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CN108418213B - Source-load interaction-based power system scheduling method - Google Patents

Source-load interaction-based power system scheduling method Download PDF

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CN108418213B
CN108418213B CN201810224587.2A CN201810224587A CN108418213B CN 108418213 B CN108418213 B CN 108418213B CN 201810224587 A CN201810224587 A CN 201810224587A CN 108418213 B CN108418213 B CN 108418213B
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邢超
李胜男
马红升
陈勇
刘明群
周鑫
何廷一
和鹏
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
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Abstract

The application provides a source-load interaction power system scheduling method, the influence of electricity price factors is fully considered, not only is the unit combination determined, but also the social welfare is maximized. In this model, the wind power output is assumed to be determined, as is the price elastic demand curve. Meanwhile, the electricity demand in each time period comprises an elastic demand and a non-elastic demand, but the electricity demand in the consumer surplus of the objective function is only the elastic demand, and the consumer surplus of the non-elastic demand is 0. The mathematical model constructed in the way can comprehensively reflect the actual demand response of the user; in the process of determining the constraint condition of the objective function, the electric power system can be ensured to run safely and reliably, the decision variables meet some conventional constraint conditions, the constraint conditions after demand response are also considered, so that the model better reflects the actual situation, accurate basis is provided for later scheduling decision, the model is solved by using a sequence optimization algorithm, and the operation amount is greatly reduced.

Description

Source-load interaction-based power system scheduling method
Technical Field
The application relates to the technical field of power grid control, in particular to a source load interaction-based power system scheduling method.
Background
With the large-scale development and utilization of industrial revolution for hundreds of years, the driven fossil energy is facing the practical problems of resource exhaustion, serious pollution emission and the like, and the problems of environmental pollution, climate change and the like also seriously affect the sustainable development of human beings. Energy production and consumption modes based on the re-fossil energy are urgently needed to be changed. Meanwhile, intermittent new energy power generation represented by wind energy and solar energy in the world is generally in an accelerated development stage.
However, the basic feature of the power system is to ensure the balance of supply and demand of energy, and in the traditional dispatching mode, although the electric energy is difficult to store in a large amount, the primary energy of traditional power generation processes such as thermal power, hydroelectric power and nuclear power can be stored, so that the output of the electric energy is controllable. The power system typically only considers random uncertainties from the demand side and meets the predicted load demand by scheduling the start-stop and output of the generator set, i.e., using a mode of generating electricity to track the load. The essential difference between intermittent new energy power generation represented by wind power generation and solar power generation and traditional power generation is that primary energy, namely wind energy and solar energy, cannot be stored, and the power output of the intermittent new energy power generation can be controlled only under the constraint of the primary energy. Therefore, when large-scale wind power is connected to the power system, the power generation itself becomes uncontrollable, and thus randomness occurs on both sides of the power system source load.
At present, the power system scheduling model considering intermittent new energy access mainly includes two types of source-load interactive scheduling models, namely, scheduling considering only power supply side control and source-load interactive scheduling model considering demand side response. The former only uses the control resource at the power supply side as a scheduling means, and simultaneously restrains the output fluctuation of the load and the intermittent new energy. Due to the fact that adjustable resources of the power supply side are limited, with large-scale access of intermittent energy sources, the traditional method of scheduling only from the power supply side is difficult to meet actual requirements. And the demand side response has the characteristics of low cost and flexible control, so that the demand side response resource is included in the traditional scheduling model, and the method is a feasible and effective method. The demand side response is taken as a new decision-making means and incorporated into a scheduling system, and a day-ahead scheduling model of the system is constructed on the basis of the new decision-making means, so that the purpose of improving the operation benefit is achieved, at present, uncertainty of the demand side control means is less in consideration, and the model is not beneficial to application in practice, so that scheduling decision errors are caused, and the traditional model is high in solving complexity and large in calculation amount.
Disclosure of Invention
The application provides a source-load interaction power system scheduling method, which aims to solve the problems that currently, uncertainty of a demand side control means is considered a little, and a model is not beneficial to application in practice, so that scheduling decision errors are caused, and the traditional model is high in solving complexity and large in calculation amount.
