A Comprehensive Energy Economic and Environmental Dispatch Optimization Method and System

Abstract

The present invention discloses a comprehensive energy economic environment dispatch optimization method and system. The method includes: obtaining basic data of the generator sets and loads in the comprehensive energy system, where the comprehensive energy system includes thermal power generator sets, renewable energy generator sets, and energy storage devices; constructing a dynamic economic environment dispatch model of the comprehensive energy system including thermal power, wind power, photovoltaic, and energy storage according to the basic data and the economic and environmental objectives to be considered; introducing crossover, mutation, and chain strategies to improve the optimization ability of the mayfly optimization algorithm when calculating the optimal result; combining the improved mayfly algorithm, the renewable energy output control strategy, and the comprehensive energy dynamic economic environment dispatch model to construct a comprehensive energy capacity allocation system, and further obtaining the optimal scheme for the economic environment dispatch of each power generation system. The method realizes the optimization of the economic and environmental indicators of the power system on the premise of ensuring the utilization rate and stable output of renewable energy.

Description

Technical Field

[0001] The technical solution of the present invention belongs to the field of power system dispatching, and specifically relates to a comprehensive energy economic environment dispatching optimization method and system. Background Art

[0002] The global energy crisis and climate change have led more and more countries to reduce the proportion of fossil fuels in the energy system and attach importance to the development and utilization of various renewable energy sources such as wind energy, solar energy, and tidal energy to address issues such as energy shortages and the greenhouse effect. Although countries have made efforts to save energy and reduce emissions to achieve these goals, the problems faced are still very serious. The characteristics of the uncertainty and uneven spatio-temporal distribution of intermittent renewable energy power generation have increased the dispatching cost of the power system while increasing the power generation of renewable energy. Therefore, it is necessary to continuously study how to improve the utilization rate of wind and photovoltaic power generation while increasing the installed capacity of renewable energy sources such as wind power and photovoltaic power, and further develop clean energy technologies to ensure sufficient and reliable power supply.

[0003] The integrated energy system considering wind power, photovoltaic power, thermal power, and energy storage devices, as the main component of the energy Internet, based on electricity, improves the utilization rate of renewable energy through multi-energy complementarity, which is the main direction of the development of China's energy and power. However, at the current stage in China, thermal power generation is still the mainstay. While increasing the installed capacity of renewable energy, more thermal power units are needed as spinning reserves to participate in grid peak shaving. With the development of energy storage technology, energy storage power stations can effectively suppress the intermittency of wind and light, regulate the output of units, and can play a good role in grid peak shaving. Despite these advantages, at the current stage, due to high costs, electrochemical energy storage devices cannot be widely deployed in grid dispatching. Therefore, how to rationally optimize the economic environment dispatching system including renewable energy devices using energy storage facilities with a certain capacity limit has become the key problem to be solved currently. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a comprehensive energy economic environment dispatching optimization method and system, which takes into account wind power generation, photovoltaic power generation, thermal power generation, and energy storage devices, and realizes the optimization of the economic and environmental indicators of the power system on the premise of ensuring the utilization rate of renewable energy and the stable operation of the power grid.

[0005] The first aspect of the embodiment of the present invention for solving this technical problem proposes a comprehensive energy economic environment dispatching optimization method, which optimizes the output of each part of the integrated energy system based on an improved mayfly optimization algorithm and a renewable energy output control strategy. The specific steps of this method are as follows:

[0006] Obtain the operating data of the generating units, pollutant emission data, and basic data of the load in the integrated energy system, where the integrated energy system includes thermal power generating units, renewable energy generating units, and energy storage devices;

[0007] According to the basic data of the generating units and the load, construct a dynamic economic environmental dispatch model for the integrated energy system including thermal power, wind power, photovoltaic, and energy storage;

[0008] As a further improvement of the present invention, based on the mayfly intelligent algorithm and aiming to improve the optimization effect of the algorithm, construct an improved mayfly optimization algorithm as an optimization tool for the model. Among them, the position of the improved mayfly optimization algorithm is updated by variable weights, chaotic initialization, mutation, and chain movement;

[0009] Combine the improved intelligent algorithm, renewable energy output control strategy, and integrated energy dynamic economic environmental dispatch model, define the objective function of the dispatch model as the fitness of the intelligent algorithm, construct an integrated energy capacity allocation system, and then obtain the optimal solution for the economic environmental dispatch of each power generation system.

