved sparrow search algorithm is used to solve the objective function to obtain the optimized intraday scheduling plan. In this step, further improvement of the sparrow search algorithm can better solve the virtual power plant optimization scheduling problem, so that it can jump out of the local optimum and obtain the global optimal solution. The ability is stronger, thereby being able to obtain a more economical optimization scheduling plan.

[0082] The low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm according to the present invention is characterized in that the step 2) introduces an adaptive weight factor to adjust its follower mechanism, which is specifically:

[0083] In this step, a dynamic adjustment method is used for efficient optimization. An adaptive inertia weight is added in the follower position update stage. The adaptive inertia weight can make the current individual affect the optimization of the next generation of individuals, thereby improving the efficiency of follower optimization. The adaptive inertia weight formula is as follows:

[0084]

[0085] Where: min With ω max For the weight adjustment parameters, they are set to 0.8 and 0.3 respectively;

[0086] m is used as a tuning parameter and is set to 12;

[0087] The low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm according to the present invention is characterized in that the adaptive inertia weight formula is added to the formula of the follower position update stage to obtain:

[0088]

[0089] The low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm according to the present invention is characterized in that the step 2) improves the globality of the search and uses a tangent flight strategy to disturb the optimal solution, which is specifically:

[0090] In this step, in view of the problems that the population diversity of the sparrow search algorithm decreases near the global optimal solution and is prone to falling into the local optimal solution, the tangent flight strategy in the tangent search algorithm is referred to, and the individuals in the optimization process are disturbed to improve the algorithm's global search and ability to jump out of the local optimal value, as described in the following formula:

[0091] X t+1 =X t +step*tan(θ).

[0092] According to the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention, it is characterized in that the step 2) performs population initialization through Circle chaos mapping to improve the global search capability, introduces an adaptive weight factor to adjust its follower mechanism to improve the globality of the search, and uses a tangent flight strategy to disturb the optimal solution to ensure the diversity of spatial search and improve convergence accuracy.

[0093] The low-carbon economic optimization scheduling method of a virtual power plant based on the improved sparrow search algorithm of the present invention has the following beneficial effects:

[0094] 1. The low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention establishes a virtual power plant model for a carbon trading mechanism, uses an improved sparrow search algorithm to solve the deficiencies in the prior art, achieves faster convergence speed and higher optimization efficiency, and obtains a better scheduling solution;

[0095] 2. The low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention uses an improved sparrow search algorithm, introduces an adaptive weight update mechanism and a multi-strategy fusion exploration method to improve the search capability and convergence speed of the algorithm, and uses the improved sparrow search algorithm to solve the established model to find a scheduling solution with the lowest cost and that meets all constraints;

[0096] 3. The low-carbon economic optimization scheduling method of the virtual power plant based on the improved sparrow search algorithm of the present invention is highly efficient, versatile, and has ideal effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 A modeling structure diagram for establishing a low-carbon economic optimization scheduling model of a virtual power plant according to the virtual power plant low-carbon economic optimization scheduling method based on the improved sparrow search algorithm of the present invention;

[0098] Figure 2 It is a schematic diagram of an embodiment of a low-carbon economic optimization scheduling method for a virtual power plant based on an improved sparrow search algorithm of the present invention;

[0099] Figure 3 It is a time-of-use electricity price and gas cost diagram of the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention;

[0100] Figure 4 The electric load diagram of the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention;

[0101] Figure 5 It is a heat load diagram of the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention;

[0102] Figure 6 A wind power output diagram of a low-carbon economic optimization scheduling method for a virtual power plant based on an improved sparrow search algorithm of the present invention;

[0103] Figure 7 The photovoltaic output diagram of the low-carbon economic optimization scheduling method of the virtual power plant based on the improved sparrow search algorithm of the present invention;

[0104] Figure 8 It is a diagram of the electric balance dispatch result of the low-carbon economic optimization dispatch method of the virtual power plant based on the improved sparrow search algorithm of the present invention;

[0105] Fig. 9 This is a diagram of the thermal balance scheduling results of the low-carbon economic optimization scheduling method of the virtual power plant based on the improved sparrow search algorithm of the present invention. DETAILED DESCRIPTION

[0106] The following is a further description of the technical means, creative features, objectives and effects achieved by the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention in conjunction with the accompanying drawings and embodiments.

