CN117200334A - Multi-energy scheduling method and device considering new energy uncertainty - Google Patents
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Abstract
The invention provides a multi-energy scheduling method and device considering new energy uncertainty. According to the invention, through load prediction, power generation prediction and carbon emission accounting, a low-carbon scheduling model is established, and resource allocation is performed, so that an optimal allocation scheme of each controllable load of the virtual power plant is obtained, namely, the adjustment electric quantity of each controllable load in each period is obtained. The controllable load is introduced to participate in power grid dispatching, so that the peak shaving capacity of the power grid is improved, the access capacity of new energy in the power grid is improved, and the safe, high-quality and high-efficiency operation of the power grid is ensured. Furthermore, the invention determines the optimal allocation scheme of each controllable load through the low-carbon scheduling model with carbon emission allowance as constraint and with the lowest running cost as target, and comprehensively considers the carbon emission and running cost factors. The controllable load operation is controlled by the determined optimal allocation scheme, and the carbon emission reduction target is realized at lower cost while the access capability of new energy sources in the power grid is improved.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a multi-energy scheduling method and device considering new energy uncertainty.
Background
Along with the large-scale rapid development of the distributed power supply, the contradiction between the scheduling space of the traditional power grid regulation resource and the intermittent new energy scale is remarkable, and the power grid faces the outstanding problems that the new energy utilization rate and the scheduling lean level are to be improved. Improving the new energy access capability and reducing the carbon emission in the running process of the power grid are important directions for future work.
Because of the randomness and intermittence of the new energy output, the new energy output is difficult to accurately predict, and the scheduling operation of the power grid is difficult. The uncertainty of the new energy source influences the scheduling capability of the power grid, so that the fluctuation of the electric energy quality in the power grid is large, and the stable operation of the power grid is influenced. In order to ensure the stable operation of the power grid, the problem of wind and light abandoning often occurs in the actual operation of the power grid, so that the consumption efficiency of new energy is reduced.
The limitation of the power grid on new energy consumption is urgent to provide a scheme capable of improving the access capability of new energy in the power grid so as to meet the target requirements of carbon emission reduction.
Disclosure of Invention
The invention provides a multi-energy scheduling method and device considering new energy uncertainty, which can improve the access capability of new energy in a power grid and realize the aim of carbon emission reduction.
In a first aspect, the present invention provides a multi-energy scheduling method that accounts for new energy uncertainty, comprising: obtaining a carbon emission allowance of low-carbon operation of a virtual power plant; performing carbon emission accounting on generator sets of various energy sources in the virtual power plant to obtain unit carbon emission of the generator sets of various energy sources; carrying out new energy power generation prediction and load prediction on the virtual power plant to obtain new energy power generation power and predicted load power of the virtual power plant in each period of the day; based on the new energy power generation, the load power and the unit carbon emission are predicted, and a low-carbon scheduling model which takes the carbon emission allowance as constraint and the lowest running cost as a target is established; based on a low-carbon scheduling model, performing resource allocation to obtain an optimal allocation scheme of each controllable load of the virtual power plant, wherein the optimal allocation scheme comprises the adjustment electric quantity of each controllable load in each period; and controlling the controllable load to operate based on the optimized distribution scheme of each controllable load.
In a second aspect, an embodiment of the present invention provides a multi-energy scheduling apparatus that accounts for new energy uncertainty, the apparatus including: a communication module and a processing module; the communication module is used for acquiring the carbon emission allowance of the low-carbon operation of the virtual power plant; performing carbon emission accounting on generator sets of various energy sources in the virtual power plant to obtain unit carbon emission of the generator sets of various energy sources; the processing module is used for carrying out new energy power generation prediction and load prediction on the virtual power plant to obtain new energy power generation power and predicted load power of the virtual power plant in each period of the day; based on the new energy power generation, the load power and the unit carbon emission are predicted, and a low-carbon scheduling model which takes the carbon emission allowance as constraint and the lowest running cost as a target is established; based on a low-carbon scheduling model, performing resource allocation to obtain an optimal allocation scheme of each controllable load of the virtual power plant, wherein the optimal allocation scheme comprises the adjustment electric quantity of each controllable load in each period; and controlling the controllable load to operate based on the optimized distribution scheme of each controllable load.
In a third aspect, embodiments of the present invention provide a multi-energy scheduling device accounting for new energy uncertainty, the multi-energy scheduling device comprising a memory storing a computer program and a processor for invoking and running the computer program stored in the memory to perform the steps of the method as described in the first aspect and any possible implementation manner of the first aspect.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described in the first aspect and any possible implementation manner of the first aspect.
