CN116415740B - Two-stage robust optimization method for virtual power plant based on saddle uncertainty - Google Patents
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Abstract
The disclosure provides a two-stage robust optimization method of a virtual power plant based on saddle uncertainty, which acts on a source network load storage linkage environment, and comprises the following steps: obtaining a conventional model of a virtual power plant, correcting an endogenous uncertainty reference distribution by using a local discrete super , introducing a relative entropy and a box-type polyhedral uncertainty set to optimize the theory model, and generating an endogenous-exogenous uncertainty model; and solving a two-stage robust optimization model by using a column and constraint generation algorithm and combining a strong dual theorem and a linearization theory. And the economical efficiency and the low carbon coordinated operation of the cold-hot electricity virtual power plant are realized. Compared with a common robust optimization model, the two-stage robust optimization model is more flexible, and the optimization performance can be improved while the robustness is maintained.
Description
Technical Field
The disclosure relates to the technical field of virtual power plant optimization, in particular to a two-stage robust optimization method of a virtual power plant based on saddle uncertainty.
Background
The virtual power plant (Virtual Power Plant, VPP) integrates a plurality of distributed energy systems (such as solar energy, wind energy, energy storage and the like) and energy resources such as traditional generator sets, load-dissipating and the like through means such as the internet and smart grid technology and the like to form a virtual power production, scheduling and transaction system. The virtual power plant can be regarded as an integrated management system of energy, can furthest improve the utilization efficiency of energy, optimize the operation and the dispatch of system, reduce the power production cost, and provide more convenient, flexible and diversified energy service for end users. The method has the advantages of reducing wind and light abandoning, ensuring the stability of the power grid, improving the flexibility of the system, reducing the influence of distributed energy on the power grid, and being an important mode for promoting carbon emission reduction and ensuring the stability of the power grid.
However, most of the existing researches are aimed at virtual power plant model construction, and the uncertainty of the distributed power supply output, the internet power price and the energy consumption load is reduced, so that the effectiveness of the virtual power plant optimization scheduling is influenced by neglecting the uncertainty. In uncertainty analysis, classification and modeling of uncertainty in the absence of a priori information directly relates to the accuracy of the virtual power plant optimization method. In the virtual power plant optimization problem, the robust optimization method replaces the exact probability distribution of random variables with an uncertain set, and the scheduling scheme of the system under the worst scene is obtained through an optimization means, so that the system meets the requirements of actual engineering more. For example, in the prior art, the publication number CN115422728A, entitled "virtual power plant optimization control system based on robust optimization of random planning" is applied to the chinese invention, which discloses a virtual power plant optimization control system based on robust optimization of random planning, including a virtual power plant optimization control module for safe operation of an economic and power grid, a virtual power plant random planning optimization control module for random renewable energy, and a virtual power plant adaptive robust optimization control module for random renewable energy, where the virtual power plant optimization control module for safe operation of the economic and power grid includes a virtual power plant economic dispatch model and a virtual power plant safety dispatch model. The virtual power plant optimization control system based on the robust optimization of the random programming disclosed by the invention considers the coordinated operation control mode among distributed power sources, energy storage and demand side users in an area under the condition of random renewable energy sources, and has the characteristics of presenting stable power output to a large power grid under the support of intelligent coordinated regulation and decision, so that a new path is opened up for safe and efficient utilization of new energy power.
Because the utilization of wind and light energy in the power grid system is mature, related application scenes are also researched, and the system can realize expected effects and optimization effects in the virtual power plant which fully utilizes wind and light to generate electricity. However, in some environments with more complex using modes of new energy, for example, after new energy such as source side biogas cogeneration distributed energy is adopted, new parameters are introduced, and damage is caused to an existing wind-light stabilizing system, so that a model designed in the prior art is difficult to match with an actual environment, and reasonable optimization cannot be made.
Disclosure of Invention
In order to solve at least one of the above technical problems, further accurately optimize a virtual power plant in a complex environment, the present disclosure provides a two-stage robust optimization method of the virtual power plant based on saddle uncertainty, acting on a source network load storage linkage environment, the method includes:
s1: acquiring a conventional model of the virtual power plant, establishing a theory model on the basis of the conventional model, and finding exogenous uncertainty and endogenous uncertainty based on the theory model;
s2: correcting the endogenous uncertainty reference distribution by using local discrete super , introducing relative entropy and a box-type polyhedral uncertainty set to optimize the theory model, and generating an endogenous-exogenous uncertainty model of the virtual power plant;
S3: describing a source side biogas cogeneration distributed energy source by using an in-out uncertainty model of a virtual power plant, wherein load can be transferred on a load side, and the energy conversion relation of electric-heat-cold multifunctional coupling equipment between load equipment and electric heat energy storage on a storage side can be reduced, so that a flexible load demand response adjustment link is represented;
s4: taking the economical efficiency and the carbon emission reduction target of the virtual power plant as convergence, and establishing a two-stage robust optimization model of the virtual power plant based on theory uncertainty risk;
s5: and solving a two-stage robust optimization model of the virtual power plant by using a column and constraint generation algorithm and combining a strong dual theorem and a linearization theory.
And the economical efficiency and the low carbon coordinated operation of the cold-hot electricity virtual power plant are realized.
Preferably, the theory discrete super and discrete sub specifications are introduced in the step S1 when creating the saddle model. in the random process, super (super martingal) is defined as: is provided withIs->，/>For two random sequences, for arbitrary +.>There is->，/>Is->And>then call itAbout->Super , abbreviated as->Super , or upper ; if->Then->Is sub (sub martingal), or lower .
