CN115758763A - Multi-energy flow system optimal configuration method and system considering source load uncertainty - Google Patents
Multi-energy flow system optimal configuration method and system considering source load uncertainty Download PDFInfo
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Abstract
The invention belongs to the technical field of multi-energy flow systems, and provides a multi-energy flow system optimal configuration method and system considering source load uncertainty. The method comprises the steps of determining a fluctuation range of equivalent uncertain quantity; based on information gap decision and a condition risk value theory, a deterministic optimization configuration model is combined to construct a multi-energy flow system optimization configuration model; constructing a target function of the total planning cost by taking the maximum loss which can be borne by a decision maker as a limiting condition according to the set risk coefficient and evasion coefficient; based on electric heating cold air load data, wind power photovoltaic output data and equipment parameter data, under the condition of the maximum equivalent uncertain quantity fluctuation range, according to a target function and an uncertain variable model of the total planning cost, combining a constraint condition and a multi-energy flow system optimization configuration model, and constructing a multi-energy flow system optimization configuration model considering source load uncertainty; and solving the optimal configuration model of the multi-energy flow system considering the source load uncertainty to obtain the optimal configuration scheme of the multi-energy flow system.
Description
Technical Field
The invention belongs to the technical field of multi-energy flow systems, and particularly relates to a multi-energy flow system optimal configuration method and system considering source load uncertainty.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The interweaving of energy safety, environmental pollution and climate warming causes significant changes in energy consumption structure and utilization. An Integrated Energy System (IES) takes an electric power network as a main body, is coupled with other Energy structures such as a natural gas network, a cold/heat network, a distributed Energy network and the like, and simultaneously relies on an internet technology to perform coordination, efficient and flexible management and control and interaction of a multi-Energy flow System.
However, the optimal configuration of the current integrated energy system has the following problems:
(1) The influence of the short-term uncertainty of the renewable energy output or the long-term uncertainty of the load on the planning cost is considered only unilaterally, so that the cost generated by the load increase exceeds the maximum loss which can be borne by an investor.
(2) The multi-energy flow coupling is an important feature of IES, and existing source load uncertainty analysis is not combined with the multi-energy flow system.
Disclosure of Invention
The invention provides a method and a system for optimizing and configuring a multi-energy flow system, aiming at the problem of source load uncertainty caused by wind-light output prediction deviation and load increase prediction deviation in the optimizing and configuring of the multi-energy flow system.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a method for optimally configuring a multi-energy flow system, which accounts for source load uncertainty.
A multi-energy flow system optimal configuration method considering source load uncertainty comprises the following steps:
acquiring electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data;
calculating the fluctuation range of the equivalent uncertain quantity according to the actual fluctuation range of the electric heating cold air load;
based on information gap decision and a condition risk value theory, a deterministic optimization configuration model is combined to construct a multi-energy flow system optimization configuration model;
constructing a target function of the total planning cost by taking the maximum loss which can be borne by a decision maker as a limiting condition according to the set risk coefficient and evasion coefficient;
based on the electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data, under the condition of the maximum equivalent uncertain quantity fluctuation range, according to a target function and an uncertain variable model of the total planning cost, combining a constraint condition and a multi-energy flow system optimization configuration model, and constructing a multi-energy flow system optimization configuration model considering source load uncertainty;
and solving the optimal configuration model of the multi-energy flow system considering the source load uncertainty to obtain the optimal configuration scheme of the multi-energy flow system.
Further, the fluctuation range of the equivalent uncertainty is as follows:
and has xi 1 +ξ 2 +ξ 3 +ξ 4 ＝1
In the formula (I), the compound is shown in the specification,
for equivalent uncertainty variable fluctuation range, xi 1 、ξ 2 、ξ 3 、ξ 4 Respectively indicating electric heat and coldWeight coefficient of fluctuation range of air load, alpha e 、α h 、α c 、α g Respectively showing the actual fluctuation range of the electric heating and cooling air load.
Further, the deterministic optimization configuration model includes:
considering the comprehensive economy of the planning scheme, and taking the minimum total planning cost as an optimization target;
wherein C represents the total planned cost in T years, C inv,t Total investment cost in the t year, C op,t Total operating cost for the t year; tau is t Coefficient, τ, for year t in relation to cash discount rate r t ＝(1+r) -t ；C inv,t Is defined as follows:
in the formula, σ k Investment cost, P, for k unit of capacity of the energy supply unit k For commissioning capacity of energy supply unit k, N k,t The built number of the energy supply unit k in the t year; rho m Investment cost for m unit capacity of energy storage equipment, E m For the commissioning capacity of the energy storage device m, Y m,t The quantity of the energy storage devices m built in the t year; c op,t Is defined as follows:
in the formula, N d D, the number of continuous days of a typical day in a year, and D typical days in the year;
respectively representing the operation cost of an energy supply unit, the operation cost of energy storage equipment, the electricity purchasing cost of an energy junction from an external power grid and the gas purchasing cost of an external natural gas grid in the t year, the d typical day and the h period;
represents the operating cost of the energy supply unit k per unit power,
representing the total operating power of the energy supply unit k in the h period;
the operating cost of the energy storage unit m per unit power is shown,
respectively representing the total charging power and the discharging power of the energy storage device m in the h period;
represents the market electricity rate for the h period,
representing the active transaction amount of the energy hub and the external power grid;
the unit price of the natural gas in the market in the h period is shown,
representing the amount of natural gas purchased by the energy hub from outside.
