CN115085293A - Distributed energy interaction method for regional energy Internet and power distribution network - Google Patents

Distributed energy interaction method for regional energy Internet and power distribution network Download PDF

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CN115085293A
CN115085293A CN202210705327.3A CN202210705327A CN115085293A CN 115085293 A CN115085293 A CN 115085293A CN 202210705327 A CN202210705327 A CN 202210705327A CN 115085293 A CN115085293 A CN 115085293A
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power
distribution network
energy
power distribution
internet
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马腾飞
裴玮
邓卫
肖浩
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Institute of Electrical Engineering of CAS
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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
    • H02J3/38—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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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]

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Abstract

The invention relates to a distributed energy interaction method for a regional energy Internet and a power distribution network; firstly, respectively establishing a power distribution network and a regional energy Internet energy interaction optimization model; on the basis, a distributed energy interaction solving method of the power distribution network and a plurality of regional energy internets based on an alternative direction multiplier method is provided; the energy interaction method not only overcomes the defect that the privacy of each interaction subject is difficult to protect by a centralized optimization algorithm, but also simultaneously considers the coordination interaction of active power and reactive power, and realizes the flexible interaction and distributed operation optimization of the regional energy Internet and the power distribution network.

Description

Distributed energy interaction method for regional energy Internet and power distribution network
Technical Field
The invention relates to a distributed energy interaction method for a regional energy Internet and a power distribution network, and belongs to the technical field of power distribution network energy management.
Background
With the vigorous construction and development of the regional energy Internet, the trend of continuous deep fusion and interaction is presented by the regional energy Internet and the power distribution network. The regional energy Internet integrates various resources such as wind, light, electricity, heat and natural gas and can be regarded as multi-energy producers and consumers, so that the interaction between the regional energy Internet and the power distribution network is bidirectional, electricity can be purchased from the power distribution network to meet local requirements, and redundant electricity can be sold to the power distribution network to increase income; in addition, the regional energy Internet can realize multi-energy complementation and substitute energy, has larger interaction potential, and meanwhile, the distributed power supply also has certain reactive power compensation capability, so that the interaction between the regional energy Internet and the power distribution network is multidirectional and deeply fused bidirectional interaction. How to construct the interactive optimization model of regional energy Internet and distribution network energy, realize the flexibility and deep interaction of regional energy Internet and distribution network, be one of the problems that need to be solved in the field of distribution network energy management scheduling operation.
At present, relevant researches aiming at interactive operation of regional energy Internet and a power distribution network exist, for example, Chinese invention patent CN112598224A 'an interactive game scheduling method for a park integrated energy system group and the power distribution network', discloses a master-slave game interactive scheduling method for the park integrated energy system group and the power distribution network, and realizes active interaction between the park integrated energy system group and the power distribution network; the Chinese patent CN113393126A 'alternative parallel collaborative optimization scheduling method for a high energy consumption park and a power grid', discloses an alternative collaborative optimization scheduling method for a high energy consumption park and a power grid, and uses an alternative direction multiplier method to perform distributed solution; however, most of the existing methods do not consider the operation constraint of the power distribution network and the coordination interaction of reactive power, the multi-directional deep interaction between the regional energy Internet and the power distribution network is difficult to realize, and meanwhile, many methods adopt centralized optimization to solve, which is not beneficial to protecting the privacy and safety of participating bodies; therefore, how to simultaneously realize the active and reactive interaction between the regional energy Internet and the power distribution network and how to realize the distributed coordination interaction between the regional energy Internet and the power distribution network is an important problem to be solved urgently in the technical field of power distribution network energy management.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a distributed energy interaction method for a regional energy Internet and a power distribution network, wherein energy interaction optimization models of the regional energy Internet and the power distribution network are respectively established; on the basis, a distributed energy interaction method of a plurality of regional energy source internet and a power distribution network based on an alternating direction multiplier method is provided; the energy interaction method overcomes the defects that a centralized optimization algorithm is difficult to protect the privacy of each interaction subject and the coordination interaction of active power and reactive power is difficult to take into account simultaneously, and realizes the flexible interaction and distributed operation optimization of the regional energy Internet and the power distribution network.