The relation between wind power and energy storage decisions and electricity price distribution in the planning problem is complex, and explicit expression of probability models of the wind power and energy storage decisions and electricity price distribution cannot be established under the condition of non-priori. Inspired by the concepts of super and sub , the concepts of local discrete super and local discrete sub are proposed for the endogenous uncertainty, and local discrete super/sub further resolve the endogenous and exogenous uncertainties in the power system planning problem from the aspect of stochastic processes, and a mathematical expression form of the endogenous-exogenous uncertainties is given.
Preferably, the endogenous uncertainty is defined by theory, and the local discrete super is used for describing the expected electricity price reduction process caused by near zero marginal cost under the current capacity decision of source side equipment of the virtual power plant, namely the electricity price is the endogenous uncertainty of the virtual power plant. Modeling of the electricity price, an endogenous uncertainty, is converted into a probability distribution correction reference distribution using a local discrete super , the uncertainty being described by means of relative entropy.
Preferably, the exogenous uncertainty comprises source side wind power equipment and methane cogeneration equipment; modeling the uncertainty of wind power through relative entropy; the biogas equipment adopts box type uncertainty set description, and random uncertainty of the cold-hot electric load is described by using polyhedron uncertainty set. Because the uncertainty of the traditional box type uncertainty set is uncontrollable, the result is over-conservative, and great economy is sacrificed, and the polygon uncertainty set is added with an adjustable uncertainty coefficient based on the box type uncertainty set, so that the conservation can be controlled by changing the uncertainty coefficient. The prior art is therefore generally described using a set of polyhedral uncertainties. The random uncertainty including the cold-hot electric load described in the invention is also described by using a polyhedron uncertainty set. The applicant has found that biogas plants are not suitable for using a polyhedral uncertainty set description. Because the biogas yield prediction scale is generally larger than the robust optimization scheduling strategy time unit, if the description is carried out by adopting a polyhedral uncertainty set, a larger error can appear after calculation, and the error can not be eliminated through the optimization of a later model, and the problem can be solved by modeling the biogas yield uncertainty by selecting a box type uncertainty set.
Preferably, in the step S3, the process adopts constant temperature fermentation, the biomass raw material amount produced every day is approximately regarded as a constant value, and the influence of the concentration of biomass waste liquid is ignored, so that the theoretical yield of the daily biogas is obtained.
Preferably, the flexible load demand response adjusting link in the step S3 comprises flexible cold-hot electricity adjustable loads, and the flexible cold-hot electricity adjustable loads can be divided into transferable loads and load reduction according to the adjusting mode. Wherein TL is capable of time shifting within the allowable transition time range; RL has higher flexibility and can relieve the system energy pressure by reducing its own power or interrupting operation in a time range that allows curtailment;
the energy storage equipment ensures that the energy storage charge state is consistent at the end of a planning period and does not exceed the capacity limit value of the equipment;
the cold and hot electric equipment in the virtual power plant takes biomass methane and electric energy as primary energy sources and writes a cold and hot electric power balance equation matrix form.
Preferably, in the step S4, in the establishing a two-stage robust optimization model of the virtual power plant based on theory uncertainty risk, the overall optimization objective of the virtual power plant system considers economy and low carbon property, the overall operation cost optimization objective of the virtual power plant is operation and maintenance cost, electricity purchasing cost, start-stop cost and load adjustment cost, and the overall carbon bank cost optimization objective of the virtual power plant is combined to form an objective function; and matrixing the optimization target and the constraint condition.
Preferably, in the step S5, a column and constraint generation algorithm is used for solving, the column and constraint generation algorithm divides a two-stage robust optimization model corresponding to a formula into a main problem and a sub-problem, the decomposed main problem and sub-problem are simplified in form, an uncertain variable set is substituted into the robust equation, after linearization by a large M method, the sub-problem is converted into a mixed integer linear optimization model, a group of initial worst source load data is given at first, and an initial cost upper limit, a initial cost lower limit, a maximum iteration number and a maximum allowable error are defined; substituting the worst source load data into the main problem type to solve, updating the lower bound LC of the function, and outputting the optimized switch variable value; substituting the optimized value of the switch variable into the sub-problem to solve, and updating the worst condition source load data and the function upper bound UC; and calculating a two-stage result error UC-LC, judging whether the error is smaller than an allowable value, if the error is in accordance with the allowable value, exiting the operation, if the error is not in accordance with the allowable value, adding a variable, updating a constraint condition of a main problem and the current iteration number, and carrying out loop solution until the condition is met or the maximum iteration number is reached.
The invention has the beneficial effects that: the invention discloses a two-stage robust optimization method of a virtual power plant based on saddle uncertainty, which is based on a virtual power plant in a source network charge storage linkage scene and on the theory, considers the network side online electricity price endophytic uncertainty and source side wind power output, methane yield, charge side cold-heat-electricity load power and other exogenous uncertainties, fully considers typical equipment such as wind power generation, methane cogeneration units, electric heating, electric refrigeration, transferable loads, load-reducible, electric heating-cold energy storage and the like, establishes a mathematical model thereof, and describes energy conversion processes of different equipment and flexible load demand response adjustment links. According to the method, the economical efficiency and the carbon emission reduction target of the virtual power plant are comprehensively considered, and a two-stage robust optimization model of the virtual power plant is established in theory of uncertainty risk: in the first stage, fine tuning of the strategy is realized by adding some robustness constraints, so that the initial solution is ensured to have certain robustness, and certain optimization performance can be maintained even under the condition of uncertainty change; in the second stage, the objective of the optimization problem is to maximize the optimization performance while maintaining robustness, typically by an objective function with robustness constraints. According to the invention, a column and constraint generation algorithm is adopted, and a strong dual theorem and a linearization theory are combined to solve a two-stage robust optimization model, so that the economical efficiency and the low-carbon coordinated operation of the cold-hot electric virtual power plant are realized. Compared with the prior art, the method has the advantages that compared with a common robust optimization model, the two-stage robust optimization model is more flexible, and the optimization performance can be improved while the robustness is maintained.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
FIG. 1 is a schematic flow diagram of a two-stage robust optimization method for a virtual power plant based on saddle uncertainty.
