CN110752599B - Distributed power supply grid-connected configuration method - Google Patents
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Abstract
The embodiment of the invention provides a distributed power supply grid-connected configuration method, which comprises the steps of constructing an upper layer objective function, wherein the upper layer objective function takes the net income of the asset full life cycle as the maximum target, and takes the installation position and the capacity of a distributed power supply as upper layer decision variables; constructing a lower-layer objective function, wherein the lower-layer objective function takes the minimum expected value of the net load peak-valley difference after the distributed power supply is absorbed as a target, and takes the electricity price as a lower-layer decision variable; and solving the upper layer objective function and the lower layer objective function to obtain an objective result, and completing the configuration of the distributed power supply based on the objective result. The method solves the problem that how a self-operated power distribution network main body coordinates the configuration of new energy power generation and the excavation and management of user side resources in the process of planning and operating the power distribution network; by simultaneously considering the planning-operation problem of the self-operation power distribution network, a double-layer opportunity constraint model combining a distributed power supply and demand response is established.
Description
Technical Field
The invention belongs to the field of power distribution network planning, and particularly relates to a distributed power supply grid-connected configuration method.
Background
In the prior art, incremental power distribution investment business needs to be gradually released to market main bodies meeting conditions according to requirements favorable for promoting the construction and development of a power distribution network and improving the power distribution operation efficiency. This means that a separate power distribution network operator will be present, which will be referred to herein as a private power distribution network. The self-operated power distribution network main body can face a decision problem of how to coordinate new energy power generation configuration and user side resource mining and management in the process of planning and operating the power distribution network, and obtain greater comprehensive benefits. Distributed Generation (DG) and Demand Response (DR) loads are important interactive resources in a self-operation power distribution network mode, and if a demand response mechanism can be incorporated into a DG grid-connected planning process, the two are managed in a coordinated mode, so that the purposes of optimizing a power utilization mode and improving the economic operation level of a power grid can be achieved.
At present, scholars at home and abroad make some researches on DG and demand response planning methods in power distribution networks. However, planning models are built from different angles, single planning research is carried out on DGs or demand responses in a public power distribution network, research is not carried out on investment decision problems of new energy configuration of a self-operated power distribution network, the cooperative optimization configuration problem of the DGs and the demand responses and the time sequence characteristics of probability distribution of the DGs and loads at different time periods and load demand variable quantities in a demand response mechanism are not considered, and operation optimization factors are not considered in planning. In order to reduce the influence of distributed power supply grid connection on a self-operation power distribution network and improve the economic benefit and the resource utilization efficiency of the self-operation power distribution network, it is necessary to research a distributed power supply grid connection configuration method.
Disclosure of Invention
The invention provides a distributed power supply grid-connected configuration method. Under the big background of the self-operation power distribution network, the problem that how a self-operation power distribution network main body coordinates the configuration of new energy power generation and the excavation and management of user side resources in the power distribution network planning and operating process is solved by considering the influence of demand response on the self-operation power distribution network planning; by simultaneously considering the problem of planning-operation of the self-operation power distribution network, a double-layer opportunity constraint model of the combination of the distributed power supply and the demand response is established, namely, the purpose of optimal configuration of the distributed power supply in the self-operation power distribution network is achieved by considering the influence of various factors.
In order to solve the above technical problem, an embodiment of the present invention provides the following technical solutions:
a distributed power grid connection configuration method comprises the steps of,
constructing an upper-layer objective function, wherein the upper-layer objective function takes the net income of the asset in the whole life cycle as the maximum target and takes the installation position and the capacity of the distributed power supply as upper-layer decision variables;
constructing a lower-layer objective function, wherein the lower-layer objective function takes the minimum expected value of the net load peak-valley difference after the distributed power supply is absorbed as a target, and takes the electricity price as a lower-layer decision variable;
and solving the upper layer objective function and the lower layer objective function to obtain an objective result, and completing the configuration of the distributed power supply based on the objective result.
