Scheduling method and device for source network load storage and charging integrated energy system

Abstract

The invention provides a scheduling method and device for a source-network-load storage and charging integrated energy system, and solves the problems of low intelligent optimization scheduling efficiency, low economic benefit and low environmental benefit of the source-network-load storage and charging integrated energy system. The method comprises the following steps: constructing a target function based on an optimization target; constructing a multi-target operation optimization model based on the target function and the constraint condition; obtaining an optimal individual solution set based on a dynamic multi-target whale optimization algorithm of a three-scale fuzzy consistent judgment matrix, and obtaining a target value based on the optimal individual solution set and the multi-target operation optimization model; and scheduling based on the target value.

Description

Technical Field

[0001] The present invention relates to the technical field of energy systems, and in particular to a scheduling method and device for a source-grid-load-storage-charging integrated energy system. Background Art

[0002] The source-grid-load-storage-charging integrated energy system involves the integration of five elements: source (renewable energy sources such as solar energy and wind energy), grid (power network), load (electricity load), storage (energy storage system) and charging (charging facilities), which leads to high complexity in system design and optimization, difficulty in achieving efficient real-time monitoring and control, and difficulty in coping with the instability and unpredictability of renewable energy. Since the source-grid-load-storage-charging integrated energy system is a relatively new field, the technology involved is not yet fully mature, resulting in various challenges in practical applications; and the system integrates multiple energy forms and technologies, and the complexity of its operation and control requires advanced management and optimization strategies; in addition, the economic feasibility of key technologies such as energy storage systems directly affects their widespread application in integrated energy systems, and related policies and incentives will also affect the speed of technology promotion; efficient energy management and scheduling rely on accurate data analysis and real-time information exchange, and existing data processing and information technology may not be sufficient to support the optimal operation of the system. Therefore, there are some problems in the existing technology of the source-grid-load-storage-charging integrated energy system, mainly including:

[0003] Structural transformation challenge: With the structural transformation of the energy system, how to effectively integrate and manage the diverse energy supply and demand relationships is a challenge.

[0004] Difficulties in scheduling and optimization: The integrated energy system involves technical research and comprehensive application in multiple links and fields. It is difficult to optimize and coordinate the operation of related equipment in the system to improve the operation level of the local power grid.

[0005] Grid volatility problem: The randomness and volatility of renewable energy have brought greater peak-shaving pressure to the distribution network, and the safe and stable operation of the power system faces challenges.

[0006] Insufficient intelligence in energy management: Although existing technologies have applied technologies such as user energy consumption monitoring and electric vehicle charging, there is still room for improvement in the intelligent promotion of energy management algorithms and platforms.

[0007] Economic and life issues of energy storage systems: The energy storage systems involved in optimized scheduling frequently switch between charging and discharging, which may increase the cost of calling up energy storage resources and affect the service life of the system. Summary of the invention

[0008] In view of this, an embodiment of the present invention provides a scheduling method and device for a source-grid-load-storage-charging integrated energy system, which solves the problems of low intelligent optimization scheduling efficiency, economic benefits and environmental benefits of the source-grid-load-storage-charging integrated energy system.

[0009] In the first aspect, an embodiment of the present invention provides a scheduling method and device for a source-grid-load-storage integrated energy system, including: constructing an objective function based on the optimization objective; constructing a multi-objective operation optimization model based on the objective function and constraints; obtaining the optimal individual solution set based on a dynamic multi-objective whale optimization algorithm based on a three-scale fuzzy consistent judgment matrix, and obtaining the target value based on the optimal individual solution set and the multi-objective operation optimization model; and scheduling based on the target value.

[0010] In one embodiment, the dynamic multi-objective whale optimization algorithm based on the three-scale fuzzy consistent judgment matrix obtains the optimal individual solution set, including: obtaining the basic parameters of the multi-objective whale optimization algorithm, initializing the objective function values ​​of the population position and the initial individual position; establishing a three-scale fuzzy consistent judgment matrix based on the multi-objective whale optimization algorithm and the three-scale analytic hierarchy process; obtaining an evaluation index based on the three-scale fuzzy consistent judgment matrix; iteratively updating the individual position based on the evaluation index; and using the dominance principle and crowding degree to update the local optimal solution in the population to obtain the optimal individual solution set of the population.