The invention provides a multi-energy scheduling method and a device for accounting for new energy uncertainty, which are characterized in that a low-carbon scheduling model with carbon emission allowance as a constraint and lowest running cost as a target is established by carrying out load prediction, power generation prediction and carbon emission accounting, and resource allocation is carried out based on the low-carbon scheduling model to obtain an optimal allocation scheme of each controllable load of a virtual power plant, wherein the optimal allocation scheme comprises the adjustment of electric quantity of each controllable load in each period; and controlling the controllable load to run based on the optimized distribution scheme of each controllable load. On one hand, the controllable load is introduced to participate in power grid dispatching, the peak shaving capacity of the power grid is improved, the access capacity of new energy in the power grid is improved, and the safe, high-quality and high-efficiency operation of the power grid is ensured. On the other hand, the invention determines the optimal allocation scheme of each controllable load through the low-carbon scheduling model with carbon emission allowance as constraint and with the lowest running cost as target, and comprehensively considers the carbon emission and running cost factors. The controllable load operation is controlled by the determined optimal allocation scheme, and the carbon emission reduction target is realized at lower cost while the access capability of new energy sources in the power grid is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an application scenario of a multi-energy scheduling method according to an embodiment of the present invention, in which new energy uncertainty is taken into account;
FIG. 2 is a schematic flow chart of a multi-energy scheduling method according to an embodiment of the present invention, in which new energy uncertainty is taken into account;
FIG. 3 is a schematic diagram of a multi-energy scheduling device according to an embodiment of the present invention, wherein the multi-energy scheduling device accounts for uncertainty of new energy;
fig. 4 is a schematic structural diagram of a multi-energy scheduling device according to an embodiment of the present invention, where uncertainty of new energy is taken into account.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In the description of the present application, "/" means "or" unless otherwise indicated, for example, A/B may mean A or B. "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. Further, "at least one", "a plurality" means two or more. The terms "first," "second," and the like do not limit the number and order of execution, and the terms "first," "second," and the like do not necessarily differ.
In embodiments of the application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion that may be readily understood.
Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules but may, alternatively, include other steps or modules not listed or inherent to such process, method, article, or apparatus.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made with reference to the accompanying drawings of the present invention by way of specific embodiments.
Fig. 1 is a schematic diagram of an application scenario of a multi-energy scheduling method according to an embodiment of the present invention, where uncertainty of new energy is taken into account.
Under the background of the energy revolution in China, the energy Internet rapidly develops, and the smart grid and the electric power Internet of things are strongly fused deeply, so that the smart grid and the electric power Internet of things become an important form of a new generation energy system. The virtual power plant is a power coordination management system which is used as a special power plant to participate in the operation of an electric power market and a power grid by realizing the aggregation and coordination optimization of distributed power sources such as a distributed power source, an energy storage system, a controllable load, an electric vehicle and the like through an advanced information communication technology and a software system.
The virtual power grid can focus on important characteristics of synchronous power generation of an energy Internet production side and a consumption side, relies on a new generation intelligent control technology and an interactive business mode of clean development of aggregation optimization of 'source network charge storage', aggregates multiple types, multiple energy flows and multiple main resources with electricity as a center, realizes multi-energy complementation of the power supply side and flexible interaction of the load side, and provides a prospective solution for solving the difficult problem of clean energy consumption and low-carbon energy transformation.
Illustratively, as shown in FIG. 1, a virtual power plant includes a distributed power source, a controllable load, an energy storage system, and a conventional load. The distributed power sources comprise wind power plants, photovoltaic power plants and conventional power plants. The controllable load comprises an electric automobile charging pile, electric heating, commercial complex double storage, intelligent building, central heating and other adjustable loads.
It should be noted that the virtual power plant realizes energy interaction with the large power grid through a connecting wire between the virtual power plant and the large power grid. When the virtual power plant generates more power than electricity, the virtual power plant supplies power to the large power grid. When the power generation of the virtual power plant is smaller than the power consumption, the large power grid supplies power to the virtual power plant.
According to the multi-energy scheduling method considering the uncertainty of the new energy, provided by the embodiment of the invention, the economic scheduling model of the multi-energy virtual power plant and the coordinated optimization algorithm are researched. The method is applied to improving the clean energy access capability and reducing the carbon emission in the running process of the power grid. According to the invention, firstly, aiming at the uncertainty of clean energy, the economic dispatch technology research of the multi-energy virtual power plant is provided, a scene generating method which simultaneously considers wind-solar uncertainty and correlation is constructed, and the output characteristics of the virtual power plant for multi-type energy aggregation are accurately described. On the basis, a multi-energy virtual power plant economic dispatching model is established, and a virtual power plant dispatching strategy and an optimization algorithm are provided with the aim of overall operation economy. Research is carried out on a coordination strategy and an optimization algorithm of the multi-energy virtual power plant, and carbon emission characteristics of internal resources of the virtual power plant are researched by considering the types, the capacities and the distribution conditions of resources at the user side; and taking the whole carbon emission quota of the virtual power plant as a constraint boundary, establishing a low-carbon scheduling operation model, and providing a virtual power plant coordination strategy and an internal optimization algorithm for low-carbon operation of a support system.
As shown in fig. 2, the embodiment of the invention provides a multi-energy scheduling method considering new energy uncertainty. The execution body is a multi-energy scheduling device. The multi-energy scheduling method comprises steps S101-S105.
S101, obtaining carbon emission limits of low-carbon operation of the virtual power plant.
In some embodiments, the virtual power plant includes a distributed power source, a controllable load, an energy storage system, and a conventional load.
Exemplary distributed power sources include wind farms, photovoltaic farms, and conventional farms.
Illustratively, the controllable loads include transferable loads and adjustable loads. Wherein, adjustable load is electric heating, intelligent building, centralized heating and commercial complex double storage. The transferable load may be an electric vehicle charging stake.
Optionally, the embodiment of the invention can also acquire the historical operation data, the installation data and the operation constraint data of each device in the virtual power plant.