In order to achieve the purpose, the invention adopts the technical scheme that:
a distributed energy interaction method for regional energy Internet and a power distribution network comprises the following steps:
step 1: aiming at minimizing the operation cost, comprehensively considering the operation constraint of the power distribution network, and establishing an energy interaction optimization model of interaction between the power distribution network and the regional energy Internet;
step 2: the method comprises the steps that with the aim of minimizing operation cost, operation constraints of regional energy Internet equipment are comprehensively considered, and an energy interaction optimization model of interaction between a regional energy Internet m and a power distribution network is established; the regional energy Internet equipment comprises energy conversion equipment and energy storage equipment;
and step 3: on the basis of the power distribution network and regional energy Internet energy interaction optimization model established in the step 1 and the step 2, establishing a distributed energy interaction solving method of the regional energy Internet and the power distribution network;
further, in the step 1, an energy interaction optimization model of the power distribution network and the regional energy Internet is established: the operation target of the power distribution network is to minimize the comprehensive operation cost, an objective function can be expressed by an equation (1) and consists of 7 items, wherein the item 1 is the network loss cost of the power distribution network, the item 2 is the electricity selling income of the power distribution network to each regional energy source internet, the item 3 is the reactive power regulation cost paid by the power distribution network to each regional energy source internet, and the items 4 to 7 are energy interaction cost; the operation constraint is expressed by an equation (2), the 1 st to 2 nd equations respectively express node active and reactive power balance constraints, the 3 rd equation expresses branch voltage constraint, the 4 th equation expresses branch current constraint, and the 5 th to 6 th equations respectively express node voltage constraint and branch current constraint; the method for establishing the energy interaction optimization model of the power distribution network specifically comprises the following steps:
step (1-1): determining an objective function of an energy interaction optimization model of the power distribution network according to the formula (1):
Figure BDA0003706007700000021
in the formula, λ loss Is a network loss cost coefficient, yuan/kW.h; t is an element {1,2, …, T } which is a time period set; b is a branch set, and B is a branch set,
Figure BDA0003706007700000022
r ij is the resistance of the branch ij and,
Figure BDA0003706007700000023
represents the square value of the current of branch ij at time t; omega is a regional energy Internet set, and m is a regional energy Internet index;
Figure BDA0003706007700000024
selling or buying electricity price per kW.h for the power distribution network at the moment t;
Figure BDA0003706007700000025
the regional energy Internet at the time t participates in the compensation electricity price, yuan/kVar, of the reactive power regulation of the power distribution network;
Figure BDA0003706007700000026
indicating that the power distribution network expects interactive power with the m regional energy source Internet at the moment t,
Figure BDA0003706007700000031
the active power of the interaction between the regional energy Internet m and the power distribution network at the moment t is shown;
Figure BDA0003706007700000032
representing the reactive power that the distribution grid expects to interact with the regional energy internet m,
Figure BDA0003706007700000033
the reactive power which is expected to interact with the power distribution network by the regional energy Internet m is represented;
Figure BDA0003706007700000034
and
Figure BDA0003706007700000035
is a Lagrange multiplier, and rho is a penalty coefficient;
step (1-2): determining an operation constraint condition of an energy interaction optimization model of the power distribution network according to the following formula (2):
Figure BDA0003706007700000036
in the formula, N is a distribution network node set, B is a branch set,
Figure BDA0003706007700000037
representing the square of the current of branch ij at time t,
Figure BDA0003706007700000038
representing the square of the voltage at node i at time t,
Figure BDA0003706007700000039
and
Figure BDA00037060077000000310
respectively representing the maximum and minimum voltage squared values,
Figure BDA00037060077000000311
and
Figure BDA00037060077000000312
represent the maximum and minimum current squared values, respectively; omega is regional energy Internet set, m is regional energy Internet index, omega j A regional energy Internet set for an access node j;
Figure BDA00037060077000000313
the active power of the mth regional energy Internet interacting with the node j at the time t is represented, the power which is expected to interact with the mth regional energy Internet by the power distribution network at the time t is represented, a positive value represents that electricity is expected to be sold to the regional energy Internet, and a negative value represents that electricity is expected to be purchased from the regional energy Internet;
Figure BDA00037060077000000314
representing the basic active load of the node j;
Figure BDA00037060077000000315
the reactive power of the injection node j of the regional energy Internet m is represented, a positive value represents that the regional energy Internet m is expected to provide the reactive power, and a negative value represents that the regional energy Internet m is expected to absorb the reactive power;
Figure BDA00037060077000000316
representing the reactive power injected into the node j by the reactive compensator;
Figure BDA00037060077000000317
representing the base reactive load of node j; δ (j) represents a set of branch end nodes with j as a head-end node; phi (j) represents a branch head node set taking j as a tail node; r is ij And x ij The resistance and reactance value of the branch ij are respectively; k is the branch index with j as the head-end node,
Figure BDA00037060077000000318
and
Figure BDA00037060077000000319
respectively flowing into the active power and the reactive power of the branch k from the node j in the time period t;
Figure BDA00037060077000000320
and
Figure BDA00037060077000000321
respectively the active power and the reactive power on the branch ij in the t period;
further, the step 2 specifically includes the following steps:
step (2-1): determining an energy interaction objective function of the regional energy Internet m according to the formula (3):
Figure BDA0003706007700000041
wherein the content of the first and second substances,
Figure BDA0003706007700000042
the operating cost of the regional energy Internet m;
Figure BDA0003706007700000043
denotes natural gas purchased from the regional energy internet m at time t,
Figure BDA0003706007700000044
and
Figure BDA0003706007700000045
respectively representing active power and reactive power of the power distribution network expected to interact with the mth regional energy Internet at the moment t;
Figure BDA0003706007700000046