[0011] In one embodiment, it includes: obtaining an objective function value group based on multiple individual positions obtained during the iterative update process; sorting the objective function values ​​in the objective function value group according to preset conditions; and obtaining the congestion degree based on the sorted objective function value group.

[0012] In one embodiment, it also includes: obtaining the convergence and diversity of the optimal individual solution set to verify the performance of the dynamic multi-objective whale optimization algorithm of the three-scale fuzzy consistent judgment matrix.

[0013] In one embodiment, the optimization objectives include: maximum daily operating income, minimum microgrid operating cost, and minimum microgrid carbon emissions.

[0014] In one embodiment, the constraint conditions include: at least one of: microgrid power load balance constraint, power generation output power limitation, and upper and lower limit constraints on energy storage system power and capacity.

[0015] In the second aspect, an embodiment of the present invention provides a scheduling device for a source-grid-load-storage integrated energy system, comprising: a function construction module, used to construct an objective function based on an optimization objective; a model construction module, used to construct a multi-objective operation optimization model based on the objective function and constraints; an algorithm design module, used to obtain an optimal individual solution set based on a dynamic multi-objective whale optimization algorithm based on a three-scale fuzzy consistent judgment matrix, and obtain a target value based on the optimal individual solution set and the multi-objective operation optimization model; a scheduling module, used to perform scheduling based on the target value.

[0016] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the above methods.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0018] In a fifth aspect, an embodiment of the present invention provides a computer program product, comprising a computer program / instructions, characterized in that when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0019] The embodiments of the present invention provide a method and device for scheduling a source-grid-load-storage-charging integrated energy system, and the scheduling method includes: constructing an objective function based on an optimization objective; constructing a multi-objective operation optimization model based on the objective function and constraints; obtaining an optimal individual solution set based on a dynamic multi-objective whale optimization algorithm of a three-scale fuzzy consistent judgment matrix, and obtaining a target value based on the optimal individual solution set and the multi-objective operation optimization model; and scheduling based on the target value. The present invention effectively improves the intelligent optimization scheduling efficiency of the source-grid-load-storage-charging integrated energy system by introducing a multi-objective optimization algorithm, significantly improves daily operating income, reduces microgrid operating costs and microgrid carbon emissions, and has good economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Shown is a structural schematic diagram of a source-grid-load-storage-charging integrated energy system provided by an embodiment of the present invention.

[0021] Figure 2 The figure shows a flow chart of a scheduling method for a source-grid-load-storage-charging integrated energy system provided by an embodiment of the present invention.

[0022] Figure 3 Shown is a flow chart of a method for obtaining an optimal individual solution set provided by an embodiment of the present invention.

[0023] Figure 4 The figure shows an optimization result obtained based on the NSGA-II algorithm with the goal of maximizing daily operating profit, provided by an embodiment of the present invention.

[0024] Figure 5 The figure shows an optimization result obtained based on the DMOWOA algorithm with the goal of maximizing daily operating profit, provided by an embodiment of the present invention.

[0025] Figure 6The figure shows an optimization result obtained based on the MOPSO algorithm with the goal of maximizing daily operating profit, provided by an embodiment of the present invention.

[0026] Figure 7 The figure shows an optimization result obtained based on the NSGA-II algorithm with the goal of minimizing the microgrid operation cost provided by an embodiment of the present invention.

[0027] Figure 8 The figure shows an optimization result obtained based on the DMOWOA algorithm with the goal of minimizing the operating cost of a microgrid provided by an embodiment of the present invention.

[0028] Fig. 9 The figure shows an optimization result obtained based on the MOPSO algorithm with the goal of minimizing the microgrid operation cost provided by an embodiment of the present invention.

[0029] Fig.10 The figure shows an optimization result obtained based on the NSGA-II algorithm with the goal of minimizing carbon emissions of a microgrid provided by an embodiment of the present invention.