The application provides a method for dispatching a source load interaction-based power system, which comprises the following steps:
acquiring the electricity consumption and the corresponding electricity price of each time interval of a user;
calculating to obtain a self-elasticity coefficient and a cross-elasticity coefficient according to the power consumption and the corresponding electricity price of each time interval;
generating an electricity price elastic matrix according to the self-elasticity coefficient and the cross elasticity coefficient;
generating an electricity price response model by using the electricity price elastic matrix, the electricity consumption of each period of the user and the corresponding electricity price;
generating a price elastic demand curve and an uncertainty set corresponding to the price elastic demand curve according to the electricity price response model;
linearizing the price elasticity demand curve to obtain a linear price elasticity curve and an uncertainty set corresponding to the linear price curve;
acquiring an uncertainty set of wind power output, a linear fuel cost function, a linear price elasticity curve and an uncertainty set corresponding to the linear price curve, and establishing a day-ahead scheduling model, wherein the day-ahead scheduling model comprises a target function and a plurality of constraint conditions;
acquiring an uncertainty set and a linear fuel cost function of wind power output, and establishing a day-ahead scheduling model by using the uncertainty set of the wind power output, the linear fuel cost function, a linear price elasticity curve and an uncertainty set corresponding to the linear price curve, wherein the day-ahead scheduling model comprises a target function and a plurality of constraint conditions;
solving the objective function by using an order optimization algorithm and a plurality of constraint conditions to obtain an optimal solution;
and executing a corresponding scheduling scheme according to the optimal solution.
According to the technical scheme, the source-load interaction power system scheduling method is provided, the influence of the electricity price factor is fully considered, the unit combination is determined, and the social welfare is maximized. The part defines some loads with electricity demand in real life but not influenced by electricity price as 'inelastic demand', such as hospitals and schools; the corresponding 'elastic demand' is defined as the change of the electricity demand with the change of the electricity price. In addition, in the model, the wind power output is assumed to be determined, and the price elasticity demand curve is also determined. While the power demand in each time segment contains both elastic and inelastic demand, the power demand in the consumer residue of the objective function is only elastic since the consumer residue of the inelastic demand is 0. The mathematical model constructed in the way can comprehensively reflect the actual demand response of the user; in the process of determining the constraint condition of the objective function, the electric power system can be ensured to run safely and reliably, the decision variables meet some conventional constraint conditions, the constraint conditions after demand response are also considered, so that the model better reflects the actual situation, accurate basis is provided for later scheduling decision, the model is solved by using a sequence optimization algorithm, and the operation amount is greatly reduced.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a flow chart of a method for source-to-load interactive power system scheduling;
FIG. 2 is a graph of price elastic demand;
FIG. 3 is a graph of demand and supply curves;
FIG. 4 is a plot of the piecewise function approximated price elastic demand.
Detailed Description
As shown in fig. 1, a method for scheduling a source-load interactive power system according to an embodiment of the present application includes:
step 11: and acquiring the electricity consumption and the corresponding electricity price of each time interval of the user.
The target user group and the typical users are determined, generally, large and medium-sized industrial and commercial users are main sources for providing demand response potential, and in addition, the target users can be expanded to first industry, small industrial and commercial users, residential users and the like according to specific demands. The specific electricity rates for each time period may include specific demand response items implemented by each place, such as peak-to-valley electricity rates (TOU), peak-to-peak electricity rates (CPP), real-time electricity Rates (RTP), and so on.
Step 12: and calculating to obtain the self-elasticity coefficient and the cross-elasticity coefficient according to the power consumption and the corresponding electricity price of each time interval.
The types of responses of the user to the electricity price include two types: single-period responses and multi-period responses. The single-period response is that the user only responds to the electricity price of the electricity in the current time period, increases or decreases the use and consumption of the electricity, and does not readjust the electricity load; for the multi-period response, the user responds to the electricity prices in different periods, namely, the user adjusts the own electricity utilization scheme according to the electricity price condition in each period. Compared with single-period response, the multi-period response is more consistent with the actual power utilization condition. In the multi-period response scene model, the elasticity coefficients are divided into self elasticity coefficients and cross elasticity coefficients which are respectively used for representing the electricity price response of the user to the current period and the electricity price response to other periods.
Step 13: and generating an electricity price elastic matrix according to the self-elasticity coefficient and the cross elasticity coefficient.
Step 14: and generating an electricity price response model by using the electricity price elastic matrix, the electricity consumption of each period of the user and the corresponding electricity price.
Step 15: and generating a price elastic demand curve and an uncertainty set corresponding to the price elastic demand curve according to the electricity price response model.