[0010] The second aspect of the embodiment of the present invention to solve this technical problem is to build an integrated energy economic environmental dispatch system, including:

[0011] An acquisition module for acquiring the basic data of the generating units and the load in the integrated energy system;

[0012] A model establishment module for constructing an economic environmental dispatch model for the integrated energy system considering various constraints based on the operating data, pollutant emission data, and basic data of the load of the generating units within the dispatch period;

[0013] A calculation module for calculating the integrated energy dynamic economic environmental dispatch model combined with the improved mayfly optimization algorithm and the renewable energy control strategy to determine the output power dispatch plan of each power generation device in the integrated energy system;

[0014] An output module for outputting the output power allocation result.

[0015] The beneficial effects of the present invention compared with the prior art are reflected in:

[0016] (1) The integrated energy dynamic economic environment scheduling model provided by the embodiments of the present invention takes the total cost of the system as the economic goal of the integrated energy system, comprehensively considers the operating costs of thermal power units, wind power generation, photovoltaic devices, and energy storage devices, and considers the power balance constraints, operating state constraints, and change rate constraints of each device of the system to achieve the economic optimum of the integrated energy system; takes the pollutant emissions of thermal power units as the environmental goal of the integrated energy system, considers wind power devices and photovoltaic devices as environmentally friendly power generation devices to achieve the environmental optimum of the integrated energy system; according to the output control strategy of renewable energy, optimizes the energy supply of the integrated energy system, and does not aim at simply increasing the power generation of renewable energy, so as to achieve the stable output of renewable energy power generation during the entire scheduling period.

[0017] (2) The integrated energy economic environment scheduling optimization method of the present invention is not limited to the economic environment scheduling optimization method, and can also be extended to the optimization methods of other devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly show the technical solutions of the embodiments of the present invention, the drawings required in the embodiments are briefly introduced below. Obviously, the described drawings are only a part of the embodiments of the present invention, rather than all the content. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic structural diagram of the integrated energy economic environment scheduling optimization system of the present invention;

[0020] Figure 2 It is a flowchart of the implementation of the improved mayfly algorithm provided by an embodiment of the present invention;

[0021] Figure 3 It is a schematic structural diagram of the implementation of the renewable energy control strategy provided by an embodiment of the present invention;

[0022] Figure 4 It is the Pareto solution set obtained by the calculation module provided by an embodiment of the present invention;

[0023] Figure 5 It is a comparison of the renewable energy grid-connected power scheduling schemes provided by an embodiment of the present invention;

[0024] Figure 6 It is the final scheduling result obtained by an integrated energy economic environment scheduling optimization system of the present invention;

[0025] Figure 7 It is a flowchart of the implementation of the integrated energy economic environment scheduling optimization system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. At the same time, the professional terms used in the present invention are only for explaining the specific implementation manners, rather than showing the implementation manners of the present invention.

[0027] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following will be described in detail with reference to the accompanying drawings and through specific embodiments.

[0028] The global energy crisis and climate change have caused more and more countries to start reducing the proportion of fossil fuels in the energy system, and attaching importance to the development and utilization of various renewable energy sources such as wind energy, solar energy and tidal energy to address issues such as energy shortage and greenhouse effect. Although countries have made efforts to save energy and reduce emissions to achieve these goals, the problems faced are still very severe. Although large-scale wind turbines and photovoltaic devices are connected to the power system, due to the uncertainty and uneven spatio-temporal distribution characteristics of the intermittent renewable energy power generation, the increase in wind-solar power generation also correspondingly increases the dispatching cost of the power system. In order to ensure the stable operation of the power system, more thermal power units are added to the power grid peak shaving as rotating reserves. With the development of energy storage technology, energy storage power stations can effectively suppress the intermittency problems of wind and solar energy, regulate the output of units, and can play a good role in power grid peak shaving. Despite these advantages, at the present stage, electrochemical energy storage devices cannot be widely invested in power grid dispatching due to high costs. With more and more renewable energy devices added to the system, the dispatching problem of the power grid becomes more and more complex. Therefore, how to reasonably optimize the hybrid dynamic economic emissions including renewable energy devices by using energy storage facilities with a certain capacity limit has become a key problem that power system operators in various countries need to solve.

[0029] Therefore, the integrated energy economic environment dispatching optimization system provided by the present invention controls the output of renewable energy and improves the optimization ability of the solution algorithm, so as to ensure the stability of the output of renewable energy while solving economic and environmental problems, and finally obtain the optimal economic environment dispatching optimization plan.

