CN112701687B - Robust optimization operation method of gas-electricity distribution network system considering price type combined demand response - Google Patents

Robust optimization operation method of gas-electricity distribution network system considering price type combined demand response Download PDF

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CN112701687B
CN112701687B CN202110095674.4A CN202110095674A CN112701687B CN 112701687 B CN112701687 B CN 112701687B CN 202110095674 A CN202110095674 A CN 202110095674A CN 112701687 B CN112701687 B CN 112701687B
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CN112701687A (en
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刘天琪
张琪
何川
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Sichuan University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract

The invention discloses a robust optimization operation method of a gas-electricity distribution network system considering price type joint demand response, which aims at maximizing social benefits of the distribution network system and combines set related operation constraints to establish a deterministic optimization operation model of the gas-electricity comprehensive energy distribution network system; processing nonlinear alternating current power flow equation constraints of the power distribution network by adopting a second-order cone relaxation method, and providing a distribution network system optimal power flow optimization method combining enhanced second-order cone planning and air pressure recovery; aiming at key uncertainty factors existing in the operation of the distribution network system, an uncertainty set is constructed by using a robust optimization theory, and then a column and constraint generation algorithm is adopted to decompose a robust optimization operation model of the distribution network system into a main-sub problem frame for solving, so that a safe robust scheduling scheme capable of coping with the uncertainty of the system is obtained. On the basis of an optimized dispatching model of the distribution network system, the invention provides the method for improving the operation economy and flexibility of the distribution network system by using price type gas-electricity combined demand response.

Description

Robust optimization operation method of gas-electricity distribution network system considering price type combined demand response
Technical Field
The invention relates to the technical field of optimization operation of comprehensive energy systems, in particular to a robust optimization operation method of a gas-electricity distribution network system considering price type joint demand response.
Background
With the push of the energy internet, renewable energy is gradually replacing traditional fossil fuels such as coal and petroleum, and gradually becoming a main primary energy. Increasing the proportion of renewable energy resources in an energy system is a future development direction of the industry in the global energy field, and the rapid development of renewable energy resources such as wind energy, solar energy and the like becomes an important method for solving energy crisis and promoting energy conservation and emission reduction at home and abroad. With the continuous expansion of the scale of the renewable energy sources accessed to the power system, the phenomena of wind abandoning and light abandoning are frequently caused by the randomness and the intermittence of the renewable energy sources such as wind power, photoelectricity and the like, and the problem of the output and the consumption of the faced new energy sources is more severe. In order to solve the problems of low comprehensive energy efficiency of access of renewable clean energy and traditional energy, an energy interconnection sharing network characterized by interconnection, low carbon, high efficiency, multi-source coordination and the like is developed at the same time, and a comprehensive energy system with multi-source high-efficiency utilization and multi-element main body cooperation is formed.
The gas-electricity comprehensive energy system is an important component for the development of an energy internet, is a necessary trend for the future development of an energy integration system, and has important significance for improving the energy utilization efficiency, promoting the development and consumption of renewable energy sources, saving energy and reducing emission. The natural gas system utilizes the slow inertia of the natural gas system to convert surplus electric energy such as new energy power generation and the like into natural gas for energy storage, and further achieves the bidirectional flow of energy in the system through energy conversion devices such as a gas turbine set, an electric-to-gas device and the like. During the valley of electricity utilization, the electricity-to-gas equipment can convert redundant electric energy (mainly surplus renewable energy for power generation) into natural gas for storage; during the peak period of electricity utilization, the system converts the natural gas into electric energy through the gas turbine.
The gas-electricity integrated energy distribution network system is an important component of an integrated energy system and is a key development direction of a future urban energy network. The energy flow management and economic dispatching mechanisms of the traditional power distribution network and the traditional gas distribution network are relatively separated, and the independent dispatching operation mode of the traditional power distribution network and the traditional gas distribution network cannot meet the operation requirement of complementation of various energy sources. The gas-electricity integrated energy distribution network system is positioned at the tail end of energy transmission and is the most typical existing form of the integrated energy system. Which is the part with the closest interaction with the user and at the same time the part with the greatest energy loss. The convex optimization method for researching the optimal power flow of the gas-electricity integrated energy distribution network system provides support for the coordinated optimization operation and planning design of the gas-electricity integrated energy distribution network; the influence of key uncertainty in system operation is considered, and the flexibility and the economy of operation of the gas-electricity comprehensive energy distribution network system can be improved by performing coordinated optimization operation on the system in combination with a gas-electricity combined load demand response mechanism.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a two-stage robust optimization operation method of a gas-electricity integrated energy distribution network system considering price type gas-electricity combined demand response, which converts a non-convex optimization problem into a convex optimization problem by using a method of combining enhanced second-order cone planning and air pressure recovery and improves the relaxation precision of a model. And on the basis, the key uncertainty of system operation is considered, and a price type gas-electricity combined demand response mechanism is introduced to effectively improve the economy and safety of the operation of the distribution network system and the consumption capacity of new energy. The technical scheme is as follows:
a robust optimization operation method of a gas-electric distribution network system considering price type combined demand response comprises the following steps:
step 1: establishing an electric power and natural gas distribution network system model which comprises an electric power alternating current DistFlow power flow model and a natural gas transmission pipeline power flow model;
step 2: establishing a coupling equipment operation model which comprises a gas unit operation model and an electric-to-gas equipment operation model;
and step 3: respectively modeling price type electricity and gas load demand responses based on a segmented step-shaped load quotation curve, and introducing price type gas-electricity combined demand responses;
and 4, step 4: constructing a gas-electricity comprehensive energy distribution network system deterministic optimization operation model taking the maximized distribution network system social benefit as an optimization target and considering various operation constraint conditions of a distribution network and price type gas-electricity combined demand response;
and 5: processing the nonlinear power distribution network power flow equation constraint by using a second-order cone relaxation method, processing the natural gas power flow equation constraint by using a method of combining enhanced second-order cone planning and air pressure recovery, and converting a nonlinear gas-electricity comprehensive energy distribution network system optimization scheduling model into a mixed integer second-order cone planning problem to solve;
and 6: considering the output of renewable distributed energy and the uncertainty of gas and electric loads in system operation, constructing a corresponding uncertainty set by using a robust optimization theory, establishing a two-stage adjustable robust optimization scheduling model of the gas and electric comprehensive energy distribution network system considering price type gas and electric combined demand response, and converting the two-stage adjustable robust second-order cone optimization scheduling model into a main-sub problem frame for solving by using a column and constraint generation algorithm;
and 7: inputting structural data, equipment parameters and operation parameters of the gas-electricity comprehensive energy distribution network system, and solving a two-stage adjustable robust optimization operation model of the distribution network system by adopting a commercial solver Gurobi to obtain a robust optimization scheduling scheme of the distribution network system.
Further, the power and natural gas distribution network system model in the step 1 is specifically as follows:
(1) The DistFlow alternating current power flow model of the power distribution network:
Figure BDA0002914150360000031
Figure BDA0002914150360000032
Figure BDA0002914150360000033
Figure BDA0002914150360000034
Figure BDA0002914150360000035
Figure BDA0002914150360000036
in the formula: γ (j) is a branch end node set which takes j as a head end node in the power distribution network; r is ij 、X ij Respectively representing the resistance and reactance value of the ij section of the distribution line; I.C. A ij,t The current of the distribution line ij section at the moment t; v it Is the voltage magnitude of node i; v jt Is the voltage magnitude of node j; p st Is the generated power P of the gas turbine set s at time t at The consumed power of the electric-to-gas equipment a at the moment t;
Figure BDA0002914150360000041
the active power transmitted to the power distribution network for the superior power grid at the moment t; />
Figure BDA0002914150360000042
The reactive power transmitted to the power distribution network by the superior power grid at the moment t; p wt Generating capacity of a w-th typhoon electric field at the time t; p is ij,t The active power of the distribution line ij section in the power distribution network at the moment t; q ij,t Indicating reactive power of an ij section of a distribution line in the power distribution network at the moment t; p jk,t The active power of the jk section of the distribution line; q jk,t The reactive power of the jk section of the distribution line; p dt The actual value of the active load d at the moment t; q dt The actual value of the reactive load d at the moment t; />
Figure BDA0002914150360000043
The load loss value of the load d at the moment t; chi-type food processing machine dt The power factor of the electrical load at time t; />
Figure BDA0002914150360000044
The maximum current value allowed to pass through the section ij of the distribution line at the moment t; />
Figure BDA0002914150360000045
The lowest and highest allowed voltage values for node i. />
(2) A natural gas transmission pipeline trend model:
Figure BDA0002914150360000046
Figure BDA0002914150360000047
Figure BDA0002914150360000048
Figure BDA0002914150360000049
π mt ≥π nt
in the formula: g st Is the gas consumption of the gas turbine unit s at time t, G at Respectively the gas production rate of the electric gas conversion equipment a at the moment t;
Figure BDA00029141503600000410
the natural gas flow transmitted from the upper-level gas network to the gas distribution network at the time t; />
Figure BDA00029141503600000411
And &>
Figure BDA00029141503600000412
Minimum and maximum pressure limits for natural gas network node n, respectively; g mn,t Is the tide transmitted by the natural gas pipeline mn at the moment t>
Figure BDA00029141503600000413
The maximum power flow transmitted by the natural gas pipeline mn; g no,t Is the tide transmitted by the natural gas pipeline no at time t; g gt Is the actual value of the load g at time t; />
Figure BDA00029141503600000414
The load loss value is the load g at the moment t; pi mt And pi nt The gas pressures of the natural gas network nodes m and n are respectively; k mn Is a Weymouth characteristic parameter of a natural gas pipeline; y (n) is a set of tail end nodes where branches with the node n as a head end node are located in the natural gas network; GU is the set of gas turbine units.
