Multi-time-scale scheduling method and device for virtual power plant, equipment and medium

Abstract

The invention relates to the technical field of energy scheduling, in particular to a multi-time-scale scheduling method, device, equipment and medium for a virtual power plant, and the method comprises the steps: solving a day-ahead optimal scheduling model of a target virtual power plant, obtaining a day-ahead scheduling scheme of the virtual power plant, carrying out the parameter recognition of the day-ahead scheduling scheme, and obtaining a comprehensive time period optimal parameter set; and based on the rolling correction optimization model, performing iterative fine tuning on the multiple period parameters by using the comprehensive period optimal parameter set to obtain a t + m-1 period optimal parameter set, and correcting actual parameters of a target t + m period by using the t + m-1 period optimal parameter set to obtain corrected parameters of the target t + m period, and making a virtual power plant multi-time scale scheduling scheme by using the corrected parameters in the target t + m time period. Therefore, the problems that in an existing virtual power plant, the number of energy sources is large, operation modes are variable, load side model parameters are difficult to accurately obtain, and then an efficient virtual power plant optimization scheduling scheme is difficult to construct are solved.

Description

Technical Field

[0001] The present invention relates to the field of energy scheduling technology, and in particular to a multi-time scale scheduling method, device, equipment and medium for a virtual power plant based on load-side state feedback. Background Art

[0002] Concerns about energy and environmental issues have become a key bottleneck restricting global economic and social development. In order to cope with the dual crises of energy and environment, many countries have promulgated various renewable energy policies and energy development plans. Vigorously increase the proportion of renewable energy generation to reduce carbon emissions. However, the uncertainty and intermittency of renewable energy have brought huge challenges to the power system. Sufficient flexibility is needed to cope with the fluctuations of renewable energy and maintain the balance of load and energy supply. Under this situation, it is urgent to further optimize resource planning and upgrade the development of energy systems. Virtual power plants are an effective way to promote the optimization and development of energy systems by unified management of distributed energy, energy storage, flexible electricity and heat loads, etc. However, there are many difficulties in the implementation process. The main difficulties are as follows:

[0003] (1) Virtual power plants have many types of equipment, complex models, and variable operating modes. It is difficult to accurately obtain the model parameters on the load side, making it difficult to build an efficient virtual power plant optimization scheduling solution.

[0004] (2) There are uncertain factors in virtual power plants, such as wind and solar power output and load fluctuations. How to ensure the robust operation of virtual power plant scheduling plans in a multi-source uncertain environment is another difficulty;

[0005] (3) Existing research has not considered using virtual power plants as a means to simultaneously optimize multiple energy sources in the context of the energy revolution, thereby breaking down the barriers between independent operations of energy sources and promoting energy complementarity and consumption. It only considers the overall operational safety of the system, volatility handling, or multi-energy green trading, ignoring the operating characteristics and setting value of units in the multi-energy system during the balance interaction process. Summary of the invention

[0006] The present invention provides a multi-time scale scheduling method, device, equipment and medium for a virtual power plant to solve the problems in existing virtual power plants, such as large amount of energy, changeable operation modes, difficulty in accurately obtaining load side model parameters, and thus difficulty in building an efficient virtual power plant optimization scheduling solution.

[0007] The first aspect of the present invention provides a multi-time scale scheduling method for a virtual power plant, comprising the following steps: solving a pre-constructed day-ahead optimization scheduling model of a target virtual power plant to obtain a day-ahead scheduling plan for the virtual power plant; performing parameter identification on the day-ahead scheduling plan for the virtual power plant to obtain an optimal parameter set for a comprehensive period; based on a pre-constructed rolling correction optimization model, using the optimal parameter set for the comprehensive period to iteratively fine-tune parameters for multiple periods to obtain an optimal parameter set for the t+m-1 period, wherein 0≤t≤23, m≥1; correcting the actual parameters of the target t+m period of the target virtual power plant according to the optimal parameter set for the t+m-1 period to obtain the corrected parameters of the target t+m period, and using the corrected parameters of the target t+m period to formulate a multi-time scale scheduling plan for the virtual power plant.

[0008] Optionally, solving a pre-built day-ahead optimization scheduling model of a target virtual power plant to obtain a day-ahead scheduling plan for the virtual power plant includes:

[0009] Decomposing the day-ahead optimization scheduling model into a mixed integer linear programming model and a non-convex maximum-minimum model;

[0010] The mixed integer linear programming model and the non-convex maximum-minimum model are solved by using a column constraint generation algorithm to obtain the day-ahead dispatching plan for the virtual power plant.

[0011] Optionally, the iterative fine-tuning of multiple time period parameters using the comprehensive time period optimal parameter set based on the pre-built rolling correction optimization model to obtain the t+m-1 time period optimal parameter set includes:

[0012] Performing interval sampling on the optimal parameter set for the comprehensive period to obtain an initial population;

[0013] Based on the rolling correction optimization model, the initial population is used to iteratively fine-tune the parameters of the target virtual power plant during the t-1 period until the number of iterations reaches a preset value, and the optimal parameter set for the t-1 period is output;

[0014] The interval sampling and fine-tuning process is iteratively performed on the optimal parameter set for the t-1 period until the parameter fine-tuning for the t+m-1 period is completed, and the optimal parameter set for the t+m-1 period is output.