Further, the optimized configuration model of the multi-energy flow system comprises:
in the formula (I), the compound is shown in the specification,
to account for the year t operating cost of the conditional risk assessment,
is an expected value of annual operating costs; establishing a wind speed and illumination intensity scene set under a typical day d in the t year by using Latin hypercube sampling; n is a radical of an alkyl radical w ，n s For the corresponding number of scenes, pi (n) w )、π(n s ) Is the probability of each scene;
is the nth of the typical day of the t year at d time h w ，n s Running cost and storage of energy supply equipment under set of wind speed and illumination sceneThe operation cost of the equipment, the electricity purchasing cost and the gas purchasing cost can be reduced.
Further, the objective function of the total planning cost is:
C′＝C+μCVaR
wherein C' represents a cost function accounting for CVaR; c (-) expresses an investment planning cost function; sigma represents an avoidance coefficient and represents the proportion of the investment cost acceptable by an investor higher than a predicted value; mu represents a risk coefficient reflecting the level of aversion of the investor to the risk; d represents a decision variable; x represents an uncertain variable.
Further, the uncertain variable model comprises:
in the formula, α represents a fluctuation range of the indeterminate amount; c (-) expresses an investment planning cost function; sigma represents an avoidance coefficient and represents the proportion of the investment cost acceptable by an investor higher than a predicted value; d represents a decision variable; x represents an uncertain variable, h and g represent equality constraint and inequality constraint respectively; Γ denotes the predicted value of the uncertain variable X around it
Set of fluctuation ranges.
Further, the constraints include:
1) Energy hub constraint
The relationship between the upper limit and the lower limit of the output of the energy supply unit is as follows:
in the formula, P k,min For the lower active power output limit, P, of the energy supply unit k k,max The active power output upper limit of the energy supply unit k is set;
the maximum transmission power of the energy hub and an external power grid;
2) Climbing power of CHP unit
In the formula (I), the compound is shown in the specification,
for electric power of cogeneration units, theta up 、θ down The upward and downward climbing rates of the cogeneration unit are respectively;
3) Energy storage equipment operation and charge-discharge energy constraint
Q t,d,0 ＝Q t,d,H
In the formula, Q t,d,h In order to store the energy, the energy storage capacity,
charging and discharging efficiency of the energy storage device;
4) Supply and demand matching constraints
Supply and demand matching constraints ensure that the installation capacity of the electric heating and cooling air energy supply unit in the t year is greater than or equal to the load peak value of the electric heating and cooling air;
in the formula (I), the compound is shown in the specification,
respectively showing the peak value of the electric heating and cooling load in the t year,
respectively representing the capacity of the energy hub output to the load part in the t year;
5) Other constraints
N k,0 ≤N k,t-1 ≤N k,t ≤N k,T
Y k,0 ≤Y k,t-1 ≤Y k,t ≤Y k,T
In the formula, N k,0 ，N k,T ，Y k,0 ，Y k,T The initial commissioning amount and the maximum commissioning amount of the energy supply device and the energy storage device are respectively represented.
A second aspect of the invention provides a multiple energy flow system optimal configuration system that accounts for source-to-load uncertainty.
A multi-energy flow system optimal configuration system accounting for source-to-load uncertainty, comprising:
a data acquisition module configured to: acquiring electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data;
an uncertainty amount fluctuation range determination module configured to: calculating the fluctuation range of the equivalent uncertain quantity according to the actual fluctuation range of the electric heating cold air load;
a multi-energy flow system optimization configuration model building module configured to: based on information gap decision and a condition risk value theory, a deterministic optimization configuration model is combined to construct a multi-energy flow system optimization configuration model;
an objective function determination module configured to: constructing a target function of the total planning cost by taking the maximum loss which can be borne by a decision maker as a limiting condition according to the set risk coefficient and evasion coefficient;
a multi-energy flow system optimization configuration model building module accounting for source-to-load uncertainty configured to: based on the electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data, under the condition of the maximum equivalent uncertain quantity fluctuation range, according to a target function and an uncertain variable model of the total planning cost, combining a constraint condition and a multi-energy flow system optimization configuration model, and constructing a multi-energy flow system optimization configuration model considering source load uncertainty;
a solving module configured to: and solving the optimal configuration model of the multi-energy flow system considering the source load uncertainty to obtain the optimal configuration scheme of the multi-energy flow system.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for optimal configuration of a multi-energy flow system taking into account source load uncertainty as described in the first aspect above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for optimized configuration of a multi-energy flow system taking into account source load uncertainty as described in the first aspect above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
the invention takes the influence of uncertainty on the planning cost on both sides of the source load into consideration and quantifies the uncertainty risk loss, so that the loss compensation added by investors due to the increase of the load is greatly reduced.