[0107] Example 1

[0108] like Figure 1-2 As shown:

[0109] 1) Combined with the carbon trading mechanism, a low-carbon economic optimization dispatch model for virtual power plants is established:

[0110] A carbon trading mechanism model is established. According to the current development status of my country's carbon trading market, the determination of the carbon emission quota of the virtual power plant in this embodiment 1 is based on a certain amount of carbon emissions initially allocated to the virtual power plant for free. The National Development and Reform Commission has issued a document stipulating the "China Regional Power Grid Baseline Emission Factor", which can determine the regional unit electricity carbon emission allocation coefficient in turn. For the established virtual power plant, the carbon emission sources are gas turbines (Micro Turbine, MT) and gas boilers (Gas Boiler, GB), and the allocated carbon emission quotas are:

[0111]

[0112] Where: θ is the regional unit electricity carbon emission allocation coefficient; is the heat and electricity power output of the micro-turbine at time t; is the wind power and photovoltaic power at time t; λ is the conversion coefficient of electricity; is the thermal power output of the gas boiler at time t.

[0113] The model of carbon emission cost is as follows: the carbon emission in the virtual power plant is positively correlated with the output of the micro-turbines and gas boilers, and its carbon emission at time t is:

[0114]

[0115] Where: β MT , β GB is the carbon emission coefficient of micro turbines and gas boilers.

[0116] The model of carbon trading costs is: In order to activate the carbon trading market, virtual power plants need to actively participate in the following trading rules: if emissions exceed the quota, additional emission rights must be purchased; if emissions are lower than the quota, excess emission rights can be sold. Its carbon trading cost at time t is:

[0117] C t =μ(E r,t -E p,t )

[0118] Where: μ is the price in the carbon trading market, and the carbon price is the average of the typical daily transaction prices in the national carbon market.

[0119] The objective function and constraints of the virtual power plant are modeled, and a low-carbon economic optimization dispatch model for the virtual power plant is established. Taking the economic optimality under the safe operation of the virtual power plant as the goal, a virtual power plant economic dispatch model considering the participation in the carbon trading market is constructed based on a university campus in East China. The objective function of the virtual power plant proposed in this paper is to maximize the comprehensive benefit, that is, to minimize the comprehensive operating cost, which is:

[0121]

[0122] In the formula, C buy is the energy purchase cost, C t is the carbon trading cost, C op is the operation and maintenance cost, F min To minimize the overall operating cost.

[0123] Where T is a dispatch cycle. buy , Operation and maintenance cost C op The specific formula is as follows:

[0124]

[0125] In the formula, is the electric power purchased and sold from the grid at time t; is the price of electricity purchased and sold from the power grid at time t; is the amount of natural gas purchased from the gas grid at time t; is the price of natural gas purchased from the gas grid at time t; C h , C pv , C wt , C ESThey are the operation and maintenance costs of thermal power units, photovoltaic power generation units, wind power units and energy storage equipment respectively.

[0126] According to the objective function, the constraints that need to be met when operating a virtual power plant participating in the carbon trading market include: wind power and photovoltaic power constraints, energy storage system constraints, thermal unit power balance, electric power constraints, and gas grid balance constraints.

[0127] Wind power and photovoltaic power constraints are:

[0128] 0≤P wt ≤P wtmax

[0129] 0≤P pv ≤P pvmax

[0130] Where P wt , P pv are the power of wind power and photovoltaic power respectively.

[0131] The energy storage system constraints are:

[0132] S ES,t0 -S ES,t =0

[0133] S ES,min ≤S ES,t ≤S ES,max

[0134]

[0135] In the formula, S ES,t0 is the initial charge state of energy storage; S ES,t is the state of charge of the energy storage at time t; η c , η d are the efficiencies of energy storage in charging and discharging states respectively; E is the rated capacity of energy storage.

[0136] The power balance constraint of the thermal unit is:

[0137]

[0138] In the formula, are the heat generation power of the heat pump and other thermal energy equipment at time t respectively; is the heat load at time t.

[0139] The electrical balance constraint is:

[0140]

[0141] In the formula, is the power consumption of the heat pump at time t; is the power generation of the micro-turbine at time t; PES,t,c , P ES,t,d are the charging and discharging power of energy storage at time t respectively; is the electrical load at time t.

[0142] The gas network balance constraint is:

[0143]

[0144] In the formula, is the gas volume purchased by the micro-turbine from the gas grid at time t; is the gas volume purchased by the gas boiler from the gas grid at time t; is the gas purchase volume from the gas grid at time t.

[0145] 2) The principle and mechanism of the sparrow search algorithm in this embodiment are as follows:

[0146] The objective function is solved by using the improved sparrow search algorithm to obtain the optimized intraday scheduling plan. The sparrow search algorithm is a new type of swarm intelligence algorithm, which is based on the imitation of the foraging and anti-predation behavior of the sparrow population. Its principle is as follows:

[0147] Assume that the population of m sparrows is as follows:

[0148]

[0149] Where X is a randomly generated sparrow population; x is each individual sparrow; d is the dimension of the population, which is the same as the number of parameters to be optimized; and m is the number of sparrows.