[0015] Optionally, based on the rolling correction optimization model, the initial population is used to iteratively fine-tune the parameters of the target virtual power plant during the t-1 period until the number of iterations reaches a preset value, and the optimal parameter set for the t-1 period is output, including:

[0016] Constructing an optimal scheduling plan for the future 24 hours of the virtual power plant in the t-1 period of the target virtual power plant according to the initial population, and determining the planned output in the t-1 period according to the optimal scheduling plan for the future 24 hours of the virtual power plant in the t-1 period;

[0017] Obtaining the actual output of the target virtual power plant during period t-1, and constructing a balancing cost function according to the planned output during period t-1 and the actual output during period t-1;

[0018] Solving the equilibrium cost function based on a quantum genetic algorithm to obtain a plurality of fitness values, and sorting the plurality of fitness values ​​to obtain sorted fitness values;

[0019] Performing quantum rotation and evolution on the fitness values ​​after the sorting to obtain the next generation population;

[0020] The above process is iteratively performed on the next generation population based on the rolling correction optimization model until the number of iterations reaches a preset value, the parameter fine-tuning of the t-1 period is completed, and the optimal parameter set of the t-1 period is output.

[0021] A second aspect of the present invention provides a multi-time scale scheduling device for a virtual power plant, comprising:

[0022] A solution module is used to solve the pre-built day-ahead optimization scheduling model of the target virtual power plant to obtain the day-ahead scheduling plan of the virtual power plant;

[0023] A parameter identification module, used to identify parameters of the day-ahead dispatching plan of the virtual power plant to obtain an optimal parameter set for a comprehensive period;

[0024] A rolling correction module, for iteratively fine-tuning multiple time period parameters using the comprehensive time period optimal parameter set based on a pre-built rolling correction optimization model, so as to obtain the optimal parameter set for the t+m-1 time period, where 0≤t≤23, m≥1;

[0025] A correction module is used to correct the actual parameters of the target t+m period of the target virtual power plant according to the optimal parameter set of the t+m-1 period, obtain the corrected parameters of the target t+m period, and use the corrected parameters of the target t+m period to formulate a multi-time scale scheduling plan for the virtual power plant.

[0026] Optionally, the solution module includes:

[0027] A decomposition unit, used for decomposing the day-ahead optimization scheduling model into a mixed integer linear programming model and a non-convex maximum-minimum model;

[0028] A solving unit is used to solve the mixed integer linear programming model and the non-convex maximum and minimum model by using a column constraint generation algorithm to obtain the day-ahead scheduling plan of the virtual power plant.

[0029] Optionally, the rolling correction module includes:

[0030] A sampling unit, used for performing interval sampling on the optimal parameter set of the comprehensive time period to obtain an initial population;

[0031] A fine-tuning unit, configured to iteratively fine-tune the parameters of the target virtual power plant in the t-1 period by using the initial population based on the rolling correction optimization model until the number of iterations reaches a preset value, and output an optimal parameter set for the t-1 period;

[0032] The multi-period iteration unit iteratively performs interval sampling and fine-tuning processes on the optimal parameter set of the t-1 period until the parameter fine-tuning of the t+m-1 period is completed, and outputs the optimal parameter set of the t+m-1 period.

[0033] Optionally, the fine-tuning unit includes:

[0034] The first construction subunit is used to construct the optimal scheduling scheme of the virtual power plant in the future 24 hours of the target virtual power plant in the t-1 period according to the initial group, and determine the planned output in the t-1 period according to the optimal scheduling scheme of the virtual power plant in the future 24 hours of the t-1 period;

[0035] The second construction subunit is used to obtain the actual output of the target virtual power plant in the t-1 period, and to construct a balancing cost function according to the planned output in the t-1 period and the actual output in the t-1 period;

[0036] A sorting subunit, used for solving the equilibrium cost function based on a quantum genetic algorithm to obtain a plurality of fitness values, and sorting the plurality of fitness values ​​to obtain sorted fitness values;

[0037] An evolution subunit, used for performing quantum rotation and evolution on the fitness values ​​after the sorting to obtain the next generation population;

[0038] The single-period iteration unit is used to iteratively execute the above process on the next generation population based on the rolling correction optimization model until the number of iterations reaches a preset value, completes the t-1 period parameter fine-tuning, and outputs the t-1 period optimal parameter set.

[0039] An embodiment of the third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the multi-time scale scheduling method of the virtual power plant as described in the above embodiment.

[0040] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the multi-time-scale scheduling method for a virtual power plant as described above.

[0041] The multi-time-scale scheduling method, device, equipment and medium of the virtual power plant proposed in the embodiment of the present invention fully consider the characteristics of the virtual power plant such as the large number of equipment types, complex models, and changeable operation modes, as well as the prediction uncertainty problem of the power supply side and load side of the virtual power plant, and construct a two-stage robust optimization model of the virtual power plant with a min-max-min structure. The two-stage robust optimization model of the virtual power plant is used to obtain state parameters, thereby solving the problem that the load side model parameters are difficult to accurately obtain, and the load side model parameters are updated using the state parameters, thereby realizing the simultaneous optimization of multiple energy sources, breaking the barriers between independent operation of energy sources, and promoting energy complementarity and absorption. The operating characteristics and setting values ​​of the units in the multi-energy system during the balanced interaction process are fully considered.