The invention constructs a model by the evasion coefficient, the risk coefficient and the fluctuation range of the uncertain quantity, only needs the representation of the risk accepting or aversion capacity of investors and the fluctuation interval of the uncertain quantity, does not need the boundary of the uncertain quantity, has less needed uncertain quantity information and less calculated quantity, reduces the calculated quantity and ensures that the calculated result is more accurate.
The electric heating and cooling air multi-energy flow system optimized configuration scheme designed by the invention is lower in planning cost, more reasonable in configuration scheme and lower in conservative degree of planning strategy.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a diagram of a typical energy terminal framework shown in the present invention;
FIG. 2 is a flow chart of a method for optimal configuration of a multi-energy flow system accounting for source load uncertainty according to the present invention;
FIG. 3 is a diagram illustrating the effectiveness of evasive factor setting analysis in accordance with the present invention;
fig. 4 is a graph illustrating the effectiveness of the risk factor setting analysis of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a segment, or a portion of code, which may comprise one or more executable instructions for implementing the logical function specified in the respective embodiment. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Example one
The embodiment provides a method for optimizing and configuring a multi-energy flow system, which takes source load uncertainty into account, and is applied to a server for illustration. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network server, cloud communication, middleware service, a domain name service, a security service CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. In this embodiment, the method includes the steps of:
acquiring electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data;
calculating the fluctuation range of the equivalent uncertain quantity according to the actual fluctuation range of the electric heating cold air load;
based on information gap decision and a condition risk value theory, a deterministic optimization configuration model is combined to construct a multi-energy flow system optimization configuration model;
constructing a target function of the total planning cost by taking the maximum loss which can be borne by a decision maker as a limiting condition according to the set risk coefficient and evasion coefficient;
based on the electricity, heat, cold and air load data, the wind power photovoltaic output data and the equipment parameter data, under the condition of the maximum equivalent uncertain quantity fluctuation range, according to a target function and an uncertain variable model of the total planning cost, and in combination with a constraint condition and a multi-energy flow system optimization configuration model, constructing a multi-energy flow system optimization configuration model considering source load uncertainty;
and solving the optimal configuration model of the multi-energy flow system considering the source load uncertainty to obtain the optimal configuration scheme of the multi-energy flow system.
The specific scheme of the embodiment can be realized by referring to the following contents:
1. modeling of multi-energy flow system
In the integrated energy system, energy flows of various forms of electric heat and cold air flow in the system, so that advantage complementation and coordinated utilization among different energy sources are effectively promoted, and the architecture of a typical Energy Hub (EH) is shown in fig. 1.
EH internal energy production equipment is Wind Turbines (WT) and photovoltaic units (PV); the energy storage equipment comprises an electric energy storage device (ES) and a thermal energy storage device (TS); an Electric Boiler (EB) of an electric heat coupling conversion equipment; an electric refrigerator (EC) as an electric cold coupling conversion device; a Combined Heat and Power (CHP) unit of gas-electric-thermal coupling conversion equipment; gas boiler (gas boiler, GB) for gas-heat coupling conversion equipment; the heat-cold coupling conversion equipment is an Absorption Chiller (AC).
The EH takes the electric energy and the natural gas purchased by the upstream power grid company and the natural gas company as input, and reasonably distributes the electric energy and the natural gas to each energy coupling device and each energy storage device through the supplement of the internal energy production device, so that the energy of each energy flow of electricity, heat, cold and gas is balanced, and the load requirements of electricity, heat, cold and gas at the user side are met. The input and output coupling relation of the energy hub is as follows:
L＝CP+E (1)
L＝[L e L h L c L g ] T (2)
E＝[E e E h 0 0] T (5)
in the formula: the matrix L represents an electricity, heat, cold and gas load output matrix; matrix C represents an energy conversion matrix; matrix P represents an energy input matrix; matrix E represents an energy storage power matrix; l is e ,L h ,L c ,L g Representing electrical, thermal, cold, gas loads; eta 1 Which represents the efficiency of the transformer, is,
representing the electric efficiency, omega, of a cogeneration unit 1 、ω 2 Distributing coefficients for the electric energy; alpha is alpha 1 ，α 2 Distribution coefficient of natural gas, eta EB In order to improve the heating efficiency of the electric boiler,
for the heating efficiency, eta, of cogeneration units GB The heating efficiency of the gas boiler is shown, and lambda is a heat load distribution coefficient; eta EC For the refrigerating efficiency of the electric refrigerator, eta AC The refrigeration efficiency of the absorption type refrigerator is improved;
electric energy of energy supply terminal for external network dg The electric energy input for an internal distributed generation (dg),
natural gas energy purchased from an external natural gas grid; e e Representing power of the electricity storage apparatus, E h Is the heat storage device power.
2. IGDT-based load output uncertainty modeling
According to the characteristics of low prediction precision and less uncertain quantity information, the long-term uncertainty caused by the increase of the cooling, heating and power loads is processed by applying an information gap decision theory. The model of the cost optimization problem belongs to an IGDT robust model under a risk avoidance strategy, namely, the load output uncertainty is considered to enable a system cost function target to become very poor, a negative worst boundary target is set, the target is ensured to be in the boundary, and the maximum fluctuation degree of uncertain parameters is searched.