Detailed Description
The present disclosure is described in further detail below with reference to the drawings and the embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant content and not limiting of the present disclosure. It should be further noted that, for convenience of description, only a portion relevant to the present disclosure is shown in the drawings.
In addition, embodiments of the present disclosure and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The model established by the virtual power plant comprises a thermodynamic model, a chemical model, a control model and the like. These are widely used in the prior art. However, virtual power plants encounter a variety of complex scenarios during use. If the optimization is not carried out, various restrictions and hidden dangers exist in the use process, and the overall efficiency is greater than that of the optimization. There are therefore a number of optimisation methods for virtual power plants in the prior art. However, the initial model can only be optimized for simple scenes, for example, only wind, light and electricity under ideal conditions are accessed, and with the continuous increase of power requirements, energy diversification is a necessary trend. In the last decades, the proportion of renewable energy sources has been increasing because of the importance of environmental protection, as well as the advances in technology. However, unlike conventional energy sources, renewable energy sources have a tendency to "disperse" and also to provide "wave" energy. Because neither wind energy nor light energy is concentrated nor is it a sustainable source of energy. While "renewable energy" is "decentralized" and "fluctuating", this means that future power supplies will be more and more "decentralized" and will be more and more "fluctuating". Therefore, besides the great development of wind, light and water, the energy conversion capacity of the rural biogas cogeneration distributed energy sources and the electric-heat-cold multi-energy coupling equipment between the electric heating and energy storage at the storage side is greatly developed. The source network charge storage linkage environment is a novel power system which realizes information sharing and coordination control between source network charge storages and high-efficiency utilization and optimal configuration of energy sources in the power system through an intelligent technology and an informatization means.
In the source network charge storage linkage environment, the information of each link can be monitored and controlled in real time, so that the optimal configuration and the efficient utilization of energy are realized. Specifically, the source network load storage linkage environment includes the following aspects:
source side control: the energy is intelligently controlled in all links such as collection, transmission, storage and utilization, so that the optimal configuration and the efficient utilization of the energy are realized.
Network side control: the power network is optimized and scheduled by monitoring and controlling the running state and the load condition of the power network in real time.
Load side control: the balance and optimization of the power load are realized by monitoring and controlling the power consumption requirement of the power consumer in real time.
Storage side control: by monitoring and controlling the running state and the load condition of the power storage equipment in real time, the power storage is optimized and scheduled.
Through the coordination control of the links, the source network charge storage linkage environment can realize comprehensive monitoring and management of energy, so that the energy utilization efficiency is improved, the energy consumption and emission are reduced, and sustainable development is realized. The diversified energy sources enable the optimization mode of the virtual power plant in the prior art to not meet the requirements.
Most of the existing virtual power plant scheduling researches aim at exogenous uncertainties such as resources, environments, weather and the like, influence of the endogenous uncertainties on a scheduling scheme is ignored, and particularly influence of a decision process on the uncertainties is easily caused, so that decision results are over-conserved and lack of prospective, and further the problem of energy storage equipment investment redundancy is caused. The endogenous uncertainty concept originates from operational theory and is also expressed as decision-dependent in the context of stochastic planning. The method mainly comprises the steps that a decision result directly influences the future distribution of uncertainty, or the moment of decision action influences the probability distribution of uncertainty. However, the above concepts cannot give a strict mathematical definition, and it is difficult to distinguish the attribute of uncertainty without prior information, which hinders the next planning modeling. There is also a growing concern about in power system related research. In the power supply planning problem, the endogenetic uncertain factor of the electricity price cannot describe the distribution characteristics by using a scene set method, the corresponding electricity price fluctuation range lacks basis, and the provided method cannot guide the planning and scheduling problem of the virtual power plant.
In order to achieve the above object, the present invention provides the following technical solutions, in which EP is abbreviated as electricity price hereinafter, referring to electricity price:
A virtual power plant fuzzy opportunity constraint optimization scheduling method considering uncertainty of source load and user will comprises the following steps:
step one: acquiring a conventional model of the virtual power plant, establishing a theory model on the basis of the conventional model, and finding exogenous uncertainty and endogenous uncertainty based on the theory model;
wherein, based on the specifications of the discrete super and the discrete sub in the theory, the local discrete super and the local discrete sub are proposed to characterize the local probability dependence relationship of the uncertainty variable of the planning problem and the planning decision variable. The invention introduces saddle point theory, simply called saddle theory. The definition of a saddle point refers to a stagnation point (point with a first derivative of 0) that is not a local minimum, called a saddle point. The mathematical meaning is: the gradient (first derivative) value of the objective function at this point is 0, but one direction from the point of change is the maximum point of the function and the other direction is the minimum point of the function.
as a way to describe the random process, adapt to random sequences, for any->The method comprises the following steps:
，
in the method, in the process of the invention,indicating the expected value of the variable in brackets, ">For two random process variables, < >>Is->Is a function of (2).