the active power of interaction between the regional energy Internet m and the power distribution network is expected at the moment t, a positive value indicates that the regional energy Internet is expected to buy power from the power distribution network, and a negative value indicates that the regional energy Internet is expected to sell power to the power distribution network;
Figure BDA0003706007700000047
the method comprises the steps that reactive power which is expected to interact with a power distribution network is represented by a regional energy Internet m, positive values indicate that the reactive power is expected to be provided for the power distribution network, and negative values indicate that the reactive power is expected to be absorbed from the power distribution network;
Figure BDA0003706007700000048
and
Figure BDA0003706007700000049
is a lagrange multiplier; rho is a penalty coefficient;
step (2-2): determining an energy balance constraint of the regional energy Internet m according to the formula (4):
Figure BDA00037060077000000410
wherein the first equation in equation (4) represents the electrical power balance, the second equation represents the thermal power balance, and the third equation represents the natural gas power balance;
Figure BDA00037060077000000411
for the photovoltaic power generation power of the regional energy internet m at time t,
Figure BDA00037060077000000412
is the electrical load at time t;
Figure BDA00037060077000000413
and
Figure BDA00037060077000000414
charging and discharging power of the electric energy storage at the time t respectively;
Figure BDA00037060077000000415
and
Figure BDA00037060077000000416
the natural gas power input by the gas turbine and the gas boiler at the time t respectively;
Figure BDA00037060077000000417
and
Figure BDA00037060077000000418
respectively the electrical efficiency and the thermal efficiency of the gas turbine,
Figure BDA00037060077000000419
to gas boiler efficiency;
Figure BDA00037060077000000420
and
Figure BDA00037060077000000421
the heat storage power and the heat release power of the heat storage device at the moment t are respectively;
step (2-3): determining the operation constraints of a gas turbine, a gas boiler, an electric energy storage device and a thermal energy storage device in the regional energy Internet m according to the formulas (5), (6) and (7):
Figure BDA00037060077000000422
wherein
Figure BDA00037060077000000423
And
Figure BDA00037060077000000424
respectively obtaining minimum and maximum interactive power constraint values of the regional energy Internet m;
Figure BDA00037060077000000425
and
Figure BDA00037060077000000426
minimum and maximum output power constraint values for the gas turbine, respectively;
Figure BDA00037060077000000427
and
Figure BDA00037060077000000428
respectively the minimum and maximum output power constraint values of the gas boiler;
Figure BDA0003706007700000051
wherein
Figure BDA0003706007700000052
Representing the stored energy of the electrical energy store at time t,
Figure BDA0003706007700000053
the energy loss rate for the electrical energy storage,
Figure BDA0003706007700000054
and
Figure BDA0003706007700000055
respectively the charging and discharging efficiency of the electrical energy storage,
Figure BDA0003706007700000056
and
Figure BDA0003706007700000057
the maximum charging and discharging power for the electrical energy storage,
Figure BDA0003706007700000058
in the form of a binary variable, the variable,
Figure BDA0003706007700000059
and
Figure BDA00037060077000000510
the minimum and maximum stored energy of the electric energy storage are obtained, and the last term is used for indicating that the stored energy of the electric energy storage in the initial and final states of the operation cycle is equal;
Figure BDA00037060077000000511
wherein
Figure BDA00037060077000000512
Represents the heat storage amount of the heat storage energy at the moment t,
Figure BDA00037060077000000513
the heat loss rate of the heat energy storage is,
Figure BDA00037060077000000514
and
Figure BDA00037060077000000515
respectively the heat charging efficiency and the heat discharging efficiency of the heat energy storage,
Figure BDA00037060077000000516
and
Figure BDA00037060077000000517
respectively the maximum heat charging and heat discharging power of the heat energy storage,
Figure BDA00037060077000000518
in the form of a binary variable, the variable,
Figure BDA00037060077000000519
and
Figure BDA00037060077000000520
the minimum and maximum heat storage quantity of the heat energy storage are obtained, and the last item is used for indicating that the heat storage quantity of the heat energy storage in the initial and final states of the operation period is equal;
further, the specific steps of step 3 are as follows:
step (3-1): initializing maximum iteration number k of distributed energy interaction alternate iteration algorithm max Precision of iterative convergence
Figure BDA00037060077000000521
Initializing lagrange multipliers
Figure BDA00037060077000000522
And
Figure BDA00037060077000000523
initializing a penalty coefficient rho and iteration times k; active power for initializing interaction between energy Internet expectation and power distribution network in each region
Figure BDA00037060077000000524
And reactive power
Figure BDA00037060077000000525
Step (3-2): receiving active power expected to interact from energy source internet of each region by power distribution network
Figure BDA00037060077000000526
And reactive power
Figure BDA00037060077000000527
Solving the formulas (1) and (2) to obtain the active power of the interaction between the expected power distribution network and the regional energy Internet m
Figure BDA00037060077000000528
And reactive power
Figure BDA00037060077000000529
Step (3-3): for regional energy Internet m, receiving active power expected to interact with the power distribution network from the power distribution network
Figure BDA00037060077000000530
And reactive power
Figure BDA00037060077000000531
Solving equations (3) - (7) to obtain the active power expected to interact with the distribution network
Figure BDA00037060077000000532
And reactive power
Figure BDA00037060077000000533
Step (3-4): updating the lagrangian multiplier according to equation (8):
Figure BDA0003706007700000061
step (3-5): updating the iteration times: k is k + 1;
step (3-6): judging the convergence condition of the algorithm, and if the iteration termination condition is met (9):
Figure BDA0003706007700000062
and (4) stopping iteration, otherwise, returning to the step (3-2) of the flow and repeating the calculation until a convergence condition or the maximum iteration number is met.
Compared with the prior art, the invention has the advantages that:
(1) the invention simultaneously realizes the interaction of the power distribution network and the active power and the reactive power of the regional energy Internet;
(2) the distributed energy interactive solution of the power distribution network and the multiple regional energy internets is realized by using an alternating direction multiplier method, and the distributed coordinated operation control of the regional energy internets is realized;
(3) the distributed energy interaction method provided by the invention can realize distributed solution optimization of the operation strategies of the power distribution network and the regional energy Internet, and protect the security of the privacy information of each participating subject.