Further, the coupling device operation model in step 2 is specifically as follows:
(1) Electric-to-gas equipment operation model:
Figure BDA00029141503600000415
Figure BDA00029141503600000416
in the formula: i is at The working state of the electric gas conversion equipment is set; g at The amount of natural gas produced by the electric gas conversion equipment;
Figure BDA00029141503600000417
the maximum power consumption of the electric gas conversion equipment; />
Figure BDA0002914150360000051
Is the energy conversion coefficient; HHV is high calorific value; />
Figure BDA0002914150360000052
The working efficiency of the electric gas conversion equipment is improved;
(2) The gas unit operation model is as follows:
Figure BDA0002914150360000053
Figure BDA0002914150360000054
Figure BDA0002914150360000055
Figure BDA0002914150360000056
SU st ≥su s ·(I st -I s(t-1) ),SU st ≥0
SD st ≥sd s ·(I s(t-1) -I st ),SD st ≥0
Figure BDA0002914150360000057
Figure BDA0002914150360000058
in the formula: GU is a set of gas units; i is st The working state of the gas unit at the moment t is shown, and if the value is 1, the gas unit is put into operation; i is s(t-1) The working state of the gas turbine set at the time t-1 is shown; p st The generated power of the gas turbine set s at the moment t;
Figure BDA0002914150360000059
is the heat rate curve of the gas unit s; />
Figure BDA00029141503600000510
And &>
Figure BDA00029141503600000511
Respectively the minimum and maximum limits of the output of the gas turbine set; />
Figure BDA00029141503600000512
And &>
Figure BDA00029141503600000513
Respectively indicating a starting time counter and a stopping time counter of the gas unit at the time of t-1; />
Figure BDA00029141503600000514
And &>
Figure BDA00029141503600000515
Respectively indicating the minimum starting time and the minimum stopping time of the unit; su s And sd s The heat consumption cost generated when the gas unit s is started and stopped once respectively; SU st And SD st The cost generated by starting and stopping the gas unit s at the moment t is respectively; UR s And DR s Respectively the upward climbing rate and the downward climbing rate of the unit. (3) other constraints: the gas turbine set and the electric gas conversion equipment connected with the same power system node cannot operate simultaneously;
I st +I at ≤1,fors,a∈N(j)ands∈GU
in the formula: n (j) is a set of devices connected to node j; the gas turbine set s and the electric power conversion equipment a are connected to the same power system node j.
Further, the price type gas-electricity combined demand response model in step 3 is specifically as follows:
(1) Price type electric load demand response model:
Figure BDA00029141503600000516
Figure BDA00029141503600000517
Figure BDA00029141503600000518
Figure BDA0002914150360000061
Figure BDA0002914150360000062
Figure BDA0002914150360000063
Figure BDA0002914150360000064
Figure BDA0002914150360000065
in the formula: p f,dt Predicting an electrical load value at time t;
Figure BDA0002914150360000066
an electrical load value which is involved in the demand response for time t when ^ h>
Figure BDA0002914150360000067
Positive indicates a transfer out of the transferable electrical load, and negative indicates a transfer into the transferable electrical load; alpha is alpha dt The electric load proportion which can participate in demand response for the load d at the time t; I.C. A dt Type indicating variable for demand response for load d at time t, if I dt =1 denotes the reduction of the load d at time t or the transfer of a partial load, I dt If =0, it means that the load d will be shifted to a partial load at time t; />
Figure BDA0002914150360000068
The maximum value of the allowed load d at the moment t; />
Figure BDA0002914150360000069
The total amount of the load d reduced in all the scheduling periods; p dht The load value of the load d on the h section at the time t; />
Figure BDA00029141503600000610
The maximum load value of the load d on the h section at the moment t; />
Figure BDA00029141503600000611
And &>
Figure BDA00029141503600000612
A demand response counter for respectively representing the loads d at the time t-1; />
Figure BDA00029141503600000613
And &>
Figure BDA00029141503600000614
Respectively indicating the minimum holding and interruption time of the demand response of the load d; m is a relaxation constant with a value of 10000.
(2) Gas load demand response model:
Figure BDA00029141503600000615
Figure BDA00029141503600000616
Figure BDA00029141503600000617
Figure BDA00029141503600000618
/>
Figure BDA00029141503600000619
Figure BDA00029141503600000620
Figure BDA00029141503600000621
Figure BDA00029141503600000622
in the formula: g f,gt Predicting an air load value for the time t;
Figure BDA00029141503600000623
an air load value which is involved in the demand response for time t when ^ s>
Figure BDA00029141503600000624
Positive indicates a transfer out of transferable gas load, and negative indicates a transfer into transferable gas load; alpha (alpha) ("alpha") gt The gas load proportion which is the load g at the moment t and can participate in demand response; I.C. A gt Type indicating variable for demand response for load g at time t, if I gt =1 denotes the time t load g is reduced or a partial load is transferred, I gt If =0, it means that the load g will be shifted to a partial load at time t; />
Figure BDA0002914150360000071
The total amount of the load g reduced in all the scheduling periods; g ght The load value of the load g on the h section at the moment t; />
Figure BDA0002914150360000072
The maximum load value of the load g on the h section at the moment t; />
Figure BDA0002914150360000073
And &>
Figure BDA0002914150360000074
A demand response counter for respectively representing the load g at the time t-1; />
Figure BDA0002914150360000075
And &>
Figure BDA0002914150360000076
Respectively indicating the minimum holding and interruption time of the load g for carrying out demand response; />
Figure BDA0002914150360000077
The maximum value of the allowed load g at time t.
Furthermore, the deterministic optimization operation model of the gas-electricity integrated energy distribution network system in the step 4 is specifically as follows:
(1) An objective function: the day-ahead coordinated optimization operation model of the gas-electricity integrated energy distribution network system takes the maximization of social benefits of the integrated energy distribution network system as an optimization target;
Figure BDA0002914150360000078
in the formula: t is a time index; d. g is node indexes of electric and gas loads respectively;
Figure BDA0002914150360000079
respectively charging to the upper-level power grid and charging to the upper-level natural gas grid; c. C coal Is the unit coal cost; />
Figure BDA00029141503600000710
Respectively unit punishment cost of the power loss load and the gas loss load; c. C dht 、c dht Respectively obtaining unit energy supply benefits corresponding to the electric load d and the air load g on the h section at the time t;
(2) And (3) new energy output constraint:
0≤P wt ≤P f,wt
in the formula: p f,wt Predicting the generated power of the fan w at the time t;
(3) And (3) power exchange constraint of the distribution network system and a superior network:
Figure BDA00029141503600000711
Figure BDA00029141503600000712
Figure BDA00029141503600000713
in the formula: p in,min 、P in,max Respectively limiting the minimum active power and the maximum active power exchanged between the power distribution network and a superior power grid; q in,min 、Q in,max Respectively limiting the minimum reactive power and the maximum reactive power exchanged between the power distribution network and a superior power grid; g in,min 、G in,max Minimum and maximum gas purchase power limits for gas distribution network exchange with natural gas suppliers, respectively;
(4) And (3) load loss constraint of the distribution network system:
Figure BDA0002914150360000081
Figure BDA0002914150360000082
in the formula, G dt Is the actual value of the load d at time t;
Figure BDA0002914150360000083
the load loss value of the load d at the time t.