[0030] Figure 1 is a schematic structural diagram of the integrated energy economic environment dispatching optimization system of the present invention. The structure of the system includes:

[0031] S101, an acquisition module, configured to acquire the operation data of the generating units, the pollutant emission data, and the basic data of the load of the integrated energy system, wherein the integrated energy system includes thermal power generating units, renewable energy generating units, and energy storage devices;

[0032] In this embodiment, the basic data may include, but are not limited to, at least one of the following: electrical load power, power generation power and power generation power change rate of a generator set, power generation cost of a generator set, predicted power generation power of wind power and photovoltaic power, transmission loss coefficient of the system, pollutant emissions of a thermal power unit, energy transfer formula and charge-discharge constraint conditions of an energy storage device, etc.

[0033] S102. A model establishment module is configured to construct a comprehensive energy system economic environment dispatch model considering various constraint conditions based on the operation data of the generator set, pollutant emission data, and basic data of the load within the dispatch period.

[0034] In this embodiment, the comprehensive energy system economic environment dispatch model may include, but are not limited to, at least one of the following: thermal power unit operation cost model, renewable energy device operation cost model, energy storage device operation cost model, thermal power unit pollutant emission model.

[0035] S103. A calculation module is configured to calculate a comprehensive energy system dynamic economic environment dispatch model combining an improved mayfly optimization algorithm and a renewable energy control strategy to determine the output power dispatch scheme of each power generation device in the comprehensive energy system.

[0036] In this embodiment, an improved mayfly optimization algorithm is constructed as an optimization tool for the model. Among them, the position of the improved mayfly optimization algorithm is updated by variable weights, chaotic initialization, mutation, and chain movement; then the constructed objective function and constraint conditions are input into the improved mayfly algorithm, and at the same time, the influence of the renewable energy output control strategy on the renewable energy processing is considered, and finally a set of reference power dispatch schemes is obtained.

[0037] S104. An output module is configured to output the optimal scheme for the power distribution of each unit.

[0038] In this embodiment, each set of power dispatch schemes output by the calculation module is stored, and then input into the Pareto compromise solution selection scheme preset in the system, and finally the optimal comprehensive energy system power dispatch scheme is obtained and output to the user interface.

[0039] Figure 2 It is a flowchart for implementing the calculation of the output power results of each unit in the comprehensive energy economic environment dispatch optimization method provided by the embodiment of the present invention. The calculation process is as follows:

[0040] S301: Obtain the basic data based on the thermal power unit, wind turbine, photovoltaic device, energy storage device, and load, determine the weight coefficient ω and the proportionality coefficient Q, and start running the mayfly optimization algorithm in the calculation module;

[0041] S302: Initialize the positions and velocities of the female and male mayfly populations, establish the fitness formula for the mayfly population based on the objective function, evaluate the fitness values of the initialized population, and select the local and global optimal solutions;

[0042] S303: Based on the relationship between the male mayflies and the mayflies at the global optimal position, update the positions and velocities of the male mayflies through the male population position update formula. Based on the position relationship between the male mayflies and the female mayflies, update the positions and velocities of the female mayflies through the female population position update formula;

[0043] S304: Evaluate the fitness values of the individuals in the population after position update based on the established fitness function, and select the new global and local optimal values;

[0044] S305: Generate a certain number of offspring populations based on the crossover strategy and mutation strategy, sort the offspring and parents according to the fitness values to select a new generation of female and male mayfly populations;

[0045] S306: Determine whether the current iteration has reached the number of iterations. If so, output a power distribution plan for the thermal power units in the integrated energy system. Otherwise, return to S303.

[0046] In this embodiment, parameters such as the number of iterations three, the weight coefficient ω, and the proportional coefficient Q of the algorithm operation are set, and then the algorithm operation is started.

[0047] In this embodiment, the positions and fitnesses of the mayfly population are initialized. The initialized position matrix of the male mayflies is as follows:

[0048]

[0049] where, X l is the male population after the th iteration, is the position of the i-th individual in the j-th dimension after the th iteration, N is the scale of the population, and D is the dimension of the problem to be solved, i.e., the number of units.

[0050] The initialized position matrix of the female mayflies is as follows:

[0051]

[0052] where, Y l is the female population after the th iteration. The female and male mayflies have the same scale, both N.

[0053] Taking the objective function as the fitness function of the population and substituting the positions of the male mayfly population into the fitness function, the corresponding fitness matrix of the male mayfly population can be obtained as follows:

[0054]

[0055] Among them, is the fitness matrix of male mayflies, is the fitness corresponding to the Nth male mayfly in the l-th iteration. Substituting the positions of the female mayfly population into the fitness function, the fitness matrix corresponding to the female population can be obtained as follows:

[0056]

[0057] Among them, is the fitness matrix of female mayflies, is the fitness corresponding to the Nth female mayfly in the l-th iteration.