[0042] 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

[0043] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 A schematic diagram of a flow chart of a multi-time scale scheduling method for a virtual power plant provided by an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a process for solving a day-ahead optimization scheduling model for a target virtual power plant provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a process of iteratively fine-tuning multiple time period parameters using a comprehensive time period optimal parameter set provided in an embodiment of the present invention;

[0047] Figure 4 A block diagram of a multi-time scale scheduling device for a virtual power plant provided by an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] Figure 1 A schematic flow chart of a multi-time scale scheduling method for a virtual power plant provided in an embodiment of the present invention.

[0051] like Figure 1 As shown, the multi-time scale scheduling method of the virtual power plant includes the following steps:

[0052] In step S101, a pre-constructed day-ahead optimization scheduling model of a target virtual power plant is solved to obtain a day-ahead scheduling plan for the virtual power plant.

[0053] In some embodiments, solving a pre-built day-ahead optimization scheduling model of a target virtual power plant to obtain a day-ahead scheduling plan for the virtual power plant includes:

[0054] Decompose the day-ahead optimization scheduling model into a mixed integer linear programming model and a non-convex maximum-minimum model;

[0055] The column constraint generation algorithm is used to solve the mixed integer linear programming model and the non-convex maximum and minimum model to obtain the day-ahead dispatching scheme of the virtual power plant.

[0056] It should be noted that the main bodies of virtual power plant management include gas turbines, cogeneration units, fans, photovoltaics, batteries, and electrical and thermal loads. Among them, fans and photovoltaics are the main power output units, batteries and distribution networks play the role of balancing the supply and demand of system electricity. Gas turbines use natural gas as fuel to mainly produce electricity, but the waste heat generated by the machine can also be recycled.

[0057] In the actual implementation process, the pre-built day-ahead optimization scheduling model of the target virtual power plant includes the objective function, inequality constraints and equality constraints, among which:

[0058] The objective function of the day-ahead optimization scheduling model is divided into six parts, where F(t) represents the total operating cost of the virtual power plant in the tth period:

[0059]

[0060] in, The total cost of power supply and heat supply by the power-heat cogeneration unit in the virtual power plant in the tth period is:

[0061]

[0062] In the formula, α, β, γ, θ, δ, is the operating cost coefficient of the cogeneration unit, is the power output of the cogeneration unit at time t, in MW, is the thermal output of the cogeneration unit at time t, in MW.

[0063] The total cost of supplying power to the distributed thermal power unit in the tth period is:

[0064]

[0065] In the formula, a, b, c are the operating cost coefficients of distributed thermal power units. P is the penalty or compensation fee for distributed thermal power units under the peak load ancillary service market mechanism. D t G It is the power output of the distributed thermal power unit at time t, in MW.

[0066] is the charging and discharging cost of energy storage in the tth period, Compensation for energy storage participating in the auxiliary peak load market:

[0067]

[0068] In the formula, C ESS is the energy storage charging and discharging cost coefficient, are the charging power and discharging power of the energy storage at time t respectively.

[0069] The compensation cost of the adjustable load in the tth period is:

[0070]

[0071] In the formula, is the adjustable load compensation cost coefficient, is the power variation of the adjustable load.

[0072] The revenue of the virtual power plant from purchasing and selling electricity in the tth period is:

[0073]

[0074] In the formula, is the electricity price for external purchase at time t, is the power sold to external parties at time t.

[0075] The revenue from the virtual power plant supplying power to the load in the tth period is:

[0076]

[0077] In the formula, are the profit coefficients of the virtual power plant supplying electricity and heat load at time t, They are respectively the power supplied by the virtual power plant for electricity and heat load at time t.

[0078] The inequality constraints of the day-ahead optimization scheduling model include:

[0079] The output constraints of wind power, photovoltaic power, gas turbine and thermal power units are expressed as follows:

[0080]

[0081] In the formula, is the power output of each unit at time t, in MW, They are the upper and lower limits of the power output of each unit, in MW.

[0082] The thermal output constraint of the cogeneration unit is expressed as:

[0083]

[0084] In the formula, is the thermal output of the electric heating unit at time t, in MW, and They are the upper and lower limits of thermal output of thermal power units, in MW.

[0085] The thermal-electric coupling constraint of the combined heat and power unit is expressed as:

[0086]

[0087] In the formula, C m is the heat-to-electricity ratio of the cogeneration unit under back pressure conditions, K is a constant, is the power output of the cogeneration unit at time t, in MW, P emax is the upper limit of the electric power of the cogeneration unit, c v is the absolute value of the curve slope of the cogeneration unit under pure condensing working conditions, is the thermal output of the cogeneration unit at time t, in MW.

[0088] Thermal power constraint, its expression is:

[0089]

[0090] In the formula, is the thermal output of the combined heat and power generation unit during period t, in MW, is the thermal output of the electric heating equipment during period t, in MW, is the heat load during period t, in MW.