[0150]

[0151] In the formula, G x is the fitness function matrix; g represents the fitness of the individual. The population will change according to the change of fitness value, and the whole will be more adaptable to the change of environment. In this process, there will be some individuals with higher fitness value in the group, and these individuals will get prey first in the search process. In the iterative process, the update of their position is shown as follows:

[0152]

[0153] Where t is the number of iterations of the population, X i,j is the information of the i-th sparrow at the j-th dimension, iter max is the maximum number of iterations.

[0154] The follower's position update is expressed as:

[0155]

[0156] Where Xp is the optimal position occupied by the discoverer in the sparrow population at this time, X worst It is the global worst position of the individual in the current population.

[0157] When the proportion of individuals aware of danger reaches 10%-20% of the entire population, the population feels dangerous and conducts anti-predation. The mathematical expression is:

[0158]

[0159] Where, X best is the current global optimal position, β is the control parameter of the step size, and it follows the N(0,1) distribution. K∈[-1,1]. i is the fitness value of the ith individual, f g is the global optimal value of the population in the current situation, f w is the global worst value of the population under the current situation, and ε is a constant.

[0160] Further improvements to the sparrow search algorithm can better solve the virtual power plant optimization scheduling problem, enabling it to escape from the local optimum and obtain a stronger ability to obtain the global optimal solution, thereby obtaining a more economical optimization scheduling plan.

[0161] 3) Combining the principle and mechanism of the above-mentioned sparrow search algorithm, this embodiment 1 uses the improved sparrow search algorithm to solve the low-carbon economic optimization scheduling model of the established virtual power plant to find the scheduling scheme with the lowest cost and satisfying all constraints. This step is specifically divided into: initializing the population through Circle chaos mapping to improve the global search ability, introducing an adaptive weight factor to adjust its follower mechanism to improve the globality of the search, and using the tangent flight strategy to perturb the optimal solution. There are three parts in total:

[0162] Since the SSA algorithm has the problem of uneven initialization component layout during initialization, it is considered to combine the Circle chaotic map with the SSA algorithm to improve the global search ability of the algorithm. The optimized function is:

[0163]

[0164] Where n is the dimension of the solution; d1, d2, and d3 are constants whose values ​​can be adjusted to change the effect of chaotic mapping.

[0165] In the sparrow search algorithm, the followers follow the discoverer to find the optimal solution, which results in weak independence of the followers. Therefore, a dynamic adjustment method is used here to perform efficient optimization. An adaptive inertia weight is added in the follower position update phase. This adaptive inertia weight can make the current individual affect the optimization of the next generation of individuals, thereby improving the efficiency of follower optimization. The introduced adaptive weight formula is as follows:

[0166]

[0167] Where: min With ω max are weight adjustment parameters, which are set to 0.8 and 0.3 respectively; m is an adjustment parameter, which is set to 12 in this paper.

[0168] The follower update formula after adding adaptive weights is:

[0169]

[0170] In view of the problems that the sparrow search algorithm has reduced population diversity near the global optimal solution and is prone to falling into the local optimal solution, the tangent flight strategy in the tangent search algorithm (TSA) is referred to. The sparrow individuals in the optimization process are disturbed to improve the algorithm's global search and ability to jump out of the local optimal value. The formula is:

[0171] X t+1 =X t +step*tan(θ)

[0172] Through this embodiment 1, it can be found that the low-carbon economic optimization scheduling method of a virtual power plant based on the improved sparrow search algorithm of the present invention improves the global search capability by initializing the population through Circle chaos mapping; introduces an adaptive weight factor to adjust its follower mechanism to improve the globality of the search; and uses a tangent flight strategy to disturb the optimal solution to ensure the diversity of spatial search and improve convergence accuracy.

[0173] Example 2

[0174] refer to Figure 3 to Figure 9 In order to verify the beneficial effects of the virtual power plant low-carbon economic optimization scheduling method based on the improved sparrow search algorithm of the present invention, a simulation comparative experiment is conducted, as follows:

[0175] The virtual power plant of a university campus in East China is taken as the research object. One operation cycle is 24 hours and the unit operation time is 1 hour. The equipment installed in the system includes photovoltaic, wind turbine, energy storage, micro gas turbine, waste heat boiler, gas boiler and heat pump. The invention adopts the time-of-use electricity price and time-of-use gas cost mechanism as follows Figure 3As shown in Figure 2, the electrical load, thermal load, wind power output and photovoltaic output are as follows: Figure 4 to Figure 7 As shown, this embodiment 2 first uses the carbon trading mechanism to establish a low-carbon economic optimization scheduling model for virtual power plants. The improved sparrow search algorithm is used to introduce an adaptive weight update mechanism and a multi-strategy fusion exploration method to improve the algorithm's search capability and convergence speed. The improved sparrow search algorithm is used to solve the established model to find the scheduling solution with the lowest cost and that meets all constraints. The solved electrical balance and thermal balance results are shown in Figure 8 and Fig. 9 shown.