Furthermore, the method for processing the natural gas flow equation constraint by combining the enhanced second-order cone programming and the air pressure recovery in the step 5 specifically comprises the following steps:
(1) Processing the flow equation constraint of the nonlinear gas distribution network by using an enhanced second-order cone relaxation method, processing a target function on the premise of basic second-order cone relaxation, namely adding a penalty term related to the node air pressure difference:
Figure BDA0002914150360000084
(G mn,t ) 2 +(K mn π nt ) 2 ≤(K mmπmt ) 2
in the formula: phi is a mn For the penalty coefficient of the pressure difference between the two ends of the transmission pipeline mn of the gas distribution network, omega p Is the collection of all the transport pipes.
(2) The method for processing the power flow equation of the gas distribution network by combining the enhanced second-order cone programming and the air pressure recovery specifically comprises the following steps:
Figure BDA0002914150360000085
Figure BDA0002914150360000086
Figure BDA0002914150360000087
σ mn,t ≥0
in the formula:
Figure BDA0002914150360000088
solving the natural gas trend obtained by using an enhanced second-order cone programming method; rho is a penalty coefficient; sigma mn,t Is an intermediate quantity and has no practical significance.
Further, in step 6, a corresponding uncertainty set is constructed by using a robust optimization theory, a two-stage adjustable robust optimization scheduling model of the gas-electricity integrated energy distribution network system considering price type gas-electricity combined demand response is established, and the two-stage adjustable robust second-order cone optimization scheduling model is converted into a main-sub problem frame by using a column and constraint generation algorithm to solve the following concrete steps:
(1) Constructing uncertainty set of electric power, natural gas load and new energy output, and determining uncertainty
Adding a budget constraint to the aggregate to adjust the conservative form of the result:
Figure BDA0002914150360000091
Figure BDA0002914150360000092
Figure BDA0002914150360000093
/>
in the formula: u shape E 、U G 、U W Respectively, an uncertainty set of electrical load, gas load and wind-power output; NT, ND, NG, NW are the scheduling time interval, the electric load, the air load and the number of wind farms respectively;
Figure BDA0002914150360000094
respectively are binary indexes of the electric load uncertainty aggregate; />
Figure BDA0002914150360000095
Respectively are binary indexes of the gas load uncertainty aggregate; />
Figure BDA0002914150360000096
Respectively representing binary indexes of the wind power output uncertainty aggregate; delta d 、Δ g 、Δ w Respectively the uncertainty budget of the electrical load, the gas load and the wind-power output; />
Figure BDA0002914150360000097
The prediction deviations of the electric load, the gas load and the wind power output are respectively; />
Figure BDA0002914150360000098
And &>
Figure BDA0002914150360000099
Respectively, uncertain electric load, air load and wind power output value; />
Figure BDA00029141503600000910
And &>
Figure BDA00029141503600000911
Respectively predicting values of electric load, gas load and wind power output; />
Figure BDA00029141503600000912
Figure BDA00029141503600000913
The set of real numbers are ND × NT, NG × NT, and NW × NT, respectively.
(2) Considering the unit combination, the scheduling arrangement and the scheduling correction measures of the units of the basic scene, establishing a two-stage adjustable robust optimization scheduling model of the distribution network system:
Figure BDA00029141503600000914
Ax≤d,x∈{0,1}
Bx+Cy+Dv＝0
Ex+Fy≤e
Figure BDA00029141503600000915
Figure BDA00029141503600000916
in the formula: x represents a binary variable related to the start-stop state of the unit, the electric-to-gas equipment and the demand response indication mark; y and z respectively represent real-time correction values of distribution network system scheduling adjusted under a basic scene and according to system uncertainty; v is a safety violation value representing the power loss load, the air loss load and the air abandoning amount of the distribution network system; u is an uncertain variable related to the uncertainty of the electric load, the air load and the wind-power output; a, B, C, D, E, F, K r G, H, I, L, M, N and P are corresponding matrix parameters; a is T ,b T ,c T ,d,e,
Figure BDA0002914150360000101
q is a corresponding vector parameter; f (x, y) is a feasible field.
(3) Converting the two-stage adjustable robust second-order cone optimization scheduling model into a main-sub problem framework by using a column and constraint generation algorithm to solve:
1) Setting maximum security violation threshold epsilon of worst scene of gas-electricity integrated energy distribution network system max And iteration counter l =1;
2) Solving the main problem to obtain the optimal result I st ，I at ，I dt ，I gt The sub-problem is brought into to test the operation safety of the gas-electricity comprehensive energy distribution network system;
3) Optimal results I from the main problem st ，I at ，I dt ，I gt Solving the sub-problems, and identifying to obtain the electrical load of the worst scene maximum safety violation of the distribution network system
Figure BDA0002914150360000102
Air load->
Figure BDA0002914150360000103
And wind power generation->
Figure BDA0002914150360000104
4) If the obtained maximum security violation of the worst scene is smaller than the set threshold, stopping iteration; otherwise, according to the worst scenario in this kth iteration
Figure BDA0002914150360000105
And &>
Figure BDA0002914150360000106
Generating CCG constraint, and returning to the step 2) to continue iteration;
by continuously solving the main problem of optimal scheduling, checking the sub-problems of safety check of the distribution network system and returning the worst scene to the main problem, the optimal solution of the robust optimization problem is obtained.
Furthermore, the gas-electricity integrated energy distribution network system structure data in step 7 comprises a distribution network system topological structure and distribution line/transmission pipeline parameters, the equipment parameters comprise a generator set cost coefficient, the number of electricity-to-gas equipment, upper and lower output limits and the capacity of a wind driven generator, and the distribution network operation parameters comprise energy market price, joint demand response proportion limit and electricity and gas load prediction data.
The invention has the beneficial effects that:
1) The method for enhancing the second-order cone planning is superior to the second-order cone relaxation method in the aspect of processing the relaxation precision problem of the optimal power flow of the gas distribution network, but when the gas distribution network is overloaded, some nodes reach the upper limit and the lower limit of the air pressure, the precision after relaxation can not reach the operation requirement, and the precision needs to be improved by combining an air pressure recovery method. The method for combining the enhanced second-order cone planning and the air pressure recovery can obviously reduce the convex relaxation error of the natural gas flow, and can keep higher precision under the condition of light and heavy loads of the system.
2) By combining the coupling characteristic between the electric power system and the natural gas system, the electric and gas loads can be transferred in and out in different time periods through a certain proportion of demand response under the guidance of energy prices. Introducing electrical load demand responses may alleviate congestion levels in the distribution grid, while introducing electrical load demand responses may alleviate power shortage problems in the distribution grid. The coordination optimization scheduling strategy considering price type gas-electricity combined demand response is beneficial to reducing the system operation cost and improving the economy and flexibility of the operation of the distribution network system.
3) The two-stage robust optimization model can ensure the operation safety of the gas-electricity comprehensive energy distribution network system under the condition that the electric load, the gas load and the wind power generation are uncertain, and more distributed generator sets are put into the two-stage robust optimization model to provide enough system climbing capacity on the whole, so that the distribution network system can safely and economically carry out day-ahead coordinated optimization scheduling arrangement when the distribution network system faces the uncertainty of operation.
Drawings
FIG. 1 is a flow chart of the steps of the method of the present invention.
FIG. 2 is a stepped, segmented graph of a price type demand response.
Fig. 3 is a diagram of an embodiment of a distribution network of a gas-electricity integrated energy system formed by coupling an IEEE33 node power system and a 20 node natural gas system.
FIG. 4 is a flow chart of a column and constraint generation algorithm.
Fig. 5 is a comparison graph of net electricity and gas load values after the distribution network system performs price type gas-electricity combined demand response.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments.
In order to explain the technical solutions disclosed in the present invention in detail, the present invention will be further described with reference to the accompanying drawings and specific examples.
The invention discloses a two-stage robust optimization operation method of a gas-electricity integrated energy distribution network system considering price type gas-electricity combined demand response. The specific implementation step flow is shown in fig. 1, and the technical scheme of the invention comprises the following steps:
step 1: and establishing a power and natural gas distribution network system model which mainly comprises a power alternating current power flow model and a natural gas transmission pipeline power flow model.
(1.1) a distribution network DistFlow alternating current power flow model: the operation of the power distribution network needs to meet the energy flow node balance conservation law, a node voltage drop equation, a branch current and power equation and the like; the transmission capacity of a distribution line is limited by the limits of its upper and lower bounds on the current that passes and the limits of its upper and lower bounds on the bus voltage.