In conclusion, the interaction method can simultaneously realize the active power and reactive power interaction between the regional energy Internet and the power distribution network, and overcomes the defect that the prior art is mostly only used for active power interaction; the distributed energy interaction method of the power distribution network and the multiple regional energy resource internets based on the alternating direction multiplier method overcomes the defect that a centralized optimization algorithm is difficult to protect privacy of interaction main bodies, and flexible interaction and distributed operation optimization of the regional energy resource internets and the power distribution network are achieved.
Drawings
Fig. 1 is a frame diagram of a distributed energy interaction method of regional energy internet and a distribution network according to the present invention;
FIG. 2 is a schematic diagram of a regional energy Internet system of the present invention;
FIG. 3 is an overall flow chart of the distributed energy interaction method of the regional energy Internet and the power distribution network according to the invention;
fig. 4 is a flowchart of a distributed energy interaction solving method for the regional energy internet and the power distribution network according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and the implementation flow.
As shown in a framework diagram of the distributed energy interaction method of the regional energy internet and the power distribution network in fig. 1, the power distribution network and each regional energy internet are different operation subjects, the regional energy internet is in grid-connected operation through different nodes and performs active and reactive energy interaction with the power distribution network, and finally distributed optimal operation of the whole system is achieved.
As shown in the schematic diagram of the regional energy internet system of fig. 2, the regional energy internet system includes photovoltaic, gas turbine, gas boiler, electrical energy storage and thermal energy storage; the regional energy Internet is connected with the power distribution network and the natural gas network, active energy and reactive energy interaction of the power distribution network is participated, self operation strategies are continuously optimized, and the terminal electric load and heat load requirements are met while self benefits are maximized.
The invention discloses a distributed energy interaction method for a regional energy Internet and a power distribution network, which comprises the following steps of firstly, respectively establishing energy interaction optimization models of the regional energy Internet and the power distribution network; on the basis, a distributed energy interaction method of a plurality of regional energy source internet and a power distribution network based on an alternating direction multiplier method is provided; the distributed energy interaction method not only overcomes the defect that a centralized optimization algorithm is difficult to protect privacy of interaction subjects, but also simultaneously considers the coordination interaction of active power and reactive power, and realizes the flexible interaction and distributed operation optimization of the regional energy Internet and the power distribution network. Specifically, the distributed energy interaction method comprises the following steps:
step 1: and aiming at minimizing the operation cost, comprehensively considering the operation constraint of the power distribution network, and establishing an energy interaction optimization model of interaction between the power distribution network and the regional energy Internet.
The operation target of the power distribution network is to minimize the operation cost, the objective function can be expressed by an equation (1), the objective function is composed of 7 items, the item 1 is the network loss cost of the power distribution network, the item 2 is the electricity selling income of the power distribution network to each regional energy source internet, the item 3 is the reactive power regulation cost paid by the power distribution network to each regional energy source internet, and the items 4 to 7 are energy interaction cost. The operation constraint is expressed by an equation (2), wherein equations 1-2 in the equation (2) respectively express node active balance constraint and reactive balance constraint, equation 3 expresses branch voltage constraint, equation 4 expresses branch current constraint, and equations 5-6 respectively express node voltage constraint and branch current constraint. The method for establishing the energy interaction optimization model of the power distribution network specifically comprises the following steps:
step (1-1): determining an objective function of an energy interaction optimization model of the power distribution network according to the formula (1):
Figure BDA0003706007700000071
in the formula, λ loss Is a network loss cost coefficient, yuan/kW.h; t is an element {1,2, …, T } which is a time period set; b is a branch set, and B is a branch set,
Figure BDA0003706007700000072
Figure BDA0003706007700000073
r ij is the resistance of the branch ij and,
Figure BDA0003706007700000074
represents the square value of the current of branch ij at time t; omega is a regional energy Internet set, and m is a regional energy Internet index;
Figure BDA0003706007700000075
selling or buying electricity price per kW.h for the power distribution network at the moment t;
Figure BDA0003706007700000076
the regional energy Internet at the time t participates in the compensation electricity price, yuan/kVar, of the reactive power regulation of the power distribution network;
Figure BDA0003706007700000077
indicating that the power distribution network expects interactive power with the m regional energy source Internet at the moment t,
Figure BDA0003706007700000081
the active power of interaction between the regional energy Internet m and the power distribution network at the moment t is expressed;
Figure BDA0003706007700000082
representing the reactive power that the distribution network expects to interact with the regional energy internet m,
Figure BDA0003706007700000083
the reactive power which is expected to interact with the power distribution network by the regional energy Internet m is represented;
Figure BDA0003706007700000084
and
Figure BDA0003706007700000085
p is a penalty factor for lagrange multipliers.
