Multi-energy complementary intelligent optimization and control method

Abstract

The invention provides a multi-energy complementary intelligent optimization and control method, and belongs to the technical field of new energy, and the method comprises the steps: collecting and setting basic data needed by the operation of a wind-solar-storage all-in-one machine; constructing a wind-solar-storage multi-energy complementary operation simulation optimization model; defining decision variables of the optimization model; a heuristic feasible solution strategy is set, the utilization rate of wind energy and light energy is maximized through the strategy, and meanwhile energy supply and demand balance is achieved through coordinated optimization of an energy storage system; and solving the optimization model by adopting an improved artificial bee colony algorithm based on a heuristic feasible solution strategy to obtain an optimal operation scheme of the wind-solar-storage all-in-one machine in each time period. The method can improve the utilization efficiency of new energy and enhance the stability and reliability of the power grid.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a multi-energy complementary intelligent optimization and control method. Background Art

[0002] Challenges of new energy access: With the large-scale grid connection of renewable energy such as wind power and solar power, the volatility and uncertainty of the power system have increased significantly, which has put forward higher requirements for the stable operation and energy balance of the power grid.

[0003] Limitations of storage technology: Although energy storage technology is crucial to balancing the supply and demand of the power grid, current energy storage technology faces problems such as energy density limitations and environmental impacts.

[0004] Low energy collection efficiency and utilization rate: New energy has the advantages of being clean, environmentally friendly, and renewable, but it also has problems such as instability and low energy density. For example, solar energy and wind energy are intermittent and unstable, and their collection efficiency is greatly affected by the weather. Summary of the invention

[0005] The object of the present invention is to solve at least one of the technical drawbacks.

[0006] To this end, the purpose of the present invention is to propose a multi-energy complementary intelligent optimization and control method to solve the problems mentioned in the background technology and overcome the shortcomings of the prior art.

[0007] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a multi-energy complementary intelligent optimization and control method, comprising: step S1, collecting and setting basic data required for the operation of a wind-solar-storage integrated machine;

[0008] Step S2, constructing a wind-solar-storage multi-energy complementary operation simulation optimization model, wherein the objective function of the optimization model is F, as follows:

[0009]

[0010] Where T is the total number of simulation periods; Mf t is the wind power generation in period t; Mg t is the photovoltaic power generation in period t; C t is the operating cost of period t; a, b, c are weight coefficients used to balance power generation and operating costs;

[0011] Step S3, defining decision variables of the optimization model;

[0012] Step S4, setting a heuristic feasible solution strategy, through which the utilization rate of wind energy and solar energy is maximized, and at the same time, energy supply and demand balance is achieved through coordinated optimization of the energy storage system, and the strategy includes:

[0013] (1) Real-time monitoring and collection of wind energy real-time power generation Mf t 、Real-time power generation of light energy Mg t , power load demand D t and the status of the energy storage system;

[0014] (2) The principle of priority is adopted for electricity load demand, and wind and solar energy are fully utilized in each period;

[0015] (3) When the total amount of wind and solar power generation exceeds the power load demand, the excess power will be stored in the energy storage system first;

[0016] (4) When the total amount of wind and solar power generation is lower than the power load demand, the energy storage system is prioritized for energy replenishment;

[0017] Step S5, using an improved artificial bee colony algorithm based on a heuristic feasible solution strategy to solve the optimization model, and obtain the optimal operation plan of the wind-solar-storage integrated machine in each time period.

[0018] Preferably, any of the above schemes comprises: wind energy and solar energy resource data, parameters of the energy storage system, electricity load demand curve, operating costs and constraints.

[0019] Preferably, in any of the above solutions, in S2, the constraints of the optimization model are as follows:

[0020] (1) Energy balance constraint: Mf t +Mg t +Ms t ≥D t ;

[0021] (2) Energy storage system constraints: 0 ≤ Ms t ≤capacity upper limit;

[0022] (3) Equipment operation constraints: Mf t Mg t ≤Rated power generation capacity;

[0023] Among them: Ms t D is the output power of the energy storage system during time period t; t is the electricity load demand in time period t.