[0058] In this embodiment, in order to obtain the optimal allocation scheme, two search methods are set, that is, different position update formulas for the two mayfly populations. Based on the relationship between male mayflies and the mayflies at the global optimal position, the positions and velocities of male mayflies are updated. Based on the position relationship between male mayflies and female mayflies, the positions and velocities of female mayflies are updated. Male mayflies update their positions by jumping near the water surface, that is, adding the unit velocity of mayflies to the current position. Male mayflies have gregariousness and will not have a large moving speed. They will only search near the optimal position. This operation mode is more conducive to searching for the optimal output configuration of each unit in the integrated energy system. Different from male mayflies, female mayflies do not gather in one place but will be attracted by males and move towards the male positions. The large-scale movement of female mayflies is conducive to obtaining more power allocation schemes for each generator set.

[0059] In this embodiment, the specific position update formula of the male mayfly is as follows:

[0060]

[0061] Among them, is the velocity of the i-th male mayfly at the th search, is the position of the i-th male mayfly at the th search, k1 and k2 are positive attraction coefficients, rp is the distance between the local optimal position and the i-th male mayfly at the current search, rg is the distance between the global optimal position and the i-th male mayfly, p best and g best are the local optimal position and the global optimal position respectively.

[0062] The position update formula of the female population is as follows:

[0063]

[0064] Among them, is the position of the i-th female mayfly after search update, is the speed of the i-th female mayfly after the search update, fl is the random walk coefficient of the female mayfly, e is a random number that changes with the number of iterations between -1 and 1, k3 is the attraction coefficient of the fixed female mayfly, and r is the distance between the male mayfly and the female mayfly.

[0065] In this embodiment, in order to enhance the search ability of the algorithm and improve the speed of the algorithm to find the optimal solution, a chaotic mapping initialization strategy, a crossover strategy, a mutation strategy, and a chain movement are introduced to generate new individuals to enhance the global search ability and search speed.

[0066] The purpose of optimizing the initialization is to obtain a better position matrix at the initial stage and maintain the randomness of the initial population as much as possible, so as to quickly obtain the output power of each generating unit of the optimal integrated energy system. The chaotic mapping initialization operation of this embodiment is as follows:

[0067]

[0068] x i and y i are the initial positions of the male and female mayflies improved by introducing the logistic chaotic mapping. ub and lb represent the upper and lower limits of the mayfly position search; z i is the logistic mapping chaotic sequence with the same dimension as x i where μ is an adjustable parameter, which is a vector randomly generated between 0 and 1. When the value is 4, all values of z can be chaotic.

[0069] In the offspring of the mayfly algorithm, the best male and female individuals are mated in turn to produce offspring. This method is likely to cause the algorithm to fall into a local optimum during iteration. To improve the global search ability, crossover and mutation operations are added to the offspring mayflies. Referring the search results with poor fitness values to the search results with better fitness values is beneficial to improve the local search ability of the mayfly to obtain the optimal solution. The crossover formula is as follows:

[0070]

[0071] Among them, Q is a random number between -1 and 1 with the same dimension as x and y. x1 and y1 are the individuals of the i-th different search method sorted in order of fitness. offspring1 and offspring2 are two newly generated individuals. At the same time, the search results of the new individuals are randomly perturbed and mutated. In order to minimize ineffective mutations as much as possible, the offspring are mutated within the search range, and the method for obtaining the mutation value ψ is improved. The mutation value is increased in the early stage, and mutations are carried out globally as many times as possible; in the later stage, the mutation value is correspondingly decreased to improve the mutation efficiency. Adding a certain degree of random search method to the mayfly individuals helps to prevent the search from falling into a local optimum and being unable to find the global optimum solution. The random perturbation mutation is as follows:

[0072] mutnew = new + ψ (9)

[0073] Among them, mutnew is the mutated offspring; new is the newly generated individual after crossover; ψ is the random mutation value.

[0074] New individuals are generated by performing chain motion on the search results, that is, the multiple search results are averaged to determine whether a better search result is generated. The chain motion can be determined according to the following formula:

[0075]

[0076] In any of the above embodiments, the objective function of the integrated energy system economic environment dispatch model to be solved is used as the basis, including the total operating cost objective and pollutant emission objective of the integrated energy system. The renewable energy can be wind energy or solar energy, which is not limited here. In this embodiment, wind power and photovoltaic are taken as examples of new energy power generation for analysis.

[0077] ① The total operating cost objective includes the power generation costs of thermal power units, wind turbines, photovoltaic devices, and the energy storage costs of energy storage power stations.