In some embodiments, the historical operating data includes historical power generation data for the wind farm, historical power generation data for the photovoltaic farm, historical load power for the controllable load, historical load power for the conventional load, and historical charge-discharge power for the energy storage system.
In some embodiments, the installed data includes installed capacity of each distributed power source, installed capacity of each type of load, and installed capacity of the energy storage system.
In some embodiments, the operational constraint data includes output constraints of each distributed power source, power and regulation constraints of each controllable load, power and capacity constraints of the energy storage system, and power constraints of each type of load.
S102, performing carbon emission accounting on the generator sets of various energy sources in the virtual power plant to obtain the unit carbon emission of the generator sets of various energy sources.
As a possible implementation manner, the multi-energy scheduling device may obtain the unit carbon emission of the generator set of various energy sources based on steps S1021-S1023.
S1021, analyzing historical operation data of the generator sets of various energy sources to obtain historical energy consumption and historical power generation of each generator set.
S1022, carrying out carbon content accounting on the historical energy consumption of each generator set to obtain the historical carbon emission of each generator set.
S1023, determining the unit carbon emission of each generator set based on the historical carbon emission and the historical power generation of each generator set.
Wherein the unit carbon emission amount is a carbon emission amount corresponding to the unit power generation amount.
S103, carrying out new energy power generation prediction and load prediction on the virtual power plant to obtain new energy power generation power and predicted load power of the virtual power plant in each period of the day.
As a possible implementation manner, the multi-energy scheduling device may establish an equivalent model of the new energy power generation device, and determine the new energy power generation power based on the equivalent model of the new energy power generation device and the predicted weather data.
For example, the multi-energy scheduling device may determine a wind power generation equivalent model in the wind farm based on the following formula.
Wherein Q is * WT Represents rated power, Q of wind generating set WT (v) Representing the power generation power of a wind generating set, v * Indicating rated wind speed, v in Represents cut-in wind speed, v out The cut-off wind speed is indicated, and v is the real-time wind speed.
It should be noted that the wind speed curve of the wind generating set approximates to the Weibull distribution, and the probability density formula is shown below.
f w (v)＝(k/γ)(v/γ) k-1 exp[-(v/γ) k ]；
Wherein f w (v) Represents probability density, k is a shape factor, γ is a scale factor, v represents wind speed, exp is an exponential function based on e.
As one possible implementation manner, establishing the power scheduling scheme prediction model further includes: and determining the photovoltaic power generation power at each time point in the target area based on the predicted power generation power at each time point of each photovoltaic unit in the target area.
For example, the multi-energy scheduling device may determine the photovoltaic power generation equivalent model based on the following formula.
Wherein Q is PVg,t Represents the power generation of the g-th photovoltaic generator set at the time t, Q PVfg,t The predicted power generation power epsilon of the g-th photovoltaic generator set at the t moment in the daytime is represented PV,t Indicating the predicted deviation at time t, N G Indicating the number of photovoltaic generator sets.
The predicted deviation ε is calculated by using the method of the present invention PV,t Obeys a normal distribution with a mean value of 0. The standard deviation formula of the normal distribution is shown below.
Wherein sigma PV,t Represents the standard deviation, Q of the normal distribution PVfg,t The predicted power generation power of the g-th photovoltaic generator set at the t moment in the daytime is represented, Q PVNg The rated capacity of the g-th photovoltaic generator set is shown.
In order to consider the influence of uncertainty in wind power generation and photovoltaic power generation, it is necessary to describe a method for opportunistic constraint planning for wind power generation and photovoltaic power generation.
The opportunity constraint is designed to process uncertainty conditions and convert the uncertainty conditions into deterministic constraints. Opportunistic constraint planning is a method of stochastic planning that solves the problem of constraint containing random variables and having to make decisions before the implementation of the random variables is observed.
In some embodiments, the process of converting random variables in constraints into deterministic constraint forms using opportunistic constraint planning is shown below.
Pr{h(x)≥ξ}≥α；
Where h (x) represents a linear function of the decision vector x, ζ represents a random variable, α represents a probability density, and Pr { h (x) > ζ } represents a probability of h (x) > ζ.
For a given confidence probability η (0.ltoreq.η.ltoreq.1), a constant Ka is present such that ka=inf { k|k=Φ -1 (η) }; in this way, the opportunistic constraint programming can be converted into deterministic constraint, namely, h (x) is more than or equal to Ka is satisfied. Where ka is a constant, inf is a lower-bound function, k=Φ -1 (eta) is a probability density function.
Therefore, the probability that the sum of the power generated by wind power and photovoltaic power is located in a safety interval is larger than the confidence probability is satisfied, and the uncertainty of the power generated by wind power and photovoltaic power is converted into the probability constraint of certainty, so that reasonable prediction of the power generated by new energy is possible, and the rationality of the power generation prediction of the new energy is improved.
As another possible implementation manner, the multi-energy scheduling device may construct a new energy power generation prediction model based on historical operation data of a new energy power generation set in the virtual power plant and historical weather data of an area where the virtual power plant is located; and predicting the new energy power generation power of the virtual power plant in each period of the day based on the new energy power generation prediction model and the predicted weather data of each period of the day in the region where the virtual power plant is located.
By way of example, the multi-energy scheduling apparatus may predict the new energy generation power for each period of the day through steps 11 to 16.