Figure BDA0002914150360000121
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Figure BDA0002914150360000122
Figure BDA0002914150360000123
Figure BDA0002914150360000124
Figure BDA0002914150360000125
Figure BDA0002914150360000126
In the formula: γ (j) is a branch end node set which takes j as a head end node in the power distribution network; r ij 、X ij Respectively representing the resistance and reactance values of the sections ij of the distribution line; i is ij,t The current of the distribution line ij section at the moment t; v it Is the voltage magnitude of node i; p st 、P at The power generation power and the power consumption power of the g gas turbine set and the electric gas conversion equipment a at the moment t are respectively;
Figure BDA0002914150360000127
the active power transmitted to the power distribution network for the superior power grid at the moment t; />
Figure BDA0002914150360000128
The reactive power transmitted to the power distribution network by the superior power grid at the moment t; p wt The power generation amount of the w-th typhoon electric field at the time t; p ij,t Active power of an ij section of a distribution line at the moment t of a power grid is assigned; q ij,t Assigning reactive power of the distribution line ij section of the power grid at the moment t; p dt Is the actual value of the load d at time t; />
Figure BDA0002914150360000129
The load loss value of the load d at the moment t; chi shape dt The power factor of the electrical load at time t; />
Figure BDA00029141503600001210
The maximum current value allowed to pass through the section ij of the distribution line at the moment t; />
Figure BDA00029141503600001211
The lowest and highest allowed voltage values for node i.
(1.2) natural gas transmission pipeline power flow model: the natural gas flow of the distribution network may be represented by a non-linear relationship between node gas pressure and pipeline characteristics. Under certain conditions, the well-known Weymouth equation is used to approximate the steady state flow of natural gas. The operation of the distribution network must also satisfy the node balance conservation law of energy, the limitation of pipeline transmission flow, the limitation of node air pressure and the like.
Figure BDA0002914150360000131
Figure BDA0002914150360000132
Figure BDA0002914150360000133
Figure BDA0002914150360000134
π mt ≥π nt
In the formula: g st 、G at Respectively the gas consumption and the gas production of the gas unit s and the electric gas conversion equipment a at the moment t;
Figure BDA0002914150360000135
the natural gas flow transmitted from the upper-level gas network to the gas distribution network at the time t; />
Figure BDA0002914150360000136
And &>
Figure BDA0002914150360000137
Minimum and maximum pressure limits for natural gas network node n, respectively; g mn,t Is the tide transmitted by the natural gas pipeline at the time t; g dt Is the actual value of the load g at time t; />
Figure BDA0002914150360000138
The load loss value is the load g at the moment t; pi mt And pi nt The gas pressures of the natural gas nodes m and n are respectively; k mn Is the Weymouth characteristic parameter of a natural gas pipeline.
Step 2: and establishing a coupling equipment operation model which comprises a gas unit operation model for gas to electricity conversion and a P2G equipment operation model for electricity to gas conversion. The operation of the distribution network system is constrained by the operation of the coupling device.
(2.1) an operation model of the electric gas conversion equipment: electrical gas conversion plants are considered as gas sources in the natural gas network and as loads in the electrical power system. The relationship between the gas production and the consumed electrical energy of the electric gas conversion device is as follows.
Figure BDA0002914150360000139
/>
Figure BDA00029141503600001310
In the formula: i is at The working state of the electric gas conversion equipment is set; g at The amount of natural gas produced by the electric gas conversion equipment;
Figure BDA00029141503600001311
the maximum power consumption of the electric gas conversion equipment; />
Figure BDA00029141503600001312
Is an energy conversion coefficient and has>
Figure BDA00029141503600001313
HHV is high calorific value, and its value is 1.026MBtu/kcf; />
Figure BDA00029141503600001314
The working efficiency of the electric gas conversion equipment is improved.
(2.2) a gas unit operation model: gas units are considered loads in the natural gas network and power sources in the electrical power system. In a power distribution network, the output of a gas turbine unit is limited by the maximum and minimum capacity, and the minimum unit start-stop time, the unit start-stop cost constraint and the upper and lower climbing rate constraint should be met.
Figure BDA0002914150360000141
Figure BDA0002914150360000142
Figure BDA0002914150360000143
Figure BDA0002914150360000144
SU st ≥su s ·(I st -I s(t-1) ),SU st ≥0
SD st ≥sd s ·(I s(t-1) -I st ),SD st ≥0
Figure BDA0002914150360000145
Figure BDA0002914150360000146
In the formula: GU is a set of gas units; i is st The working state of the gas turbine set is set; p st The generated power of the gas turbine set s at the moment t;
Figure BDA0002914150360000147
is the heat rate curve of the gas unit s; />
Figure BDA0002914150360000148
And &>
Figure BDA0002914150360000149
Are respectively asMinimum and maximum limits of gas unit output; />
Figure BDA00029141503600001410
And &>
Figure BDA00029141503600001411
Respectively indicating a starting time counter and a stopping time counter of the gas turbine set at the time of t-1; />
Figure BDA00029141503600001412
And &>
Figure BDA00029141503600001413
Respectively indicating the minimum starting time and the minimum stopping time of the unit; su s And sd s The heat consumption cost generated when the unit s is started and stopped once is respectively; SU st And SD st The cost generated by starting and stopping the gas unit s at the moment t is respectively; UR s And DR s Respectively the upward climbing rate and the downward climbing rate of the unit.
(2.3) other constraints: the gas turbine set and the electric gas conversion equipment connected to the same power system node cannot operate simultaneously.
I st +I at ≤1,fors,a∈N(j)ands∈GU
In the formula: n (j) is a set of devices connected to node j; the gas turbine set s and the electric power conversion equipment a are connected to the same power system node j.
And step 3: and respectively modeling price type electricity and gas load demand responses, and introducing price type gas-electricity combined demand responses. As shown in fig. 2, the trend of the demand response of gas and electricity loads along with the change of energy prices can be simulated by using the stepped load quotation curve, and the energy consumption of the loads is monotonically reduced along with the increase of the energy prices. The energy market price change is responded through flexibly reducing the load and transferring the power utilization time of the load, the output of renewable energy is better matched, and the running cost of a distribution network system is reduced.
(3.1) price type electric load demand response model: can participate in price type demandThe responsive electrical load is limited by a ratio, the proportionality coefficient alpha dt ∈[0,1](ii) a Considering that the electrical load cannot be changed frequently according to market prices, the demand response of the electrical load is limited, similar to the minimum start-stop time constraint of the unit. I is dt 1 represents that the electric load d is transferred out by a certain amount of load at the time t and needs to be maintained in a transferred-out state in a certain period of time; i is dt A value of 0 indicates that the electrical load d is switched to a certain amount of load at the time t, and the switched state needs to be maintained for a certain period of time thereafter. To avoid large variations in load, the amount of electrical load that can be responded to needs to be limited to a certain range within the total scheduling period.
Figure BDA0002914150360000151
Figure BDA0002914150360000152
Figure BDA0002914150360000153
Figure BDA0002914150360000154
Figure BDA0002914150360000155
Figure BDA0002914150360000156
Figure BDA0002914150360000157
Figure BDA0002914150360000158
In the formula: p f，dt 、
Figure BDA0002914150360000159
Forecasting electric load values at the time t and participating in demand response respectively; when/is>
Figure BDA00029141503600001510
Positive indicates a transfer out of the transferable electrical load, and negative indicates a transfer into the transferable electrical load; alpha is alpha dt The electric load proportion which can participate in demand response for the load d at the time t; i is dt A type indicating variable for carrying out demand response on the load d at the moment t; if I dt =1 denotes the time t load d is reduced or a partial load is transferred, I dt If =0, it means that the load d will be shifted to a partial load at time t; />
Figure BDA00029141503600001511
The total amount of the load d reduced in all the scheduling periods; p dht The load value of the load d on the h section at the moment t; />
Figure BDA00029141503600001512
The maximum load value of the load d on the h section at the moment t; />
Figure BDA00029141503600001513
And &>
Figure BDA00029141503600001514
A demand response counter for load d at time t-1;
Figure BDA00029141503600001515
and &>
Figure BDA00029141503600001516
Respectively, the minimum hold and interrupt times for the load d to respond to the demand.
(3.2) price type gas load demand response model: similar to the price type electrical load demand response, it will not be described herein.