[0091] Energy storage charging and discharging constraints are expressed as:

[0092]

[0093] In the formula, and are the discharge or charging power of the energy storage at time t, in MW, is the energy state of energy storage at time t, in MWh; SOC max They are respectively the charging and discharging power and energy state upper limit of the energy storage, in MW.

[0094] Adjustable load constraint, its expression is:

[0095]

[0096] In the formula, is the upper limit of the control capacity of the controlled load, is the power variation of the adjustable load.

[0097]

[0098] Where S is the time period during which dispatch control measures can be taken.

[0099] The indoor temperature constraint of the heating area is expressed as:

[0100] T in min≤T in (t)≤T in max

[0101] Where, T in (t) is the indoor temperature of the heating area at time t, T in min and T in max is the upper and lower limits of indoor temperature for heating in northern winter.

[0102] The equality constraints of the day-ahead optimization scheduling model include:

[0103] The electric power balance constraint is expressed as:

[0104]

[0105] In the formula, on the left side of the equation, They are the thermal power unit, gas turbine, photovoltaic, wind power output and energy storage discharge power at time t, in MW. On the right side of the equation, They are the energy storage discharge power, electric load, electric heating load, the power of the virtual power plant participating in the grid purchase and sales of electricity in the source-grid-load-storage integrated system at time t, and the change in adjustable load power, in MW.

[0106] The electric heat conversion power constraint is expressed as:

[0107]

[0108] In the formula, is the electric power consumed by the electric heating equipment at time t, in MW, is the heat output of the electric heating equipment at time t, in MW, eb is the electrothermal conversion efficiency.

[0109] Energy state constraint of energy storage equipment, its expression is:

[0110]

[0111] In the formula, λ soc is the energy storage efficiency, η ESS For energy storage charging efficiency, and are the discharge or charging power of the energy storage at time t, in MW, It is the energy state of energy storage at time t-1, in MWh.

[0112] The thermal model constraint of the flexible load heating area is expressed as:

[0113] QUR DLC (t)+k4(T out (t-1)-T in (t-1)) = k3T in (t)-k2T in (t-1)

[0114] In the formula, Q DLC (t) is the heat dissipation of the flexible heat load at time t, T in (t) is the indoor temperature of the heating area at time t, T out (t) is the outdoor temperature of the heating area at time t, and k1, k2, k3, and k4 are the corresponding constant coefficients.

[0115] Furthermore, since wind turbines, photovoltaic output and basic load have strong uncertainties, the influence of uncertain factors needs to be added to the model. The constructed uncertainty set is:

[0116]

[0117] In the formula, μ wt (t), μpv (t), μ p (t), μ q (t) are the uncertain variables of wind turbine, photovoltaic output power, and electrical and thermal base loads, respectively. are the predicted value column vectors of wind turbine, photovoltaic output power, and electric and thermal base loads at time t, They are the maximum allowable deviations of wind turbine, photovoltaic output power, and electric and thermal basic loads, respectively. They are positive numbers, and N is the number of time periods.

[0118] If only the deterministic model is considered, the matrix representation of the deterministic optimization scheduling model of the virtual power plant is:

[0119]

[0120] Where c is the coefficient column vector of the objective function, D, K, F, G, I μ is the coefficient matrix corresponding to the constraint conditions, d, ξ, k are constant column vectors, is a constant column vector of the predicted values ​​for each period, and x and y are optimization variables.

[0121] The idea of ​​robust optimization is to optimize the scheduling plan of the objective function based on the constructed uncertainty set and considering the uncertain variable μ changing towards the worst case in the uncertainty set U. Therefore, the robust optimization model (i.e., the day-ahead optimization scheduling model) constructed in the embodiment of the present invention is:

[0122]

[0123] The min outside the curly brackets is the outer one-stage optimization problem, and the optimization variable is x. The max min inside the curly brackets is the inner two-stage optimization problem, and the optimization variables are μ and x. Ω is the range of y under the selected x and μ conditions, which can be expressed as:

[0124]

[0125] In the formula, y| represents Dy≥d, Ky=ξ, Fx+Gy≥k, I μ y=μ, which is the value range of y under the four limiting conditions; γ, λ, ν, and π are the corresponding dual variables in the inner two-stage optimization problem.

[0126] Furthermore, if Figure 2 As shown, the embodiment of the present invention decomposes the day-ahead optimization scheduling model into two problems for solution, wherein the main problem is a mixed integer linear programming problem, and the sub-problem is a non-convex maximum and minimum problem. The two problems are iterated with each other and the upper and lower bounds are continuously updated to make the problem converge faster. When the difference between the upper and lower bounds reaches the allowable range, the algorithm terminates and finally finds the optimal solution to the problem.

[0127] The main problem structure is:

[0128]

[0129] In the formula, α is an auxiliary variable, n is the current number of iterations, and y l is the solution after the lth iteration, is the value of the uncertain variable μ under the worst conditions after the lth iteration.