[0030] Fig.11 The figure shows an optimization result obtained based on the DMOWOA algorithm with the goal of minimizing carbon emissions from a microgrid provided by an embodiment of the present invention.

[0031] Fig.12 The figure shows an optimization result obtained based on the MOPSO algorithm with the goal of minimizing carbon emissions of a microgrid provided by an embodiment of the present invention.

[0032] Fig.13 The figure shows a daily operating profit forecast diagram after optimization of a scheduling method for a source-grid-load-storage integrated energy system provided by an embodiment of the present invention.

[0033] Fig.14 The figure shows an operating cost prediction diagram after optimization of a scheduling method for a source-grid-load-storage-charging integrated energy system provided by an embodiment of the present invention.

[0034] Fig.15 The figure shows a carbon emission prediction diagram after optimization obtained by a scheduling method for a source-grid-load-storage-charging integrated energy system provided by an embodiment of the present invention.

[0035] Fig.16 Shown is a structural schematic diagram of a scheduling device for a source-grid-load-storage-charging integrated energy system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1:

[0038] In order to solve the problems existing in the prior art in the source-grid-load-storage integrated energy system, the present invention provides a source-grid-load-storage-charge integrated energy system operation optimization scheduling method based on a dynamic multi-objective whale optimization algorithm (DMOWOA) of a three-scale fuzzy consistent judgment matrix, aiming to solve the multi-objective operation optimization scheduling problem of the source-grid-load-storage-charge integrated energy system, especially the balance between the highest daily operating income, the lowest microgrid operating cost and the lowest microgrid carbon emissions. Figure 1 Shown is a structural schematic diagram of a source-grid-load-storage-charging integrated energy system provided by an embodiment of the present invention. Figure 2 The figure shows a flow chart of a scheduling method for a source-grid-load-storage-charging integrated energy system provided by an embodiment of the present invention.

[0039] The dispatching method of the source-grid-load-storage-charging integrated energy system is specifically described as follows:

[0040] Step 01: Construct an objective function based on the optimization goal.

[0041] Among them, the optimization objectives include: the highest daily operating income, the lowest microgrid operating cost and the lowest microgrid carbon emissions. A multi-objective operation optimization model of the source-grid-load-storage-charge integrated energy system is constructed, with the highest daily operating income, the lowest microgrid operating cost and the lowest microgrid carbon emissions as the optimization objectives. Specifically,

[0042] The cost of purchasing electricity from the external power grid is expressed as:

[0043]

[0044] Where d is the number of operating periods per day; T is the time step length; P BE (t) is the power exchanged with the external power grid during period t; C BE (t) is the electricity price of the external power grid during period t; s(t) is the exchange status with the external power grid, with exchange represented by 1 and no exchange represented by 0.

[0045] The daily charging income expression is:

[0046] S C =C dl ×C co (2)

[0047] In the formula, C d1 Daily charging capacity; C co This is the unit price of the charging service fee.

[0048] The expression of daily photovoltaic power generation income is:

[0049] S G =G dl ×G co (3)

[0050] In the formula, G d1 is the daily photovoltaic power generation; G co The unit price of electricity purchase cost.

[0051] The daily wind power generation revenue expression is:

[0052] S F =F dl ×F co (4)

[0053] In the formula, F d1 F is the daily wind power generation; co The unit price of electricity purchase cost.

[0054] The expression of daily energy storage peak-valley cash-out income is:

[0055] S N =F dl ×F co (5)

[0056] In the formula, F d1 F is the daily wind power generation; co The unit price of electricity purchase cost.

[0057] The objective functions include: daily operating revenue function, microgrid operating cost function and microgrid carbon emission function.

[0058] According to the above formulas (1) to (5), we can get:

[0059] The daily operating profit function is:

[0060] S=S C +S G +S F +S N -C e (6)

[0061] Among them, S C is the daily charging income; S G is the daily photovoltaic power generation income; S F is the daily wind power generation income; S N C is the cash income from daily energy storage peak and valley; eThe cost of purchasing electricity from the external power grid.