The basic model of this theory is as follows:
in the formula, f is an objective function, d represents a decision variable, and X represents an uncertain variable; h. g represents equality constraint and inequality constraint respectively; Γ denotes the predicted value around which the uncertain variable X surrounds
Set of fluctuation ranges.
The model of the uncertain variables described with the envelope constraint is as follows:
in the formula, alpha represents the fluctuation range of the uncertain quantity, C (-) represents an investment planning cost function, and sigma represents an avoidance coefficient and represents the proportion of the investment cost which is acceptable by an investor and is higher than a predicted value.
For modeling of IGDT with multiple uncertain variables, the prior art generally uses a method of weighting multiple uncertain variable fluctuations to form an equivalent uncertain variable fluctuation range. The equivalent uncertainty fluctuation range is expressed as:
and has xi 1 +ξ 2 +ξ 3 +ξ 4 ＝1 (9)
In the formula (I), the compound is shown in the specification,
for equivalent uncertainty variable fluctuation range, xi 1 、ξ 2 、ξ 3 、ξ 4 Weight coefficient, alpha, representing the fluctuation range of electric heating and cooling air load e 、α h 、α c 、α g Respectively showing the actual fluctuation range of the electric heating and cooling air load.
3. CVaR-based wind-solar output uncertainty modeling
Aiming at the characteristic that the renewable energy output probability is accurately depicted, the adaptive degree of the modeling scheme to the wind power photovoltaic short-term uncertainty is adjusted by applying the condition risk value theory. In the embodiment, uncertainty of output of wind power and photovoltaic units is simulated by using a scene set method.
3.1 construction of wind-solar output scene set
In the embodiment, a Latin hypercube sampling is used for establishing a scene set of wind speed and illumination intensity. The Latin hypercube sampling essence is a layered random sampling method, can accurately reflect the theoretical distribution of random variables and avoid repetition, and the characteristic of equidistant sampling ensures that a sampling area is uniformly covered by sampling points. Let x be 1 ,x 2 ,...x k Generating N scenes for k mutually independent uncertain variables, wherein the nth scene generation process comprises the following steps:
in the formula (I), the compound is shown in the specification,
is the inverse of the cumulative distribution function of the kth uncertain variable.
For multi-random variable sampling, N samples of each variable and samples of other variables need to be randomly combined, and the total number of finally generated scenes is N k And (4) respectively. In order to reduce the solving burden and improve the solving efficiency, a synchronous back substitution elimination method is used for reducing scenes.
3.2 Construction of CVaR model
CVaR develops from VaR, which is the model for risk value assessment in the financial field, vaR, which refers to the maximum possible loss value expected for an investment given a confidence level.
The mathematical expression for VaR is:
wherein beta is a confidence level, f is a loss function caused by the fluctuation of an uncertain variable, d is a decision variable, X is an uncertain variable, and integral
Representing the probability that the loss function f (d, X) is not greater than α.
The risk value theory VaR characterizes the maximum possible loss value of an investor under the risk confidence, but does not characterize the risk loss exceeding the confidence, and lacks the measure of the tail risk, thereby influencing the risk loss decision. The meaning of conditional risk value theory CVaR refers to the expectation that the system risk loss exceeds the VaR part at a given confidence level and at a given time.
The mathematical expression for CVaR is:
where Γ represents the predicted value around which the uncertain variable X surrounds
The set of fluctuation ranges of (c).
The conditional risk value CVaR represents an expected value of the maximum possible loss part exceeding the VaR, and the defect that the tail risk cannot be measured by the VaR is overcome. The optimal configuration model of the multi-energy flow system based on the CVaR theory is modeled as follows:
C′＝C+μCVaR (13)
in the formula, C' represents a cost function for calculating CVaR, C is an expected running cost value, and mu represents a risk coefficient, and reflects the aversion level of an investor to risks.
4. Optimized configuration model accounting for uncertainty
4.1 deterministic optimal configuration model
4.1.1 objective function
And considering the comprehensive economy of the planning scheme, the optimal configuration model of the electric heating and cooling air multi-energy flow system takes the minimum total planning cost as an optimization target, including investment cost and operation cost.
Wherein C represents the total planned cost in T years, C inv,t Total investment cost in the t year, C op,t Total operating cost for year t; tau is t Coefficient, τ, for year t in relation to cash discount rate r t ＝(1+r) -t 。C inv,t Is defined as follows:
in the formula, σ k Investment cost, P, for k unit of capacity of the energy supply unit k For commissioning capacity of energy supply unit k, N k,t The built number of the energy supply unit k in the t year; rho m Investment cost for m unit capacity of energy storage equipment, E m For the commissioning capacity of the energy storage device m, Y m,t The built number of energy storage devices m in the t year. C op,t Is defined as follows:
in the formula, N d D, the number of continuous days of a typical day in a year, and D typical days in the year;
respectively representing the operation cost of an energy supply unit, the operation cost of energy storage equipment, the electricity purchasing cost of an energy junction from an external power grid and the gas purchasing cost of an external natural gas grid in the t year, the d typical day and the h period;
represents the operating cost of the energy supply unit k per unit power,
representing the total operating power of the energy supply unit k in the h period;
the m unit power operation cost of the energy storage unit is shown,
respectively representing the total charging power and the discharging power of the energy storage device m in the h period;
represents the market electricity rate for the h period,
representing the active transaction amount of the energy hub and the external power grid;
the unit price of the market natural gas in the h period,
representing the amount of natural gas purchased by the energy hub from outside.