[0078] The operating function expression of the thermal power unit considering the valve point effect is as follows:

[0079]

[0080] Among them, f 1c is the total power generation cost of the thermal power unit, N is the number of thermal power generation units, P i，t is the power generation power of the i-th unit at the t-th hour, a i , b i , c i are the fuel cost coefficients of the thermal power generator, T is the time period required to calculate the total cost, g i and h i are the valve loading coefficients of a single generator, P imin is the lower limit of the active power of the i-th generator.

[0081] Meanwhile, considering the high manufacturing costs and depreciation costs of wind turbines, photovoltaic panels and energy storage power stations, the total costs of wind power, photovoltaic and energy storage devices are as follows:

[0082]

[0083] Among them, c w , c pv , c bat are the cost coefficients of wind power, photovoltaic and energy storage devices respectively, J and K are the numbers of wind power devices and photovoltaic devices respectively, represents the power output of the i-th wind turbine unit at time t, represents the power output of the i-th photovoltaic power station at time t, P t bat represents the power output or input of the energy storage power station at time t.

[0084] Therefore, the total operating cost objective of the integrated energy system is as follows:

[0085] F C = f 1c + f 2c (13)

[0086] ② Pollutant emission target

[0087] When the system generates electricity, thermal power units will produce a large amount of carbon dioxide and other polluting gases, which will have a great impact on the environment. As environmentally friendly renewable energy generation methods, increasing the corresponding generation proportion of wind power and photovoltaic power can reduce pollutant emissions. Therefore, pollutant emissions from wind power and photovoltaic power generation do not need to be considered.

[0088] The pollutant emission formula of thermal power units can be expressed as follows:

[0089]

[0090] Among them, o i , p i , q i , θ i , represents the pollutant emission coefficient of the i-th thermal generator.

[0091] For an economic environment dispatch model with multiple solution objectives, multiple objectives are combined into a whole through the weight coefficient w. When the magnitudes of each objective differ greatly, introducing the proportionality coefficient Q into the fitness function can enable the optimization algorithm to obtain a better compromise solution. The objective function formula is as follows:

[0092] min F = wF C +Q(1 - w)F E (15)

[0093] where F is the objective function that combines the operating cost and pollutant emissions, F C is the operating cost of the integrated energy system, F E is the pollutant emissions of the integrated energy system, w is the weight coefficient that changes the proportion of the operating cost and pollutant emissions in the objective function, Q is the proportionality coefficient, and the value of Q depends on the order of magnitude difference between different objectives.

[0094] The use of the objective function promotes the search algorithm to obtain the optimal operating cost and pollutant emissions under this weight coefficient. For the entire integrated energy system, it is also necessary to compare the operating cost and pollutant emissions corresponding to a set of weight coefficients to obtain the most suitable weight coefficient and compromise solution. The best compromise solution is obtained by running the Pareto satisfaction formula after normalizing each item of data, and finally the solution with the highest satisfaction is selected as the optimal compromise solution.

[0095] The membership function for normalization is as follows:

[0096]

[0097] where φ k，i is the satisfaction of the i-th solution in the k-th objective in the Pareto solution set, f k max and f k min are the upper and lower limit constraints of the k-th objective.

[0098] By calculating the satisfaction of each objective, and then running the satisfaction function to calculate the satisfaction of each set of objective solutions, the formula is as follows:

[0099]

[0100] where φ i is the satisfaction of the final solution, n is the number of objectives, in this embodiment n is 2, that is, the total cost and emissions of the integrated energy system; I is the number of solutions in the Pareto solution set.

[0101] In some embodiments, based on any of the above embodiments, the integrated energy economic environment dispatch optimization method further includes: establishing constraint conditions according to the economic environment dispatch model. Optionally, the constraint conditions include at least one of the following: system power balance constraint, generator output power constraint, and energy storage device capacity constraint.

[0102] ① System power balance constraint

[0103]

[0104] Among them, P t load is the load demand at time t, and P t loss is the transmission loss at time t. When the output power of the energy storage power station dispatch is greater than zero, the sum of the output powers of the thermal power unit, wind turbine unit, photovoltaic power station, and energy storage power station should be equal to the total system load and transmission loss; when the output power of the energy storage power station is less than zero, the output power does not consider the energy storage power station.