[0062] The microgrid operation cost function is:

[0063] c ECO (t) = c PG (t)+c ES (t)+c EX (t) (7)

[0064] In the formula, C ECO (t) is the microgrid operation cost, C PG (t) is the cost of photovoltaic and wind power generation, C ES (t) is the operating cost of the energy storage system, C EX (t) is the cost of purchasing electricity from the external power grid.

[0065] The carbon emission function of microgrid is:

[0066] e CD (t) = e CD_PG (t)+e CD_ES (t)+e CD_GP (t) (8)

[0067] Where, is the carbon emissions caused by the real-time power consumption of the microgrid; e CD_PG (t) is the carbon emissions generated by photovoltaic and wind power generation; CD_ES (t) is the carbon emission of the energy storage system during charging and discharging; e CD_GP (t) Carbon emissions from supplying electricity to the external grid.

[0068] There are three objective functions, namely, the above-mentioned daily operation revenue function, microgrid operation cost function and microgrid carbon emission function are the final three objective functions of the model; among them, the external power grid purchase cost expression Ce, the daily charging revenue expression S C 、 Daily photovoltaic power generation income expression S G 、 Daily wind power generation revenue expression S F The expression of daily energy storage peak-valley cash-out income S N It is the calculation function expression of each part of the income and the calculation function expression of the cost mentioned in the daily operating income calculation formula S.

[0069] Optionally, there may be nonlinear and conflicting relationships between the three objective functions. The carbon emissions of the microgrid are affected by the real-time power consumption of the microgrid, photovoltaic and wind power, energy storage system, and power generation from four sources of the external power grid. The carbon emissions generated by these four sources are not necessarily linearly related to the corresponding costs; the same benefits are affected by S C , S G , S F , S NThe influence of the four factors and their corresponding costs are not necessarily linear. For example, in a certain interval [A, B], as the cost of photovoltaic and wind power increases, the carbon emissions generated are less, but the overall benefits are not necessarily maximized. If in the interval [B+1, C], as the cost of photovoltaic and wind power increases, the carbon emissions generated are more, but the overall benefits may reach the maximum. Therefore, in general, there is a high probability that there is a nonlinear and conflicting relationship between the three. If you want to get a specific and accurate relationship, you can calculate it based on the historical data and forecast data of the three under the same conditions.

[0070] Step 02: Construct a multi-objective operation optimization model based on the objective function and constraints.

[0071] The constraints include: at least one of the microgrid power load balance constraint, power generation output limit, and energy storage system power and capacity upper and lower limit constraints.

[0072] Microgrid power load balance constraints:

[0073] P GG (t)+P FP (t)+P ES (t) = P CP (t) (9)

[0074] Where P GG (t) is the photovoltaic power output, P FP (t) is the wind power output, P ES (t) is the output power of the energy storage system, P CP (t) is the power load power.

[0075] Power generation output limit:

[0076] Photovoltaic power generation power constraints:

[0077] In the formula, is the minimum power of the photovoltaic generator set, is the maximum power of the photovoltaic generator set.

[0078] Wind power generation capacity constraints:

[0079] In the formula, is the minimum power of the photovoltaic generator set, is the maximum power of the photovoltaic generator set.

[0080] Energy storage system power and capacity upper and lower limits:

[0081] Remaining capacity SOC constraint limit: SOC min≤SOC ES ≤SOC max ;

[0082] In the formula, SOC min is the minimum remaining capacity of the energy storage system, SOC max is the maximum remaining capacity of the energy storage system.

[0083] Energy storage system charging and discharging power constraints:

[0084] In the formula, is the minimum charging and discharging power of the energy storage system, It is the maximum charging and discharging power of the energy storage system.

[0085] Step 03: A dynamic multi-objective whale optimization algorithm based on a three-scale fuzzy consistent judgment matrix is ​​used to obtain an optimal individual solution set, and a target value is obtained based on the optimal individual solution set and the multi-objective operation optimization model.