Assuming that a certain uncertainty in the planning problem is defined as a discrete stochastic process The corresponding uncertainty variable is +.>Decision variables in the optimization problem are defined as random discrete processes +.>Decision variable is +.>. Random processAnd->Can construct procedure->：
，
Wherein:for the time increment in probability space, +.>Representation->Time of day variable->Is expected to be andeverywhere the same->Representation->At->Go up to generate->Sub-algebra. If the uncertainty variable and the decision variable satisfy the above formula, the idea according to the theory can be considered as +.>Decision variable +.>Can be constructed as-> of (a) i.e. "". The decision variable may be "" with respect to the uncertainty variable, representing that the uncertainty variable is exogenous uncertainty, equation (2) may be considered asMoment->Decision variable before time ∈ ->Uncertainty variable +.>For decision variables->Conditional desire and decision variable->Is irrelevant; further, it is (2)It can also be considered desirable to be->Decision variable +.>Is also irrelevant, and the condition expectations are still +.>。
The relation between wind power and energy storage decisions and electricity price distribution in the planning problem is complex, and explicit expression of probability models of the wind power and energy storage decisions and electricity price distribution cannot be established under the condition of non-priori. Inspired by the concepts of super and sub , the concepts of local discrete super and local discrete sub are proposed for endogenous uncertainty:
，
Wherein:is an uncertain variable->Corresponding random procedure, < > on >>Is->Corresponding random procedure, < > on >>Is->Is as much as desired->Is->Corresponding random procedure, < > on >>Is->Is not limited to the above-described embodiments. />An upper limit of a running period is planned; formulae (3) and (4) correspond to local discrete super and local discrete sub , which can express +.>And->I.e. the current decision quantity has a dependency on the uncertainty variable in the current and future, or the decision result makes the uncertainty variable in the current or future hope ∈>Will locally decrement or increment. the discriminant describes the dependency of decision timing on this uncertainty more closely than bayesian dependencies. and local discrete super/sub further resolve endogenous and exogenous uncertainties in the power system planning problem from the aspect of stochastic processes, giving a mathematical representation of the endogenous-exogenous uncertainties. Both endogenous and exogenous uncertainties are very important factors in saddles. Saddle points are extreme points of a function, and both endogenous and exogenous uncertainties can affect the extreme position and nature of the function. In the optimization problem of the invention, the influence of the endogenous uncertainty and the exogenous uncertainty on the optimization result needs to be considered to determine whether the optimal solution is stable and reliable.
Step two: correcting the endogenous uncertainty reference distribution by using local discrete super , introducing relative entropy and a box-type polyhedral uncertainty set to optimize the theory model, and generating an endogenous-exogenous uncertainty model of the virtual power plant; according to theory model, taking into account the net side net electricity price in-situ uncertainty, source side wind power output, methane yield, load side cold-heat-electricity load power and other in-situ uncertainty, correcting the in-situ uncertainty reference distribution by using local discrete super , and integrating relative entropy, box type and polyhedron uncertainty to describe in-situ uncertainty model. The Relative entropy (Relative entropy) is an important concept in information theory, also called KL divergence (Kullback-Leibler divergence). It measures the "difference" or "distance" between two probability distributions.
In the aspect of endophytic uncertainty, defined by theory, the current source side equipment capacity decision of the virtual power plant can be described by using local discrete super , and the expected electricity price reduction process caused by near zero marginal cost is that the electricity price is the endophytic uncertainty of the virtual power plant. Modeling of this endogenous uncertainty in electricity prices is converted into a probability distribution correction reference distribution using a local discrete super , the uncertainty being described by means of relative entropy:
Under ideal conditions, it is assumed that historical electricity price dataSatisfy normal distribution->Historical price->Is represented by the discrete process of (a):
，
wherein:for an empty set of decision sets, it is indicated that there is no decision space before planning has not started. The above equation indicates that the electricity price expectation will not change under passive side device capacity decision conditions. In the prior test, the wind power permeability is increased to lower the electricity price, and the electricity price +_in the beginning of planning can be constructed according to the prior information>Is greater than %>：
，
For initial electricity priceThe distribution is corrected to obtain a corrected reference distribution->. According to the big theorem, the local discrete super is expected to converge to the mean value, and the reference distribution correction process can be constructed as follows:
，
wherein:is the number of samples; />Correction factors for mean and variance; />And estimating an increment factor for the wind power permeability. After correction, the reference distribution is ensured to approach the electricity price distribution of the actual source side equipment after operation, and the real reference value of the reference distribution is exerted.
Describing a set of electricity price uncertainty using relative entropy:
，
wherein:is an uncertainty variable of wind power error; />Is wind power generationSample space of rate error; />And->The actual probability density function of the electricity price and the corrected probability density function of the reference electricity price. While the actual wind power distribution When not measurable, a set of uncertainty in electricity prices for different years can be constructed based on relative entropy:
，
wherein:is->A collection of annual electricity price uncertainties; />For planning period->Actual distribution of annual electricity prices;to program a cycle.
A robust opportunity constraint is established for the endogenous uncertainty of electricity price:
/>，
wherein:is a price risk factor; />Is +.>The individual nodes are->The electricity price at the moment; /> For electricity price probability, < >>The probability quantiles; the above-mentioned reference electricity price distribution set is +.>In the worst case, the future electricity price +.>Higher than a certain limit value of history synchronization>The probability of (2) is still greater than the probability threshold +.>。
In the aspect of exogenous uncertainty, the source side wind power and methane cogeneration equipment cannot influence the current and future output fluctuation, does not change the expectation of future wind power distribution, belongs to the virtual power plant exogenous uncertainty, and models the uncertainty of wind power by means of relative entropy. Relative entropy of wind powerExpressed as:
，
wherein:is an uncertainty variable of wind power error; />Is the sample space of wind power error; />Andis an actual probability density function of wind power error and a reference probability density function of wind power error. The actual wind power distribution is not measurable at present, so as to ensure the actual wind power distribution +. >And generating a reference distribution->Is based on the relative entropy to construct a wind power uncertainty set +.>The following are provided:
，
wherein:for the distance threshold between the reference distribution and the actual distribution, the chi-square distribution method is adopted to obtain the sampling typical scene number +.>Then selecting corresponding divergence value:
，
chi-square distribution is a statistical distribution. It describes the statistical relationship between two random variables reflecting the degree of independence of the two random variables. When the random variables are completely independent, the chi-square distribution relation is satisfied between the random variables. The density function of the chi-square distribution contains a degree of freedom parameter which reflects the degree of correlation between random variables, with a larger value indicating a smaller correlation.