[0105] ② Output power constraint of the generator set

[0106] The output power constraint of the thermal power unit is as follows:

[0107] P i min ≤P i ≤P i max (19)

[0108] A rapid increase or decrease in output power will damage the generator set. By setting the limit value of power change, the output power is controlled within a certain range. The ramp constraint is as follows:

[0109]

[0110] Among them, P i min and P i max are the minimum and maximum output limits of the i-th unit; P i up and P i down are the rising limit and falling limit of the power change of the i-th unit respectively;

[0111] ③ Capacity constraint of the energy storage power station

[0112] The power constraint of the energy storage device is as follows:

[0113]

[0114] Among them is the maximum power of the energy storage power station; is the maximum charging power; P t bat is the charging or discharging power of the energy storage power station at time, when P t bat is greater than zero, the energy storage power station discharges, and when it is less than zero, the power station charges. To ensure the continuity of the dispatch, the charge and discharge amounts are set to be equal within a dispatch period.

[0115] The energy constraint of the energy storage device is as follows:

[0116]

[0117] Among them, E t and E t+1 are the electricity quantities of the energy storage power station at time t and the next moment respectively; it is assumed that the charging and discharging efficiencies of the energy storage power station are equal, both being η bat ; E max represents the capacity of the battery, and the battery power at each moment must be within the constraint range.

[0118] Figure 3 is the implementation structure diagram of the renewable energy control strategy provided by an embodiment of the present invention. The renewable energy output control strategy can regard the wind turbine, photovoltaic device and energy storage device as a whole, and aims to improve the power stability of renewable energy access to the power grid. This strategy can divide the wind power and photovoltaic output into high-power periods and low-power periods. During the periods when the wind turbine or photovoltaic output is large, a part of the power connected to the power grid is correspondingly reduced and stored by the energy storage device. During the periods when the wind turbine or photovoltaic output is small, the power connected to the power grid is increased as much as possible, and at this time the energy storage device discharges. As Figure 4 shown, after the wind power and photovoltaic power are connected to the system, the power is first reduced in terms of stability according to the set data, then the volatility of the generated power is analyzed, and the controlled and reduced data is transmitted to the algorithm calculation layer for optimization. Then, the wind power and photovoltaic power output by the algorithm are transmitted to the charge and discharge control part of the energy storage, and finally the wind power, photovoltaic power and energy storage power processed by multiple control instructions are output.

[0119] In order to further illustrate the application effect of the integrated energy economic environment scheduling optimization method provided by the present invention, the following will be demonstrated and analyzed in combination with specific examples.

[0120] In this example, the scheduling interval of the system power is 1 hour, the scheduling length is 24 hours, and 10 thermal power units, 1 wind turbine, 1 photovoltaic device, and 1 energy storage device are used for scheduling optimization. The formulas and models of this embodiment have been described in other embodiments and will not be elaborated here.

[0121] Step 1, obtain the operating data of the generating units, pollutant emission data and basic data of the load of the integrated energy system, where the integrated energy system includes thermal power generating units, renewable energy generating units and energy storage devices.

[0122] Step 2: Based on the operation data, pollutant emission data, and basic load data of the generating units within the scheduling period, construct a comprehensive energy system economic and environmental scheduling model considering various constraints. It should include the operating cost model of thermal power units, the operating cost model of renewable energy devices, the operating cost model of energy storage devices, and the pollutant emission model of thermal power units.

[0123] The functional expression of the operating cost model of thermal power units is as follows:

[0124]

[0125] The functional expression of the total cost model of wind turbines, photovoltaic devices, and energy storage devices is as follows:

[0126]

[0127] The functional expression of the pollutant emission model of thermal power units is as follows:

[0128]

[0129] Table 1 The power generation parameters of the generating units are as follows:

[0130]

[0131] Step 3: Establish the fitness function formula for algorithm search according to the system operation cost and pollutant emission targets as follows:

[0132] min F = wF C +Q(1 - w)F E

[0133] Step 4: Run the improved mayfly optimization algorithm in the calculation module to obtain a set of target solutions

[0134] (4.1) Set the number of iterations during algorithm search to 1000 times, the search dimension to the number of thermal power units, wind turbines, photovoltaic devices, and energy storage devices, the search scale of the algorithm to 100, and preset the search weight w of the mayfly algorithm. Initialize the positions and velocities of the female and male mayfly populations using the chaos mapping strategy, and establish the fitness formula of the mayfly population according to the objective function to evaluate the fitness values of the initialized population and select the local optimal and global optimal solutions;

[0135] (4.2) Based on the relationship between the male mayflies and the mayflies at the global optimal position, update the positions and velocities of the male mayflies through the male population position update formula, and based on the position relationship between the male mayflies and the female mayflies, update the positions and velocities of the female mayflies through the female population position update formula;

[0136] (4.3) Evaluate the fitness values of individuals in the population after position update based on the established fitness function, and select the new global optimal and local optimal values;

[0137] (4.4) Generate a certain number of offspring populations based on the crossover strategy, mutation strategy, and chain movement. Sort the offspring and parents according to the fitness values to select the new generation of female and male mayfly populations;

[0138] (4.5) Determine whether the current iteration has reached 1000 times. If it has reached, output the power distribution scheme of the thermal power units in the integrated energy system corresponding to the weight coefficient w. Otherwise, return to step (4.2).