[0024] Preferably, in any of the above schemes, in S3, the decision variable X of the optimization model is defined as the wind power generation Mf in each period t and photovoltaic power generation Mg t , expressed as:

[0025] X={Mf1,Mg1,...,Mf T Mg T}.

[0027] Preferably, in any of the above schemes, in S4, the load demand priority principle is as follows:

[0028] In each time period, the current power load demand is met first:

[0029] D t =min(Mf t +Mg t +Ms t , power load demand), where Ms t is the output power of the energy storage system.

[0030] Preferably, in S4, when Mf t +Mg t >D t When the power is full, the excess electricity is stored in the energy storage system first; if the capacity of the energy storage system is full, the output of the wind turbine and photovoltaic components is dynamically adjusted.

[0031] Preferably, in S4, when Mf t +Mg t <D t When the energy storage system is started, the stored electricity is dispatched according to priority to supplement the load gap. If the energy storage is insufficient, an external power dispatch signal is issued according to the grid demand.

[0032] Preferably, in any of the above solutions, in S5, the use of an improved artificial bee colony algorithm based on a heuristic feasible solution strategy to solve the optimization model includes:

[0033] (1) Initializing the population according to the decision variables. After the initialization is completed, the population is used as the input of the algorithm;

[0034] (2) Calculate the fitness and normalize the fitness;

[0035] (3) Use artificial bee colony algorithm to design different search update rules;

[0036] (4) Set the convergence conditions. If the convergence conditions are met, output the current optimal solution X*, that is, the optimal operation plan of the wind-solar-storage integrated machine in each time period.

[0037] Preferably, any of the above schemes is to set the objective function

[0038] Among them, U t represents the total energy utilization rate of the wind-solar-storage system in period t; C t represents the total cost of energy storage operation and grid operation;

[0039] For each solution Xi Calculate the objective function value F(X i ), the goal is to find the solution that maximizes F(X).

[0040] Normalize the fitness value:

[0041]

[0042] Preferably, any of the above solutions uses an artificial bee colony algorithm to design different search update rules, including:

[0043] (1) Worker bee search:

[0044] Each worker bee revolves around the current solution i Generate new solution X i ': in is the random perturbation factor, X k is a solution randomly selected from the population;

[0045] (2) Observation bee selection:

[0046] According to the probability P(X i ) Select solutions with high fitness for further search:

[0047] (3) Scout Bee Update:

[0048] If the solution is X i After a long period of no improvement, the scout bee randomly generates a new solution instead: X i =X new .

[0049] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0050] 1. Improve the efficiency of new energy utilization

[0051] According to the characteristics of wind, solar and storage energy, an optimization model including objective function and constraints was established. The objective function maximizes the utilization rate and economy of new energy, and the constraints include energy balance, energy storage system capacity and equipment operation constraints.

[0052] Through the multi-energy complementary strategy, wind energy, solar energy and energy storage systems are organically combined to achieve the coordinated utilization of different energy resources, effectively solving the intermittent and volatility problems of new energy power generation.

[0053] The "all-inclusive" heuristic strategy is adopted to maximize the use of renewable energy, reduce the abandonment of wind and solar power, and improve the overall energy efficiency. Under the premise of ensuring system stability, wind and solar energy are used first, and dynamically adjusted in combination with the energy storage system to achieve efficient use of energy.

[0054] 2. Improve system economy

[0055] The optimization model comprehensively considers the renewable energy power generation and operating costs, balances energy utilization and economy through the objective function, and reduces the operating costs of the power system.

[0056] The optimization problem is solved quickly based on the improved artificial bee colony algorithm (IABC). The algorithm combines heuristic strategies, has strong global optimization capabilities and high computational efficiency, can quickly obtain the optimal operation plan, and improves the economic benefits and computational efficiency of the plan.