Preferably, the net profit of the asset life cycle is obtained at least based on the operation profit of the self-operated power distribution network in the planning period, the cost of purchasing power to the superior main network and the installation, operation and maintenance cost of the distributed power supply.
Preferably, the constraints of the upper layer objective function include a single-node distributed power supply capacity constraint and a permeability constraint.
Preferably, the post-consumer payload peak-to-valley difference expected value of the distributed power supply is obtained at least based on a high peak value of the system payload and a low valley value of the system payload.
Preferably, the constraint conditions of the lower layer objective function comprise a power flow equality constraint, a demand response power supplier constraint, a demand response power consumption constraint and an opportunity inequality constraint.
Preferably, the solving of the upper layer objective function and the lower layer objective function to obtain the objective result includes,
respectively obtaining an upper layer decision variable and a lower layer decision variable based on the initial time sequence sample;
obtaining a time sequence sample of the load demand variation quantity based on the initial time sequence sample, the lower layer decision variable and the load demand response;
and performing load flow calculation based on the initial time sequence sample and the time sequence sample of the load demand variation, and judging whether the upper-layer decision variable is a target result based on the result of the load flow calculation.
Preferably, before deriving the upper layer decision variable and the lower layer decision variable, respectively, based on the initial time series samples, including,
and obtaining an initial time sequence sample based on the probability distribution of the distributed power supply and the load.
Preferably, the determining whether the upper decision variable is a target result based on the result of the power flow calculation includes,
when the result of the load flow calculation shows that the electrical parameter is within a preset range, the upper-layer decision variable is shown as a target result;
and when the result of the power flow calculation shows that the electrical parameter is not in the preset range, the upper-layer decision variable is not the target result.
Preferably, the obtaining the initial time sequence sample based on the probability distribution of the distributed power sources and the loads includes processing the probability distribution of the distributed power sources and the loads by using an MLHS technology to obtain the initial time sequence sample.
Preferably, the solving of the upper layer objective function and the lower layer objective function includes,
and solving the upper layer objective function and the lower layer objective function by applying a random simulation technology and combining a nested ESGA mixed intelligent algorithm.
Based on the disclosure of the above embodiments, it can be known that the embodiments of the present invention have the following beneficial effects:
under the big background of the self-operation power distribution network, the problem that how a self-operation power distribution network main body coordinates the configuration of new energy power generation and the excavation and management of user side resources in the power distribution network planning and operating process is solved by considering the influence of demand response on the self-operation power distribution network planning; by simultaneously considering the problem of planning-operation of the self-operation power distribution network, a double-layer opportunity constraint model of the combination of the distributed power supply and the demand response is established, namely, the purpose of optimal configuration of the distributed power supply in the self-operation power distribution network is achieved by considering the influence of various factors.
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Fig. 1 is a schematic flow chart of a distributed power supply grid connection configuration method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method for obtaining an initial timing sample according to an embodiment of the present invention.
Detailed Description
The following detailed description of specific embodiments of the present invention is provided in connection with the accompanying drawings, which are not intended to limit the invention.
It will be understood that various modifications may be made to the embodiments disclosed herein. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It should also be understood that, although the invention has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of the invention, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as not to obscure the present disclosure with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings,
as shown in fig. 1, an embodiment of the present invention provides a distributed power grid connection configuration method, where the method includes,
constructing an upper-layer objective function, wherein the upper-layer objective function takes the net income of the asset in the whole life cycle as the maximum target and takes the installation position and the capacity of the distributed power supply as upper-layer decision variables;
constructing a lower-layer objective function, wherein the lower-layer objective function takes the minimum expected value of the net load peak-valley difference after the distributed power supply is absorbed as a target, and takes the electricity price as a lower-layer decision variable;
and solving the upper layer objective function and the lower layer objective function to obtain an objective result, and completing the configuration of the distributed power supply based on the objective result.