Step (1-2): determining the operation constraint conditions of the energy interaction optimization model of the power distribution network according to the formula (2):
Figure BDA0003706007700000086
in the formula, N is a distribution network node set, B is a branch set,
Figure BDA0003706007700000087
representing the square of the current of branch ij at time t,
Figure BDA0003706007700000088
represents the square of the voltage at node i at time t,
Figure BDA0003706007700000089
and
Figure BDA00037060077000000810
respectively representing the maximum and minimum voltage squared values,
Figure BDA00037060077000000811
and
Figure BDA00037060077000000812
represent the maximum and minimum current squared values, respectively; omega is regional energy Internet set, m is regional energy Internet index, omega j A regional energy Internet set for an access node j;
Figure BDA00037060077000000813
the active power of the mth regional energy Internet interacting with the node j at the time t is represented, the power which is expected to interact with the mth regional energy Internet by the power distribution network at the time t is represented, a positive value represents that electricity is expected to be sold to the regional energy Internet, and a negative value represents that electricity is expected to be purchased from the regional energy Internet;
Figure BDA00037060077000000814
representing the basic active load of the node j;
Figure BDA00037060077000000815
the reactive power of the injection node j of the regional energy Internet m is represented, a positive value represents that the regional energy Internet m is expected to provide the reactive power, and a negative value represents that the regional energy Internet m is expected to absorb the reactive power;
Figure BDA00037060077000000816
representing the reactive power injected into the node j by the reactive compensator;
Figure BDA00037060077000000817
representing the base reactive load of node j; δ (j) represents a set of branch end nodes with j as a head-end node; phi (j) represents a branch head node set taking j as a tail node; r is ij And x ij The resistance and reactance value of the branch ij are respectively; k is the branch index with j as the head-end node,
Figure BDA00037060077000000818
and
Figure BDA00037060077000000819
respectively flowing into the active power and the reactive power of the branch k from the node j in the time period t;
Figure BDA00037060077000000820
and
Figure BDA00037060077000000821
respectively active power and reactive power on the branch ij in the t time period;
step 2: the method comprises the following steps of taking the minimum operation cost as a target, comprehensively considering the operation constraints of regional energy Internet energy conversion equipment and energy storage equipment, and establishing an energy interaction optimization model of interaction of a regional energy Internet m and a power distribution network, wherein the energy interaction optimization model specifically comprises the following steps:
step (2-1): determining an energy interaction objective function of the regional energy Internet m according to the formula (3):
Figure BDA0003706007700000091
wherein the content of the first and second substances,
Figure BDA0003706007700000092
the operating cost of the regional energy Internet m;
Figure BDA0003706007700000093
denotes natural gas purchased from the regional energy internet m at time t,
Figure BDA0003706007700000094
and
Figure BDA0003706007700000095
respectively representing active power and reactive power of the power distribution network expected to interact with the mth regional energy Internet at the moment t;
Figure BDA0003706007700000096
the active power of interaction between the regional energy Internet m expectation and the power distribution network at the moment t is represented, a positive value represents that the regional energy Internet expects to purchase power from the power distribution network, and a negative value represents that the regional energy Internet expects to purchase power from the power distribution networkIndicating a desire to sell electricity to the distribution grid;
Figure BDA0003706007700000097
the method comprises the steps that reactive power which is expected to interact with a power distribution network is represented by a regional energy Internet m, positive values indicate that the reactive power is expected to be provided for the power distribution network, and negative values indicate that the reactive power is expected to be absorbed from the power distribution network;
Figure BDA0003706007700000098
and
Figure BDA0003706007700000099
is a lagrange multiplier; rho is a penalty coefficient;
step (2-2): determining an energy balance constraint of the regional energy Internet m according to the formula (4):
Figure BDA00037060077000000910
wherein the first equation in equation (4) represents the electrical power balance, the second equation represents the thermal power balance, and the third equation represents the natural gas power balance;
Figure BDA00037060077000000911
for the photovoltaic power generation power of the regional energy internet m at time t,
Figure BDA00037060077000000912
is the electrical load at time t;
Figure BDA00037060077000000913
and
Figure BDA00037060077000000914
charging and discharging power of the electric energy storage at the time t respectively;
Figure BDA00037060077000000915
and
Figure BDA00037060077000000916
the natural gas power input by the gas turbine and the gas boiler at the time t respectively;
Figure BDA00037060077000000917
and
Figure BDA00037060077000000918
respectively the electrical efficiency and the thermal efficiency of the gas turbine,
Figure BDA00037060077000000919
to gas boiler efficiency;
Figure BDA00037060077000000920
and
Figure BDA00037060077000000921
the heat storage power and the heat release power of the heat storage device at the moment t are respectively;
step (2-3): determining the operation constraints of a gas turbine, a gas boiler, an electric energy storage device and a thermal energy storage device in the regional energy Internet m according to the formulas (5), (6) and (7):
Figure BDA00037060077000000922
wherein
Figure BDA00037060077000000923
And
Figure BDA00037060077000000924
respectively limiting the minimum and maximum interactive power of the regional energy Internet m;
Figure BDA00037060077000000925
and
Figure BDA00037060077000000926
minimum and maximum output power constraint values for the gas turbine, respectively;
Figure BDA00037060077000000927
and
Figure BDA00037060077000000928
respectively the minimum and maximum output power constraint values of the gas boiler;
Figure BDA0003706007700000101
wherein
Figure BDA0003706007700000102
The stored energy representing the electrical stored energy at time t,
Figure BDA0003706007700000103
the energy loss rate for the electrical energy storage,