[0057] 3. Enhance grid stability and reliability

[0058] The use of energy storage systems alleviates the time mismatch between power generation and load and achieves a dynamic balance of energy.

[0059] The optimization process fully considers the equipment operation constraints, avoids overload or abnormal operation, and improves the stability and reliability of power grid operation.

[0060] 4. Provide intelligent optimization solutions

[0061] This method adopts an intelligent optimization algorithm (improved artificial bee colony algorithm), which has global optimization capability and efficient optimization performance, and can quickly find the optimal operation plan of the multi-energy complementary system.

[0062] The method is highly scalable and can flexibly adjust the optimization objectives and constraints according to actual needs to adapt to the new energy access needs in different scenarios.

[0063] 5. Environmentally friendly and sustainable

[0064] By increasing the utilization rate of wind and solar energy, we reduce dependence on traditional fossil energy, reduce greenhouse gas emissions, and meet the goals of carbon neutrality and sustainable development.

[0065] 6. Wide application value

[0066] It is suitable for large-scale integrated wind, solar and storage energy systems in various scenarios, including new energy grid connection, power dispatching, microgrid optimization and other fields, and has broad promotion value.

[0067] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0069] Figure 1 Flow chart of a multi-energy complementary intelligent optimization and control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0071] The multi-energy complementary intelligent optimization and control method proposed in the present invention is based on a wind, solar and storage multi-energy complementary integrated device, and realizes efficient utilization and intelligent control of new energy through optimization models and intelligent algorithms.

[0072] like Figure 1 As shown, the multi-energy complementary intelligent optimization and control method of the embodiment of the present invention includes the following steps:

[0073] Step S1, collecting and setting the basic data required for the operation of the wind-solar-storage integrated machine.

[0074] In the embodiment of the present invention, the basic data includes: wind and light energy resource data, parameters of the energy storage system, power load demand curve, operating costs, constraints and other key system parameters. Among them, wind and light energy resource data include: hourly changes in wind speed and light intensity, etc. The parameters of the energy storage system include: battery capacity, charging and discharging efficiency, etc.

[0075] Step S2, construct a simulation optimization model for wind, solar and storage multi-energy complementary operation.

[0076] Specifically, based on the above basic data, an optimization model is established, and the objective function F and constraint conditions of the model are set.

[0077] (1) Objective function

[0078] Maximize the new energy utilization rate and economy of the wind, solar and storage integrated system. The expression is:

[0079]

[0080] Where T is the total number of simulation periods; Mf t is the wind power generation in period t; Mg t is the photovoltaic power generation in period t; C t is the operating cost of period t; a, b, c are weight coefficients used to balance power generation and operating costs.

[0081] (2) Constraints

[0082] The constraints of the optimization model are as follows:

[0083] 1) Energy balance constraint: Mf t +Mg t +Ms t ≥D t ;

[0084] 2) Energy storage system constraints: 0≤Ms t ≤capacity upper limit;

[0085] 3) Equipment operation constraints: Mf t Mg t ≤Rated power generation capacity;

[0086] Among them: Ms t D is the output power of the energy storage system during time period t; t is the electricity load demand in time period t.

[0087] Step S3, defining the decision variables of the optimization model.

[0088] Specifically, the decision variable X of the optimization model is defined as the wind power generation Mf in each period t and photovoltaic power generation Mg t , expressed as:

[0089] X={Mf1,Mg1,...,Mf T Mg T} (2)

[0090] Step S4, setting a heuristic feasible solution strategy, that is, designing a "collect as much as possible" heuristic feasible solution strategy, through which the utilization rate of wind energy and solar energy is maximized, and at the same time, energy supply and demand balance is achieved through coordinated optimization of the energy storage system. The strategy includes:

[0091] (1) Real-time monitoring and collection of wind energy real-time power generation Mf t 、Real-time power generation of light energy Mg t , power load demand D t And the state of the energy storage system. The state of the energy storage system includes the remaining capacity and the charging and discharging efficiency.