And 11, performing time division on historical operation data of a new energy generator set in the virtual power plant and historical weather data of an area where the virtual power plant is located to obtain power generation amount and weather data of each period in a historical period.
In some embodiments, the weather data includes illumination intensity, temperature, and wind speed.
And 12, determining a plurality of training samples by taking the generated energy of each period in the historical period as output and the weather data of each period in the historical period as input.
And 13, performing scene clustering on the training samples to obtain clustered samples of the scenes.
And 14, training a neural network based on the clustering samples of the multiple scenes to obtain a new energy power generation prediction model.
And 15, obtaining predicted weather data of each period in the day.
And step 16, inputting the predicted weather data of each time period in the day into a power generation prediction model, and predicting to obtain the predicted power generation of each time period in the day.
As a possible implementation manner, the multi-energy scheduling device may construct a load prediction model based on historical operation data of each load in the virtual power plant and historical weather data of an area where the virtual power plant is located; and predicting and obtaining the predicted load power of each load of the virtual power plant in each period of the day based on the load prediction model and the predicted weather data of each period of the day in the region where the virtual power plant is located.
Illustratively, the multi-energy scheduling apparatus may predict the predicted load power for each period of time during the day based on steps 21-27.
And step 21, performing data fitting by taking historical weather data as independent variables and historical operation data of each load as dependent variables to obtain a fitting regression module.
Wherein the historical weather data includes historical temperature data, historical humidity data, and historical precipitation data.
And 22, inputting the historical weather data into a fitting regression module to obtain fitting load data.
Step 23, taking the historical weather data and the fitting load data as input and the historical operation data of each load as output to generate a training sample.
And step 24, training the neural network based on the training sample to obtain a neural network module.
And step 25, determining a load prediction model based on the fitting regression module and the neural network module.
Step 26, obtaining predicted weather data of each period in the day.
And step 27, inputting the predicted weather data of each time period in the day into a load prediction model, and predicting to obtain the predicted load data of each time period in the day.
Wherein the predicted load data may be a predicted load power.
S104, based on the new energy power generation, the predicted load power and the unit carbon emission, a low-carbon scheduling model with carbon emission allowance as constraint and the lowest running cost as target is established.
As a possible implementation manner, the multi-energy scheduling device may build a two-stage optimization model based on steps S1041-S1043.
S1041, constructing an objective function by taking the lowest running cost of the virtual power plant as a target.
For example, the multi-energy scheduling apparatus may construct an objective function based on the following formula.
Wherein,represents the unit cost of electricity generation of the jth electricity generation device in the ith period, < >>Represents the generated power of the jth power generation device of the ith period,/th power generation device of the ith period>Representing the unit cost of the kth energy storage device of the ith period,/>Representing the charge-discharge power of the kth energy storage device of the ith period, +.>Represents the ith period z
The unit cost of adjustment of the individual controllable loads,representing the regulated power of the ith period of time, z-th energy storage device,>unit cost representing scheduled power between virtual power plant and large grid in the ith period, +.>And the power consumption of the power plant is calculated according to the power consumption of the power plant, the power generation equipment and the energy storage equipment, and the power consumption of the power plant is calculated according to the power consumption of the power plant.
The power generation equipment includes wind power generation equipment, photovoltaic power generation equipment and conventional power generation equipment. The charge and discharge power of the energy storage device includes a charge power or a discharge power.
S1042, based on the new energy power generation, the predicted load power and the unit carbon emission, determining the constraint condition.
In some embodiments, the constraint conditions include a power balance constraint, a node voltage constraint, an upper and lower output limit and a climbing rate constraint of a conventional generator set, an upper and lower adjustment limit and an adjustment rate constraint of a controllable load, a charge and discharge power constraint of an energy storage system, a maximum output constraint of wind power generation, a maximum output constraint of photovoltaic power generation and a scheduling electric quantity constraint.
The power balance constraint is, for example, that the difference in the amount of generated load of the virtual power plant is equal to the scheduled power between the virtual power plant and the large power grid.
It should be noted that, for any period, the sum of the total power generation amount of wind power generation, photovoltaic power generation and conventional power generation and the discharge power of the energy storage system is the power generation amount of the period; the sum of the load power of the controllable load and the conventional load, and the charging power of the energy storage system is determined as the load amount of the period.
The load power of the controllable load is the load power of the controllable load after being regulated. The electricity consumption valley period is the load power of the controllable load after the electric quantity is increased. The electricity consumption peak period is the load power after the controllable load reduces the electric quantity.
It is understood that the distributed energy storage system is disposed at a plurality of nodes in the virtual power plant. The control device of each energy storage system can control the charge and discharge states and the charge and discharge power of the energy storage system based on the real-time electric quantity of the energy storage system. As such, during the same period, different energy storage systems may be in different charge and discharge states. For example, the energy storage system 1 is in a charged state. The energy storage system 2 is in a discharged state.
Illustratively, the node voltage constraints are such that the node voltages of the nodes within the virtual power plant meet a voltage criterion. For example, the node voltage of each node should be varied in a range of 50.+ -. 0.2Hz.
Illustratively, the upper and lower limits of the output force of a conventional generator set should meet design criteria. If the output power of the conventional generator set is larger than the minimum limit value and smaller than the maximum limit value. The ramp rate of a conventional genset should be less than a set point.