And the uncertainty set corresponding to the price elastic demand curve is used for reflecting an uncertainty model of the price elastic demand curve, wherein the uncertainty model comprises a deviation range and a constraint condition of the uncertainty of the price elastic demand curve.
The uncertainty of the demand side response is influenced by the habit of the user and can be seen by the electricity price response model, and particularly, as shown in fig. 2, for a given certain price p0, the corresponding demand is uncertain (the range of d 0). Similarly, for a given demand d0, the corresponding price may also vary within a certain range (range of p 0). Thus, the price elastic demand curve is
Figure GDA0003049063570000041
Or
Figure GDA0003049063570000042
Wherein
Figure GDA0003049063570000043
Representing the deviation of uncertainty for describing the price elastic demand curve,
Figure GDA0003049063570000044
may be given a reference point
Figure GDA0003049063570000045
The value of the parameter that is decided,
Figure GDA0003049063570000046
for node b's price of electricity during time period t,
Figure GDA0003049063570000047
is the price elasticity value given at node b in the time period t.
Curves as piecewise functions, for each of the price elastic demand curves
Figure GDA0003049063570000048
Corresponding to
Figure GDA0003049063570000049
Allowed in the range
Figure GDA00030490635700000410
Internal change in which
Figure GDA00030490635700000411
Represents a reference value of the reference electricity prices,
Figure GDA00030490635700000412
is that
Figure GDA00030490635700000413
The deviation of (a) is determined,
Figure GDA00030490635700000414
is that
Figure GDA00030490635700000415
The upper limit of (3). The uncertainty set corresponding to the price elastic demand curve is
Figure GDA00030490635700000416
Figure GDA00030490635700000417
Is that
Figure GDA00030490635700000418
The deviation of (a) is determined,
Figure GDA00030490635700000419
is that
Figure GDA00030490635700000420
T denotes a time period set, B denotes a bus, K denotes a price elastic demand curve.
Step 16: and linearizing the price elasticity demand curve to obtain a linear price elasticity curve and an uncertainty set corresponding to the linear price curve.
Generally, as electricity prices rise, demand will decrease. However, some power consumption is not affected by the price of power. This section is defined herein as "inelastic demand" and another section of demand varies with the price of electricity, which is defined herein as "elastic demand". The power demand, corresponding to the greatest social welfare, is defined as the sum of the consumer surplus and the supplier surplus. There is an infinite marginal value due to the inelastic demand component. The present application assumes that the portion of the consumer's remaining inelastic demand is a constant.
And step 17: the method comprises the steps of obtaining an uncertainty set of wind power output and a linear fuel cost function, and establishing a day-ahead scheduling model by utilizing the uncertainty set of the wind power output, the linear fuel cost function, a linear price elasticity curve and an uncertainty set corresponding to the linear price curve, wherein the day-ahead scheduling model comprises a target function and a plurality of constraint conditions.
Wind power is difficult to accurately characterize wind power output due to its intermittent nature. Assuming that the wind power is output at
Figure GDA00030490635700000421
Within the interval in which
Figure GDA00030490635700000422
A predicted value representing the wind output of bus b during time period t, an
Figure GDA00030490635700000423
Figure GDA00030490635700000424
Each represents highAbove and below the maximum allowed deviation value. The interval may typically be generated by using a quantile. For example, this document may provide
Figure GDA00030490635700000425
And
Figure GDA00030490635700000426
equal to the 0.95 quantile and the 0.05 quantile of the uncertain wind output, respectively. Actual wind power output
Figure GDA00030490635700000427
Is allowed to any value within a given interval. The present embodiment uses a cardinal uncertainty set to adjust the conservatism of the proposed model. For this method, the integer π is introduced herebAs a base budget to limit the number of time periods that the wind output is away from its predicted value at bus b. For example, ifbSet to 0, the wind output fluctuation at each bus is assumed to be small and can be approximated by a predicted value. If pibA significant fluctuation in wind output is considered to occur for no more than six time periods, 6. It can be considered that the "budget parameter" pibCan be used to adjust the conservatism of the system. For any given budget πbLess than 24, the optimal solution obtained based on the uncertainty set is still feasible, with a high probability (e.g., when π is between the given upper and lower limits of any possible wind outputbAnd when the probability is more than or equal to 8 percent, a robust optimization unit guarantee scheme is feasible, and the probability is higher than 95 percent). Under this arrangement, at each bus b, when the wind output reaches its upper, lower or predicted value and the total number of periods in which the wind output is not at its predicted value, the occurrence of a worst-case wind output situation should not exceed the budget value πb. Thus, the uncertain set of wind power outputs is
Figure GDA0003049063570000052
Figure GDA0003049063570000053
A predicted value representing the wind output of bus b over time period t; and
Figure GDA0003049063570000054
represents the maximum deviation value above and below the allowable value, respectively; pibAs a base budget to limit the number of time periods that the wind output is away from its predicted value at bus b;
Figure GDA0003049063570000055
and
Figure GDA0003049063570000056
is a binary variable, T represents a set of time periods, R|B|×|T|And representing a real number set, wherein the dimensionality of the real number set is | B | × | T |, B is a node set, and T is a time period set. The uncertainty set of the wind power output is used for reflecting an uncertainty model of the wind power output, wherein parameters such as an upper limit and a lower limit of the wind power output, constraint conditions of the wind power output in the power supply process and the like can be included, and the fluctuation range of the wind power supply can be determined by determining the uncertainty set.