[0086] Step 04: Scheduling based on the target value.

[0087] Embodiment 2:

[0088] The model constructed in the above embodiment provides expressions of individual positions, objective functions and constraints. If the expressions of the objective function and the constraints are in explicit form, the individual positions in the explicit expressions can be directly obtained; if the expressions of the objective function and the constraints are in implicit form, that is, the output is restricted by using the prediction model and the output is obtained as the target value, then the input of the model can be used as the individual position.

[0089] refer to Figure 3 As shown, the steps of obtaining the optimal individual solution set by the dynamic multi-objective whale optimization algorithm based on the three-scale fuzzy consistent judgment matrix described in step 03 of the above embodiment include:

[0090] Step 031: Obtain the basic parameters of the multi-objective whale optimization algorithm, the objective function values ​​of the initial population position and the initial individual position.

[0091] Step 032: Establish a three-scale fuzzy consistent judgment matrix based on the multi-objective whale optimization algorithm and the three-scale hierarchical analysis method.

[0092] Specifically, given m candidate solutions, a matrix consisting of m candidate solutions is constructed, where each row represents a candidate solution and each column represents an attribute position. The candidate solution is the population individual in DMOWOA, and there are as many candidate solutions as there are individuals in the population. Based on the candidate solutions, the evaluation index corresponding to each candidate solution is obtained.

[0093] The matrix composed of m candidate solutions is The evaluation index is Among them, n is the number of attributes of the candidate solution, and i is the candidate solution sequence number. Furthermore, the comprehensive quality of the candidate solution is evaluated based on the three-scale dynamic hierarchical analysis method.

[0094] The analytic hierarchy process is a comprehensive index evaluation method based on the fuzzy consistent judgment matrix. The three-scale method can make the established judgment matrix more consistent and comprehensive, and enhance the accuracy of the evaluation results. Specifically, the three-scale fuzzy consistent matrix is ​​established by formulas (10)-(13) (it can be established according to the candidate solution).

[0095]

[0096] Step 033: Obtain evaluation indicators based on the three-scale fuzzy consistent judgment matrix.

[0097] The evaluation index is obtained through formulas (14)-(15).

[0098]

[0099] Where i, j are the candidate solution numbers, and l is the candidate solution attribute.

[0100] Step 034: Iteratively update the individual position based on the evaluation index.

[0101] According to the obtained evaluation index p and the size of the parameter |A| of the multi-objective whale optimization algorithm, the individual position (the symbol of the individual position in the formula is X, which is the candidate solution in the three-scale hierarchical analysis method. X is an n-dimensional vector, that is, each X corresponds to a row in the candidate solution matrix, and each component of X corresponds to a column in the candidate solution matrix) is updated. The update formulas are shown in (16)-(19).

[0102]

[0103] D * =|X * (it)-X(it)| (17)

[0104] D1=|C·X * (it)-X(it)| (18)

[0105] D2=||C·X rand -X(it)| (19)

[0106] The individual positions are updated using formulas (16)-(19), that is, the candidate solution matrix is ​​updated once, and then the evaluation index is calculated according to formulas (10)-(15).

[0107] The parameter A is obtained through formulas (11)-(13) as the basis for selecting the update formula.

[0108] C=2·r1 (20)

[0109]

[0110] A=2a·r2-a (22)

[0111] Where r1 is a random vector between [0,1], r2 is a random number between [0,1], a represents the convergence factor, which decreases linearly from 2 to 0 as the number of iterations increases, it is the current number of iterations, and it max is the maximum number of iterations, and X* is the population optimal solution.

[0112] Step 035: Use the dominance principle and crowding degree to update the local optimal solution in the population to obtain the optimal individual solution set of the population.