[0139] Step 5, determine the weight coefficient ω. By linearly changing the value of the weight coefficient ω from 0 to 1, and running the mayfly search algorithm multiple times to obtain a set of Pareto front solutions, and use the satisfaction formula to obtain the best weight coefficient w with the highest satisfaction value, as well as the corresponding operating cost and pollutant emissions of this w.

[0140] In this embodiment, the integrated energy system model is respectively used to search for the optimal operating cost and pollutant emissions by using the particle swarm optimization (PSO) algorithm, moth-flame optimization (MFO) algorithm, mayfly optimization (MA) algorithm, and improved mayfly optimization (IMA) algorithm, and the search results of the four algorithms are compared.

[0141] Table 2 Comparison of operating results of different algorithms

[0142]

[0143] Table 3 Output power of each power generation device in the integrated energy system

[0144]

[0145] The results of this embodiment show that an integrated energy economic environment dispatch optimization method of the present invention can obtain lower operating costs and pollutant emissions, and the line loss of the system that obtains the optimal search result is also the smallest.

[0146] Figure 4 It is the Pareto solution set obtained by the calculation module provided by an embodiment of the present invention. Figure 4 It shows the comparison of the Pareto solution sets obtained by the objective function with the proportional coefficient Q and the objective function without the proportional coefficient Q. The horizontal axis represents the system pollutant emissions, and the vertical axis represents the system operating cost. It can be seen that the proposed proportional coefficient can effectively promote the integrated energy system to obtain a better unit power dispatch scheme.

[0147] Figure 5It is a comparison of the renewable energy grid-connected power scheduling scheme provided by an embodiment of the present invention. By using the renewable energy output control strategy and not using the renewable energy output control strategy for the integrated energy system, the comparison of the 24-hour renewable energy scheduling power curve is obtained. It can be seen that through the renewable energy output control strategy, the integrated energy system can obtain a stable output of wind power, photovoltaic power and energy storage with stable output and small fluctuations, increasing the system's ability to cope with uncertainties.

[0148] Figure 6 It is the final scheduling result obtained by the integrated energy economic environment scheduling optimization system provided by an embodiment of the present invention. Figure 6 It shows a stacked bar chart of the optimized scheduling power of each unit obtained by improving the mayfly algorithm. In the figure, the legends G1-G10 represent 10 units, and the legend renewable energy represents the sum of wind power, photovoltaic power and energy storage charge and discharge power. It can be seen from the figure that the output of each unit in the integrated energy system is relatively uniform, and the output of wind power, photovoltaic power and energy storage is stable.

[0149] Those skilled in the art should understand that the present invention is described with reference to the flowcharts, model block diagrams and simulation diagrams of the system operation of the method and system provided by the embodiments of the present invention. It should be understood that the content to be expressed in the flowcharts and model block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to devices such as general-purpose computers, special-purpose computers, and embedded processors to generate a scheduling system, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a general-purpose system for implementing the specified functions in one or more processes and / or one or more blocks in the model block diagram. Figure 1 A general-purpose system for implementing the specified functions in one process or multiple processes and / or one or more blocks in the model block diagram.