Linear fuel cost function and linear price elasticity curve
Fuel cost function of unit in actual production
Figure GDA0003049063570000057
Can be expressed as a quadratic function, using N-piece linear functions to approximate the fuel cost function
Figure GDA0003049063570000058
Figure GDA0003049063570000059
And
Figure GDA00030490635700000510
is the intercept and slope of the jth segment function,
Figure GDA00030490635700000511
is an auxiliary variable, T represents a set of time periods, B represents a set of nodes, Gb represents a generator set at node B,
Figure GDA00030490635700000512
for the portion of node b that is inelastically demanded during time period t,
Figure GDA00030490635700000513
is a binary variable indicating whether generator i is on node b during time period t.
Assuming that the load on each bus includes both inelastic and elastic elements, the demand and supply curves can be modeled as shown in fig. 4. Power supply and demand at intersection
Figure GDA0003049063570000061
The balance is achieved, and as the day-ahead scheduling optimization model takes the power price factor into consideration, the objective function needs to ensure the maximum social welfare. In addition, in the model, the wind power output is assumed to be a determined value, and a price elasticity demand curve is also determined. The demand and supply curves are simulated as shown in fig. 3.
The finally determined day-ahead scheduling model includes an objective function and constraints,
the objective function is
Figure GDA0003049063570000062
T represents a set of time periods, B represents a set of nodes, Gb represents a generator set at node B,
Figure GDA0003049063570000063
representing the starting cost of generator i at node b,
Figure GDA0003049063570000064
for the cost of shutdown of generator i at node b,
Figure GDA0003049063570000065
for the amount of power generated by generator i during time period t at node b,
Figure GDA0003049063570000066
for the actual power demand of node b during time period t,
Figure GDA0003049063570000067
is the integral of the price elastic demand curve of node b over time period t,
Figure GDA0003049063570000068
as a function of the fuel cost of generator i at node b,
Figure GDA0003049063570000069
is a binary variable indicating whether generator i is activated at node b during time period t,
Figure GDA00030490635700000610
is a binary variable that indicates whether generator i is off at node b for time period t. The objective function maximizes social welfare and omits the constant part.
The constraint conditions comprise unit technical constraint conditions, system constraint conditions and demand response constraint conditions,
the technical constraint condition of the unit is
Figure GDA00030490635700000611
Figure GDA00030490635700000612
The two constraints represent a minimum on-time and a minimum off-time limit.
Figure GDA00030490635700000613
Figure GDA00030490635700000614
The two constraints are the start and stop state variables of the computer group.
Figure GDA0003049063570000071
The constraint is to enforce upper and lower power output limits for less gensets.
Figure GDA0003049063570000072
Figure GDA0003049063570000073
The two constraints are the slope limits for each unit implemented.
The system constraint condition is
Figure GDA0003049063570000074
This constraint ensures load balancing.
Figure GDA0003049063570000075
The constraint is a transmission line capacity limit.
The demand response constraint condition is
Figure GDA0003049063570000076
The constraint imposes a lower and upper limit on demand.