Wherein:the upper quantile is distributed for the chi-square of the degree of freedom of N-1, so that the wind power can be ensured to be not less than +.>The probability of (2) being included in the uncertainty set +.>Is a kind of medium.
Describing the exogenous uncertainty of wind power as a robust opportunity constraint:
/>，
wherein:for the wind power actual output power, < >>And->Nodes +.>The actual wind curtailment rate and wind curtailment rate limit of the wind farm; /> For the reference wind power distribution set obtained in the foregoing>In the mean that even in the worst distribution case, the probability that the wind abandoning rate is smaller than a given value is still larger than the probability threshold +. >。
Other equipment aspects of exogenous uncertainty are that the biogas equipment adopts a box type uncertainty set, and random uncertainty of the cold-hot electric load is described by using a polyhedron uncertainty set:
，
wherein:daily biogas production and daily biogas production are respectively treated by->Predicted values of electric, thermal and cold load power at the moment; />Daily biogas production and daily biogas production are respectively treated by->Real values of electric, thermal and cold load power at moment; />Respectively represent methane and load->Is determined by the maximum deviation rate of (2); />And the variable is 0-1, and the uncertainty coefficient of the daily output of the methane and the different moments of the cold and hot electric loads is represented.
Step three: and describing a source side biogas cogeneration distributed energy source by using an in-out uncertainty model of the virtual power plant, wherein load can be transferred on a load side, and the energy conversion relation of an electric-heat-cold multifunctional coupling device between load equipment and electric heat energy storage on a storage side can be reduced, so that a flexible load demand response adjustment link is represented.
In the production link of the biogas, the technological process adopts constant-temperature fermentation, the biomass raw material quantity produced every day is approximately regarded as a constant value, and the influence of the concentration of biomass waste liquid is ignored, so that the theoretical yield of the biogas every day is obtained:
，
wherein:is the daily output of methane; />The gas yield of biomass raw materials; />Daily yield of biomass feedstock. The biogas production process is accompanied with the generation of accessory products such as organic fertilizers and the like, so that the production cost can be further reduced. The organic fertilizer production is deduced from the law of conservation of mass as follows:
，
Wherein:daily yield of organic fertilizer; />Biomass feedstock density; />Is the density of methane; />For the residue of the methane tankThe content of the organic fertilizer.
The biomass energy utilization process also needs to meet the biogas yield constraint:
，
the flexible load demand response adjusting link, the flexible cold and hot electricity adjustable load can be divided into a transferable load (transferable load, TL) and a Reducible Load (RL) according to the adjusting mode. Wherein TL is capable of time shifting within the allowable transition time range; RL has greater flexibility and can relieve system power pressure by reducing its own power or interrupting operation in a time frame that allows curtailment.
In the transferable load, the allowable transfer time period of TL is set asThe method is characterized in that the method meets the constraint conditions of a transfer power range, a minimum continuous operation duration, a transfer electric quantity conservation and the like in the adjustment process:
，
wherein: upper corner markRespectively representing electric, thermal, cold load and power types; />For regulating the later->Moment transferable load->Is a power of (2); />Is a variable 0-1, representing->Moment transferable load->The value 0 represents +.>The time does not participate in regulation, otherwise, the representatives participate in regulation; />Respectively is the transferable load after regulation- >Minimum and maximum power of (2); />For transferring load->Is a minimum transition length of (2); />To adjust the front->Moment transferable load->Is set, is provided.
In the load reduction, the allowable transition period of the RL is set to beIt should meet constraints such as a cut power range, a continuous cut transition range, etc. during the adjustment:
，
for regulating the later->Load can be reduced at the moment->Is a power of (2); />Is a variable 0-1, representing->Moment transferable load->The value 0 represents +.>The time participates in regulation; />Respectively, can reduce load after adjusting>Minimum and maximum power of (2); />To cut load->Minimum, maximum cut-down duration of (c).
The relationship between the electrical/thermal/cold load and the flexible load at the moment is:
，
the energy storage equipment ensures that the energy storage charge state is consistent at the end of a planning period, and the capacity limit value of the equipment is not exceeded:
，
wherein:the maximum and minimum values of the stored energy y are respectively; />Respectively->Time energy storage->Charging and discharging power of (a); />Is a variable 0-1, representing +.>Time energy storage->A value of 1 indicates a charge state and a value of 0 indicates a discharge state; />Respectively store energy->Maximum and minimum values of charge and discharge energy power; />Is the energy charging and discharging efficiency.
The cold-hot electric equipment in the virtual power plant takes biomass biogas and electric energy as primary energy sources, and the matrix form of the cold-hot electric power balance equation is shown as a formula (26). Wherein: Respectively->Electric, thermal, cold at momentLoad power; />Is->Biogas consumption at moment CHP; />The heat value of the methane is; />Respectively->The power grid interaction power, the wind power output power and the electric power absorbed by the electric heating and refrigerating equipment at moment; />The power generation and heating efficiencies of the methane cogeneration CHP are respectively; />Is the electric heating efficiency; />Is the electric refrigeration efficiency;respectively->And the charging and discharging power of electric, thermal and cold energy storage at any time.
For the sake of more clear expression of the meaning of the coupled energy supply equation, split description is made. Wherein:L representing a load demand vector;Irepresenting a conversion efficiency matrix;E representing a driving energy vector of the energy supply system;D represents the cold-hot electricity energy storage output power vector,
/>，
step four: combining a virtual power plant source-network-load-storage model, comprehensively considering the economy and carbon emission reduction targets of the virtual power plant, taking the economy and the carbon emission reduction targets of the virtual power plant as convergence, and establishing a virtual power plant two-stage robust optimization model based on theory uncertainty risk.