[0130] The sub-problem structure is:

[0131] max μ∈U min y∈Ω(x,μ) c T y

[0132] Since the formula of the sub-problem is a non-convex maximum problem, it is difficult to solve directly. Therefore, the embodiment of the present invention also uses the duality theorem to convert the maximum-minimum problem into a single-level bilinear programming problem, which can be expressed as:

[0133]

[0134] According to the above derivation, the column constraint generation (C&CG) algorithm is used to solve the main problem and the single-level bilinear programming problem and sub-problems of the mixed integer linear optimization model to obtain the day-ahead dispatching plan of the virtual power plant.

[0135] In step S102, parameters of the day-ahead dispatch plan of the virtual power plant are identified to obtain an optimal parameter set for a comprehensive period.

[0136] In step S103, based on the pre-built rolling correction optimization model, multiple time period parameters are iteratively fine-tuned using the comprehensive time period optimal parameter set to obtain the t+m-1 time period optimal parameter set, where 0≤t≤23 and m≥1.

[0137] In some embodiments, based on a pre-built rolling correction optimization model, multiple time period parameters are iteratively fine-tuned using the comprehensive time period optimal parameter set to obtain the t+m-1 time period optimal parameter set, including:

[0138] Perform interval sampling on the optimal parameter set for the comprehensive period to obtain the initial population;

[0139] Based on the rolling correction optimization model, the initial population is used to iteratively fine-tune the parameters of the target virtual power plant in the t-1 period until the number of iterations reaches the preset value, and the optimal parameter set for the t-1 period is output;

[0140] The interval sampling and fine-tuning process is iteratively performed on the optimal parameter set for the t-1 period until the parameter fine-tuning for the t+m-1 period is completed, and the optimal parameter set for the t+m-1 period is output.

[0141] It should be noted that the pre-built rolling correction optimization model is:

[0142]

[0143] Among them, y t+1 is the aggregation parameter for time period t+1, x t+1 is the scheduling plan vector of all devices in the next scheduling period, A t+1 ,b t+1 is the t+1th row of the corresponding matrix. t is the actual operation status of the equipment in the scheduling period t.

[0144] According to the current operating status, energy storage SOC and other information, the matrix G in the constraint condition is updated to G'(x t ), the vector P is updated to P', and the virtual power plant obtains the adjustable capacity range in the next dispatch.

[0145] The intraday real-time correction optimization problem of the virtual power plant becomes a deterministic optimization problem. Therefore, real-time online correction can reduce or even eliminate the conservatism caused by uncertainty. The rolling correction results can provide a basis for parameter selection in the day-ahead aggregation optimization problem. For example, if the lower limit of the system power is frequently modified, the weight coefficient of the lower limit of the modified scheduling time period can be appropriately increased. The heuristic parameter correction method can be expressed as:

[0146]

[0147] Among them, π pm,t is the weight coefficient corresponding to the lower limit of the scheduling period t, k is a constant column vector, is the modified weight coefficient of the scheduling period t, P DA and p RO They are the lower bound parameters in the day-ahead optimization scheduling model and the intraday rolling correction model respectively.

[0148] In the actual implementation process, first, the optimal parameter set for the comprehensive period is obtained as the initial parameter set for the t-1 period, and the initial parameter set for the t-1 period is sampled at intervals to obtain the initial population; based on the rolling correction optimization model, the optimal scheduling plan for the virtual power plant in the t-1 period of the target virtual power plant in the future 24 hours is constructed according to the initial population, and the planned output of the t-1 period is determined according to the optimal scheduling plan for the virtual power plant in the future 24 hours of the t-1 period; the actual output of the target virtual power plant in the t-1 period is obtained, and a balance cost function is constructed according to the planned output of the t-1 period and the actual output of the t-1 period; the balance cost function is solved based on the quantum genetic algorithm to obtain multiple fitness values, and the multiple fitness values ​​are sorted to obtain the sorted fitness values; the sorted fitness values ​​are quantum rotated and evolved to obtain the first generation population; and then the first generation population is obtained using the first generation population. The first generation population constructs a new t-1 period future 24h virtual power plant optimization scheduling plan for the target virtual power plant, and determines the new t-1 period planned output based on the new t-1 period future 24h virtual power plant optimization scheduling plan; constructs a new balance cost function based on the new t-1 period planned output and the initially obtained t-1 period actual output; solves the new balance cost function based on the quantum genetic algorithm to obtain multiple new fitness values, and sorts the multiple new fitness values ​​to obtain new sorted fitness values; performs quantum rotation and evolution on the new sorted fitness values ​​to obtain the second generation population; then performs the same operation process as the first generation population on the second generation population, iterates this process to continuously update the population until the number of iterations reaches the preset value G, completes the t-1 period parameter fine-tuning, and outputs the optimal parameter set for the t-1 period.