Figure BDA00029141503600001517
Figure BDA00029141503600001518
Figure BDA0002914150360000161
Figure BDA0002914150360000162
Figure BDA0002914150360000163
Figure BDA0002914150360000164
Figure BDA0002914150360000165
Figure BDA0002914150360000166
In the formula: g f，gt 、
Figure BDA0002914150360000167
Respectively predicting an air load value and an air load value participating in demand response at the moment t; when/is>
Figure BDA0002914150360000168
To be positive, indicating that the rotation-out is rotatableGas load transfer, otherwise, the gas load is transferred to the transferable; alpha is alpha gt The gas load proportion which is the load g at the moment t and can participate in demand response; i is gt A type indicating variable for carrying out demand response on the load g at the moment t; if I gt =1 denotes the time t load g is reduced or a partial load is transferred, I gt If =0, it means that the load g will be shifted to a partial load at time t; />
Figure BDA0002914150360000169
The total amount of the load g reduced in all the scheduling periods; g ght The load value of the load g on the h section at the moment t; />
Figure BDA00029141503600001610
The maximum load value of the load g on the h section at the moment t; />
Figure BDA00029141503600001611
And &>
Figure BDA00029141503600001612
A demand response counter for load g at time t-1;
Figure BDA00029141503600001613
and &>
Figure BDA00029141503600001614
Respectively, the minimum hold and interrupt times for the load g to respond to the demand.
And 4, step 4: and constructing a distribution network deterministic optimization operation model of the gas-electricity integrated energy system, which takes the social benefits of the maximized integrated energy distribution network system as an objective function and considers various operation constraint conditions such as the energy balance of the distribution network and the distribution network node and the price type gas-electricity combined demand response.
(4.1) objective function: the day-ahead coordinated optimization operation model of the gas-electricity integrated energy distribution network system takes the maximization of social benefits of the integrated energy distribution network system as an optimization target. The social benefit of the distribution network system is the difference between the energy supply benefit and the operation cost. The energy supply benefits include benefits of delivering electrical energy and natural gas to the load side; the operation cost mainly comprises the cost of purchasing corresponding energy from a superior power grid and an air grid, the operation cost of the traditional coal-fired unit and the addition of the loss load into the objective function in the form of a penalty item. The operation maintenance cost of the gas turbine unit, including the scheduling cost and the start-stop cost, is calculated according to the gas purchase cost purchased from the gas distribution network root node to the superior gas network.
Figure BDA0002914150360000171
In the formula: t is a time index; d. g is node indexes of electric and gas loads respectively;
Figure BDA0002914150360000172
respectively charging to the upper-level power grid and charging unit charge to the upper-level natural gas grid; c. C coal Is the unit coal cost; />
Figure BDA0002914150360000173
Figure BDA0002914150360000174
Respectively unit punishment cost of the power loss load and the gas loss load; c. C dht 、c ght And respectively obtaining the unit energy supply benefits corresponding to the electric load d and the air load g on the h-th section at the time t.
(4.2) new energy output constraint: the wind farm scheduling at each time is limited by the predicted available wind power generation.
0≤P wt ≤P f,wt
In the formula: p is f,wt And predicting the generated power of the w-th fan at the time t.
(4.3) power exchange constraint between the distribution network system and the superior network:
Figure BDA0002914150360000175
Figure BDA0002914150360000176
Figure BDA0002914150360000177
in the formula: p in,min 、P in,max Respectively limiting the minimum active power and the maximum active power exchanged between the power distribution network and a superior power grid; q in,min 、Q in,max Respectively limiting the minimum reactive power and the maximum reactive power exchanged between the power distribution network and a superior power grid; g in,min 、G in,max Minimum and maximum gas purchase power limits for the gas distribution network to exchange with the natural gas supplier, respectively.
And (4.4) loss load constraint of the distribution network system: the power loss load power is to forcibly cut off the load on the premise of ensuring the safe operation of the system as much as possible, and if the load is not cut off, the safety problems of system voltage out-of-limit or tidal current out-of-limit and the like may exist. The load loss amount cannot be larger than the load amount of the node itself.
Figure BDA0002914150360000178
Figure BDA0002914150360000179
And 5: the method combining Enhanced Second-Order Cone Programming (ESOCP) and Pressure Recovery (PR) is utilized to process the constraint of the natural gas flow equation, and a nonlinear gas-electricity comprehensive energy system distribution network optimization scheduling model is converted into a mixed integer Second-Order Cone Programming (MISOCP) problem.
(5.1) the enhanced second-order cone relaxation method is used for processing the flow equation constraint of the gas distribution network: and processing the target function on the premise of the relaxation of the basic second-order cone, namely adding a penalty term related to the air pressure difference of the node. The method can tighten the original second-order cone relaxation equation as much as possible.
Figure BDA0002914150360000181
(G mn,t ) 2 +(K mn π nt ) 2 ≤(K mn π mt ) 2
In the formula: phi is a mn And punishing coefficients for the air pressure difference at two ends of the transmission pipeline mn of the gas distribution network.
And (5.2) considering that the enhanced second-order cone relaxation method can not ensure that the power flow relaxation precision can meet the operation requirement under the condition of heavy-load operation of the system, in order to obtain an operation scheme applicable to a scheduling mechanism, the invention provides a method (ESOCP & PR) for combining enhanced second-order cone planning with air pressure recovery, and a high-quality node air pressure solution of the original Weymouth natural gas power flow equation is obtained through the air pressure recovery, so that the high precision of the second-order cone relaxation of the gas distribution network is ensured.
Figure BDA0002914150360000182
Figure BDA0002914150360000183
Figure BDA0002914150360000184
σ mn,t ≥0
In the formula:
Figure BDA0002914150360000185
the natural gas trend is obtained by solving by using an enhanced second-order cone programming method.
And 6: and constructing a corresponding uncertainty set by using a robust optimization theory, establishing a two-stage robust optimization scheduling model of the gas-electricity integrated energy distribution network system considering price type gas-electricity combined demand response, and solving the robust second-order cone optimization model by using a column and constraint generation algorithm (CCG).
(6.1) constructing an uncertain set of electric power, natural gas load and new energy output: and adding a budget constraint in the uncertain combination set to adjust the conservative type of the obtained result.
Will not determine the extent of variation of the elements such as
Figure BDA0002914150360000186
Is limited to the interval->
Figure BDA0002914150360000187
Within. The uncertainty budget Δ is between 0 and ND, and as Δ increases, more of the total elements will deviate from their mean, resulting in a more conservative solution. Budget Δ, for example, from a collection of uncertainties of power loads d Take on values between 0 and ND when Δ d When added, more elements of the collection will deviate from their mean, resulting in a more conservative solution. Consider a binary variable->
Figure BDA0002914150360000191
The calculation amount is increased, and the relaxation is considered as the interval [0,1 ]]Continuous variable between the two steps, thereby greatly increasing the operation speed.
Figure BDA0002914150360000192
/>
Figure BDA0002914150360000193
Figure BDA0002914150360000194
In the formula: u shape E 、U G 、U W Respectively corresponding to the uncertainty collection of the electric load, the gas load and the wind-power output; NT, ND, NG, NW are the number of scheduling periods, electrical loads, air loads, and wind farms, respectivelyAn amount;
Figure BDA0002914150360000195
respectively binary indexes in the electric load uncertainty aggregate; />
Figure BDA0002914150360000196
Respectively are binary indexes of the gas load uncertainty aggregate; />
Figure BDA0002914150360000197
Respectively representing binary indexes of the wind power output uncertainty aggregate; delta d 、Δ g 、Δ w Respectively the uncertainty budget of the electrical load, the gas load and the wind-power output; />
Figure BDA0002914150360000198
The predicted deviations are the electrical load, the gas load and the wind power output respectively.
(6.2) the distribution network system two-stage adjustable robust optimization scheduling model:
the two-stage adjustable robust optimization scheduling model established in the step is described by adopting a simplified model in a general form. The adjustable robust optimization model takes into account the unit combination and scheduling arrangement of the underlying scenario, and models the scheduling corrective measures of the units to cope with real-time changes of uncertain parameters. During real-time optimization scheduling, the output of the unit can be adjusted to track changes of electric power, natural gas load or wind power generation.
Wherein x represents binary variables related to the start-stop state of the unit, the electric-to-gas equipment, the demand response indicating sign and the like; y and z respectively represent real-time correction values of distribution network system scheduling adjusted under a basic scene and according to system uncertainty; v is a safety violation value representing the distribution network system such as power loss load, air abandonment quantity and the like; u is an uncertainty variable related to the uncertainty of the electrical load, the air load and the wind-power output.