[0092] In this step, the above real-time data is collected through sensors and controllers, and uploaded to the control system for analysis and calculation.

[0093] (2) The principle of priority is adopted to configure electricity load demand, and wind energy and solar energy are fully utilized in each time period.

[0094] Specifically, the load demand priority principles are as follows:

[0095] In each time period, the current power load demand is met first:

[0096] D t =min(Mf t +Mg t +Ms t , power load demand) (3)

[0097] Ms t is the output power of the energy storage system.

[0098] (3) Configure excess power storage strategy: When the total amount of wind and solar power generation exceeds the power load demand, the excess power will be stored in the energy storage system first.

[0099] Specifically, when the total amount of wind and solar power generation (Mf t +Mg t ) exceeds the power demand D t , that is, when Mf t +Mg t >D t When the power is off, the excess power is stored in the energy storage system (battery or other energy storage device) first.

[0100] If the capacity of the energy storage system is full, the control system dynamically adjusts the output of wind turbines and photovoltaic modules to avoid energy waste.

[0101] (4) Configure insufficient power replenishment strategy: When the total amount of wind and solar power generation is lower than the power load demand, the energy storage system is prioritized to replenish energy.

[0102] Specifically, when the total amount of wind and solar power generation (Mf t +Mg t ) is lower than the power demand D t , that is, when Mf t +Mg t <D t When the load is insufficient, the energy storage system is started and the stored electricity is dispatched according to priority to supplement the load gap. If the energy storage is insufficient, an external power dispatch signal is issued according to the needs of the power grid, such as calling thermal power or other backup power sources.

[0103] Through real-time optimization algorithms, wind, solar and storage are globally scheduled to achieve a dynamic balance between energy flow, storage and demand, maximize the utilization of new energy, reduce waste and lower operating costs.

[0104] In summary, by setting the heuristic feasible solution strategy in this step, the following three points can be achieved:

[0105] 1. In each period, give priority to fully utilizing wind energy and solar energy;

[0106] 2. If the power generation exceeds the load demand, the excess power will be stored;

[0107] 3. If the power generation is insufficient, the energy storage system will be prioritized to supplement energy.

[0108] Step S5, the improved artificial bee colony algorithm (IABC) based on the heuristic feasible solution strategy is used to solve the optimization model and obtain the optimal operation plan of the wind-solar-storage integrated machine in each period. By incorporating the "all-receivable" heuristic strategy, better directional guidance is provided for the initial solution and search process.

[0109] Specifically, an improved artificial bee colony algorithm based on a heuristic feasible solution strategy is used to solve the optimization model, including:

[0110] (1) Initialize the population according to the decision variables. After initialization, the generated initial population is used as the input of the algorithm.

[0111] First, define the real number encoding:

[0112] Decision variables X = [Mf1, Mf2, ..., Mf T ,Mg1,Mg2,...,Mg T ], representing the wind and solar power generation in each period. Real number coding simplifies the representation of solutions, avoids the complexity of traditional discrete coding, and improves search efficiency.

[0113] Then initialize the population:

[0114] 1. Randomly generate a population S = {X1, X2, ..., X N}, where N is the population size, X i is a candidate solution;

[0115] 2. Each solution X i The components of are randomly generated within the allowed range:

[0116] Mf t ∈[Mf min ,Mf max ],Mg t ∈[Mg min Mg max ](4)

[0117] 3. After initialization, the population is used as the input of the algorithm.

[0118] (2) Calculate the fitness and normalize it.

[0119] First, let the objective function be:

[0120]

[0121] Among them, U t represents the total energy utilization rate of the wind-solar-storage system in period t; C t Represents the total cost of energy storage operation and grid operation.

[0122] Then calculate the fitness: for each solution X i Calculate the objective function value F(X i ), the goal is to find the solution that maximizes F(X).