[0113] Specifically, an objective function value group is obtained based on multiple individual positions obtained during the iterative update process; the objective function values ​​in the objective function value group are sorted according to preset conditions; and the congestion degree is obtained based on the sorted objective function value group. The congestion degree calculation formula is shown in (23):

[0114]

[0115] in, are the maximum and minimum values ​​of the objective function, respectively (where the subscript m is the serial number of the objective function, where m = 1, 2, and 3 refer to the highest daily operating income, the lowest microgrid operating cost, and the lowest microgrid carbon emissions, respectively. For the goal of the highest daily operating income, a minus sign can be added in front to change the goal of seeking the maximum value to seeking the minimum value of the target to maintain consistency); They are the objective function values ​​of the previous and next individual after arranging them in descending order.

[0116] The result is output when the iteration termination is satisfied. The objective function mentioned in the algorithm provided in this embodiment is the three objective functions of the model constructed above, which respectively refer to the highest daily operating income, the lowest microgrid operating cost and the lowest microgrid carbon emissions. For the goal of the highest daily operating income, a minus sign can be added in front to change the goal of seeking the maximum value to seeking the minimum value of the target to maintain consistency. The algorithm continuously updates the individual position, and then uses the individual position to calculate the target value. The constraints mentioned in the above model are used to limit the values ​​of the individual positions in this algorithm.

[0117] Embodiment three:

[0118] The scheduling method of a source-grid-load-storage integrated energy system described in the present invention also includes: obtaining the convergence and diversity of the optimal individual solution set to verify the performance of the dynamic multi-objective whale optimization algorithm of the three-scale fuzzy consistent judgment matrix.

[0119] Convergence and diversity are used as indicators to measure the distribution and diversity of solutions. The calculation formulas of convergence and diversity are shown in (24)-(25).

[0120]

[0121]

[0122] Where N is the number of non-dominated solutions (that is, the individual positions obtained), f is the solution set objective function value obtained by DMOWOA, f′ is the true objective function value, and the smaller the μ value, the better the convergence of the algorithm; d f and d m are the distance between extreme solutions in the solution set obtained by the algorithm and the distance between boundary solutions of each target, d i is the distance between two consecutive solutions in the solution set obtained by the algorithm, is the average value of the solution. The smaller the θ value is, the more uniform the distribution of solutions obtained by the algorithm is.

[0123] Embodiment 4:

[0124] The scheduling method of the source-grid-load-storage-charging integrated energy system provided in the present invention is experimentally verified as follows:

[0125] First, the performance of DMOWOA is verified on the multi-objective benchmark functions ZDT1, ZDT2 and ZDT3, and the performance of DMOWOA algorithm is compared with other optimization algorithms. The description of the benchmark functions is shown in Table 1.

[0126] Table 1 Description of benchmark functions

[0127]

[0128] The optimization results obtained by comparing the DMOWOA proposed in the present invention with NSGA-II and MOPSO are as follows: Figure 4-12 The comparison between convergence and diversity is shown in Table 2-3.

[0129] Table 2 Convergence comparison

[0130]

[0131] Table 3 Comparison of distribution

[0132]

[0133] from Figure 4-12 As can be seen from Table 1-2, compared with the other two mainstream algorithms, the algorithm proposed in the present invention uses the analytic hierarchy process to select the global optimal solution, and the optimal solution set obtained by the algorithm operation is closer to the real Pareto frontier, and has better convergence and distribution. Therefore, the method provided by the present invention constructs a multi-objective optimization scheduling model, comprehensively considers the highest daily operating income, the lowest microgrid operating cost and the lowest microgrid carbon emissions, and constructs a multi-objective operation optimization scheduling model suitable for the source-grid-load-storage-charging integrated energy system. It has practical application value and improves the intelligent optimization scheduling of the source-grid-load-storage-charging integrated energy system.