Figure BDA0002914150360000201
Ax≤d,x∈{0,1}
Bx+Cy+Dv＝0
Ex+Fy≤e
Figure BDA0002914150360000202
Figure BDA0002914150360000203
In the formula: a, B, C, D, E, F, K r G, H, I, L, M, N and P are corresponding matrix parameters; a is T ,b T ,c T ,d,e,
Figure BDA0002914150360000204
q is the corresponding vector parameter.
(6.3) Main-sub problem framework:
the main problem is the scheduling arrangement problem of the gas-electricity integrated energy distribution network system, the social benefits of system operation under a basic scene are maximized mainly according to predicted values of load and new energy output, and meanwhile, the day-ahead scheduling result can be adjusted in a self-adaptive and safe mode according to actual values of uncertain parameters. Compared with an adaptive robust optimization model which maximizes the system social benefit under the worst scenario as an objective function, the method can provide a useful day-ahead scheduling result for a system scheduling mechanism.
The constraint conditions comprise basic scene constraints and constraints about worst scenes, the worst scenes are obtained by solving an iterative subproblem, and the worst scenes refer to the largest load loss scenes; the subproblem is the problem of identifying the worst scene, and the scene causing the maximum security violation is found by using a double-layer max-min problem, namely
Figure BDA0002914150360000205
The method is converted into a single-layer bilinear maximization optimization problem through a dual theory. Solving and proposing under main problem-subproblem framework by adopting Column and Constraint Generation (CCG) algorithmThe two-stage robust optimization scheduling model.
The flow chart of the CCG method is shown in fig. 4, and the specific steps include the following 4 steps:
1) Setting maximum security violation threshold epsilon of worst scene of gas-electricity integrated energy distribution network system max And iteration counter l =1;
2) Solving the main problem to obtain the optimal result I st ，I at ，I dt ，I gt The sub-problem is brought into to test the operation safety of the gas-electricity comprehensive energy distribution network system;
3) Optimal results I from the main problem st ，I at ，I dt ，I gt Solving the sub-problems, and identifying to obtain the electrical load of the worst scene maximum safety violation of the distribution network system
Figure BDA0002914150360000211
Air load->
Figure BDA0002914150360000212
And wind power generation->
Figure BDA0002914150360000213
4) If the obtained maximum security violation of the worst scene is smaller than the set threshold, stopping iteration; otherwise, according to the worst scenario in this kth iteration
Figure BDA0002914150360000214
And &>
Figure BDA0002914150360000215
Generating CCG constraints and returning to step 2) to continue the iteration.
By continuously solving the main problem of optimal scheduling, checking the sub-problems of safety check of the distribution network system and returning the worst scene to the main problem, the CCG method can ensure that the optimal solution of the robust optimization problem is obtained. In addition, identifying the worst-case sub-problem in the uncertainty set seeks electrical loads, air loads, and wind power generation that can lead to the greatest security violations. Wherein the identified worst scenario number is equal to the number of iterations of the CCG method. The number of iterations is not fixed and depends on the uncertainty budget of the system, electrical load, air load, wind power generation, and system parameters.
The effects of the present invention will be described in detail below with reference to specific examples.
(1) Introduction to the examples.
As shown in FIG. 3, an example of a gas-electricity integrated energy distribution network system is constructed by using an IEEE33 node power system and a 20 node natural gas system. Adding a wind driven generator at each of nodes 5 and 21 of an IEEE33 node power system; the gas turbine set GU1 and the P2G equipment are connected to a node 6 of a power system and a node 5 of a natural gas system; the gas turbine unit GU2 is connected to a node 12 of a power system and a node 9 of a natural gas system; the 3 coal-fired units GU3-GU5 are respectively connected to nodes 28, 10 and 15 of the power distribution network. The interval is 1 hour, and the scheduling period is 24 hours. The test tool used Matlab2018a programming software and a GUROBI 9.0 commercial solver.
(2) Description of embodiment scenarios.
In order to verify the economic and flexible advantages of the two-stage robust optimization scheduling scheme, four algorithms 1-4 of deterministic optimization without considering gas-electricity combined demand response, deterministic optimization with considering gas-electricity combined demand response, robust optimization without considering gas-electricity combined demand response and robust optimization with considering gas-electricity combined demand response are set; to verify the effectiveness of the enhanced second-order cone programming and barometric pressure recovery combined (ESOCP & PR) method, the following equations 1.1-1.3 are set; in order to verify the advantages of the price type gas-electricity combined demand response, four types of operation modes of not considering the demand response, only considering the electric load demand response, only considering the gas load response and considering the gas-electricity combined demand response are set, and the calculation examples 2.1 to 2.4 are set as the following table 1.
Example 1: deterministic optimization of demand response without consideration of gas-electricity combination;
example 2: considering the deterministic optimization of the gas-electricity combined demand response;
example 3: robust optimization without considering gas-electricity combined demand response;
example 4: robust optimization of gas-electricity combined demand response is considered;
EXAMPLES 1.1: performing relaxation by using an original second-order cone programming (SOCP) method;
example 1.2: carrying out relaxation by using an enhanced second-order cone programming (ESOCP) method;
example 1.3: the method of enhancing the second-order cone and combining the pressure recovery (ESOCP & PR) is applied.
TABLE 1 demand response ratios of examples 2.1-2.4
Figure BDA0002914150360000221
(3) Examples analysis of results.
Table 2 shows the error comparison for solving with three methods at different load ratios, from which can be obtained: the ESOCP & PR method can ensure better precision under the conditions of light load and heavy load of a gas distribution network, and has better effect on the aspect of loosening precision compared with a second-order cone loosening and second-order cone planning enhancing method.
TABLE 2 comparison of errors for different methods
Figure BDA0002914150360000231
Table 3 shows the distribution network system optimization operation results under different DR types, which are easily obtained: electrical load demand response may mitigate potential gas distribution network blockage; the air load demand response may mitigate the power shortage during peak hours by reducing the power supply to the electrical to gas device. Compared with single electric load or gas load demand response, the gas-electricity combined demand response can fully utilize the coupling characteristic between the electricity, so that the operation of a distribution network system is more flexible and economic, and the social benefit is improved while the operation cost of the system is reduced.
Table 3 distribution network system optimization operation results under different DR types
Figure BDA0002914150360000232
Table 4 shows the distribution network system optimized scheduling results in different operation modes, which are easily obtained by comparison: the robust optimization scheduling method considering the uncertainty of system operation can ensure the economy and safety of the operation of the distribution network system under the condition of uncertainty of electricity, air load and wind power generation, and generally speaking, more units are used for providing enough system climbing capacity so as to sacrifice certain economy for the safety of the system operation. Price type gas-electricity combined demand response is introduced, flexible adjustment is carried out through guidance of energy market price, the operation cost can be obviously reduced, and social benefits of a distribution network system are improved.
TABLE 4 EXAMPLES 1-4 scheduling results
Figure BDA0002914150360000241
Fig. 5 shows a comparison of net electricity and gas load values after the price type gas-electricity combined demand response. Compared to no DR, part of the power load is shifted mainly from peak hours 11-13 and 19-21 to off-peak hours 1-9, while a large natural gas load is shifted from heavy load hours 9-21 to other scheduling hours to maximize the social benefit of the system. As the proportion of the combined gas and electricity demand response increases, more load is shifted to off-peak hours. This means that the larger the participation ratio of DR, the larger the space available for adjustment on the load side, and the higher the operational flexibility of the distribution network system.
The above description is only an embodiment of the present invention, but not intended to limit the scope of the present invention, and all equivalent changes or substitutions made by using the contents of the present specification and the drawings, which are directly or indirectly applied to other related arts, should be included within the scope of the present invention.