[0123] Finally, normalize the fitness: To improve the calculation stability, normalize the fitness value:

[0124]

[0125] (3) Use artificial bee colony algorithm to design different search update rules.

[0126] Bee behavior modeling: Based on the three bee roles (worker bees, observer bees, and scout bees) of the artificial bee colony algorithm, different search update rules are designed. Different search update rules are designed using the artificial bee colony algorithm, including:

[0127] 1. Worker Bee Search:

[0128] Each worker bee revolves around the current solution i Generate new solution X i ':

[0129]

[0130] in, is the random disturbance factor, X k is a solution randomly selected from the population.

[0131] 2. Observe bee selection:

[0132] According to the probability P(X i ) Select solutions with high fitness for further search:

[0133]

[0134] 3. Scout Bee Update:

[0135] If the solution is X i After a long period of no improvement, the scout bee randomly generates a new solution instead: X i =X new (Randomly generated).

[0136] (4) Set the convergence conditions. If the convergence conditions are met, output the current optimal solution X*, that is, the optimal operation plan of the wind-solar-storage integrated machine in each time period.

[0137] 1. Set the convergence condition (i.e. stop condition)

[0138] The convergence of the algorithm is determined according to the following criteria:

[0139] 1) The fitness change of the current optimal solution X* is lower than the preset threshold:

[0140] ΔF(X*)=|F(X* new )-F(X* old )|<μ (9)

[0141] Among them, μ is the preset accuracy threshold.

[0142] 2) Reach the maximum number of iterations Iter max .

[0143] 2. Output results: If the convergence conditions are met, output the current optimal solution X*, that is, the optimal operation plan of the wind-solar-storage integrated machine in each time period.

[0144] This step uses algorithm convergence acceleration: through fitness normalization and scout bee mechanism, local convergence problems are avoided and global optimization performance is improved.

[0145] In summary, through the optimization solution of this step, the following four points can be achieved:

[0146] 1. Initial population generation: Generate the initial solution set according to the real number code;

[0147] 2. Fitness evaluation: Calculate the fitness of each solution based on the objective function F;

[0148] 3. Search update: Iteratively update the solution based on the artificial bee colony algorithm optimization mechanism;

[0149] 4. Convergence judgment: When the improvement of the solution is lower than the preset threshold, the optimal solution is output.

[0150] The multi-energy complementary intelligent optimization and control method of the embodiment of the present invention adopts a multi-energy complementary strategy to achieve efficient operation of the energy system and significant reduction of carbon emissions. This strategy involves optimizing the utilization of energy by combining different types of energy resources, such as wind energy, solar energy, etc., through technological innovation and system optimization configuration. Multi-energy complementarity not only improves the comprehensive utilization efficiency of energy, but is also crucial to balancing and stabilizing the power system, especially when large-scale new energy is connected to the grid.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0152] It is not difficult for those skilled in the art to understand that the present invention includes any combination of the invention content and specific implementation methods of the above specification and the various parts shown in the drawings. Due to the limited space and to make the specification concise, the various schemes composed of these combinations are not described one by one. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0153] Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. Those skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-energy complementary intelligent optimization and control method, characterized in that: The steps include: Step S1, collecting and setting the basic data required for the operation of the wind-solar-storage integrated machine; Step S2, constructing a wind-solar-storage multi-energy complementary operation simulation optimization model, wherein the objective function of the optimization model is F, as follows: Where T is the total number of simulation periods; Mf t is the wind power generation in period t; Mg t is the photovoltaic power generation in period t; C t is the operating cost of period t; a, b, c are weight coefficients used to balance power generation and operating costs; Step S3, defining decision variables of the optimization model; Step S4, setting a heuristic feasible solution strategy, through which the utilization rate of wind energy and solar energy is maximized, and at the same time, energy supply and demand balance is achieved through coordinated optimization of the energy storage system, and the strategy includes: (1) Real-time monitoring and collection of wind energy real-time power generation Mf t 、Real-time power generation of light energy Mg t , power load demand D t and the status of the energy storage system; (2) The principle of priority is adopted for electricity load demand, and wind and solar energy are fully utilized in each period; (3) When the total amount of wind and solar power generation exceeds the power load demand, the excess power will be stored in the energy storage system first; (4) When the total amount of wind and solar power generation is lower than the power load demand, the energy storage system is prioritized for energy replenishment; Step S5, using an improved artificial bee colony algorithm based on a heuristic feasible solution strategy to solve the optimization model, and obtain the optimal operation plan of the wind-solar-storage integrated machine in each time period.