Illustratively, the upper and lower limits of the regulation of the controllable load are constrained such that the regulated power of the controllable load should be greater than a minimum limit and less than a maximum limit. The minimum limit value and the maximum limit value of the regulated power of the controllable load are comprehensively determined according to the operation condition and the installed capacity of the controllable load.
Illustratively, the charge and discharge power constraints of the energy storage system are such that the charge power and the discharge power of the energy storage system should satisfy greater than a minimum limit and less than a maximum limit.
For example, the multi-energy scheduling device may determine an equivalence model of the energy storage system and determine a charge-discharge power constraint of the energy storage system based on the following formula.
The charging power of the energy storage system BESS at the t hour is represented by Pc BESS (t), the maximum charging power of the energy storage system BESS is represented by PBCmax, the discharging power of the energy storage system BESS at the t hour is represented by Pd BESS (t), the maximum discharging power of the energy storage system BESS at the t hour is represented by PBDmax, the SOC of the energy storage system BESS at the t hour is represented by Soc (t), the SOC of the energy storage system BESS at the t+1hour is represented by Soc (t+1), the maximum value of the SOC of the energy storage system during operation is Socmax, the minimum value of the SOC of the energy storage system during operation is Socmin, and Snom represents the rated capacity of the energy storage system during operation. In different scenarios, the BESS charge and discharge power is also related to the specific charge and discharge strategy in the scenario.
Illustratively, the maximum output force constraint of the wind power generation is that the output power of the wind power generation should be less than a maximum limit value.
Illustratively, the maximum output force constraint of the photovoltaic power generation is that the output power of the photovoltaic power generation should be less than a maximum limit.
The scheduling power constraint is that the scheduling power between the virtual power plant and the large power grid is smaller than a set value.
S1043, establishing a low-carbon scheduling model based on the objective function and the constraint condition.
And S105, performing resource allocation based on the low-carbon scheduling model to obtain an optimal allocation scheme of each controllable load of the virtual power plant.
The optimal allocation scheme comprises the adjustment of the electric quantity of each controllable load in each period.
In some embodiments, the controllable load includes an adjustable load and a transferable load.
In some embodiments, the optimal allocation scheme includes a first optimal allocation scheme and a second optimal allocation scheme, the first optimal allocation scheme being used to determine an increased amount of power for each controllable load during each period of the electricity usage valley period; the second optimal allocation scheme is used for determining the reduction electric quantity of each controllable load in each period of the electricity consumption peak period.
For example, the multi-energy scheduling device may calculate an optimal allocation scheme of each controllable load by using a particle swarm algorithm.
Step 31, initializing algorithm parameters; the population is randomly initialized and the population is automatically initialized, the population is the adjusted electric quantity of each controllable load.
Step 32, initializing the iteration number k=1.
And step 33, calculating an objective function of the low-carbon scheduling model, and if the running cost corresponding to the current population is the minimum value of the running cost before the current iteration times, determining the current population as an optimal solution.
And step 34, updating the population based on the constraint condition of the low-carbon scheduling model.
Step 35, adding 1 to the iteration number, and judging whether the current iteration number is greater than the first iteration number; if yes, outputting an optimal solution, and exiting the iterative process; if not, repeating the steps 33-35; the optimal solution is an optimal allocation scheme of each controllable load of the virtual power plant.
It should be noted that, when the particle swarm optimization algorithm is applied, in the iterative process of each step, the population can be updated according to the following formula, so as to control the speed and the position of the particles (i.e. the adjusted electric quantity of each controllable load).
Wherein v is k Indicating the speed of the particles, v, at an iteration number k k+1 Indicating the speed of the control particles when the iteration number is k+1; x is x k Representing the spatial position of the control particles at an iteration number k, x k+1 Represents the spatial position of the control particle, pbest, at an iteration number of k+1 k Represents the self-optimal solution, gbest, of the control particle at the kth iteration k Representing a globally optimal solution for the control particles at the kth iteration c 1 Representing the first learning factor, c 2 Representing a second learning factor, r 1 And r 2 Are random numbers uniformly distributed between (0, 1), wherein omega represents an inertia factor and omega max Representing the most significant of the inertial factors Large limit, ω min Represents the minimum limit of the inertia factor, k represents the number of iterations, k max Representing the maximum number of iterations.
When the iteration number k is greater than the maximum iteration number k max The iteration process is exited to obtain the adjustment electric quantity (gbest) of each controllable load k The value is the optimal solution of the two-stage optimization model).
S106, controlling the controllable load to operate based on the optimized distribution scheme of each controllable load.
As a possible implementation, the multi-energy scheduling device may control the controllable load operation based on steps S1061-S1062.
S1061, controlling the start of the transferable load and controlling the adjustable load to increase the power consumption based on the increased electric quantity of each controllable load in each period of the electricity consumption valley period in the first optimized distribution scheme.
S1062, controlling the closing of the transferable load and controlling the reducing of the power consumption of the adjustable load based on the reduction electric quantity of each controllable load in each period of the power consumption peak period in the second optimal distribution scheme.
The multi-energy scheduling device can monitor the scheduling electric quantity between the virtual power plant and the large power grid in real time, and adjust the adjusting electric quantity of each controllable load in real time, so that the safe and stable operation of the power grid is ensured.