Claims (5)

1. A robust optimization operation method of a gas-electric distribution network system considering price type combined demand response is characterized by comprising the following steps:
step 1: establishing an electric power and natural gas distribution network system model which comprises an electric power alternating current DistFlow power flow model and a natural gas transmission pipeline power flow model;
step 2: establishing a coupling equipment operation model which comprises a gas unit operation model and an electric-to-gas equipment operation model;
and step 3: respectively modeling price type electricity and gas load demand responses based on a segmented step-shaped load quotation curve, and introducing price type gas-electricity combined demand responses;
and 4, step 4: constructing a gas-electricity comprehensive energy distribution network system deterministic optimization operation model taking the maximized distribution network system social benefit as an optimization target and considering various operation constraint conditions of a distribution network and price type gas-electricity combined demand response;
and 5: processing the nonlinear power distribution network power flow equation constraint by using a second-order cone relaxation method, processing the natural gas power flow equation constraint by using a method of combining enhanced second-order cone planning and air pressure recovery, and converting a nonlinear gas-electricity comprehensive energy distribution network system optimization scheduling model into a mixed integer second-order cone planning problem to solve;
step 6: considering the output of renewable distributed energy and the uncertainty of gas and electric loads in system operation, constructing a corresponding uncertainty set by using a robust optimization theory, establishing a two-stage adjustable robust optimization scheduling model of the gas and electric comprehensive energy distribution network system considering price type gas and electric combined demand response, and converting the two-stage adjustable robust second-order cone optimization scheduling model into a main-sub problem frame for solving by using a column and constraint generation algorithm;
and 7: inputting structural data, equipment parameters and operation parameters of the gas-electricity comprehensive energy distribution network system, and solving a two-stage adjustable robust optimization operation model of the distribution network system by adopting a commercial solver Gurobi to obtain a robust optimization scheduling scheme of the distribution network system;
the model of the power and natural gas distribution network system in the step 1 is as follows:
(1) A distribution network DistFlow alternating current power flow model:
Figure FDA0003999924050000021
Figure FDA0003999924050000022
Figure FDA0003999924050000023
Figure FDA0003999924050000024
Figure FDA0003999924050000025
V i min ≤V i ≤V i max
in the formula: γ (j) is a branch end node set which takes j as a head end node in the power distribution network; r ij 、X ij Respectively representing the resistance and reactance value of the ij section of the distribution line; i is ij,t The current of the distribution line ij section at the moment t; v it The voltage of the node i at the time t is obtained; v jt The voltage of the node j at the time t is obtained; p st Is the generated power P of the gas turbine set s at time t at The consumed power of the electric-to-gas equipment a at the moment t;
Figure FDA0003999924050000026
the active power transmitted to the power distribution network for the superior power grid at the moment t;
Figure FDA0003999924050000027
the reactive power transmitted to the power distribution network by the superior power grid at the moment t; p is wt The power generation amount of the w-th typhoon electric field at the time t; p ij,t The active power of the distribution line ij section in the power distribution network at the moment t; q ij,t Means reactive power of distribution line ij section in power distribution network at time t；P jk,t The active power of the jk section of the distribution line; q jk,t The reactive power of the jk section of the distribution line; p dt The actual value of the active load d at the moment t; q dt The actual value of the reactive load d at the moment t;
Figure FDA0003999924050000028
the load loss value of the load d at the moment t; chi shape dt The power factor of the electrical load at time t;
Figure FDA0003999924050000029
the maximum current value allowed to pass through the section ij of the distribution line at the moment t; v i Is the voltage value of node i, V i min 、V i max The lowest and highest allowed voltage values for node i;
(2) A natural gas transmission pipeline trend model:
Figure FDA00039999240500000210
Figure FDA00039999240500000211
Figure FDA00039999240500000212
Figure FDA00039999240500000213
π mt ≥π nt
in the formula: g st Is the gas consumption of the gas turbine unit s at time t, G at Respectively the gas production rate of the electric gas conversion equipment a at the moment t;
Figure FDA00039999240500000214
the natural gas flow transmitted from the superior gas network to the gas distribution network at the time t;
Figure FDA00039999240500000215
and
Figure FDA00039999240500000216
minimum and maximum pressure limits for natural gas network node n, respectively; g mn,t For the flow transmitted by the natural gas pipeline mn at time t,
Figure FDA0003999924050000031
the maximum power flow transmitted by the natural gas pipeline mn; g no,t Is the tide transmitted by the natural gas pipeline no at time t; g gt Is the actual value of the load g at time t;
Figure FDA0003999924050000032
the load loss value is the load g at the moment t; pi mt And pi nt The gas pressures of the natural gas network nodes m and n are respectively; k is mn Is a Weymouth characteristic parameter of a natural gas pipeline; y (n) is a set of tail end nodes where branches with the node n as a head end node are located in the natural gas network; GU is a set of gas units;
the operation model of the coupling equipment in the step 2 is specifically as follows:
(1) Electric gas conversion equipment operation model:
Figure FDA0003999924050000033
Figure FDA0003999924050000034
in the formula: i is at The working state of the electric gas conversion equipment is set; g at The amount of natural gas produced by the electric gas conversion equipment;
Figure FDA0003999924050000035
the maximum power consumption of the electric gas conversion equipment;
Figure FDA0003999924050000036
is the energy conversion coefficient; HHV is high calorific value;
Figure FDA0003999924050000037
the working efficiency of the electric gas conversion equipment is improved;
(2) The gas unit operation model is as follows:
Figure FDA0003999924050000038
Figure FDA0003999924050000039
Figure FDA00039999240500000310
Figure FDA00039999240500000311
SU st ≥su s ·(I st -I s(t-1) ),SU st ≥0
SD st ≥sd s ·(I s(t-1) -I st ),SD st ≥0
Figure FDA00039999240500000312
Figure FDA00039999240500000313
in the formula: GU is a set of gas units; i is st The working state of the gas unit at the moment t is shown, and if the value is 1, the gas unit is put into operation; i is s(t-1) The working state of the gas turbine set at the time t-1 is shown; p st The generated power of the gas turbine set s at the moment t;
Figure FDA00039999240500000314
is the heat rate curve of the gas unit s;
Figure FDA00039999240500000315
and
Figure FDA00039999240500000316
respectively the minimum and maximum limits of the output of the gas turbine set;
Figure FDA00039999240500000317
and
Figure FDA00039999240500000318
respectively indicating a starting time counter and a stopping time counter of the gas unit s at the time of t-1;
Figure FDA00039999240500000319
and
Figure FDA00039999240500000320
respectively indicating the minimum starting time and the minimum stopping time of the unit; su s And sd s The heat consumption cost generated when the gas unit s is started and stopped once is respectively reduced; SU st And SD st The cost generated by starting and stopping the gas unit s at the moment t is respectively; UR s And DR s Respectively the upward climbing rate and the downward climbing rate of the unit;
(3) Other constraints are as follows: the gas turbine set and the electric gas conversion equipment connected with the same power system node cannot operate simultaneously;
I st +I at ≤1,for s,a∈N(j)and s∈GU
in the formula: n (j) is a set of devices connected to node j; the gas unit s and the electric gas conversion equipment a are connected to the same power system node j;
the price type gas-electricity combined demand response model in the step 3 is concretely as follows:
(1) Price type electric load demand response model:
Figure FDA0003999924050000041
Figure FDA0003999924050000042
Figure FDA0003999924050000043
Figure FDA0003999924050000044
Figure FDA0003999924050000045
Figure FDA0003999924050000046
Figure FDA0003999924050000047
Figure FDA0003999924050000048
in the formula: p f,dt Is at t timePredicting an electric load value;
Figure FDA0003999924050000049
the value of the electrical load participating in the demand response at time t when
Figure FDA00039999240500000410
Positive indicates a transfer out of the transferable electrical load, and negative indicates a transfer into the transferable electrical load; alpha is alpha dt The electric load proportion which can participate in demand response for the load d at the time t; I.C. A dt Type indicating variable for demand response for load d at time t, if I dt =1 denotes the reduction of the load d at time t or the transfer of a partial load, I dt =0 then indicates that the load d will be transferred to the partial load at time t;
Figure FDA00039999240500000411
the maximum value of the allowed load d at the moment t;
Figure FDA00039999240500000412
the total amount of the load d reduced in all the scheduling periods; p dht The load value of the load d on the h section at the moment t;
Figure FDA00039999240500000413
the maximum load value of the load d on the h section at the moment t;
Figure FDA00039999240500000414
and
Figure FDA00039999240500000415
a demand response counter for respectively representing the load d at the time t-1;
Figure FDA00039999240500000416
and
Figure FDA00039999240500000417
respectively mean loadd minimum hold and interrupt time for demand response; m is a relaxation constant and takes the value of 10000;
(2) Gas load demand response model:
Figure FDA00039999240500000418
Figure FDA0003999924050000051
Figure FDA0003999924050000052
Figure FDA0003999924050000053
Figure FDA0003999924050000054
Figure FDA0003999924050000055
Figure FDA0003999924050000056
Figure FDA0003999924050000057
in the formula: g f,gt Predicting the gas load value at the time t;
Figure FDA0003999924050000058
is at t timeThe gas load value of the instant participation in the demand response is
Figure FDA0003999924050000059
When positive, indicating a transition out of the transferable gas load, otherwise indicating a transition into the transferable gas load; alpha is alpha gt The gas load proportion which is the load g at the moment t and can participate in demand response; I.C. A gt Type indicating variable for demand response for load g at time t, if I gt =1 denotes the time t load g is reduced or a partial load is transferred, I gt If =0, it means that the load g will be shifted to a partial load at time t;
Figure FDA00039999240500000510
the total amount of the load g reduced in all the scheduling periods; g ght The load value of the load g on the h section at the moment t;
Figure FDA00039999240500000511
the maximum load value of the load g on the h section at the moment t;
Figure FDA00039999240500000512
and
Figure FDA00039999240500000513
a demand response counter for respectively representing the load g at the time t-1;
Figure FDA00039999240500000514
and
Figure FDA00039999240500000515
respectively indicating the minimum holding time and the minimum interruption time for the load g to carry out demand response;
Figure FDA00039999240500000516
the maximum value of the allowed load g at time t.