[0150] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An economic and environmental dispatching optimization method for an integrated energy system, characterized in that It includes the following steps: First, obtain the operation data of the generating units, pollutant emission data, and basic load data of the integrated energy system. Among them, the integrated energy system includes thermal power generating units, renewable energy generating units, and energy storage devices; Then, based on the basic data of the generating units and the load, construct a dynamic economic environmental dispatch model for the integrated energy system including thermal power, wind power, photovoltaic power, and energy storage. The objective function of the model is as follows: min F=wF C +Q(1 - w)F E Among them, F is the objective function that combines the operating cost and pollutant emissions, F C is the operating cost of the integrated energy system, F E is the pollutant emission of the integrated energy system, w is the weight coefficient for changing the proportion of the operating cost and pollutant emissions in the objective function, and Q is the proportionality coefficient; Finally, combine the improved mayfly optimization algorithm (IMA) with the renewable energy output control strategy and the integrated energy dynamic economic environmental dispatch model to construct an integrated energy capacity allocation system, and then obtain the optimal solution for the economic environmental dispatch of each power generation system. The optimal solution is solved by the improved mayfly optimization algorithm, and its specific process is as follows: S1: Obtain the basic data based on thermal power generating units, wind turbine units, photovoltaic devices, energy storage devices, and loads. Define the objective function of the model as the fitness function of the mayfly optimization algorithm, determine the weight coefficient ω and the proportionality coefficient Q, and start running the IMA algorithm; S2: Initialize the positions and velocities of the female and male mayfly populations, and evaluate the fitness values according to the fitness formula, and select the local optimal and global optimal solutions; S3: Based on the relationship between the male mayfly and the mayfly at the global optimal position, update the positions and velocities of the male and female mayflies through the position update formula; The position update formula of the mayfly optimization algorithm includes the position update formula of the female mayfly population and the position update formula of the male mayfly population. The position update formula of the male population is as follows: The position update formula of the female population is as follows: Among them, and are the positions of male mayflies and male mayflies at the current iteration, and are the positions of male mayflies and male mayflies at the next iteration after position update, and are the velocities of individuals before and after iteration. k1, k2, and k3 are the attraction coefficients of the population. rp is the distance between the local optimal value and the male mayfly at the current iteration. rg is the distance between the global optimal value and the male mayfly. r is the distance between the male and female individuals, p best and g best are the local optimal position and the global optimal position of the population, respectively; S4: Evaluate the fitness values of the individuals after position update based on the fitness function, and update the global optimal and local optimal values; S5: Generate a certain number of offspring populations based on the crossover and mutation strategy, and sort the offspring and parents according to the fitness values to form a new generation of female and male mayfly populations; S6: Judge whether the current iteration has reached the number of iterations. If so, output the power allocation scheme of the thermal power generating unit in the integrated energy system; otherwise, return to S3.

2. The economic and environmental dispatch optimization method for the integrated energy system according to claim 1, wherein: The integrated energy system includes thermal power generating units, wind turbine units, photovoltaic devices, and energy storage devices.

3. The economic and environmental dispatch optimization method for the integrated energy system according to claim 1, wherein: The proportionality coefficient Q in the objective function depends on the order-of-magnitude differences between different objectives. When the order of magnitude of each objective varies greatly, introducing the weight coefficient Q into the fitness function can enable the optimization algorithm to obtain a better compromise solution.

4. The economic and environmental dispatch optimization method for the integrated energy system according to claim 1, characterized in that: The best compromise solution is obtained by running the Pareto satisfaction formula after normalizing the various data, and finally the solution with the highest satisfaction is selected as the optimal compromise solution; The membership function used for normalization is as follows: Among them, φ k,i is the satisfaction degree of the i-th solution in the k-th objective in the Pareto solution set, f k max and f k min are the upper and lower limit constraints of the k-th objective; The satisfaction function is as follows: Among them, φ i is the satisfaction degree of the final solution, n is the number of objectives, that is, the total cost and emissions of the integrated energy system; I is the number of solutions in the Pareto solution set.

5. The economic and environmental dispatch optimization method for the integrated energy system according to claim 1, characterized in that: The renewable energy output control strategy enables wind turbines, photovoltaic devices, and energy storage devices to function as a whole, with the goal of improving the power stability of renewable energy connected to the grid. The renewable energy output control strategy divides the output of wind power and photovoltaic power into high-power periods and low-power periods. During periods when the output of wind turbines or photovoltaic devices is large, a portion of the power connected to the grid is correspondingly reduced and stored in the energy storage device. During periods when the output of wind turbines or photovoltaic devices is small, the power connected to the grid is increased as much as possible. At this time, the energy storage device discharges, and finally, a stable and less fluctuating renewable energy grid-connected power is obtained.

6. The economic and environmental dispatch optimization method for the integrated energy system according to claim 1, characterized in that The method further includes: considering the valve point effect phenomenon of thermal power units; constructing a Pareto satisfaction formula for selecting multiple target values; considering corresponding constraint conditions according to the dynamic economic environment dispatch model; The constraint conditions should include the power balance constraint of the dynamic economic environment dispatch model, the power generation constraint and power ramp constraint of thermal power units, the output constraints of wind power and photovoltaic power generation, and the charge and discharge power and energy constraints of energy storage power stations.

7. A system for the integrated energy economic and environmental dispatch optimization method according to claim 1, characterized in that, including: an acquisition module for acquiring basic data of the generator sets and loads in the integrated energy system; a model establishment module for constructing an economic environment dispatch model of the integrated energy system considering various constraint conditions based on the operation data and pollutant emission data of the generator sets and the basic data of the loads during the dispatch period; a calculation module for determining the output power dispatch plan of each power generation device in the dynamic economic environment dispatch model of the integrated energy system; an output module for outputting the power distribution results of each unit.

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