Figure GDA0003049063570000077
Wherein,
Figure GDA0003049063570000078
represents the minimum uptime of generator i at node b;
Figure GDA0003049063570000079
represents the minimum down time of generator i at node b;
Figure GDA00030490635700000710
represents the minimum power generation amount of the generator i at the node b;
Figure GDA00030490635700000711
the maximum power generation amount of the generator i at the node b;
Figure GDA00030490635700000712
is a binary variable indicating whether generator i is on node b for time period t;
Figure GDA00030490635700000713
is a binary variable indicating whether the generator i is started at the node b within the time period t;
Figure GDA00030490635700000714
is a binary variable indicating whether generator i is off at node b for time period t; Ω is a transmission line connecting two nodes;
Figure GDA00030490635700000715
the actual power demand of the node b in the time period t is obtained; u shapeijThe transmission capacity of a transmission line connecting node i and node b;
Figure GDA0003049063570000081
the inelastic requirement part of the node b in the time period t;
Figure GDA0003049063570000082
for node b's maximum demand during time period t,
Figure GDA0003049063570000083
is the inelastic demand for B during time period T, T represents the set of time periods, B represents the set of nodes, Gb represents the genset at node B,
Figure GDA0003049063570000084
and (3) representing the power reduction speed constraint of the ith generating set on the node b, wherein K is a set of all segment lengths of the price elastic demand curve, and K is a certain segment in the set of all segment lengths.
Step 18: and solving the objective function by using an order optimization algorithm and a plurality of constraint conditions to obtain an optimal solution.
The main principle of the sequence optimization method is that "sequence" is easier to compare than "value", which is consistent with the life experience of people, for example, when two hands each hold one ball, it can be determined which ball is heavier by lifting, but it is difficult to accurately determine how much weight is, and similarly, although it is not enough to accurately determine how much the performance difference between two solutions is, it is rather accurate to determine whether the two solutions are good or bad. If the optimal solution of the problem is extremely huge in solution space and is not feasible or difficult in calculation amount, from the application perspective, the final result can be relaxed to be a good enough solution, namely a group of good enough solutions can be found, and the best solution, namely the target softening, is not necessarily found. Finally, each solution in a group of sufficiently good solutions is accurately evaluated, and a best solution is selected from the solutions.
Step 19: and executing a corresponding scheduling scheme according to the optimal solution.
According to the technical scheme, the source-load interaction power system scheduling method is provided, the influence of the electricity price factor is fully considered, the unit combination is determined, and the social welfare is maximized. The part defines some loads with electricity demand in real life but not influenced by electricity price as 'inelastic demand', such as hospitals and schools; the corresponding 'elastic demand' is defined as the change of the electricity demand with the change of the electricity price. In addition, in the model, the wind power output is assumed to be determined, and the price elasticity demand curve is also determined. While the power demand in each time segment contains both elastic and inelastic demand, the power demand in the consumer residue of the objective function is only elastic since the consumer residue of the inelastic demand is 0. The mathematical model constructed in the way can comprehensively reflect the actual demand response of the user; in the process of determining the constraint condition of the objective function, the power system can be ensured to run safely and reliably, the decision variables meet some conventional constraint conditions, and the constraint conditions after demand response are also considered, so that the model can better reflect the actual situation, and an accurate basis is provided for later scheduling decisions.
In another embodiment of the present application, the step 14 includes:
step 141: calculating a self-elasticity coefficient and a cross-elasticity coefficient according to the following formulas according to the power consumption and the corresponding electricity price of each time interval;
Figure GDA0003049063570000085
Figure GDA0003049063570000091
Δ q and Δ p are the relative increments of the quantity of electricity q and the price of electricity p, respectively, εiiIs a coefficient of self-elasticity, epsilonijFor the cross elastic coefficient, i and j denote the i-th and j-th periods, respectively.
Step 142: generating an electric quantity and price elastic matrix according to the self elastic coefficient and the cross elastic coefficient, wherein the electric quantity and price elastic matrix is
Figure GDA0003049063570000092
The electricity price and electricity price elastic matrix is used for describing the relative change of electricity caused by the change of electricity price.
Step 143: using the electricity price elastic matrix, the electricity consumption of each period of the user and the corresponding electricity price, generating an electricity price response model of
Figure GDA0003049063570000093
In another embodiment of the present application, the step 16 includes:
step 161: and obtaining an optimized price elastic demand curve under the assumption that the price elasticity of the price elastic demand curve is constant, wherein the optimized price elastic demand curve is
Figure GDA0003049063570000094
Wherein
Figure GDA0003049063570000095
Representing the deviation of uncertainty for describing the price elastic demand curve,
Figure GDA0003049063570000096
may be given a reference point
Figure GDA0003049063570000097
The value of the parameter that is decided,
Figure GDA0003049063570000098
for node b's price of electricity during time period t,
Figure GDA0003049063570000099
is the price elasticity value given at node b in the time period t.