2. The robust optimization operation method for the gas-electric distribution network system considering price type combined demand response as claimed in claim 1, wherein the deterministic optimization operation model for the gas-electric integrated energy distribution network system in step 4 is specifically as follows:
(1) An objective function: the day-ahead coordinated optimization operation model of the gas-electricity integrated energy distribution network system takes the maximization of social benefits of the integrated energy distribution network system as an optimization target;
Figure FDA00039999240500000517
in the formula: t is a time index; d. g is node indexes of electric and gas loads respectively;
Figure FDA00039999240500000518
respectively charging to the upper-level power grid and charging to the upper-level natural gas grid; c. C coal Is the unit coal cost;
Figure FDA00039999240500000519
Figure FDA0003999924050000061
respectively unit punishment cost of the power loss load and the gas loss load; c. C dht 、c ght Respectively obtaining unit energy supply benefits corresponding to the electric load d and the air load g on the h section at the time t;
(2) And (3) new energy output constraint:
0≤P wt ≤P f,wt
in the formula: p f,wt The predicted generated power of the fan w at the moment t;
(3) And (3) power exchange constraint of the distribution network system and a superior network:
Figure FDA0003999924050000062
Figure FDA0003999924050000063
Figure FDA0003999924050000064
in the formula: p in,min 、P in,max Respectively limiting the minimum active power and the maximum active power exchanged between the power distribution network and a superior power grid; q in,min 、Q in,max Respectively limiting the minimum reactive power and the maximum reactive power exchanged between the power distribution network and a superior power grid; g in,min 、G in,max Minimum and maximum gas purchase power limits for gas distribution network exchange with natural gas suppliers, respectively;
(4) And (3) load loss constraint of the distribution network system:
Figure FDA0003999924050000065
Figure FDA0003999924050000066
in the formula, G dt Is the actual value of the load d at time t;
Figure FDA0003999924050000067
the load loss value of the load d at the time t.
3. The robust optimization operation method of the gas-electric distribution network system considering the price type combined demand response as claimed in claim 2, wherein the method for processing the natural gas flow equation constraint by combining the enhanced second-order cone programming and the gas pressure recovery in step 5 is specifically as follows:
(1) Processing the flow equation constraint of the nonlinear gas distribution network by using an enhanced second-order cone relaxation method, and processing a target function on the premise of the basic second-order cone relaxation, namely adding a penalty term related to the node air pressure difference:
Figure FDA0003999924050000068
(G mn,t ) 2 +(K mn π nt ) 2 ≤(K mn π mt ) 2
in the formula: phi is a unit of mn For the penalty coefficient of the pressure difference between the two ends of the transmission pipeline mn of the gas distribution network, omega p The collection of all transmission pipelines;
(2) The method for processing the power flow equation of the gas distribution network by combining the enhanced second-order cone programming and the air pressure recovery specifically comprises the following steps:
Figure FDA0003999924050000071
Figure FDA0003999924050000072
Figure FDA0003999924050000073
σ mn,t ≥0
in the formula:
Figure FDA0003999924050000074
solving the natural gas trend obtained by using an enhanced second-order cone programming method; rho is a penalty coefficient; sigma mn,t Is an intermediate quantity and has no practical significance.
4. The robust optimal operation method of the gas-electric distribution network system considering the price type combined demand response, according to claim 1, is characterized in that in the step 6, a corresponding uncertainty collection is constructed by using a robust optimization theory, a two-stage adjustable robust optimal scheduling model of the gas-electric comprehensive energy distribution network system considering the price type gas-electric combined demand response is established, and the two-stage adjustable robust second-order cone optimal scheduling model is converted into a main-sub problem frame for solving by using a column and constraint generation algorithm as follows:
(1) Constructing an uncertainty set of electric power, natural gas load and new energy output, and adding a budget constraint in the uncertainty set to adjust to obtain a conservative form of a result;
Figure FDA0003999924050000075
Figure FDA0003999924050000076
Figure FDA0003999924050000077
in the formula: u shape E 、U G 、U W Respectively, an uncertainty set of electrical load, gas load and wind-power output; NT, ND, NG, NW are the number of scheduling periods, electrical loads, air loads and wind farms, respectively;
Figure FDA0003999924050000078
respectively are binary indexes of the electric load uncertainty aggregate;
Figure FDA0003999924050000079
respectively are binary indexes of the gas load uncertainty aggregate;
Figure FDA00039999240500000710
respectively representing binary indexes of the wind power output uncertainty aggregate; delta d 、Δ g 、Δ w Respectively the uncertainty budget of the electrical load, the gas load and the wind-power output;
Figure FDA0003999924050000081
the prediction deviations of the electric load, the gas load and the wind power output are respectively;
Figure FDA0003999924050000082
and
Figure FDA0003999924050000083
respectively, uncertain electric load, air load and wind power output value;
Figure FDA0003999924050000084
and
Figure FDA0003999924050000085
respectively predicting values of electric load, gas load and wind power output;
Figure FDA0003999924050000086
sets of real numbers, ND XNT, NG XNT, and NW XNT, respectively;
(2) Considering the unit combination, the scheduling arrangement and the scheduling correction measures of the units of the basic scene, establishing a two-stage adjustable robust optimization scheduling model of the distribution network system:
Figure FDA0003999924050000087
Ax≤d,x∈{0,1}
Bx+Cy+Dv＝0
Ex+Fy≤e
Figure FDA0003999924050000088
Figure FDA0003999924050000089
in the formula: x represents a unit of the generator,The electric power conversion equipment start-stop state and the demand response indication mark are related to binary variables; y and z respectively represent real-time correction values of distribution network system scheduling adjusted under a basic scene and according to system uncertainty; v is a safety violation value representing the power loss load, the air loss load and the air abandoning amount of the distribution network system; u is an uncertain variable related to the uncertainty of the electric load, the air load and the wind-power output; a, B, C, D, E, F, K r G, H, I, L, M, N and P are corresponding matrix parameters; a is T ,b T ,c T ,d,e,
Figure FDA00039999240500000810
q is a corresponding vector parameter; f (x, y) is a feasible field;
(3) And (3) converting the two-stage adjustable robust second-order cone optimization scheduling model into a main-sub problem frame by using a column and constraint generation algorithm to solve:
1) Setting maximum security violation threshold epsilon of worst scene of gas-electricity integrated energy distribution network system max And iteration counter l =1;
2) Solving the main problem to obtain the optimal result I st ，I at ，I dt ，I gt The sub-problem is brought into to test the operation safety of the gas-electricity comprehensive energy distribution network system;
3) Optimal result I from the main problem st ，I at ，I dt ，I gt Solving the sub-problems, and identifying to obtain the electrical load of the worst scene maximum safety violation of the distribution network system
Figure FDA0003999924050000091
Air load
Figure FDA0003999924050000092
And wind power generation
Figure FDA0003999924050000093
4) If the obtained maximum security violation of the worst scene is smaller than the set threshold, stopping iteration; otherwise, atElectrical load according to worst scenario maximum safety violation in this kth iteration
Figure FDA0003999924050000094
Air load
Figure FDA0003999924050000095
And wind power generation
Figure FDA0003999924050000096
Generating CCG constraint and returning to the step 2) to continue iteration;
and continuously solving the main problem of the optimized scheduling, checking the sub-problem of the safety check of the distribution network system and returning the worst scene to the main problem to obtain the optimal solution of the robust optimization problem.
5. The robust optimization operation method of the gas-electricity distribution network system considering price type combined demand response, according to claim 1, wherein the gas-electricity integrated energy distribution network system structure data in step 7 comprises distribution network system topology and distribution line/transmission pipeline parameters, the equipment parameters comprise a generator set cost coefficient, the number of electric-to-gas equipment, upper and lower output limits and capacity of a wind driven generator, and the distribution network operation parameters comprise energy market price, combined demand response proportion limit and electricity and gas load prediction data.
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