Figure BDA0003706007700000104
and
Figure BDA0003706007700000105
respectively the charging and discharging efficiency of the electrical energy storage,
Figure BDA0003706007700000106
and
Figure BDA0003706007700000107
the maximum charging and discharging power for the electrical energy storage,
Figure BDA0003706007700000108
is a binary variable and is used as a reference,
Figure BDA0003706007700000109
and
Figure BDA00037060077000001010
the minimum and maximum stored energy of the electric energy storage are obtained, and the last term is used for indicating that the stored energy of the electric energy storage in the initial and final states of the operation cycle is equal;
Figure BDA00037060077000001011
wherein
Figure BDA00037060077000001012
Represents the heat storage amount of the heat storage energy at the moment t,
Figure BDA00037060077000001013
the heat loss rate of the heat energy storage is,
Figure BDA00037060077000001014
and
Figure BDA00037060077000001015
respectively the heat charging efficiency and the heat discharging efficiency of the heat energy storage,
Figure BDA00037060077000001016
and
Figure BDA00037060077000001017
respectively the maximum heat charging and heat discharging power of the heat energy storage,
Figure BDA00037060077000001018
in the form of a binary variable, the variable,
Figure BDA00037060077000001019
and
Figure BDA00037060077000001020
the minimum and maximum heat storage quantity of the heat energy storage are obtained, and the last item is used for indicating that the heat storage quantity of the heat energy storage in the initial and final states of the operation period is equal;
and step 3: on the basis of the energy interaction optimization model of the power distribution network and the regional energy Internet established in the step 1 and the step 2, a distributed energy interaction solving method of the regional energy Internet and the power distribution network is established, as shown in FIG. 4, the specific steps are as follows:
step (3-1): initializing distributed energy interactive alternating iterationsMaximum number of iterations k of the algorithm max Precision of iterative convergence
Figure BDA00037060077000001021
Initializing lagrange multipliers
Figure BDA00037060077000001022
And
Figure BDA00037060077000001023
initializing a penalty coefficient rho and iteration times k; initializing active power of each regional energy source internet expectation and power distribution network interaction
Figure BDA00037060077000001024
And reactive power
Figure BDA00037060077000001025
Step (3-2): receiving active power expected to interact from energy source internet of each region by power distribution network
Figure BDA00037060077000001026
And reactive power
Figure BDA00037060077000001027
Solving the formulas (1) and (2) to obtain the active power of the interaction between the expected power distribution network and the regional energy Internet m
Figure BDA00037060077000001028
And reactive power
Figure BDA00037060077000001029
Step (3-3): for regional energy Internet m, receiving active power expected to interact with the distribution grid from the distribution grid
Figure BDA0003706007700000111
And reactive power
Figure BDA0003706007700000112
Solving equations (3) - (7) yields the active power expected to interact with the distribution network
Figure BDA0003706007700000113
And reactive power
Figure BDA0003706007700000114
Step (3-4): updating the lagrangian multiplier according to equation (8):
Figure BDA0003706007700000115
step (3-5): updating the iteration times: k is k + 1;
step (3-6): judging the convergence condition of the algorithm, and if the iteration termination condition of the formula (9) is met:
Figure BDA0003706007700000116
and (4) ending the iteration, otherwise, returning to the step (3-2) of the flow and repeating the calculation until a convergence condition or the maximum iteration number is met.
The above implementation steps are provided only for the purpose of describing the present invention and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims. Various equivalent substitutions and modifications can be made without departing from the spirit and principles of the invention, and are intended to be within the scope of the invention.

Claims (4)

1. A distributed energy interaction method for regional energy Internet and a power distribution network is characterized by comprising the following steps:
step 1: aiming at minimizing the operation cost, comprehensively considering the operation constraint of the power distribution network, and establishing an energy interaction optimization model of interaction between the power distribution network and the regional energy Internet;
step 2: the method comprises the steps that with the aim of minimizing operation cost, operation constraints of regional energy Internet equipment are comprehensively considered, and an energy interaction optimization model of interaction between a regional energy Internet m and a power distribution network is established; the regional energy Internet equipment comprises energy conversion equipment and energy storage equipment;
and step 3: and (3) on the basis of the power distribution network and regional energy Internet energy interaction optimization model established in the step (1) and the step (2), establishing a distributed energy interaction solving method of the regional energy Internet and the power distribution network.
2. The distributed energy interaction method for the regional energy Internet and the power distribution network according to claim 1, wherein the method comprises the following steps: the step 1 specifically comprises the following steps:
the operation target of the power distribution network is to minimize the comprehensive operation cost, an objective function is expressed by a formula (1) and consists of 7 items, the item 1 is the network loss cost of the power distribution network, the item 2 is the income of the power distribution network for selling electricity to each regional energy Internet, the item 3 is the reactive power regulation cost paid by the power distribution network to each regional energy Internet, and the items 4 to 7 are energy interaction cost; the operation constraint is expressed by an equation (2), the 1 st to 2 nd equations respectively express node active and reactive power balance constraints, the 3 rd equation expresses branch voltage constraint, the 4 th equation expresses branch current constraint, and the 5 th to 6 th equations respectively express node voltage constraint and branch current constraint; the method for establishing the energy interaction optimization model of the power distribution network specifically comprises the following steps:
step (1-1): determining an objective function of an energy interaction optimization model of the power distribution network according to the following formula (1):
Figure FDA0003706007690000011