In this embodiment, the installation position and capacity of the distributed power supply are used as upper-layer decision variables, the electricity price is used as a lower-layer decision variable, and the demand response of the user side is influenced by the electricity price, that is, the lower-layer decision variable is related to the demand response of the user side. Under the large background of the self-operation power distribution network, the problem of how to coordinate the configuration of new energy power generation and the excavation and management of user side resources in the power distribution network planning and operating process of a self-operation power distribution network main body is solved by considering the influence of demand response on the self-operation power distribution network planning; by simultaneously considering the problem of planning-operation of the self-operation power distribution network, a double-layer opportunity constraint model of the combination of the distributed power supply and the demand response is established, namely, the purpose of optimal configuration of the distributed power supply in the self-operation power distribution network is achieved by considering the influence of various factors.
In one embodiment provided by the invention, the net profit of the asset life cycle is obtained based on at least the operation profit of the self-operation power distribution network, the cost of purchasing power to the superior main network and the installation, operation and maintenance cost of the distributed power supply in the planning period. In another embodiment provided by the present invention, the constraints of the upper layer objective function include a single node distributed power capacity constraint and a permeability constraint.
In another embodiment provided by the present invention, the post-consumer payload peak-to-valley difference expected value of the distributed power source is obtained based on at least a high peak value of the system payload and a low valley value of the system payload. In one embodiment of the invention, the constraint conditions of the lower layer objective function comprise a power flow equality constraint, a demand response power supplier constraint, a demand response power consumer constraint and an opportunity inequality constraint.
In a specific embodiment, the specific steps of constructing the two-layer objective function are as follows:
1) constructing an upper level objective function
The upper layer objective function aims to maximize the net income of the full life cycle of the assets in the self-operating power distribution network.
Wherein f is the net benefit of the asset in the life cycle; f. of1The operation income of the self-operation power distribution network in the planning period is obtained; f. of2The cost of purchasing electricity to the upper level main network; f. of3Operating and maintaining costs for DG installation; f. oflossRevenue is improved for network loss; f. ofeBenefits for environmental improvements; f. ofsEarning for independent electricity sales; f. ofxThe peak eliminating benefit is obtained; y is the planning period years; y is the year of operation, d is the discount rate, and r is the inflation rate of the currency; j is the period of time, tjThe number of days corresponding to each time period in one year; cs、CeRespectively the unit electricity consumption cost, the unit electricity exhaust emission cost and Cs.jThe price of electricity sold in the self-operated power distribution network in the jth time period; ps.j、Pg.j、Pl.jThe network loss, the main network output power and the power of the self-operation power distribution network in the jth time interval are respectively; px、ZoRespectively eliminating peak values (mainly considering the avoidable capacity of the transformer substation of the self-operation distribution network after the demand response is implemented) and the investment cost of unit capacity of the transformer substation; cb.jSelling electricity and electricity prices for the main network in the jth time period; f. ofzFor DG mountingThen, the process is carried out; f. ofoOperating and maintaining costs for the DGs;
respectively the installation cost of the photovoltaic and the fan in unit capacity,
respectively the operation and maintenance costs of the photovoltaic and the fan in unit capacity; n is a radical of1、N2Respectively installing node numbers for photovoltaic and fan;
respectively the photovoltaic capacity and the fan rated capacity of the node i; ppv.i.j、Pwg.i,jThe photovoltaic power generation system and the fan are respectively the random output of the photovoltaic power generation system and the fan in the jth time period.
2) Constraints of upper layer objective function
The constraint conditions of the upper layer objective function comprise single-node DG capacity constraint and permeability constraint.
In the formula (I), the compound is shown in the specification,
DG grid-connected capacity, P, for node i accessDG.i.maxThe upper limit of the DG grid-connected capacity at the node i is set; pDG.max、PL.maxRespectively representing the total DG of the system and the maximum load capacity; pSE.maxIs the upper limit of the maximum permeability of DG.
3) Lower-layer objective function based on demand side electricity price response
And the lower-layer objective function aims at minimizing the net load peak-valley difference expected value after the DG is absorbed according to the energy supply condition of each time period of the DG and the energy consumption condition of each time period of the load.