2. The multi-energy complementary intelligent optimization and control method according to claim 1 is characterized in that: The basic data include: wind and solar energy resource data, parameters of energy storage system, electricity load demand curve, operating costs and constraints.

3. The multi-energy complementary intelligent optimization and control method according to claim 1 is characterized in that: In S2, the constraints of the optimization model are as follows: (1) Energy balance constraint: Mf t +Mg t +Ms t ≥D t ; (2) Energy storage system constraints: 0 ≤ Ms t ≤capacity upper limit; (3) Equipment operation constraints: Mf t Mg t ≤Rated power generation capacity; Among them: Ms t D is the output power of the energy storage system during time period t; t is the electricity load demand in time period t.

4. The multi-energy complementary intelligent optimization and control method according to claim 1 is characterized in that: In S3, the decision variable X of the optimization model is defined as the wind power generation Mf in each period t and photovoltaic power generation Mg t , expressed as: X={Mf1,Mg1,...,Mf T ,Mg T }。 5. The multi-energy complementary intelligent optimization and control method according to claim 1 is characterized in that: In S4, the load demand priority principle is as follows: In each time period, the current power load demand is met first: D t =min(Mf t +Mg t +Ms t , power load demand), where Ms t is the output power of the energy storage system.

6. The multi-energy complementary intelligent optimization and control method according to claim 1 is characterized in that: In S4, when Mf t +Mg t >D t When the power is high, the excess power will be stored in the energy storage system first; If the capacity of the energy storage system is full, the output of wind turbines and photovoltaic panels is dynamically adjusted.

7. The multi-energy complementary intelligent optimization and control method according to claim 1 is characterized in that: In S4, when Mf t +Mg t <D t When the energy storage system is started, the stored electricity is dispatched according to priority to supplement the load gap. If the energy storage is insufficient, an external power dispatch signal is issued according to the grid demand.

8. The multi-energy complementary intelligent optimization and control method according to claim 1 is characterized in that: In S5, the improved artificial bee colony algorithm based on the heuristic feasible solution strategy is used to solve the optimization model, including: (1) Initializing the population according to the decision variables. After the initialization is completed, the population is used as the input of the algorithm; (2) Calculate the fitness and normalize the fitness; (3) Use artificial bee colony algorithm to design different search update rules; (4) Set the convergence conditions. If the convergence conditions are met, output the current optimal solution X*, that is, the optimal operation plan of the wind-solar-storage integrated machine in each time period.

9. The multi-energy complementary intelligent optimization and control method according to claim 8, characterized in that: Assume the objective function Among them, U t represents the total energy utilization rate of the wind-solar-storage system in period t; C t represents the total cost of energy storage operation and grid operation; For each solution X i Calculate the objective function value F(X i ), the goal is to find the solution that maximizes F(X). Normalize the fitness value:

10. The multi-energy complementary intelligent optimization and control method according to claim 8, characterized in that: The artificial bee colony algorithm is used to design different search update rules, including: (1) Worker bee search: Each worker bee revolves around the current solution i Generate new solution X i ': in, is the random disturbance factor, X k is a solution randomly selected from the population; (2) Observation bee selection: According to the probability P(X i ) Select solutions with high fitness for further search: (3) Scout Bee Update: If the solution is X i After a long period of no improvement, the scout bee randomly generates a new solution instead: X i =X new .

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