The overall operation cost optimization objective of the virtual power plant is as follows:
，
wherein:the unit cycle operation cost of the ecological agriculture IES; />For operation and maintenance cost, electricity purchasing cost, start-stop cost and load adjusting cost; / >For the unit->The unit operation and maintenance cost of each unit corresponds to the unit output power; />Is->Time unit->Is a force of the (a); />For storing energy->The unit operation cost of (3); />Is->The charging/discharging power of the stored energy y at the moment. Wherein, the machine set is->The method comprises wind power, methane cogeneration, electric heating and electric refrigeration; subscript ofRespectively representing three energy storage types of electricity, heat and cold; />Is->The electricity purchase price is carried out at any time; />The single start-stop cost of the unit z; />Is a variable 0-1, representing +.>Time unit->Is in the working state of->Is->Time TL adjusts the compensation amount:
，
wherein the method comprises the steps ofCan be made intoLoad transfer->The amount of subsidy per unit power; />Is->Time RL adjusts the compensation amount:
，
wherein the method comprises the steps ofTo adjust the front->Load can be reduced at the moment->A power; />To cut load->To subsidize the amount per unit power.
The overall operation cost optimization objective of the virtual power plant is as follows:
，
wherein:the daily treatment cost of the carbon emission of the system is realized; />Penalty amount per CO2 emission; />And the carbon emission factors are used for respectively supplying power to the marsh gas and the power grid.
The overall optimization objective of the virtual power plant system considers economy and low carbon performance, and an objective function is as follows:
，
matrixing the optimization target and the constraint condition, and writing a robust optimization model:
，
Wherein:x、y0-1 variable and continuous variable column vectors, respectively; s isSRespectively as variablesxCorresponding coefficient vectors and matrixes, wherein s is a unit start-stop cost coefficient,SCalculating a matrix for the start-stop state;mas a variableyCorresponding coefficient vectors, meaning power-related cost coefficients;A、B、C、D、F、G is a coefficient matrix;a、b、c、d、uas a vector of coefficients,uis a source load predictor coefficient. Constraint 1 in formula (32) corresponds to formulas (20) and (23); constraint 2 corresponds to condition 2 of formula (26), formula (21), formula (18) and formula (25); constraint 3 corresponds to condition 1 of formula (10), formula (14), formula (24), and formula (25); constraint 4 corresponds to conditions 3 and 4 of formula (15), formula (19), formula (22) and formula (25); constraint 5 corresponds.
Step five: and solving a two-stage robust optimization model by using a column and constraint generation algorithm and combining a strong dual theorem and a linearization theory, so as to realize the economical efficiency and low-carbon coordinated operation of the cold-hot electric virtual power plant. The column and constraint generation algorithm (Column and Constraint Generation Algorithm, CCG) is an algorithm that builds a linear programming model step by step. It approximates the optimal solution by incrementally generating new columns (equivalent to variables) and rows (equivalent to constraints) rather than initially specifying the complete model. This makes it more efficient in processing highly sparse but structured models. CCG algorithms typically start with a relatively small-scale model and solve their linear programming relaxation problem to get a viable solution. It is then checked whether the feasible solution satisfies the optimality condition. If so, the solution is the optimal solution, the algorithm ends, otherwise, new variables or constraints need to be added incrementally. The most "valuable" new variables or constraints are generated and added to the model. The basis for the judgment of the "value" is the extent to which it can improve the model objective function. And solving the relaxation problem of the new linear programming model again to obtain a new feasible solution. Repeating the steps until the optimal solution is obtained. Compared with directly establishing a complete model with larger scale, the CCG algorithm has the main advantages that:
1. Avoiding the processing of large-scale models at first and being more efficient. With the progress of the solving process, the model scale gradually increases, but only a small amount of change is made on the basis of the current model each time, so that the solution is relatively easy.
2. It is more intelligent to automatically decide when new variables or constraints are needed and which ones need to be added. This can efficiently approach the optimal solution.
3. Since only a small number of variables or constraints are added at a time, the planning model and underlying data structure may be updated incrementally as implemented. This also improves algorithm efficiency. The method provides a more efficient method for solving the ultra-large scale linear programming model. This is also why the present invention chooses to employ this algorithm.
And (3) dividing the two-stage robust optimization model corresponding to the formula (32) into a main problem and a sub-problem by adopting a CCG algorithm to solve the main problem and the sub-problem. The reduced form of the decomposed main problem is:
/>，/>
sub-problem reduction form:
，
wherein:the function value of the corresponding sub-problem is the other cost except the start-stop cost of the virtual power plant; SX meansThe process vector formed by the difference value between the start-stop state of the time unit and the start-stop state at the previous time has three conditions of 0, 1 and 1;the values of the optimization variable and the uncertain variable in the 2 nd stage after the iteration are respectively taken; / >Is the maximum allowed number of iterations.
In a sub-problem of this,μ、ν、ω、πoptimizing variables for sub-problemsyThe dual variables of the relevant constraints are,the source load error vector accords with the definition of the worst condition when the new energy output of the virtual power plant is less and the load is greater, and the source load errors are respectively
；/>A variable of 0-1, which represents the uncertainty coefficient of the source load at different moments; />For the introduced continuous variable, for equivalent uncertainty coefficient +.>With dual variables->Is a product of (2); m is a sufficiently large positive number; />The uncertainty of the photovoltaic and the cold-hot electrical load periods are respectively represented.
In the first stage main problem, the variable is a set start-stop 0-1 variable x, and other constraints are iteratively input by the sub-problem except for the constraint related to x in the 1 st line in the formula (32). In the second-stage sub-problem, the variables are all continuous variables y, and the 2 nd, 3 rd, 4 th and 5 th constraint except the start and stop of the unit in the constraint formula (32).