In the formulaF is a net load peak-valley difference expected value after DG is absorbed, j is the number of time segments, and T is the total number of time segments; e (-) is the expectation function; l ism(j) The system net load at the mth simulation in the jth time interval; the calculation of the payload for any jth interval is:
in the formula, N is the total number of load nodes; delta P1iLoad demand variation after demand response implementation for the i-node; p0.liThe original load of the i node is obtained; pDGThe DG random output is given;
the load demand variation of the i node in the mth simulation in the jth period after the demand response is implemented;
the load of the i node at the mth simulation time in the jth period before the demand response is implemented;
the power generated by DG during the mth simulation in the jth period.
E(Lm(j) Calculated as follows):
where M is the number of samples in the jth period of the MLHS technique.
m is the number of simulations.
4) Constraint condition of lower-layer objective function based on demand side electricity price response
Flow equation constraint
In the formula, Pi.j、Qi.jRespectively injecting active power and reactive power into the node i in the time interval j; deltaik.jIs the voltage phase angle difference in time period j; gik、BikIs the network admittance. U shapei.jIs the voltage of node i for the jth period; a represents a node number set connected with a node i; k represents a value from the set a.
② power supplier constraint in demand response
For a power supply party, the economic benefit of the power distribution network side can be guaranteed after the demand response of the self-operation power distribution network is implemented through partial concession.
In the formula, Vi(j) The electricity price of the load of the i node in the j time period; v0.iElectricity prices before implementation for demand response; krIs the yield coefficient. Vi(j) The electricity price of the node i load in the jth time period after the demand response is implemented;
the i-node load demand variation of the power supplier in the jth period after the demand response is implemented;
the i node load of the power supplier in the jth period before the implementation of the demand response; v0.iThe electricity prices of the prior i-node loads are implemented for demand response.
Third, demand response power utilization constraint
For the electricity consumers, the unit load electricity price participating in electricity price response is ensured not to be increased after the demand response is implemented, and the load demand change value is within a certain range after the electricity price is adjusted.
ΔPi.min(j)≤E[ΔPi(j)]≤ΔPi.max(j)
In the formula,. DELTA.Pi.min、ΔPi.maxThe upper limit and the lower limit of the load demand variable quantity of the i node participating in the electricity price response in the j time interval are respectively.
Opportunistic inequality constraint
When the self-operation power distribution network operates, safe operation indexes such as node voltage, line current-carrying capacity and power reverse transmission main network in each time interval are used as opportunity constraint conditions.
In the formula, Pr {. is the probability that a certain event is true; beta is aU、βl、βgridRespectively the confidence level of the voltage, the line current-carrying capacity and the power back-off main network; i isk.j、ImaxThe current amplitude and the maximum value of the kth line in the time period j are shown; pDG.j、Pg.jAnd respectively providing the DG grid-connected total active power and the main power for the time period j. U shapeminAnd UmaxRespectively representing the lower limit and the upper limit of the node voltage allowance; u shapei.jRepresenting the voltage at node i for the jth period.
In an embodiment provided by the present invention, the solving the upper layer objective function and the lower layer objective function to obtain the objective result includes,
respectively obtaining an upper layer decision variable and a lower layer decision variable based on the initial time sequence sample;
obtaining a time sequence sample of the load demand variation quantity based on the initial time sequence sample, the lower layer decision variable and the load demand response;
and performing load flow calculation based on the initial time sequence sample and the time sequence sample of the load demand variation, and judging whether the upper-layer decision variable is a target result based on the result of the load flow calculation.
In one embodiment of the present invention, the solving the upper layer objective function and the lower layer objective function includes,
and solving the upper layer objective function and the lower layer objective function by applying a random simulation technology and combining an ESGA (Elite Strategy Genetic Algorithm, ESGA) hybrid intelligent Algorithm. The double-layer objective function is globally optimized by adopting a random simulation technology and combining with a nested ESGA (intelligent hybrid algorithm) to obtain a self-operation power distribution network location and volume-fixed planning scheme in a distributed power supply, and the obtained result is prevented from falling into local optimization.