in the formula, λ loss Is a network loss cost coefficient, yuan/kW.h; t is an element {1,2, …, T } is a time period set; b is a branch set, and B is a branch set,
Figure FDA0003706007690000012
Figure FDA0003706007690000013
r ij electricity for branch ijThe resistance is set to be in a state of being in a state of being in a state of being in a state of being in a being in a state of being in a state of being in a state of being in a state of being in a state of being in a state of being in a,
Figure FDA0003706007690000014
represents the square value of the current of branch ij at time t; omega is a regional energy Internet set, and m is a regional energy Internet index;
Figure FDA0003706007690000015
selling or buying electricity price per kW.h for the power distribution network at the moment t;
Figure FDA0003706007690000016
participating in the compensation electricity price of the reactive power regulation of the power distribution network for the regional energy Internet at the time t, Yuan/kVar;
Figure FDA0003706007690000017
indicating that the power distribution network expects interactive power with the m regional energy source Internet at the moment t,
Figure FDA0003706007690000018
the active power of interaction between the regional energy Internet m and the power distribution network at the moment t is expressed;
Figure FDA0003706007690000021
representing the reactive power that the distribution grid expects to interact with the regional energy internet m,
Figure FDA0003706007690000022
the reactive power which is expected to interact with the power distribution network by the regional energy Internet m is represented;
Figure FDA0003706007690000023
and
Figure FDA0003706007690000024
is a Lagrange multiplier, and rho is a penalty coefficient;
step (1-2): determining an operation constraint condition of an energy interaction optimization model of the power distribution network according to the following formula (2):
Figure FDA0003706007690000025
in the formula, N is a distribution network node set, B is a branch set,
Figure FDA0003706007690000026
Figure FDA0003706007690000027
representing the square of the current of branch ij at time t,
Figure FDA0003706007690000028
representing the square of the voltage at node i at time t,
Figure FDA0003706007690000029
and
Figure FDA00037060076900000210
respectively representing the maximum and minimum voltage squared values,
Figure FDA00037060076900000211
and
Figure FDA00037060076900000212
represent the maximum and minimum current squared values, respectively; omega is regional energy Internet set, m is regional energy Internet index, omega j A regional energy Internet set for an access node j;
Figure FDA00037060076900000213
the active power of the mth regional energy Internet interacting with the node j at the time t is represented, the power which is expected to interact with the mth regional energy Internet by the power distribution network at the time t is represented, a positive value represents that electricity is expected to be sold to the regional energy Internet, and a negative value represents that electricity is expected to be purchased from the regional energy Internet;
Figure FDA00037060076900000214
representing the basic active load of the node j;
Figure FDA00037060076900000215
the reactive power of the injection node j of the regional energy Internet m is represented, a positive value represents that the regional energy Internet m is expected to provide the reactive power, and a negative value represents that the regional energy Internet m is expected to absorb the reactive power;
Figure FDA00037060076900000216
representing the reactive power injected into the node j by the reactive compensator;
Figure FDA00037060076900000217
representing the base reactive load of node j; δ (j) represents a set of branch end nodes with j as a head-end node; phi (j) represents a branch head node set taking j as a tail node; r is ij And x ij The resistance and reactance value of the branch ij are respectively; k is the branch index with j as the head-end node,
Figure FDA00037060076900000218
and
Figure FDA00037060076900000219
respectively flowing into the active power and the reactive power of the branch k from the node j in the time period t;
Figure FDA00037060076900000220
and
Figure FDA00037060076900000221
respectively active and reactive power on branch ij during time period t.
3. The distributed energy interaction method for the regional energy Internet and the power distribution network according to claim 2, wherein the method comprises the following steps: the step 2 specifically comprises the following steps:
step (2-1): determining an energy interaction objective function of the regional energy Internet m according to the formula (3):
Figure FDA00037060076900000222
wherein the content of the first and second substances,
Figure FDA0003706007690000031
the operating cost of the regional energy Internet m;
Figure FDA0003706007690000032
selling or buying the electricity price of the power distribution network at the time t, yuan/kW.h;
Figure FDA0003706007690000033
the price of the natural gas at the moment t is shown,
Figure FDA0003706007690000034
natural gas purchased from regional energy Internet m at time t;
Figure FDA0003706007690000035
and
Figure FDA0003706007690000036
respectively representing active power and reactive power of the power distribution network expected to interact with the mth regional energy Internet at the moment t;
Figure FDA0003706007690000037
the active power of interaction between the regional energy Internet m and the power distribution network is expected at the moment t, a positive value indicates that the regional energy Internet is expected to buy power from the power distribution network, and a negative value indicates that the regional energy Internet is expected to sell power to the power distribution network;
Figure FDA0003706007690000038
reactive power representing interaction between regional energy Internet m expectation and power distribution networkPositive values indicate a desire to provide reactive power to the distribution grid, negative values indicate a desire to absorb reactive power from the distribution grid;
Figure FDA0003706007690000039
and
Figure FDA00037060076900000310
is a lagrange multiplier; rho is a penalty coefficient;
step (2-2): determining an energy balance constraint of the regional energy Internet m according to the following formula (4):
Figure FDA00037060076900000311
wherein, the first equation in the equation (4) represents the electric power balance, the second equation represents the thermal power balance, and the third equation represents the natural gas power balance;
Figure FDA00037060076900000312
for the photovoltaic power generation power of the regional energy internet m at time t,
Figure FDA00037060076900000313
is the electrical load at time t;
Figure FDA00037060076900000314