[0149] Furthermore, if Figure 3As shown in the figure, the optimal parameter set of period t-1 is used as the initial parameter set of period t, and the initial parameter set of period t is sampled at intervals to obtain the initial population of period t-1; based on the rolling correction optimization model, the optimal scheduling plan of the virtual power plant in the future 24 hours of period t of the target virtual power plant is constructed according to the initial population of period t-1, and the planned output of period t is determined according to the optimal scheduling plan of the virtual power plant in the future 24 hours of period t; the actual output of the target virtual power plant in period t is obtained, and a balance cost function is constructed according to the planned output of period t and the actual output of period t; the balance cost function is solved based on the quantum genetic algorithm to obtain multiple fitness values, and the multiple fitness values ​​are sorted to obtain the sorted fitness values; the sorted fitness values ​​are quantum rotated and evolved to obtain the first generation population; and then the first generation population is obtained using the first generation population. The first generation population constructs a new t-period future 24-hour virtual power plant optimization scheduling plan for the target virtual power plant, and determines the new t-period planned output based on the new t-period future 24-hour virtual power plant optimization scheduling plan; constructs a new balance cost function based on the new t-period planned output and the initial t-period actual output; solves the new balance cost function based on the quantum genetic algorithm to obtain multiple new fitness values, and sorts the multiple new fitness values ​​to obtain new sorted fitness values; performs quantum rotation and evolution on the new sorted fitness values ​​to obtain the second generation population; then performs the same operation process as the first generation population on the second generation population, iterates this process to continuously update the population until the number of iterations reaches the preset value, completes the t-period parameter fine-tuning, and outputs the optimal parameter set for the t+1 period.

[0150] The above-mentioned interval sampling and fine-tuning process is iterated according to actual needs (the period t+m that needs to be corrected), until the parameter fine-tuning of the period before the period that needs to be corrected is completed, and the optimal parameter set of the period before the period that needs to be corrected (i.e., the optimal parameter set of the period t+m-1) is output. For example, if m=2 in the period that needs to be corrected, it is necessary to fine-tune the t-1 section and the t section, and finally obtain the optimal parameter set of the t+1 period.

[0151] In step S104, the actual parameters of the target virtual power plant in the target t+m period are corrected according to the optimal parameter set of the t+m-1 period to obtain the corrected parameters of the target t+m period, and the multi-time scale scheduling plan of the virtual power plant is formulated using the corrected parameters of the target t+m period.

[0152] In the actual implementation process, the optimal parameter set of the t+m-1 period is used as the initial parameter set of the t+m period, and the initial parameter set of the t+m period is used to correct the actual parameters of the target t+m period of the target virtual power plant. Finally, a multi-time scale scheduling plan for the virtual power plant is formulated based on the corrected parameters of the target t+m period.

[0153] The multi-time scale scheduling method for a virtual power plant proposed in an embodiment of the present invention has the following beneficial effects:

[0154] (1) Taking into full consideration the characteristics of virtual power plants, such as the large number of equipment types, complex models, and variable operation modes, an operation constraint and operation cost model is constructed for each flexibility resource model;

[0155] (2) The prediction uncertainty of the virtual power plant on the power supply side and the load side is fully considered, and a two-stage robust optimization model of the virtual power plant with a min-max-min structure is constructed;

[0156] (3) Real-time updating of load-side model parameters based on state feedback can be used to solve problems such as the difficulty in accurately obtaining load-side model parameters;

[0157] (4) Considering the uncertainty of renewable energy and load forecasting in the virtual power plant and the inaccuracy of load-side model parameters, a scheduling method combining day-ahead scheduling and intraday correction of the virtual power plant is formed to obtain a multi-time scale scheduling scheme for the virtual power plant;

[0158] (5) Using virtual power plants as a means, multiple energy sources are optimized simultaneously, thereby breaking the barriers between independent operations of energy sources, promoting energy complementarity and consumption, and fully considering the operating characteristics and setting value of units in the multi-energy system during the balanced interaction process.

[0159] Next, the multi-time scale scheduling device for a virtual power plant proposed according to an embodiment of the present invention is described with reference to the accompanying drawings.

[0160] Figure 4 It is a block diagram of a multi-time scale scheduling device for a virtual power plant according to an embodiment of the present invention.

[0161] like Figure 4 As shown, the multi-time scale scheduling device 40 of the virtual power plant includes: a solution module 401, a parameter identification module 402, a rolling correction module 403 and a correction module 404.

[0162] Among them, the solution module 401 is used to solve the pre-constructed day-ahead optimization scheduling model of the target virtual power plant to obtain the day-ahead scheduling plan of the virtual power plant. The parameter identification module 402 is used to identify the parameters of the day-ahead scheduling plan of the virtual power plant to obtain the optimal parameter set for the comprehensive time period. The rolling correction module 403 is used to iteratively fine-tune the parameters of multiple time periods based on the pre-constructed rolling correction optimization model using the optimal parameter set for the comprehensive time period to obtain the optimal parameter set for the t+m-1 time period, where 0≤t≤24, m≥1. The correction module 404 is used to correct the actual parameters of the target t+m time period of the target virtual power plant according to the optimal parameter set for the t+m-1 time period, obtain the corrected parameters of the target t+m time period, and use the corrected parameters of the target t+m time period to formulate a multi-time scale scheduling plan for the virtual power plant.

[0163] In some embodiments, the solution module 401 includes:

[0164] A decomposition unit, used for decomposing the day-ahead optimization scheduling model into a mixed integer linear programming model and a non-convex maximum and minimum model;

[0165] The solving unit is used to solve the mixed integer linear programming model and the non-convex maximum and minimum model by using the column constraint generation algorithm to obtain the day-ahead dispatching plan of the virtual power plant.