In this embodiment, an upper layer decision variable and a lower layer decision variable are obtained based on an initial time sequence sample, the upper layer decision variable is an installation position and capacity of a distributed power supply, and the lower layer decision variable is a power price; then, based on the initial time sequence sample, the lower layer decision variable and the load demand response, obtaining a time sequence sample of the load demand variable quantity; and finally, performing load flow calculation based on the initial time sequence sample and the time sequence sample of the load demand variation, and judging whether the upper-layer decision variable is a target result based on the result of the load flow calculation. For example, in another embodiment provided by the present invention, the determining whether the upper layer decision variable is the target result based on the result of the power flow calculation includes indicating that the upper layer decision variable is the target result when the result of the power flow calculation indicates that the electrical parameter is within a preset range; and when the result of the power flow calculation shows that the electrical parameter is not in the preset range, the upper-layer decision variable is not the target result. That is, in the present invention, an upper layer decision variable and a lower layer decision variable are obtained based on an initial time series sample, the lower layer decision variable is a verification of the upper layer decision variable, and when a calculation result obtained based on the lower layer decision variable is within a preset range, it indicates that the upper layer decision variable is a target result, and configuration of the distributed power supply is completed based on the target result. Otherwise, recalculation is required, and the recalculation is not stopped until the calculation result obtained based on the lower layer decision variables is within the preset range.
In a specific embodiment provided by the present invention, the obtaining of the time series sample of the load demand variation based on the initial time series sample, the lower layer decision variable, and the load demand response includes the following steps:
the demand response comprises incentive-based demand response and price-based demand response, the influence of the electricity price on the load electricity utilization behavior is the largest, and the long-term economic benefit in planning and operating the self-operated power distribution network can be better reflected by considering the price-based demand response, so that only the price-based demand response is considered in the application. The price elasticity coefficient in the demand response based on the price can reflect the sensitivity degree of the load demand in each period to the electricity price reaction:
in the formula, subscripts g and w are time periods and represent the influence of electricity price in the time period g on load demand in the time period w; when g is equal to w, epsilongwThe elastic coefficient is called as self elastic coefficient, and the elastic coefficient is called as cross elastic coefficient when g is not equal to w; subscripts 0 and 1 are data before and after the electricity price adjustment respectively; i is a load node; p, V load and electricity price; Δ P and Δ V are the load demand and the price change amount, respectively.
Under the condition of knowing the price elastic coefficient epsilon, the load demand change quantity delta P in each period after the demand response is implemented can be obtained by the following formula and the initial time sequence samples of DG and load in 24 periods before the demand response is implemented.
In the formula, P0.liInitial time sequence samples of each time interval before demand response implementation are carried out for the i-node load; e is a price elastic coefficient matrix, and each element is epsilongw. The load demand variation Δ P involved in the demand response adjustment may be derived from the load initial timing sample P0And the electricity price V is formed for each period.
In another embodiment provided by the present invention, prior to deriving the upper layer decision variables and the lower layer decision variables, respectively, based on the initial time series samples, including,
and obtaining an initial time sequence sample based on the probability distribution of the distributed power supply and the load.
In an embodiment of the present invention, the obtaining the initial timing sequence sample based on the probability distribution of the distributed power sources and the loads includes processing the probability distribution of the distributed power sources and the loads by using an MLHS (latin hypercube sampling) technique to obtain the initial timing sequence sample.
In one embodiment of the present invention, obtaining an initial timing sample based on probability distributions of distributed power sources and loads includes the following steps:
by dividing time intervals and according to specific probability distribution parameters in each time interval, processing DGs and loads by using an MLHS technology to obtain initial time sequence samples of the DGs and the loads in 24 time intervals in one day, the specific process is as follows:
assuming that there are c DGs and loads in the system, the variable is H ═ H1,h2,…,hc) The system comprises a photovoltaic subject to Beta distribution, b wind turbines subject to Weibull distribution and c-a-b loads subject to normal distribution. The MLHS technology is used for processing DGs and loads which are taken from a specific probability distribution within a certain period.