Step S106 may be embodied as steps A1-A5, for example.
A1, monitoring the dispatching electric quantity between the virtual power plant and the large power grid.
And A2, if the dispatching electric quantity is larger than the set value, acquiring the real-time power generation power of the new energy source and the real-time load power of each load in the virtual power plant.
A3, updating an objective function and constraint conditions of the low-carbon scheduling model based on the real-time power generation of the new energy and the real-time load power of each load.
And A4, re-distributing resources based on the updated low-carbon scheduling model to obtain a real-time distribution scheme of each controllable load of the virtual power plant.
A5, controlling the controllable load to operate based on a real-time distribution scheme of each controllable load.
The multi-energy scheduling device can monitor the actual adjustment electric quantity of each controllable load in real time, and adjust the adjustment electric quantity of each controllable load in real time, so as to ensure the safe and stable operation of the power grid.
Step S106 may be embodied as steps B1-B4, for example.
B1, monitoring the actual adjustment electric quantity of each controllable load.
And B2, if the error between the actual adjustment quantity of each controllable load and the adjustment electric quantity in the optimal allocation scheme is larger than the set error, determining the constraint condition of the low-carbon scheduling model again based on the actual running condition of each controllable load, and updating the low-carbon scheduling model.
And B3, re-distributing resources based on the updated low-carbon scheduling model to obtain a real-time distribution scheme of each controllable load of the virtual power plant.
And B4, controlling the controllable load to operate based on a real-time distribution scheme of each controllable load.
The invention provides a multi-energy scheduling method and a device for accounting for uncertainty of new energy, which are characterized in that a low-carbon scheduling model with carbon emission allowance as constraint and lowest running cost as target is established by carrying out load prediction, power generation prediction and carbon emission accounting, resource allocation is carried out based on the low-carbon scheduling model, so as to obtain an optimal allocation scheme of each controllable load of a virtual power plant, wherein the optimal allocation scheme comprises the adjustment of electric quantity of each controllable load in each period; and controlling the controllable load to operate based on the optimized distribution scheme of each controllable load. On one hand, the controllable load is introduced to participate in power grid dispatching, the peak shaving capacity of the power grid is improved, the access capacity of new energy in the power grid is improved, and the safe, high-quality and high-efficiency operation of the power grid is ensured. On the other hand, the invention determines the optimal allocation scheme of each controllable load through the low-carbon scheduling model with carbon emission allowance as constraint and with the lowest running cost as target, and comprehensively considers the carbon emission and running cost factors. The controllable load operation is controlled by the determined optimal allocation scheme, and the carbon emission reduction target is realized at lower cost while the access capability of new energy sources in the power grid is improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 shows a schematic structural diagram of a multi-energy scheduling device according to an embodiment of the present invention, where uncertainty of new energy is taken into account. The multi-energy scheduling device 200 comprises a communication module 201 and a processing module 202.
And the communication module 201 is used for acquiring the carbon emission allowance of the low-carbon operation of the virtual power plant.
The processing module 202 is used for performing carbon emission accounting on the generator sets of various energy sources in the virtual power plant to obtain the unit carbon emission of the generator sets of various energy sources; carrying out new energy power generation prediction and load prediction on the virtual power plant to obtain new energy power generation power and predicted load power of the virtual power plant in each period of the day; based on new energy power generation, load power prediction and unit carbon emission, establishing a low-carbon scheduling model with carbon emission allowance as constraint conditions and lowest running cost; based on a low-carbon scheduling model, performing resource allocation to obtain an optimal allocation scheme of each controllable load of the virtual power plant, wherein the optimal allocation scheme comprises the adjustment electric quantity of each controllable load in each period; and controlling the controllable load to operate based on the optimized distribution scheme of each controllable load.
In one possible implementation manner, the processing module 202 specifically analyzes the historical operation data of the generator sets of various energy sources to obtain the historical energy consumption and the historical power generation of each generator set; performing carbon content accounting on the historical energy consumption of each generator set to obtain the historical carbon emission of each generator set; and determining the unit carbon emission of each generator set based on the historical carbon emission and the historical power generation of each generator set, wherein the unit carbon emission is the carbon emission corresponding to the unit power generation.
In one possible implementation manner, the processing module 202 is specifically configured to construct a new energy power generation prediction model based on historical operation data of a new energy power generating set in the virtual power plant and historical weather data of an area where the virtual power plant is located; based on the new energy power generation prediction model and the predicted weather data of each time period in the day of the region where the virtual power plant is located, predicting to obtain new energy power generation of the virtual power plant in each time period in the day; constructing a load prediction model based on historical operation data of each load in the virtual power plant and historical weather data of an area where the virtual power plant is located; and predicting and obtaining the predicted load power of each load of the virtual power plant in each period of the day based on the load prediction model and the predicted weather data of each period of the day in the region where the virtual power plant is located.
In one possible implementation manner, the processing module 202 is specifically configured to time-divide historical operation data of a new energy generator set in the virtual power plant and historical weather data of an area where the virtual power plant is located, so as to obtain power generation amount and weather data of each period in a historical period; determining a plurality of training samples by taking the generated energy of each period in the historical period as output and the weather data of each period in the historical period as input; performing scene clustering on the training samples to obtain clustered samples of a plurality of scenes; and training the neural network based on the clustering samples of the multiple scenes to obtain a new energy power generation prediction model.