4.1.2 constraints
1) Energy hub constraint
The relationship between the upper limit and the lower limit of the output of the energy supply unit is as follows:
in the formula, P k,min For the lower active power output limit, P, of the energy supply unit k k,max The active power output upper limit of the energy supply unit k is set;
the maximum transmission power of the energy hub and the external power grid.
2) Climbing power of CHP unit
In the formula (I), the compound is shown in the specification,
for electric power of cogeneration units, theta up 、θ down The upward and downward climbing rates of the cogeneration unit are respectively.
3) Energy storage equipment operation and charge-discharge energy constraint
Q t,d,0 ＝Q t,d,H (28)
Equations (24) - (25) are energy storage charging and discharging energy upper and lower limit constraints, Q t,d,h In order to store the energy, the energy storage capacity,
and the charging and discharging efficiency of the energy storage device is improved.
4) Supply and demand matching constraints
Supply and demand matching constraints ensure that the installation capacity of the electric heating and cooling air energy supply unit in the t year is greater than or equal to the load peak value of the electric heating and cooling air.
In the formula (I), the compound is shown in the specification,
respectively showing the peak value of the electric heating and cooling load in the t year,
respectively representing the capacity of the energy hub output to the load part in the t year.
5) Other constraints
N k,0 ≤N k,t-1 ≤N k,t ≤N k,T (33)
Y k,0 ≤Y k,t-1 ≤Y k,t ≤Y k,T (34)
In the formula, N k,0 ，N k,T ，Y k,0 ，Y k,T The initial commissioning amount and the maximum commissioning amount of the energy supply device and the energy storage device are respectively represented.
4.2 optimized configuration model accounting for uncertainty
By combining the deterministic optimal configuration model with the formulas (11) to (13), the optimal configuration model of the multi-energy flow system based on the CVaR theory can be obtained:
in the formula (I), the compound is shown in the specification,
to account for the year t operating cost of the conditional risk assessment,
is an expected value of annual operating costs; establishing a wind speed and illumination intensity scene set under a typical day d in the t year by using Latin hypercube sampling; n is a radical of an alkyl radical w ，n s For the corresponding number of scenes, pi (n) w )、π(n s ) Is the probability of each scene;
is the nth at the typical day d and h of the t year w ，n s The running cost of energy supply equipment, the running cost of energy storage equipment, the electricity purchasing cost and the gas purchasing cost under the condition of each wind speed and illumination scene set.
According to the characteristics of low prediction precision and less uncertain quantity information, an IGDT model of a cost optimization function is constructed to process long-term uncertainty caused by the increase of the cooling, heating and power loads. Aiming at the characteristic that the renewable energy output probability is accurately characterized, on the basis of constructing and predicting a typical scene set, a condition risk value theory is applied to carry out condition risk assessment on output fluctuation loss in the running process of the multi-energy flow system.
Finally, a multi-energy flow system optimization configuration model considering source load uncertainty is obtained, and the method is as follows:
5. Analysis by calculation example:
5.1 parameter settings
An example is set for a research object in a certain commercial district, and the structure of an energy hub is shown in figure 1. And inputting wind-solar output data, various equipment parameters, various load data, time-of-use electricity price and natural gas price data. The example is written and solved in a Cplex solver.
The planning age limit is 10 years, and the capital discount rate is 3%. The whole year is divided into 3 typical days in summer, winter and transition season. Considering short-term uncertainty of wind power photovoltaic output, setting probability distribution of a distributed photovoltaic system and wind power system output to be respectively processed according to Beta distribution and Weibull distribution, generating 10 wind power scenes and 10 photovoltaic scenes for each typical day, namely nw = ns =10, and total scenes of the whole year are shared
The number of scenes is reduced to 18 through synchronous back substitution elimination. Considering the influence of long-term uncertainty of various types of growth of electric heating and cooling air on the planning result, the annual growth rate of various types of loads is set to be 3%. Respectively applying a deterministic model and an optimized configuration model for calculating uncertainty to solve the calculation example, setting parameters as an avoidance coefficient sigma =0.2, a risk coefficient mu =0.1, and a deviation coefficient weight ratio of an electric heating and cooling air load to xi 1 :ξ 2 :ξ 3 :ξ 4 ＝1:1:1:1。
5.2 comparison of results analysis by different methods
In order to verify the effectiveness and superiority of the method provided by the scheme, 4 scenes are set for carrying out example simulation analysis. Scene 1: carrying out example analysis by using a deterministic model of the text without considering the wind-solar output uncertainty and the load prediction uncertainty; scene 2: setting a robust planning method considering multi-energy load uncertainty only by considering load uncertainty; scene 3: using an optimization configuration method for researching uncertainty by robust optimization; scene 4: a configuration method is optimized using a multi-power flow system that accounts for uncertainty herein. The system equipment capacity selection is shown in table 1, the planning result is shown in table 2, the optimized configuration cost analysis is shown in table 3, and the model calculation time comparison is shown in table 4. The fluctuation range of the load increase uncertainty is 0.107, and the planning total cost of the optimized configuration models of the scenes 1 to 4 are 426.79 ten thousand yuan, 457.12 ten thousand yuan, 510.58 ten thousand yuan and 498.65 ten thousand yuan respectively.