1) Setting the sampling times in the jth time interval as M times, F (h)i) Is a variable hiA cumulative distribution function from a particular distribution parameter is taken over a period of time.
2) And (3) averagely dividing the interval [0,1] into M equal parts, so that the probability of each interval is 1/M, and selecting the middle value of each interval.
3) Obtaining a variable h by inverse transformationiCumulative distribution function F (h)i) Is inverse function of
The variable h is obtained according to the formulaiThe m-th sampled value h obeying a certain distribution parameter in a certain periodim。
In the formula (I), the compound is shown in the specification,
representing Beta, Weibull, Normal distribution functionThe inverse function of (c).
4) And when all variables in the vector H are sampled, obtaining a c multiplied by M sample matrix corresponding to the vector H in a certain period.
As can be seen from the above, DG of 24 periods a day and the initial time sequence samples of the load form a sample matrix of c × M order.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.
Claims (9)
1. A distributed power grid-connected configuration method is characterized by comprising the following steps,
constructing an upper-layer objective function, wherein the upper-layer objective function takes the net income of the asset in the whole life cycle as the maximum target and takes the installation position and the capacity of the distributed power supply as upper-layer decision variables;
constructing a lower-layer objective function, wherein the lower-layer objective function takes the minimum expected value of the net load peak-valley difference after the distributed power supply is absorbed as a target, and takes the electricity price as a lower-layer decision variable;
solving the upper layer objective function and the lower layer objective function to obtain an objective result, and completing the configuration of the distributed power supply based on the objective result; wherein the content of the first and second substances,
solving the upper layer objective function and the lower layer objective function to obtain an objective result, wherein the target result comprises an upper layer decision variable and a lower layer decision variable which are respectively obtained based on an initial time sequence sample; obtaining a time sequence sample of the load demand variation quantity based on the initial time sequence sample, the lower layer decision variable and the load demand response; and performing load flow calculation based on the initial time sequence sample and the time sequence sample of the load demand variation, and judging whether the upper-layer decision variable is a target result based on the result of the load flow calculation.
2. The method of claim 1, wherein the asset life cycle net gain is based on at least an operational gain of a private power distribution grid during a planning period, a cost of purchasing power to an upper level primary grid, and a distributed power installation operational maintenance cost.
3. The method of claim 1, wherein the constraints of the upper layer objective function comprise a single node distributed power capacity constraint and a permeability constraint.
4. The method of claim 1, wherein the post-consumer payload peak-to-valley difference expected value for the distributed power supply is derived based on at least a high peak value of a system payload and a low valley value of the system payload.
5. The method of claim 1, wherein the constraints of the underlying objective function include a power flow equality constraint, a demand response provider constraint, and an opportunity inequality constraint.
6. The method of claim 1, wherein prior to deriving the upper layer decision variables and the lower layer decision variables, respectively, based on initial timing samples, comprising,
and obtaining an initial time sequence sample based on the probability distribution of the distributed power supply and the load.
7. The method of claim 1, wherein determining whether the upper decision variable is a target result based on the result of the power flow calculation comprises,
when the result of the load flow calculation shows that the electrical parameter is within a preset range, the upper-layer decision variable is shown as a target result;
and when the result of the power flow calculation shows that the electrical parameter is not in the preset range, the upper-layer decision variable is not the target result.
8. The method of claim 6, wherein obtaining initial timing samples based on the probability distributions of the distributed power sources and the loads comprises processing the probability distributions of the distributed power sources and the loads using MLHS techniques to obtain initial timing samples.
9. The method of claim 1, wherein solving the upper and lower layer objective functions comprises,
and solving the upper layer objective function and the lower layer objective function by applying a random simulation technology and combining a nested ESGA mixed intelligent algorithm.
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