In one possible implementation, the processing module 202 is specifically configured to construct an objective function with the lowest running cost of the virtual power plant as a goal; based on the new energy power generation, predicting load power and unit carbon emission, determining constraint conditions; and establishing a low-carbon scheduling model based on the objective function and the constraint condition.
In one possible implementation, the processing module 202 is specifically configured to perform the following steps: step 31, initializing algorithm parameters; randomly initializing a population, wherein the population is the adjusted electric quantity of each controllable load; step 32, initializing iteration times k=1; step 33, calculating an objective function of the low-carbon scheduling model, and if the running cost corresponding to the current population is the minimum value of the running cost before the current iteration times, determining the current population as an optimal solution; step 34, updating the population based on constraint conditions of the low-carbon scheduling model; step 35, adding 1 to the iteration number, and judging whether the current iteration number is greater than the first iteration number; if yes, outputting an optimal solution, and exiting the iterative process; if not, repeating the steps 33-35; the optimal solution is an optimal allocation scheme of each controllable load of the virtual power plant.
In one possible implementation, the processing module 202 is specifically configured to monitor a scheduled power amount between the virtual power plant and the large power grid; if the dispatching electric quantity is larger than the set value, acquiring real-time power generation power of new energy sources in the virtual power plant and real-time load power of each load; updating an objective function and constraint conditions of the low-carbon scheduling model based on the real-time power generation of the new energy and the real-time load power of each load; based on the updated low-carbon scheduling model, re-distributing resources to obtain a real-time distribution scheme of each controllable load of the virtual power plant; and controlling the controllable load to operate based on a real-time distribution scheme of each controllable load.
In one possible implementation, the processing module 202 is specifically configured to monitor an actual adjustment power of each controllable load; if the error between the actual adjustment quantity of each controllable load and the adjustment electric quantity in the optimal allocation scheme is larger than the set error, determining constraint conditions of the low-carbon scheduling model again based on the actual running condition of each controllable load, and updating the low-carbon scheduling model; based on the updated low-carbon scheduling model, re-distributing resources to obtain a real-time distribution scheme of each controllable load of the virtual power plant; and controlling the controllable load to operate based on a real-time distribution scheme of each controllable load.
Fig. 4 is a schematic structural diagram of a multi-energy scheduling device according to an embodiment of the present invention, where uncertainty of new energy is taken into account. As shown in fig. 4, the multi-energy scheduling apparatus 300 of this embodiment includes: a processor 301, a memory 302 and a computer program 303 stored in said memory 302 and executable on said processor 301. The steps of the method embodiments described above, such as steps 101 to 106 shown in fig. 2, are implemented when the processor 301 executes the computer program 303. Alternatively, the processor 301 may implement the functions of the modules/units in the above-described embodiments of the apparatus when executing the computer program 303, for example, the functions of the communication module 201 and the processing module 202 shown in fig. 3.
Illustratively, the computer program 303 may be partitioned into one or more modules/units that are stored in the memory 302 and executed by the processor 301 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 303 in the multi-energy scheduling device 300. For example, the computer program 303 may be divided into the communication module 201 and the processing module 202 shown in fig. 3.
The processor 301 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 302 may be an internal storage unit of the multi-energy scheduling device 300, such as a hard disk or a memory of the multi-energy scheduling device 300. The memory 302 may also be an external storage device of the multi-energy scheduling device 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the multi-energy scheduling device 300. Further, the memory 302 may also include both internal storage units and external storage devices of the multi-energy scheduling device 300. The memory 302 is used for storing the computer program as well as other programs and data required by the terminal. The memory 302 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. A multi-energy scheduling method considering new energy uncertainty is characterized by comprising the following steps:
obtaining a carbon emission allowance of low-carbon operation of a virtual power plant; performing carbon emission accounting on generator sets of various energy sources in the virtual power plant to obtain unit carbon emission of the generator sets of various energy sources;
performing new energy power generation prediction and load prediction on the virtual power plant to obtain new energy power generation power and predicted load power of the virtual power plant in each period of the day;
based on the new energy power generation, the predicted load power and the unit carbon emission, establishing a low-carbon scheduling model with carbon emission allowance as constraint and with lowest running cost as a target;
Performing resource allocation based on the low-carbon scheduling model to obtain an optimal allocation scheme of each controllable load of the virtual power plant, wherein the optimal allocation scheme comprises the adjustment electric quantity of each controllable load in each period;
and controlling the controllable load to run based on the optimized distribution scheme of each controllable load.
2. The multi-energy scheduling method according to claim 1, wherein the calculating the carbon emission of the generator sets of various energy sources in the virtual power plant to obtain the unit carbon emission of the generator sets of various energy sources comprises:
analyzing the historical operation data of the generator sets of various energy sources to obtain the historical energy consumption and the historical power generation of each generator set;
performing carbon content accounting on the historical energy consumption of each generator set to obtain the historical carbon emission of each generator set;
and determining the unit carbon emission of each generator set based on the historical carbon emission and the historical power generation of each generator set, wherein the unit carbon emission is the carbon emission corresponding to the unit power generation.