[0134] Embodiment five:

[0135] The multi-objective whale optimization algorithm (MOWOA) may encounter slow convergence and premature phenomena when facing high-dimensional problems, resulting in the inability to fully explore the solution space. The stability and consistency of MOWOA performance will also produce different results for different problem properties. In the process of studying MOWOA, the main difficulties faced include how to effectively balance the global exploration ability and local development ability of the algorithm to avoid falling into the local optimal solution, and how to improve the convergence of the algorithm and the diversity of solutions in a multi-objective environment. Therefore, this application focuses on improving the structure of the algorithm, introducing a three-scale dynamic hierarchical analysis method to enhance the accuracy of the evaluation index, adding the dominance principle and crowding ranking to update the population position, and further enhancing the effectiveness of MOWOA. The source-grid-load-storage-charge integrated energy system involves the integration of five elements: source (renewable energy such as solar energy and wind energy), grid (power network), load (electricity load), storage (energy storage system) and charging (charging facilities), resulting in high complexity in system design and optimization, difficulty in achieving efficient real-time monitoring and control, and difficulty in coping with the instability and unpredictability of renewable energy. Therefore, in the face of complex technical challenges and market environment, this application effectively improves the intelligent optimization scheduling efficiency of the source-grid-load-storage-charging integrated energy system by introducing a multi-objective optimization algorithm, significantly improves daily operating income, reduces microgrid operating costs and microgrid carbon emissions, and has good economic and environmental benefits.

[0136] (1) Model parameter setting

[0137] The charging price during valley hours is set at 0.5 yuan / kWh, the charging price during normal hours is set at 0.69 yuan / kWh, and the charging price during peak hours is set at 1.27 yuan / kWh; the effective annual duration of sunlight is 1314 hours, the proportion of photovoltaic self-generation and self-use is 100%, the peak-time cost of purchasing electricity from the external grid is 0.9538 yuan / kWh, the normal cost is 0.5558 yuan / kWh, and the valley-time cost is 0.2518 yuan / kWh.

[0138] (2) Optimization process

[0139] Based on various business data such as power generation data, energy storage data, load data, revenue data, etc., the power quality function, microgrid operation cost function and microgrid carbon emission function are established; based on past power data, the power balance constraints of supply and demand within the microgrid, the power limit of power generation output and the upper and lower limits of power and capacity of the energy storage system are established, and then a comprehensive energy system model of source, grid, load, storage and charging is established.

[0140] Set the basic parameters of MOWOA, initialize the population position, and calculate the objective function value of the initial individual position. Based on MOWOA, a three-scale dynamic analytic hierarchy process is introduced to consider the quantitative, qualitative and dynamic characteristics of decision factors, and establish a fuzzy consistent judgment matrix, so that decision makers can analyze complex problems more comprehensively.

[0141] The algorithm calculation is performed according to the method in the above-mentioned embodiment 2, and the specific implementation method is not repeated here.

[0142] (3) Experimental results

[0143] Substituting the objective function and constraints of the source-grid-load-storage integrated energy system into DMOWOA, the algorithm is run to obtain the short-term prediction of power generation and load, operation cost prediction and carbon emission prediction after optimization. Figure 13-15 shown.

[0144] Embodiment six:

[0145] This embodiment provides a scheduling device for a source-grid-load-storage-charge integrated energy system, such as Fig.16 As shown, the dispatching device of the source-grid-load-storage-charge integrated energy system includes: a function building module, a model building module, a computer design module and a dispatching module.

[0146] The function construction module is used to construct an objective function based on the optimization objectives, wherein the optimization objectives include: the highest daily operating income, the lowest microgrid operating cost, and the lowest microgrid carbon emissions.

[0147] The model building module is used to build a multi-objective operation optimization model based on the objective function and constraint conditions; wherein the constraint conditions include: at least one of microgrid power load balance constraints, power generation output power limitations, and upper and lower limit constraints on energy storage system power and capacity.

[0148] The algorithm design module is used to obtain the optimal individual solution set based on the dynamic multi-objective whale optimization algorithm of the three-scale fuzzy consistent judgment matrix, and obtain the target value based on the optimal individual solution set and the multi-objective operation optimization model.

[0149] Furthermore, the algorithm design module is also used to obtain the basic parameters of the multi-objective whale optimization algorithm, the objective function values ​​of the initialized population position and the initial individual position; establish a three-scale fuzzy consistent judgment matrix based on the multi-objective whale optimization algorithm and the three-scale hierarchical analysis method; obtain evaluation indicators based on the three-scale fuzzy consistent judgment matrix; iteratively update the individual position based on the evaluation indicators; use the dominance principle and crowding degree to update the local optimal solution in the population to obtain the optimal individual solution set of the population.