Figure GDA00030490635700000910
Is the inelastic demand for b during time t, since the demand has an inelastic component, so there is
Figure GDA00030490635700000911
In that
Figure GDA00030490635700000912
Further practical upper limits
Figure GDA00030490635700000913
To obtain
Figure GDA00030490635700000914
Thus, the inelastic component, i.e. the social welfare equals the demand curve
Figure GDA00030490635700000915
To
Figure GDA00030490635700000916
Integral of (expressed in the model as)
Figure GDA00030490635700000917
) Adding a constant (i.e. demand curve from 0 to
Figure GDA00030490635700000918
Integral of) and the integral of the supply curve from 0 to
Figure GDA00030490635700000919
In the model, the constant part is omitted for computational convenience, which will provide the same optimal solution.
Step 162: generating a corresponding piecewise function according to the optimized price elastic demand curve, referring to fig. 4, where the piecewise function is
Figure GDA0003049063570000101
Figure GDA0003049063570000102
Figure GDA0003049063570000103
Figure GDA0003049063570000104
Represents the optimized price elastic demand curve
Figure GDA0003049063570000105
To
Figure GDA0003049063570000106
The integral of (a) is calculated,
Figure GDA0003049063570000107
is the k-th segment of the piecewise function,
Figure GDA0003049063570000108
is the corresponding price at the k segments,
Figure GDA0003049063570000109
is an auxiliary variable introduced for demand at K segments, K is the set of all segment lengths of the price elastic demand curve, K is some of the set of all segment lengths.
Step 163: will be provided with
Figure GDA00030490635700001010
Maximizing to obtain a linear price elastic curve and an uncertainty set corresponding to the linear price elastic curve, wherein the linear price elastic curve is
Figure GDA00030490635700001011
When there is a certain s0So that
Figure GDA00030490635700001012
When it is established, it can prove
Figure GDA00030490635700001013
Is an approximate integral of the price elastic demand curve, i.e.
Figure GDA00030490635700001014
Is reasonable.
The uncertainty set corresponding to the linear price curve is
Figure GDA00030490635700001015
Figure GDA00030490635700001016
Figure GDA00030490635700001017
Is that
Figure GDA00030490635700001018
The deviation of (a) is determined,
Figure GDA00030490635700001019
is that
Figure GDA00030490635700001020
T represents a set of time periods, B represents a bus, K is a set of all segment lengths of the price elastic demand curve, K is a certain segment of the set of all segment lengths.
The above described embodiment proposes a price elastic demand curve and how to approximate it using a linear function. However, the actual price elastic demand curve is uncertain. When ISOs/RTOs make day-ahead scheduling decisions, the price elastic demand curve must be allowed to vary within a certain range. To adjust for conservation, parameters were introduced
Figure GDA0003049063570000111
To limit the total amount of deviation, i.e.
Figure GDA0003049063570000112
Can be changed by
Figure GDA0003049063570000113
To adjust the security of the proposed methodAnd (4) keeping the sex. The smaller the value, the less uncertainty in the demand response curve.
According to the technical scheme, the method for dispatching the source-load interactive power system is provided, the influence of the electricity price factor is fully considered, the unit combination is determined, and the social welfare is maximized. The part defines some loads with electricity demand in real life but not influenced by electricity price as 'inelastic demand', such as hospitals and schools; the corresponding 'elastic demand' is defined as the change of the electricity demand with the change of the electricity price. In addition, in the model, the wind power output is assumed to be determined, and the price elasticity demand curve is also determined. While the power demand in each time segment contains both elastic and inelastic demand, the power demand in the consumer residue of the objective function is only elastic since the consumer residue of the inelastic demand is 0. The mathematical model constructed in the way can comprehensively reflect the actual demand response of the user; in the process of determining the constraint condition of the objective function, the power system can be ensured to run safely and reliably, the decision variables meet some conventional constraint conditions, and the constraint conditions after demand response are also considered, so that the model can better reflect the actual situation, and an accurate basis is provided for later scheduling decisions.