3. The multi-energy scheduling method according to claim 1, wherein the performing new energy power generation prediction and load prediction on the virtual power plant to obtain new energy power generation and predicted load power of the virtual power plant in each period of the day comprises:
Constructing a new energy power generation prediction model based on historical operation data of a new energy power generating set in the virtual power plant and historical weather data of an area where the virtual power plant is located;
based on the new energy power generation prediction model and the predicted weather data of each time period in the day of the region where the virtual power plant is located, predicting to obtain new energy power generation of the virtual power plant in each time period in the day;
constructing a load prediction model based on historical operation data of each load in the virtual power plant and historical weather data of an area where the virtual power plant is located;
and predicting and obtaining predicted load power of each load of the virtual power plant in each period of the day based on the load prediction model and the predicted weather data of each period of the day in the region where the virtual power plant is located.
4. The multi-energy scheduling method according to claim 1, wherein the constructing a new energy power generation prediction model based on the historical operation data of the new energy generator set in the virtual power plant and the historical weather data of the area where the virtual power plant is located comprises:
time division is carried out on the historical operation data of the new energy generator set in the virtual power plant and the historical weather data of the area where the virtual power plant is located, so that the generated energy and the weather data of each period in the historical period are obtained;
Taking the generated energy of each period in the historical period as output, and taking the weather data of each period in the historical period as input, determining a plurality of training samples;
performing scene clustering on the training samples to obtain clustered samples of a plurality of scenes;
and training the neural network based on the clustering samples of the multiple scenes to obtain a new energy power generation prediction model.
5. The multi-energy scheduling method according to claim 1, wherein the establishing a low-carbon scheduling model based on the new energy generated power, the predicted load power, and the unit carbon emission with carbon emission allowance as a constraint condition for lowest running cost comprises:
constructing an objective function by taking the lowest running cost of the virtual power plant as a target;
determining a constraint condition based on the new energy generation power, the predicted load power, and the unit carbon emission;
and establishing the low-carbon scheduling model based on the objective function and the constraint condition.
6. The multi-energy scheduling method considering new energy uncertainty as claimed in claim 1, wherein the resource allocation is performed based on the low-carbon scheduling model to obtain an optimal allocation scheme of each controllable load of the virtual power plant, and the method comprises the following steps:
Step 31, initializing algorithm parameters; randomly initializing a population, wherein the population is the adjusted electric quantity of each controllable load;
step 32, initializing iteration times k=1;
step 33, calculating an objective function of the low-carbon scheduling model, and if the running cost corresponding to the current population is the minimum value of the running cost before the current iteration times, determining the current population as an optimal solution;
step 34, updating the population based on the constraint condition of the low-carbon scheduling model;
step 35, adding 1 to the iteration number, and judging whether the current iteration number is greater than the first iteration number; if yes, outputting an optimal solution, and exiting the iterative process; if not, repeating the steps 33-35; and the optimal solution is an optimal allocation scheme of each controllable load of the virtual power plant.
7. The multi-energy scheduling method according to claim 1, wherein the controlling the controllable loads based on the optimized allocation scheme of each controllable load comprises:
monitoring a scheduled power quantity between the virtual power plant and a large power grid;
if the dispatching electric quantity is larger than a set value, acquiring real-time power generation power of new energy sources in the virtual power plant and real-time load power of each load;
Updating an objective function and constraint conditions of the low-carbon scheduling model based on the real-time power generation of the new energy and the real-time load power of each load;
re-distributing resources based on the updated low-carbon scheduling model to obtain a real-time distribution scheme of each controllable load of the virtual power plant;
and controlling the controllable load to run based on a real-time distribution scheme of each controllable load.
8. The multi-energy scheduling method according to claim 1, wherein the controlling the controllable loads based on the optimized allocation scheme of each controllable load comprises:
monitoring the actual adjustment electric quantity of each controllable load;
if the error between the actual adjustment quantity of each controllable load and the adjustment electric quantity in the optimized distribution scheme is larger than the set error, determining constraint conditions of the low-carbon scheduling model again based on the actual running condition of each controllable load, and updating the low-carbon scheduling model;
re-distributing resources based on the updated low-carbon scheduling model to obtain a real-time distribution scheme of each controllable load of the virtual power plant;
and controlling the controllable load to run based on the real-time distribution scheme of each controllable load.
9. A multi-energy scheduling apparatus that accounts for new energy uncertainty, comprising:
the communication module is used for acquiring the carbon emission allowance of the low-carbon operation of the virtual power plant;
the processing module is used for carrying out carbon emission accounting on the generator sets of various energy sources in the virtual power plant to obtain the unit carbon emission of the generator sets of various energy sources; performing new energy power generation prediction and load prediction on the virtual power plant to obtain new energy power generation power and predicted load power of the virtual power plant in each period of the day; based on the new energy power generation, the predicted load power and the unit carbon emission, establishing a low-carbon scheduling model with carbon emission allowance as constraint conditions and lowest running cost; performing resource allocation based on the low-carbon scheduling model to obtain an optimal allocation scheme of each controllable load of the virtual power plant, wherein the optimal allocation scheme comprises the adjustment electric quantity of each controllable load in each period; and controlling the controllable load to run based on the optimized distribution scheme of each controllable load.
10. A multi-energy scheduling device accounting for new energy uncertainty, characterized in that it comprises a memory storing a computer program and a processor for invoking and running the computer program stored in the memory to perform the method according to any of claims 1 to 8.
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