[0150] Furthermore, the algorithm design module is also used to obtain an objective function value group based on multiple individual positions obtained during the iterative update process; sort the objective function values ​​in the objective function value group according to preset conditions; and obtain the congestion degree based on the sorted objective function value group.

[0151] A scheduling module is used to perform scheduling based on the target value.

[0152] In addition, the dispatching device of the source-grid-load-storage-charging integrated energy system also includes a verification module. The verification module is used to obtain the convergence and diversity of the optimal individual solution set to verify the performance of the dynamic multi-objective whale optimization algorithm of the three-scale fuzzy consistent judgment matrix.

[0153] Embodiment seven:

[0154] On the basis of the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0155] In some implementations of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0156] In some implementations of this embodiment, a computer program product is provided, including a computer program / instructions, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0157] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the method in the above embodiments.

[0158] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and the computer-readable storage medium may include but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0159] The computer-readable storage medium may also store at least one computer executable program / instruction, which may be, for example, a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0160] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0161] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0162] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0163] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0164] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0165] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A dispatching method for a source-grid-load-storage-charge integrated energy system, characterized in that: include: Construct an objective function based on the optimization goal; Constructing a multi-objective operation optimization model based on the objective function and constraint conditions; The dynamic multi-objective whale optimization algorithm based on three-scale fuzzy consistent judgment matrix obtains the optimal individual solution set; Obtaining a target value based on the optimal individual solution set and the multi-objective operation optimization model; Scheduling is performed based on the target value.

2. The dispatching method of the source-grid-load-storage-charging integrated energy system according to claim 1 is characterized in that: The dynamic multi-objective whale optimization algorithm based on the three-scale fuzzy consistent judgment matrix obtains the optimal individual solution set, including: Obtain the basic parameters of the multi-objective whale optimization algorithm, the objective function values ​​of the initial population position and the initial individual position; A three-scale fuzzy consistent judgment matrix is ​​established based on the multi-objective whale optimization algorithm and the three-scale analytic hierarchy process; Obtaining an evaluation index based on the three-scale fuzzy consistent judgment matrix; Iteratively updating individual positions based on the evaluation index; The dominance principle and crowding degree are used to update the local optimal solution in the population to obtain the optimal individual solution set of the population.

3. The dispatching method of the source-grid-load-storage-charging integrated energy system according to claim 2 is characterized in that: include: Obtaining a set of objective function values ​​based on a plurality of individual positions obtained during the iterative updating process; Sorting the objective function values ​​in the objective function value group according to a preset condition; The congestion degree is obtained based on the sorted objective function value group.

4. The dispatching method of the source-grid-load-storage-charging integrated energy system according to claim 1 is characterized in that: Also includes: The convergence and diversity of the optimal individual solution set are obtained to verify the performance of the dynamic multi-objective whale optimization algorithm of the three-scale fuzzy consistent judgment matrix.

5. The dispatching method of the source-grid-load-storage-charging integrated energy system according to claim 1 is characterized in that: The optimization objectives include: maximum daily operating income, minimum microgrid operating cost and minimum microgrid carbon emissions.

6. The dispatching method of the source-grid-load-storage-charging integrated energy system according to claim 1 is characterized in that: The constraint conditions include: at least one of: microgrid power load balance constraint, power generation output power limitation, and upper and lower limit constraints of energy storage system power and capacity.

7. A dispatching device for a source-grid-load-storage-charge integrated energy system, characterized in that: include: Function building module, used to build the objective function based on the optimization goal; A model building module, used for building a multi-objective operation optimization model based on the objective function and constraint conditions; An algorithm design module, used for obtaining an optimal individual solution set based on a dynamic multi-objective whale optimization algorithm of a three-scale fuzzy consistent judgment matrix, and obtaining a target value based on the optimal individual solution set and the multi-objective operation optimization model; A scheduling module is used to perform scheduling based on the target value.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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