Claims (4)

1. A method for source-load interaction power system scheduling, the method comprising:
acquiring the electricity consumption and the corresponding electricity price of each time interval of a user;
calculating to obtain a self-elasticity coefficient and a cross-elasticity coefficient according to the power consumption and the corresponding electricity price of each time interval;
generating an electricity price elastic matrix according to the self-elasticity coefficient and the cross elasticity coefficient;
generating an electricity price response model by using the electricity price elastic matrix, the electricity consumption of each period of the user and the corresponding electricity price;
generating a price elastic demand curve and an uncertainty set corresponding to the price elastic demand curve according to the electricity price response model;
linearizing the price elasticity demand curve to obtain a linear price elasticity curve and an uncertainty set corresponding to the linear price elasticity curve;
acquiring an uncertainty set and a linear fuel cost function of wind power output, and establishing a day-ahead scheduling model by using the uncertainty set, the linear fuel cost function, a linear price elasticity curve and an uncertainty set corresponding to the linear price elasticity curve of the wind power output, wherein the day-ahead scheduling model comprises a target function and a plurality of constraint conditions;
solving the objective function by using an order optimization algorithm and a plurality of constraint conditions to obtain an optimal solution;
executing a corresponding scheduling scheme according to the optimal solution;
the price elastic demand curve is
Figure FDA0003049063560000011
Or
Figure FDA0003049063560000012
Wherein
Figure FDA0003049063560000013
Representing the deviation of uncertainty for describing the price elastic demand curve,
Figure FDA0003049063560000014
is given a reference point
Figure FDA0003049063560000015
The value of the parameter that is decided,
Figure FDA0003049063560000016
for node b's price of electricity during time period t,
Figure FDA0003049063560000017
is the price elasticity value given at node b in time period t;
the uncertainty set corresponding to the price elastic demand curve is
Figure FDA0003049063560000018
Figure FDA0003049063560000019
Is that
Figure FDA00030490635600000110
The deviation of (a) is determined,
Figure FDA00030490635600000111
is that
Figure FDA00030490635600000112
T represents a time period set, B represents a bus, and K represents a price elasticity demand curve;
linearizing the price elasticity demand curve to obtain a linear price elasticity curve and an uncertainty set corresponding to the linear price elasticity curve, including:
and obtaining an optimized price elastic demand curve under the assumption that the price elasticity of the price elastic demand curve is constant, wherein the optimized price elastic demand curve is
Figure FDA00030490635600000113
Wherein
Figure FDA00030490635600000114
Representing the deviation of uncertainty for describing the price elastic demand curve,
Figure FDA00030490635600000115
is given a reference point
Figure FDA00030490635600000116
The value of the parameter that is decided,
Figure FDA00030490635600000117
for node b's price of electricity during time period t,
Figure FDA00030490635600000118
is the price elasticity value given at node b in time period t;
generating a corresponding piecewise function according to the optimized price elastic demand curve, wherein the piecewise function is
Figure FDA00030490635600000119
Figure FDA0003049063560000021
Figure FDA0003049063560000022
Figure FDA0003049063560000023
Represents the optimized price elastic demand curve
Figure FDA0003049063560000024
To
Figure FDA0003049063560000025
The integral of (a) is calculated,
Figure FDA0003049063560000026
is the k-th segment of the piecewise function,
Figure FDA0003049063560000027
is the corresponding price at the k segments,
Figure FDA0003049063560000028
is an auxiliary variable introduced for the demand at K sections, where K is a set of all section lengths of the price elastic demand curve, and K is a certain section of the set of all section lengths;
will be provided with
Figure FDA0003049063560000029
Maximizing to obtain a linear price elastic curve and an uncertainty set corresponding to the linear price elastic curve, wherein the linear price elastic curve is
Figure FDA00030490635600000210
The uncertainty set corresponding to the linear price elastic curve is
Figure FDA00030490635600000211
Figure FDA00030490635600000212
Is that
Figure FDA00030490635600000213
The deviation of (a) is determined,
Figure FDA00030490635600000214
is that
Figure FDA00030490635600000215
An upper limit of (1) with a parameter of
Figure FDA00030490635600000216
Limiting the total amount of deviation, T representing a time period set, B representing a bus, K representing a set of all segments of the price elastic demand curve, and K representing all segmentsA segment in the collection; the uncertain set of wind power output is
Figure FDA00030490635600000217
Wt b*A predicted value representing the wind output of bus b over time period t; and Wt b+，Wt b-Represents the maximum deviation value above and below the allowable value, respectively; pibAs a base budget to limit the number of time periods that the wind output is away from its predicted value at bus b;
Figure FDA00030490635600000218
and
Figure FDA00030490635600000219
is a binary variable, T represents a set of time periods, R|B|×|T|And representing a real number set, wherein the dimensionality of the real number set is | B | × | T |, B is a node set, and T is a time period set.