And substituting the uncertain variable set u into a robust equation, linearizing by a large M method, converting the sub-problem into a mixed integer linear optimization model, and solving by using a CCG algorithm. The large M method is a numerical method for solving a linear program. The nonlinear constraint condition of the linear programming problem is linearized by introducing a large M parameter into the constraint condition, so that the linear programming method such as SIMPLEX is used for solving. The solving process is as follows:
Firstly, a group of initial worst source load data is given, and an initial cost upper limit, a initial cost lower limit, a maximum iteration number and a maximum allowable error are defined; substituting the worst source load data into the main problem type to solve, updating the lower bound LC of the function, and outputting the optimized switch variable value; substituting the optimized value of the switch variable into the sub-problem to solve, and updating the worst condition source load data and the function upper bound UC; calculating a result error UC-LC of two stages, and judging whether the error is smaller than an allowable valueIf yes, the operation is stopped, if not, the variable is added +.>And updating the constraint condition of the main problem and the current iteration number to carry out loop solving until the condition is met or the maximum iteration number is reached. By means of the method, the two-stage robust optimization model is more flexible, and the optimization performance can be improved while the robustness is maintained.
In the description of the present specification, reference to the terms "one embodiment/manner," "some embodiments/manner," "example," "a particular example," "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/manner or example is included in at least one embodiment/manner or example of the application. In this specification, the schematic representations of the above terms are not necessarily for the same embodiment/manner or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/modes or examples described in this specification and the features of the various embodiments/modes or examples can be combined and combined by persons skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
It will be appreciated by those skilled in the art that the above-described embodiments are merely for clarity of illustration of the disclosure, and are not intended to limit the scope of the disclosure. Other variations or modifications will be apparent to persons skilled in the art from the foregoing disclosure, and such variations or modifications are intended to be within the scope of the present disclosure.
Claims (6)
1. The utility model provides a virtual power plant two-stage robust optimization method based on theory uncertainty, which is characterized in that the method is applied to a source network load storage linkage environment and comprises the following steps:
s1: acquiring a conventional model of the virtual power plant, establishing a theory model on the basis of the conventional model, and finding exogenous uncertainty and endogenous uncertainty based on the theory model;
S2: correcting the endogenous uncertainty reference distribution by using local discrete super , introducing relative entropy and a box-type polyhedral uncertainty set to optimize the theory model, and generating an endogenous-exogenous uncertainty model of the virtual power plant; the endogenous uncertainty is defined by theory, and the current capacity decision of source side equipment of the virtual power plant is described by using local discrete super , wherein the expected electricity price reduction process caused by near zero marginal cost is that the electricity price is the endogenous uncertainty of the virtual power plant;
the exogenous uncertainty comprises source side wind power equipment and methane cogeneration equipment; modeling the uncertainty of wind power through relative entropy; the biogas equipment adopts box type uncertainty set description, and random uncertainty of the cold-hot electric load is described by using a polyhedron uncertainty set;
s3: describing a source side biogas cogeneration distributed energy source by using an in-out uncertainty model of a virtual power plant, wherein load can be transferred on a load side, and the energy conversion relation of electric-heat-cold multifunctional coupling equipment between load equipment and electric heat energy storage on a storage side can be reduced, so that a flexible load demand response adjustment link is represented;
in the transferable load, the allowable transfer time period of TL is set as [ t ] TL0 ，t TL1 ]The method is characterized in that the method meets the constraint conditions of transfer power range, minimum continuous operation duration and transfer electric quantity conservation in the adjustment process:
Wherein: the upper corner mark alpha epsilon { e, h, c }, respectively representing electric, thermal, cold load and power types;to adjustThe power of the load alpha can be transferred at the moment t; />A variable of 0-1 represents the regulation and control state of the transferable load alpha at the moment t, and a value of 0 represents that the regulation is not participated at the moment t, and conversely represents that the regulation is participated; />Respectively the minimum and maximum power of the transferable load alpha after adjustment; />A minimum transfer duration that is a transferable load α; />The power of the load alpha can be transferred for adjusting the moment t before;
in the load reduction, the allowable transition time period of the RL is set to [ t ] RL0 ，t RL1 ]It should meet the cut power range, continuous cut transition range constraint during regulation:
the power of the load alpha can be reduced for adjusting the time t; />A variable of 0-1 represents the regulation and control state of the transferable load alpha at the moment t, and a value of 0 represents the participation of the regulation at the moment t; />Respectively the minimum and maximum power which can cut down the load alpha after adjustment; />A minimum and maximum reduction time period for which the load alpha can be reduced;
the relation between the electric/heat/cold load and the flexible load at the time t is as follows:
the energy storage equipment ensures that the energy storage charge state is consistent at the end of a planning period, and the capacity limit value of the equipment is not exceeded:
wherein: s is S y,min 、S y,max Respectively the maximum and minimum values of the stored energy y; The charging and discharging power of the energy storage y at the moment t respectively; b y，t A variable of 0-1 represents the working state of the energy storage y at the moment t, a value of 1 represents the energy charging state, and a value of 0 represents the energy discharging state; />Respectively the maximum and minimum values of the charge and discharge energy power of the energy storage y; η (eta) y The energy efficiency is the charge and discharge efficiency;
the cold-hot electric equipment in the virtual power plant takes biomass biogas and electric energy as primary energy sources, and a matrix form of a cold-hot electric power balance equation is shown as a formula (26);