[0166] In some embodiments, the rolling correction module 403 includes:

[0167] A sampling unit is used to perform interval sampling on the optimal parameter set of the comprehensive period to obtain an initial population;

[0168] A fine-tuning unit is used to iteratively fine-tune the parameters of the target virtual power plant in the t-1 period based on the rolling correction optimization model using the initial population until the number of iterations reaches a preset value, and output the optimal parameter set in the t-1 period;

[0169] The multi-period iteration unit iteratively performs interval sampling and fine-tuning processes on the optimal parameter set of the t-1 period until the parameter fine-tuning of the t+m-1 period is completed, and outputs the optimal parameter set of the t+m-1 period.

[0170] In some embodiments, the fine-tuning unit comprises:

[0171] The first construction subunit is used to construct the optimal scheduling plan of the virtual power plant in the future 24 hours of the target virtual power plant in the t-1 period according to the initial group, and determine the planned output in the t-1 period according to the optimal scheduling plan of the virtual power plant in the future 24 hours of the t-1 period;

[0172] The second construction subunit is used to obtain the actual output of the target virtual power plant in the t-1 period, and to construct a balancing cost function according to the planned output in the t-1 period and the actual output in the t-1 period;

[0173] A sorting subunit is used to solve the equilibrium cost function based on the quantum genetic algorithm to obtain multiple fitness values, and sort the multiple fitness values ​​to obtain sorted fitness values;

[0174] The evolution subunit is used to perform quantum rotation and evolution on the sorted fitness values ​​to obtain the next generation population;

[0175] The single-period iteration unit is used to iteratively execute the above process on the next generation population based on the rolling correction optimization model until the number of iterations reaches a preset value, completes the fine-tuning of the parameters in the t-1 period, and outputs the optimal parameter set in the t-1 period.

[0176] It should be noted that the aforementioned explanation of the embodiment of the multi-time scale scheduling method for a virtual power plant is also applicable to the multi-time scale scheduling device for the virtual power plant of this embodiment, and will not be repeated here.

[0177] The multi-time scale scheduling device for a virtual power plant proposed in an embodiment of the present invention has the following beneficial effects:

[0178] (1) Taking into full consideration the characteristics of virtual power plants, such as a large number of equipment types, complex models, and variable operation modes, an operation constraint and operation cost model is constructed for each flexibility resource model;

[0179] (2) The prediction uncertainty of the virtual power plant on the power supply side and the load side is fully considered, and a two-stage robust optimization model of the virtual power plant with a min-max-min structure is constructed;

[0180] (3) Real-time updating of load-side model parameters based on state feedback can be used to solve problems such as the difficulty in accurately obtaining load-side model parameters;

[0181] (4) Considering the uncertainty of renewable energy and load forecasting in the virtual power plant and the inaccuracy of load-side model parameters, a scheduling method combining day-ahead scheduling and intraday correction of the virtual power plant is formed to obtain a multi-time scale scheduling scheme for the virtual power plant;

[0182] (5) Using virtual power plants as a means, multiple energy sources are optimized simultaneously, thereby breaking the barriers between independent operations of energy sources, promoting energy complementarity and consumption, and fully considering the operating characteristics and setting value of units in the multi-energy system during the balanced interaction process.

[0183] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0184] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0185] When the processor 502 executes the program, the multi-time scale scheduling method of the virtual power plant provided in the above embodiment is implemented.

[0186] Furthermore, the electronic device further comprises:

[0187] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0188] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0189] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0190] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0191] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0192] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0193] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-time-scale scheduling method for a virtual power plant as described above.

[0194] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. 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 representations of the above terms do 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 N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0195] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0196] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0197] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0198] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0199] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0200] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0201] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A multi-time scale scheduling method for a virtual power plant, characterized in that: The following steps are involved: Solve the pre-built day-ahead optimization dispatch model of the target virtual power plant and obtain the day-ahead dispatch plan of the virtual power plant; Identify parameters of the day-ahead dispatching plan of the virtual power plant to obtain an optimal parameter set for a comprehensive period; Based on the pre-built rolling correction optimization model, the optimal parameter set of the comprehensive period is used to iteratively fine-tune the parameters of multiple periods to obtain the optimal parameter set of the t+m-1 period, where 0≤t≤23 and m≥1; According to the optimal parameter set for the t+m-1 period, the actual parameters of the target virtual power plant in the target t+m period are corrected to obtain the corrected parameters of the target t+m period, and the multi-time scale scheduling plan of the virtual power plant is formulated using the corrected parameters of the target t+m period.

2. The multi-time scale scheduling method of a virtual power plant according to claim 1, characterized in that: The method of solving the pre-built day-ahead optimization scheduling model of the target virtual power plant to obtain the day-ahead scheduling plan of the virtual power plant includes: Decomposing the day-ahead optimization scheduling model into a mixed integer linear programming model and a non-convex maximum-minimum model; The mixed integer linear programming model and the non-convex maximum-minimum model are solved by using a column constraint generation algorithm to obtain the day-ahead dispatching plan for the virtual power plant.