TABLE 1 Equipment Capacity parameter Table
TABLE 2 optimized configuration planning results
TABLE 3 optimized configuration cost analysis
TABLE 4 model calculation time comparison
Overall, the increased load has led to a year-by-year increase in the commercial site energy supply and storage configurations. Compare 4 types of scenes: 1) Compared with the scenes 1 and 2 and the scenes 3 and 4, the short-term uncertainty of wind-solar output is considered, so that the wind power and photovoltaic installation configuration is reduced, the CHP configuration is increased, and the electric energy prediction error at the source side is responded, so that the investment cost and the equipment maintenance cost are higher; 2) Compared with the scene 2, the scene 1 has the disadvantages that the uncertainty of the load increase changes the construction scheme of the GB, so that the investment cost, the operation cost and the outsourcing cost are increased; 3) Compared with scenarios 3 and 4, one more GB is added to the configuration scheme of scenario 3, which results in an increase in overall investment cost, an increase in gas purchase cost, and a decrease in electricity purchase cost, with the total cost slightly higher than that of scenario 4.
Due to the price advantage of WT, the system becomes the energy supply equipment with the most new configuration when the load is increased; the ES can deal with the fluctuation caused by long-term uncertainty of load increase, can effectively relieve the influence of wind power photovoltaic output uncertainty on a system, and is usually constructed in a matching way with a wind power photovoltaic generator set; in scenario 4, a new configuration of the coupling device EC occurs, possibly because its lower operating cost is more economically advantageous in dealing with fluctuations in the electrical cooling load.
In the solution time, the complexity of the deterministic model, the load uncertainty model and the source load uncertainty model is sequentially improved, and the solution time is sequentially prolonged. Compared with the robust optimization of the scene 3, the model constructed by the scene 4 does not need the boundary of the uncertainty, the needed uncertainty information is less, and the solving time is slightly faster than that of the scene 3.
In summary, compared with other planning methods, the method for scenario 4 constructed in this embodiment has the following advantages: 1) Considering the influence of uncertainty on the source load and the load on the planning cost and quantifying the uncertainty risk loss, in the embodiment, when the difference between the load increase and the predicted value is within 10.7%, the loss compensation added by an investor is not more than 20%; 2) Compared with the robust optimization of the scene 3, the scene 4 constructs a model by using the avoidance coefficient, the risk coefficient and the fluctuation range of the uncertain quantity, only the representation of the risk acceptance or aversion capability of an investor and the fluctuation interval of the uncertain quantity are needed, the boundary of the uncertain quantity is not needed, and the needed uncertain quantity information is less; 3) Compared with the robust optimization of the scene 3, the planning cost of the scene 4 is lower, the configuration scheme is more reasonable, and the conservative degree of the planning strategy is reduced.
5.3 avoidance coefficient analysis
The avoidance coefficient sigma represents the proportion of the investment cost acceptable by investors in the model higher than the predicted value, and mainly characterizes the loss caused by uncertainty of load increase. In this example, if the value of σ is set to 0.2 and the value of α obtained by simulation is 0.107, that is, if the increase in load is within 10.7%, the loss compensation added by the investor is not more than 20%. The result of changing the set value of σ to obtain the load increase fluctuation range α is shown in fig. 3, where the larger the value of σ, the larger the investor is willing to add a larger loss compensation, the larger the allowable load increase fluctuation range for planning, the better the robustness of the system, and the larger the planning investment of the system. Conversely, the smaller the sigma value is, the poorer the robustness of the system is, and the smaller the planning investment of the system is.
5.4 Risk coefficient analysis
The risk coefficient mu corresponds to the weight coefficient of the CVaR and represents the preference level of a decision maker to the risk, and mu is more than or equal to 0. Generally, when mu is more than or equal to 0.10, the decision maker selects a risk avoidance strategy, and a certain gain is exchanged for a smaller risk. When mu is less than or equal to 0.05, the decision maker selects the opportunity to seek the strategy, and more benefits are obtained with higher risk. In this example, the value of μ is set to 0.1, and the value of α is obtained by simulation to 0.107. The results of changing the set value of μ to obtain the load increase fluctuation range α are shown in fig. 4. If the value of mu is smaller, investors tend to avoid risks caused by wind and light output fluctuation and select other power supply modes, and the robustness of the system is better. Conversely, if the value of μ is larger, the wind-solar configuration is more, the investment risk is larger, and the robustness of the system is poorer.