[0176] Therefore, the overall cost of whether to consider carbon trading is shown in Table 1. Among them, the carbon emissions of wind turbines and photovoltaic batteries are negligible.

[0177]

[0178] Table 1 Comparison of overall benefits and costs of virtual power plants in different scenarios

[0179] As can be seen from Table 1, the strategy used in the low-carbon economic optimization scheduling method of a virtual power plant based on an improved sparrow search algorithm of the present invention is relative to direct scheduling without considering carbon trading. The total revenue is increased by about RMB 73.88 and the carbon emissions are reduced by about 390.33 kg. It can be seen that the virtual power plant scheduling strategy adopted in this article can comprehensively reduce the various costs and carbon emissions of the system, reflecting the role of the carbon trading mechanism in promoting energy conservation, emission reduction and cost reduction.

[0180] In summary, the low-carbon economic optimization scheduling method for a virtual power plant based on an improved sparrow search algorithm of the present invention establishes a virtual power plant model regarding a carbon trading mechanism, utilizes the improved sparrow search algorithm to solve the deficiencies in the prior art, achieves faster convergence speed and higher optimization efficiency, and obtains a better scheduling solution; the low-carbon economic optimization scheduling method for a virtual power plant based on an improved sparrow search algorithm of the present invention uses an improved sparrow search algorithm, introduces an adaptive weight update mechanism and a multi-strategy fusion exploration method to improve the search capability and convergence speed of the algorithm, and uses the improved sparrow search algorithm to solve the established model to find a scheduling solution with the lowest cost and that meets all constraints; the low-carbon economic optimization scheduling method for a virtual power plant based on an improved sparrow search algorithm of the present invention has high efficiency, strong versatility, and ideal effects.

[0181] However, those skilled in the art should recognize that the above embodiments are only used to illustrate the present application, and are not used to limit the present application. As long as they are within the spirit of the present application, any changes or modifications to the above embodiments will fall within the scope of the claims of the present application.

Claims

1. A low-carbon economic optimization scheduling method for a virtual power plant based on an improved sparrow search algorithm, the specific steps of which are as follows: 1) Combined with the carbon trading mechanism, a low-carbon economic optimization scheduling model for virtual power plants is established; 2) Use the improved sparrow search algorithm to solve the low-carbon economic optimization scheduling model of the established virtual power plant to find the scheduling plan with the lowest cost and meeting all constraints. This step is specifically divided into three parts: initializing the population through Circle chaos mapping to improve the global search capability, introducing an adaptive weight factor to adjust its follower mechanism to improve the globality of the search, and using the tangent flight strategy to perturb the optimal solution.

2. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 1 is characterized in that: The step 1) of establishing a low-carbon economic optimization scheduling model for a virtual power plant is specifically divided into a carbon emission quota model, a carbon emission cost model and a carbon trading cost model, as follows: 2.1) For the established virtual power plant, the carbon emission sources are gas turbines, referred to as MT and gas boilers, referred to as GB, and the allocated carbon emission quotas are: Where: θ is the regional carbon emission allocation coefficient per unit electricity; is the heat and electricity power output of the micro-turbine at time t; is the wind power and photovoltaic power at time t; λ is the conversion factor of electricity; is the thermal power output of the gas boiler at time t; 2.2) Establish a model for carbon emission costs: The carbon emissions in this model and virtual power plant are positively correlated with the output of micro-turbines and gas boilers. The carbon emissions at time t are: In the formula, β MT , β GB is the carbon emission coefficient of micro turbines and gas boilers; 2.3) Establish a model for carbon trading costs: According to the trading rules of the virtual power plant - if the emissions exceed the quota, additional emission rights must be purchased, and if the emissions are lower than the quota, the excess emission rights can be sold. The carbon trading cost at time t is: C t =μ(E r,t -AND p,t ) In the formula, μ is the price of the carbon trading market, and the carbon price is the average of the typical daily transaction prices in the national carbon market; Combining the above models, a low-carbon economic optimization scheduling model for virtual power plants was established.

3. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 2 is characterized in that: The low-carbon economic optimization dispatch model of the virtual power plant is aimed at the economic optimization under the safe operation of the virtual power plant, and constructs an economic dispatch model of the virtual power plant participating in the carbon trading market. In this model, the objective function of the virtual power plant is to maximize the comprehensive benefit, that is, to minimize the comprehensive operating cost, and its formula is: In the formula, C buy The cost of purchasing energy; C t For carbon trading costs; C op For operation and maintenance costs; F min To minimize the overall operating cost.

4. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 3 is characterized in that: The objective function of the virtual power plant is to maximize the comprehensive benefits, that is, to minimize the comprehensive operating cost. In the formula, T is a dispatch cycle, and the energy purchase cost C buy , Operation and maintenance cost C op The specific formula is as follows: In the formula, is the electric power purchased and sold from the grid at time t; is the price of electricity purchased and sold from the power grid at time t; is the amount of natural gas purchased from the gas grid at time t; is the price of natural gas purchased from the gas grid at time t; C h , C pv , C wt , C ES They are the operation and maintenance costs of thermal power units, photovoltaic power generation units, wind power units and energy storage equipment respectively.

5. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 1 is characterized in that: In the step 1) of establishing a low-carbon economic optimization dispatch model for a virtual power plant, constraints need to be met, and the constraints are: wind power and photovoltaic power constraints, energy storage system constraints, thermal unit power balance, electric power constraints, and gas grid balance constraints, as follows: 5.1) Wind and photovoltaic power constraints: 0≤P wt ≤P wtmax 0≤P pv ≤P pvmax Where P wt , P pv are the power of wind power and photovoltaic power respectively; 5.2) Energy storage system constraints: S ES,t0 -S ES,t =0 S ES,min ≤S ES,t ≤S ES,max In the formula, S ES,t0 is the initial charge state of the energy storage; S ES,t is the state of charge of the energy storage at time t; η c , η d are the efficiencies of energy storage in charging and discharging states, respectively; E is the rated capacity of energy storage; 5.3) Thermal power unit power balance constraints: In the formula, are the heat generation power of the heat pump and other thermal energy equipment at time t respectively; is the heat load at time t; 5.4) Electrical balance constraints: In the formula, is the power consumption of the heat pump at time t; is the power generation of the micro-turbine at time t; P ES,t,c , P ES,t,d are the charging and discharging power of energy storage at time t respectively; is the electrical load at time t; 5.5) The gas grid balance constraint is: In the formula, is the gas volume purchased by the micro-turbine from the gas grid at time t; is the gas volume purchased by the gas boiler from the gas grid at time t; is the gas purchase volume from the gas grid at time t.

6. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 1 is characterized in that: The step 2) in which the population is initialized by Circle chaotic mapping to improve the global search capability is specifically as follows: In this step, since the sparrow search algorithm has the problem of uneven initialization component layout during initialization, the Circle chaos map is combined with the sparrow search algorithm to improve the global search ability of the algorithm. The optimized function is: Where n is the dimension of the solution; d1, d2, and d3 are constants, and their values ​​can be adjusted to change the effect of chaotic mapping.

7. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 1 is characterized in that: The step 2) introduces an adaptive weight factor to adjust the follower mechanism, which is specifically: In this step, a dynamic adjustment method is used for efficient optimization. An adaptive inertia weight is added in the follower position update stage. The adaptive inertia weight can make the current individual affect the optimization of the next generation of individuals, thereby improving the efficiency of follower optimization. The adaptive inertia weight formula is as follows: Where: min With ω max For the weight adjustment parameters, they are set to 0.8 and 0.3 respectively; m is used as a tuning parameter and is set to 12; 8. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 7 is characterized in that: The adaptive inertia weight formula is added to the formula of the follower position update phase to obtain:

9. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 1 is characterized in that: The step 2) improves the globality of the search and uses the tangent flight strategy to perturb the optimal solution, which is specifically: In this step, in view of the problems that the population diversity of the sparrow search algorithm decreases near the global optimal solution and is prone to falling into the local optimal solution, the tangent flight strategy in the tangent search algorithm is referred to, and the individuals in the optimization process are disturbed to improve the algorithm's global search and ability to jump out of the local optimal value, as described in the following formula: X t+1 =X t +step*and(θ)。 10. The low-carbon economic optimization scheduling method for virtual power plants based on the improved sparrow search algorithm according to claim 1, characterized in that: The step 2) improves the global search capability by initializing the population through Circle chaotic mapping, introduces an adaptive weight factor to adjust its follower mechanism, improves the globality of the search, and uses a tangent flight strategy to perturb the optimal solution to ensure the diversity of spatial search and improve convergence accuracy.

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