wherein:the power of electric, heat and cold loads at the moment t respectively; m is M CHP，t The biogas amount consumed by the CHP at the time t; p is p b The heat value of the methane is; />The power grid interaction power and the wind power output power at the time t are respectively the electric power absorbed by electric heating and refrigerating equipment; />The power generation and heating efficiencies of the methane cogeneration CHP are respectively; />Is the electric heating efficiency; />Is the electric refrigeration efficiency; />Charging and discharging power of electric, hot and cold energy storage at the moment t respectively;
in order to clearly express the meaning of the coupling energy supply equation, split description is carried out; wherein: l represents a load demand vector; i represents a conversion efficiency matrix; e represents a driving energy vector of an energy supply system; d represents a cold-hot electric energy storage output power vector;
s4: taking the economical efficiency and the carbon emission reduction target of the virtual power plant as convergence, and establishing a two-stage robust optimization model of the virtual power plant based on theory uncertainty risk;
The overall operation cost optimization objective of the virtual power plant is as follows:
wherein: f (F) 1 The unit cycle operation cost of the ecological agriculture IES; c (C) om ，C grid ，C ss ，C load For operation and maintenance cost, electricity purchasing cost, start-stop cost and load adjusting cost;the unit operation and maintenance cost of the unit x is set, and the operation and maintenance cost of each unit corresponds to the output power of the unit; />The output of the unit x at the moment t; a, a y，om The unit operation and maintenance cost of the energy storage y; p (P) y，t The charging/discharging power of the energy storage y at the time t; wherein, the unit x comprises wind power, methane cogeneration, electric heating and electric refrigeration; subscript y e { EES, HES, CES }, represents three energy storage types of electricity, heat and cold respectively; a, a buy，t The electricity purchase price is t time; a, a z，ss The single start-stop cost of the unit z; b z，t Is 0-1 variable, and represents the working state of the unit z at the moment t, C TL，t Adjusting the compensation amount for time TL:
wherein the method comprises the steps ofThe unit power of the transferable load alpha is subsidized by the amount; c (C) RL，t Adjusting the compensation amount for time RL:
wherein the method comprises the steps ofLoad alpha power can be reduced for adjusting the previous t moment; />The unit power subsidy amount capable of reducing the load alpha is subsidized;
the overall operation cost optimization objective of the virtual power plant is as follows:
wherein: f (F) 2 The daily treatment cost of the carbon emission of the system is realized;in units of CO 2 Penalty amount of emission; omega b ，ω e Carbon emission factors for respectively supplying power to the marsh gas and the power grid;
The overall optimization objective of the virtual power plant system considers economy and low carbon performance, and an objective function is as follows:
min F c ＝F 1 +F 2 (31)
matrixing the optimization target and the constraint condition, and writing a robust optimization model:
wherein: x and y are 0-1 variable and continuous variable column vectors respectively; s and S are coefficient vectors and matrixes corresponding to the variable x respectively, wherein S is a unit start-stop cost coefficient, and S is a start-stop state calculation matrix; m is a coefficient vector corresponding to a variable y, and the meaning of m is a power-related cost coefficient; A. b, C, D, F, G is a coefficient matrix; a. b, c, d, u is a coefficient vector, u is a source load predicted value coefficient;
s5: solving a two-stage robust optimization model of the virtual power plant by using a column and constraint generation algorithm and combining a strong dual theorem and a linearization theory; in the first stage main problem, the variable is a set start-stop 0-1 variable x, and other constraints are iteratively transmitted by the sub-problem except for the constraint related to x in the 1 st row in the formula (32); in the second-stage sub-problem, the variables are all continuous variables y, and the constraint is the constraint of lines 2, 3, 4 and 5 except the start and stop of the unit in the formula (32).
2. The method for two-stage robust optimization of a virtual power plant based on theory uncertainty as claimed in claim 1, wherein theory discrete super and discrete sub specifications are introduced in the step S1 when theory models are established.
3. The method for two-stage robust optimization of a virtual power plant based on theory uncertainty as claimed in claim 1, wherein in the step S3, in the biogas production process, constant-temperature fermentation is adopted, the biomass raw material amount produced every day is used as a constant value, and the influence of the concentration of biomass waste liquid is ignored, so that the theoretical yield of daily biogas is obtained.
4. The theory uncertainty-based two-stage robust optimization method for the virtual power plant according to claim 1, wherein the flexible load demand response adjustment link in the step S3 comprises flexible cold-hot electricity adjustable loads, and the flexible cold-hot electricity adjustable loads are divided into transferable loads and reducible loads according to adjustment modes.
5. The uncertainty-based two-stage robust optimization method for a virtual power plant according to claim 1, wherein in the step S4, in the uncertainty risk-based two-stage robust optimization model for a virtual power plant, the overall optimization objective of a virtual power plant system considers economy and low carbon, the overall operation cost optimization objective of the virtual power plant is operation cost, electricity purchasing cost, start-stop cost and load adjustment cost, and the overall carbon bank cost optimization objective of the virtual power plant is combined to form an objective function; and matrixing the optimization target and the constraint condition.
6. The method for two-stage robust optimization of a virtual power plant based on theory uncertainty as claimed in claim 1, wherein in the step S5, a column and constraint generation algorithm is used for solving, the column and constraint generation algorithm divides a two-stage robust optimization model corresponding to a formula into a main problem and a sub-problem, the decomposed main problem and the sub-problem are simplified in form, an uncertain variable set is substituted into a robust equation, after linearization by a large M method, the sub-problem is converted into a mixed integer linear optimization model, a group of initial worst source load data is given first, and an initial cost upper limit and a lower limit, a maximum iteration number and a maximum allowable error are defined; substituting the worst source load data into the main problem type to solve, updating the lower bound LC of the function, and outputting the optimized switch variable value; substituting the optimized value of the switch variable into the sub-problem to solve, and updating the worst condition source load data and the function upper bound UC; and calculating a two-stage result error UC-LC, judging whether the error is smaller than an allowable value, if the error is in accordance with the allowable value, exiting the operation, if the error is not in accordance with the allowable value, adding a variable, updating a constraint condition of a main problem and the current iteration number, and carrying out loop solution until the condition is met or the maximum iteration number is reached.
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