The embodiment firstly analyzes typical energy supply equipment, energy storage equipment and energy coupling conversion equipment of a multi-energy flow energy supply network in the EH, and analyzes a physical mechanism and input-output characteristics of the energy supply network; secondly, an optimized configuration model objective function is constructed, constraints such as EH operation, energy storage operation, source charge supply and demand matching and the like are analyzed, prediction deviation caused by electric heating and cooling air load increase in the model is processed by applying an information gap decision theory, and risk loss caused by wind and light output prediction deviation is evaluated by applying a condition risk value theory regulation modeling scheme.
Example two
The embodiment provides a multi-energy flow system optimal configuration system considering source load uncertainty.
A multi-energy flow system optimal configuration system accounting for source-to-load uncertainty, comprising:
a data acquisition module configured to: acquiring electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data;
an uncertainty amount fluctuation range determination module configured to: calculating the fluctuation range of the equivalent uncertain quantity according to the actual fluctuation range of the electric heating cold air load;
a multi-energy flow system optimization configuration model building module configured to: based on information gap decision and a condition risk value theory, a deterministic optimization configuration model is combined to construct a multi-energy flow system optimization configuration model;
an objective function determination module configured to: constructing a target function of the total planning cost by taking the maximum loss which can be borne by a decision maker as a limiting condition according to the set risk coefficient and evasion coefficient;
a multi-energy flow system optimization configuration model building module accounting for source-to-load uncertainty configured to: based on the electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data, under the condition of the maximum equivalent uncertain quantity fluctuation range, according to a target function and an uncertain variable model of the total planning cost, combining a constraint condition and a multi-energy flow system optimization configuration model, and constructing a multi-energy flow system optimization configuration model considering source load uncertainty;
a solving module configured to: and solving the optimal configuration model of the multi-energy flow system, which accounts for the source load uncertainty, to obtain an optimal configuration scheme of the multi-energy flow system.
It should be noted here that the data acquisition module, the uncertainty fluctuation range determination module, the multi-energy flow system optimization configuration model construction module, the objective function determination module, the multi-energy flow system optimization configuration model construction module and the solving module that account for the source load uncertainty are the same as the example and the application scenario realized by the steps in the first embodiment, but are not limited to the contents disclosed in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the method for optimally configuring a multi-energy flow system taking into account source load uncertainty as described in the first embodiment above.
Example four
The embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the processor implements the steps in the method for optimally configuring a multi-energy flow system, which takes into account source load uncertainty as described in the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A multi-energy flow system optimal configuration method considering source load uncertainty is characterized by comprising the following steps:
acquiring electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data;
calculating the equivalent uncertain quantity fluctuation range according to the actual fluctuation range of the electric heating and cold air load;
based on information gap decision and a condition risk value theory, a deterministic optimization configuration model is combined to construct a multi-energy flow system optimization configuration model;
constructing a target function of the total planning cost by taking the maximum loss which can be borne by a decision maker as a limiting condition according to the set risk coefficient and evasion coefficient;
based on the electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data, under the condition of the maximum equivalent uncertain quantity fluctuation range, according to a target function and an uncertain variable model of the total planning cost, combining a constraint condition and a multi-energy flow system optimization configuration model, and constructing a multi-energy flow system optimization configuration model considering source load uncertainty;
and solving the optimal configuration model of the multi-energy flow system considering the source load uncertainty to obtain the optimal configuration scheme of the multi-energy flow system.
2. The method for optimal configuration of a multi-energy flow system considering source load uncertainty according to claim 1, wherein the fluctuation range of the equivalent uncertainty is as follows:
and has xi 1 +ξ 2 +ξ 3 +ξ 4 ＝1
In the formula (I), the compound is shown in the specification,
for equivalent uncertainty variable fluctuation range, xi 1 、ξ 2 、ξ 3 、ξ 4 Weight coefficient, alpha, representing the fluctuation range of electric heating and cooling air load e 、α h 、α c 、α g Respectively showing the actual fluctuation range of the electric heating and cooling air load.
3. The method of claim 1, wherein the deterministic optimal configuration model comprises:
considering the comprehensive economy of the planning scheme, and taking the minimum total planning cost as an optimization target;
wherein C represents the total planned cost in T years, C inv,t Total investment cost of year t, C op,t Total operating cost for the t year; tau is t Coefficient, τ, for year t in relation to cash discount rate r t ＝(1+r) -t ；C inv,t Is defined as follows:
in the formula, σ k Investment cost, P, for k unit of capacity of the energy supply unit k For commissioning capacity of energy supply unit k, N k,t The built number of the energy supply unit k in the t year; rho m Investment cost for m unit capacity of energy storage equipment, E m For the commissioning capacity of the energy storage device m, Y m,t The built quantity of the energy storage equipment m in the t year; c op,t Is defined as follows:
in the formula, N d D, the number of continuous days of a typical day in a year, and D typical days in the year;
respectively representing the operation cost of an energy supply unit, the operation cost of energy storage equipment, the electricity purchasing cost of an energy junction from an external power grid and the gas purchasing cost of an external natural gas grid in the t year, the d typical day and the h period;
represents the operating cost of the energy supply unit k per unit power,
representing the total operating power of the energy supply unit k in the h period;
the m unit power operation cost of the energy storage unit is shown,
respectively representing the total charging power and the discharging power of the energy storage device m in the h period;
represents the market electricity rate for the h period,
representing the active transaction amount of the energy hub and the external power grid;
the unit price of the market natural gas in the h period,
representing the amount of natural gas purchased by the energy hub from the outside.