2. The method of source-to-charge interactive power system scheduling as claimed in claim 1, wherein said generating a power rate response model based on the power usage and corresponding power rates for each of said time periods comprises:
calculating a self-elasticity coefficient and a cross-elasticity coefficient according to the following formulas according to the power consumption and the corresponding electricity price of each time interval;
Figure FDA0003049063560000031
Figure FDA0003049063560000032
Δ q and Δ p are the relative increments of the quantity of electricity q and the price of electricity p, respectively, εiiIs a coefficient of self-elasticity, epsilonijFor the cross elastic coefficient, i and j represent the ith and jth periods, respectively;
generating an electric quantity and price elastic matrix according to the self elastic coefficient and the cross elastic coefficient, wherein the electric quantity and price elastic matrix is
Figure FDA0003049063560000033
Using the electricity price elastic matrix, the electricity consumption of each period of the user and the corresponding electricity price, generating an electricity price response model of
Figure FDA0003049063560000034
3. The source-to-load interactive power system scheduling method of claim 1, wherein the linear fuel cost function is
Figure FDA0003049063560000035
Figure FDA0003049063560000037
And
Figure FDA0003049063560000038
is the intercept and slope of the jth segment function,
Figure FDA0003049063560000039
is an auxiliary variable, T represents a set of time periods, B represents a set of nodes, Gb represents a generator set at node B,
Figure FDA00030490635600000310
for the portion of node b that is inelastically demanded during time period t,
Figure FDA00030490635600000311
for binary variables, usingIndicating whether generator i is on node b for time period t.
4. The source-to-load interactive power system dispatching method of claim 1,
the objective function is
Figure FDA0003049063560000036
T represents a set of time periods, B represents a set of nodes, Gb represents a generator set at node B,
Figure FDA00030490635600000312
representing the starting cost of generator i at node b,
Figure FDA00030490635600000313
for the cost of shutdown of generator i at node b,
Figure FDA00030490635600000314
for the amount of power generated by generator i during time period t at node b,
Figure FDA00030490635600000412
for the actual power demand of node b during time period t,
Figure FDA00030490635600000413
is the integral of the price elastic demand curve of node b over time period t, fi bAs a function of the fuel cost of generator i at node b,
Figure FDA00030490635600000414
is a binary variable indicating whether generator i is activated at node b during time period t,
Figure FDA00030490635600000415
is a binary variable indicating whether generator i is off at node b for time period t;
the plurality of constraints comprise unit technical constraints, system constraints and demand response constraints,
the technical constraint condition of the unit is
Figure FDA0003049063560000041
Figure FDA0003049063560000042
Figure FDA0003049063560000043
Figure FDA0003049063560000044
Figure FDA0003049063560000045
Figure FDA0003049063560000046
Figure FDA0003049063560000047
The system constraint condition is
Figure FDA0003049063560000048
Figure FDA0003049063560000049
The demand response constraint condition is
Figure FDA00030490635600000410
Figure FDA00030490635600000411
Wherein,
Figure FDA00030490635600000416
represents the minimum uptime of generator i at node b;
Figure FDA00030490635600000417
represents the minimum down time of generator i at node b;
Figure FDA00030490635600000418
represents the minimum power generation amount of the generator i at the node b;
Figure FDA00030490635600000419
the maximum power generation amount of the generator i at the node b;
Figure FDA00030490635600000420
is a binary variable indicating whether generator i is on node b for time period t;
Figure FDA00030490635600000421
is a binary variable indicating whether the generator i is started at the node b within the time period t;
Figure FDA00030490635600000422
is a binary variable indicating whether generator i is off at node b for time period t; Ω is a transmission line connecting two nodes;
Figure FDA00030490635600000423
the actual power demand of the node b in the time period t is obtained; u shapeijThe transmission capacity of a transmission line connecting node i and node b;
Figure FDA00030490635600000424
the inelastic requirement part of the node b in the time period t;
Figure FDA00030490635600000425
for node b's maximum demand during time period t,
Figure FDA00030490635600000426
is the inelastic demand for B during time period T, T represents the set of time periods, B represents the set of nodes, Gb represents the genset at node B,
Figure FDA00030490635600000427
and (3) representing the power reduction speed constraint of the ith generating set on the node b, wherein K is a set of all segment lengths of the price elastic demand curve, and K is a certain segment in the set of all segment lengths.
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