3. The multi-time scale scheduling method of a virtual power plant according to claim 1, characterized in that: The pre-built rolling correction optimization model uses the comprehensive period optimal parameter set to iteratively fine-tune multiple period parameters to obtain the t+m-1 period optimal parameter set, including: Performing interval sampling on the optimal parameter set for the comprehensive period to obtain an initial population; Based on the rolling correction optimization model, the initial population is used to iteratively fine-tune the parameters of the target virtual power plant during the t-1 period until the number of iterations reaches a preset value, and the optimal parameter set for the t-1 period is output; The interval sampling and fine-tuning process is iteratively performed on the optimal parameter set for the t-1 period until the parameter fine-tuning for the t+m-1 period is completed, and the optimal parameter set for the t+m-1 period is output.

4. The multi-time scale scheduling method of a virtual power plant according to claim 3, characterized in that: Based on the rolling correction optimization model, the t-1 period parameters of the target virtual power plant are iteratively fine-tuned using the initial population until the number of iterations reaches a preset value, and the optimal parameter set for the t-1 period is output, including: Constructing an optimal scheduling plan for the future 24 hours of the virtual power plant in the t-1 period of the target virtual power plant according to the initial population, and determining the planned output in the t-1 period according to the optimal scheduling plan for the future 24 hours of the virtual power plant in the t-1 period; Obtaining the actual output of the target virtual power plant during period t-1, and constructing a balancing cost function according to the planned output during period t-1 and the actual output during period t-1; Solving the equilibrium cost function based on a quantum genetic algorithm to obtain a plurality of fitness values, and sorting the plurality of fitness values ​​to obtain sorted fitness values; Performing quantum rotation and evolution on the fitness values ​​after the sorting to obtain the next generation population; The above process is iteratively performed on the next generation population based on the rolling correction optimization model until the number of iterations reaches a preset value, the parameter fine-tuning of the t-1 period is completed, and the optimal parameter set of the t-1 period is output.

5. A multi-time scale scheduling device for a virtual power plant, characterized in that: include: A solution module is used to solve the pre-built day-ahead optimization scheduling model of the target virtual power plant to obtain the day-ahead scheduling plan of the virtual power plant; A parameter identification module, used to identify parameters of the day-ahead dispatching plan of the virtual power plant to obtain an optimal parameter set for a comprehensive period; A rolling correction module, for iteratively fine-tuning multiple time period parameters using the comprehensive time period optimal parameter set based on a pre-built rolling correction optimization model, so as to obtain the optimal parameter set for the t+m-1 time period, where 0≤t≤23, m≥1; A correction module is used to correct the actual parameters of the target t+m period of the target virtual power plant according to the optimal parameter set of the t+m-1 period, obtain the corrected parameters of the target t+m period, and use the corrected parameters of the target t+m period to formulate a multi-time scale scheduling plan for the virtual power plant.

6. The multi-time scale scheduling device for a virtual power plant according to claim 5, characterized in that: The solution module comprises: A decomposition unit, used for decomposing the day-ahead optimization scheduling model into a mixed integer linear programming model and a non-convex maximum-minimum model; A solving unit is used to solve the mixed integer linear programming model and the non-convex maximum and minimum model by using a column constraint generation algorithm to obtain the day-ahead scheduling plan of the virtual power plant.

7. The multi-time scale scheduling device for a virtual power plant according to claim 5, characterized in that: The rolling correction module comprises: A sampling unit, used for performing interval sampling on the optimal parameter set of the comprehensive time period to obtain an initial population; A fine-tuning unit, configured to iteratively fine-tune the parameters of the target virtual power plant in the t-1 period by using the initial population based on the rolling correction optimization model until the number of iterations reaches a preset value, and output an optimal parameter set for the t-1 period; The multi-period iteration unit iteratively performs interval sampling and fine-tuning processes on the optimal parameter set of the t-1 period until the parameter fine-tuning of the t+m-1 period is completed, and outputs the optimal parameter set of the t+m-1 period.

8. The multi-time scale scheduling device for a virtual power plant according to claim 7, characterized in that: The fine-tuning unit comprises: The first construction subunit is used to construct the optimal scheduling scheme of the virtual power plant in the future 24 hours of the target virtual power plant in the t-1 period according to the initial group, and determine the planned output in the t-1 period according to the optimal scheduling scheme of the virtual power plant in the future 24 hours of the t-1 period; The second construction subunit is used to obtain the actual output of the target virtual power plant in the t-1 period, and to construct a balancing cost function according to the planned output in the t-1 period and the actual output in the t-1 period; A sorting subunit, used for solving the equilibrium cost function based on a quantum genetic algorithm to obtain a plurality of fitness values, and sorting the plurality of fitness values ​​to obtain sorted fitness values; An evolution subunit, used for performing quantum rotation and evolution on the fitness values ​​after the sorting to obtain the next generation population; The single-period iteration unit is used to iteratively execute the above process on the next generation population based on the rolling correction optimization model until the number of iterations reaches a preset value, completes the t-1 period parameter fine-tuning, and outputs the t-1 period optimal parameter set.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-time scale scheduling method for a virtual power plant as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the multi-time scale scheduling method of a virtual power plant as described in any one of claims 1-4.

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