4. The method of claim 1, wherein the optimal configuration model of the multi-energy flow system comprises:
in the formula (I), the compound is shown in the specification,
to account for the year t operating cost of the conditional risk assessment,
is an expected value of annual operating costs; establishing a wind speed and illumination intensity scene set under a typical day d in the t year by using Latin hypercube sampling; n is w ，n s For the corresponding number of scenes, pi (n) w )、π(n s ) The probability of each scene;
is the nth at the typical day d and h of the t year w ，n s The running cost of energy supply equipment, the running cost of energy storage equipment, the electricity purchasing cost and the gas purchasing cost under the condition of each wind speed and illumination scene set.
5. The method of claim 1, wherein the objective function of the total planning cost is:
C′＝C+μCVaR
wherein C' represents a cost function accounting for CVaR; c (-) expresses an investment planning cost function; sigma represents an avoidance coefficient and represents the proportion of the investment cost acceptable by an investor higher than a predicted value; mu represents a risk coefficient reflecting the level of aversion of the investor to the risk; d represents a decision variable; x represents an uncertain variable.
6. The method of claim 1, wherein the uncertainty variable model comprises:
in the formula, α represents a fluctuation range of the indeterminate amount; c (-) expresses an investment planning cost function; sigma represents an avoidance coefficient and represents the proportion of the investment cost acceptable by an investor higher than a predicted value; d represents a decision variable; x represents an uncertain variable, h and g represent equality constraint and inequality constraint respectively; Γ denotes the predicted value of the uncertain variable X around it
Set of fluctuation ranges.
7. The method of claim 1, wherein the constraints comprise:
1) Energy hub restraint
The relationship between the upper limit and the lower limit of the output of the energy supply unit is as follows:
in the formula, P k,min For the lower active power output limit, P, of the energy supply unit k k,max The active power output upper limit of the energy supply unit k is set;
the maximum transmission power of the energy hub and the external power grid;
2) Climbing power of CHP unit
In the formula (I), the compound is shown in the specification,
electric power, theta, for cogeneration units up 、θ down The upward and downward climbing rates of the cogeneration unit are respectively;
3) Energy storage equipment operation and charge-discharge energy constraint
Q t,d,0 ＝Q t,d,H
In the formula, Q t,d,h In order to store the energy, the energy storage capacity,
charging and discharging efficiency of the energy storage device;
4) Supply and demand matching constraints
Supply and demand matching constraints ensure that the installation capacity of the electric heating and cooling air energy supply unit in the t year is greater than or equal to the load peak value of the electric heating and cooling air;
in the formula (I), the compound is shown in the specification,
respectively represents the peak value of the electric heating and cooling load in the t year,
respectively representing the capacity of the energy hub output to the load part in the t year;
5) Other constraints
N k,0 ≤N k,t-1 ≤N k,t ≤N k,T
Y k,0 ≤Y k,t-1 ≤Y k,t ≤Y k,T
In the formula, N k,0 ，N k,T ，Y k,0 ，Y k,T The initial commissioning amount and the maximum commissioning amount of the energy supply device and the energy storage device are respectively represented.
8. A multi-energy flow system optimal configuration system that accounts for source-to-load uncertainty, comprising:
a data acquisition module configured to: acquiring electricity, heat, cold and gas load data, wind power photovoltaic output data and equipment parameter data;
an uncertainty amount fluctuation range determination module configured to: calculating the fluctuation range of the equivalent uncertain quantity according to the actual fluctuation range of the electric heating cold air load;
a multi-energy flow system optimization configuration model building module configured to: based on information gap decision and a condition risk value theory, a deterministic optimization configuration model is combined to construct a multi-energy flow system optimization configuration model;
an objective function determination module configured to: constructing a target function of the total planning cost by taking the maximum loss which can be borne by a decision maker as a limiting condition according to the set risk coefficient and evasion coefficient;
a multi-energy flow system optimization configuration model building module accounting for source-to-load uncertainty configured to: based on the electricity, heat, cold and air load data, the wind power photovoltaic output data and the equipment parameter data, under the condition of the maximum equivalent uncertain quantity fluctuation range, according to a target function and an uncertain variable model of the total planning cost, and in combination with a constraint condition and a multi-energy flow system optimization configuration model, constructing a multi-energy flow system optimization configuration model considering source load uncertainty;
a solving module configured to: and solving the optimal configuration model of the multi-energy flow system considering the source load uncertainty to obtain the optimal configuration scheme of the multi-energy flow system.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method for optimal configuration of a multi energy flow system taking into account source load uncertainty as claimed in any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the method for optimal configuration of a multi-energy flow system taking into account source-to-load uncertainty as claimed in any one of claims 1 to 7.
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| CN116341762B (en) * | 2023-05-23 | 2023-07-25 | 武汉中元华电科技股份有限公司 | Optimal energy flow solving method and system for high-altitude wind power supply system |
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