the active power of interaction between the regional energy Internet m and the power distribution network at the moment t is expressed;
Figure FDA00037060076900000315
and
Figure FDA00037060076900000316
charging and discharging power of the electric energy storage at the time t respectively;
Figure FDA00037060076900000317
indicates the region energy at time tThe total natural gas consumption power of the source internet m;
Figure FDA00037060076900000318
and
Figure FDA00037060076900000319
the natural gas power consumed by the gas turbine and the gas boiler at the moment t respectively;
Figure FDA00037060076900000320
and
Figure FDA00037060076900000321
respectively the electrical efficiency and the thermal efficiency of the gas turbine,
Figure FDA00037060076900000322
to gas boiler efficiency;
Figure FDA00037060076900000323
and
Figure FDA00037060076900000324
the heat storage power and the heat release power of the heat storage device at the moment t are respectively;
Figure FDA00037060076900000325
represents the thermal load at time t;
step (2-3): determining the operation constraints of a gas turbine, a gas boiler, an electric energy storage device and a thermal energy storage device in the regional energy Internet m according to the formulas (5), (6) and (7):
Figure FDA00037060076900000326
wherein the content of the first and second substances,
Figure FDA00037060076900000327
and
Figure FDA00037060076900000328
respectively obtaining minimum and maximum interactive power constraint values of the regional energy Internet m;
Figure FDA00037060076900000329
the active power of interaction between the regional energy Internet m and the power distribution network at the moment t is expressed;
Figure FDA00037060076900000330
and
Figure FDA00037060076900000331
minimum and maximum output power constraint values for the gas turbine, respectively;
Figure FDA00037060076900000332
and
Figure FDA00037060076900000333
the natural gas power consumed by the gas turbine and the gas boiler at the moment t respectively;
Figure FDA00037060076900000334
in order to be able to achieve the electrical efficiency of the gas turbine,
Figure FDA00037060076900000335
to gas boiler efficiency;
Figure FDA00037060076900000336
and
Figure FDA00037060076900000337
respectively the minimum and maximum output power constraint values of the gas boiler;
Figure FDA0003706007690000041
wherein the content of the first and second substances,
Figure FDA0003706007690000042
representing the stored energy of the electrical energy store at time t,
Figure FDA0003706007690000043
the energy loss rate for the electrical energy storage,
Figure FDA0003706007690000044
and
Figure FDA0003706007690000045
respectively the charging and discharging efficiency of the electrical energy storage,
Figure FDA0003706007690000046
and
Figure FDA0003706007690000047
the maximum charging and discharging power for the electrical energy storage,
Figure FDA0003706007690000048
in the form of a binary variable, the variable,
Figure FDA0003706007690000049
and
Figure FDA00037060076900000410
the minimum and maximum stored energy of the electric energy storage are obtained, and the last term is used for indicating that the stored energy of the electric energy storage in the initial and final states of the operation cycle is equal;
Figure FDA00037060076900000411
wherein the content of the first and second substances,
Figure FDA00037060076900000412
represents the heat storage amount of the heat storage energy at the moment t,
Figure FDA00037060076900000413
the heat loss rate of the heat energy storage is,
Figure FDA00037060076900000414
and
Figure FDA00037060076900000415
respectively the heat charging efficiency and the heat discharging efficiency of the heat energy storage,
Figure FDA00037060076900000416
and
Figure FDA00037060076900000417
respectively the maximum heat charging and heat discharging power of the heat energy storage,
Figure FDA00037060076900000418
in the form of a binary variable, the variable,
Figure FDA00037060076900000419
and
Figure FDA00037060076900000420
the minimum and maximum heat storage amounts of the thermal energy storage are obtained, and the last term is used to indicate that the heat storage amounts of the thermal energy storage at the beginning and the end of the operation period are equal.
4. The distributed energy interaction method of the regional energy Internet and the power distribution network according to claim 3, characterized in that: the specific steps of the step 3 are as follows:
step (3-1): initializing maximum iteration number k of distributed energy interaction alternate iteration algorithm max Number of iterations k and iteration convergence accuracy
Figure FDA00037060076900000421
InitializationLagrange multiplier
Figure FDA00037060076900000422
And
Figure FDA00037060076900000423
initializing a penalty coefficient rho; active power for initializing interaction between energy Internet expectation and power distribution network in each region
Figure FDA00037060076900000424
And reactive power
Figure FDA00037060076900000425
Step (3-2): receiving active power expected to interact from energy source internet of each region by power distribution network
Figure FDA00037060076900000426
And reactive power
Figure FDA00037060076900000427
Solving the formula (1) and the formula (2) to obtain the active power of the interaction between the expected power distribution network and the regional energy Internet m
Figure FDA00037060076900000428
And reactive power
Figure FDA00037060076900000429
Step (3-3): for regional energy Internet m, receiving active power expected to interact with the power distribution network from the power distribution network
Figure FDA0003706007690000051
And reactive power
Figure FDA0003706007690000052
Solving equations (3) - (7) to obtain the active power expected to interact with the distribution network
Figure FDA0003706007690000053
And reactive power
Figure FDA0003706007690000054
Step (3-4): updating the lagrangian multiplier according to equation (8):
Figure FDA0003706007690000055
step (3-5): updating the iteration times: k is k + 1;
step (3-6): judging the convergence condition of the algorithm, and if the iteration termination condition is met (9):
Figure FDA0003706007690000056
and (4) ending the iteration, otherwise, returning to the step (3-2) of the flow and repeating the calculation until a convergence condition or the maximum iteration number is met.
CN202210705327.3A 2022-06-21 2022-06-21 Distributed energy interaction method for regional energy Internet and power distribution network Pending CN115085293A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116191575A (en) * 2023-03-17 2023-05-30 长电新能有限责任公司 Operation control method and system for participation of optical storage system in power grid voltage regulation auxiliary service
CN116191575B (en) * 2023-03-17 2023-08-18 长电新能有限责任公司 Operation control method and system for participation of optical storage system in power grid voltage regulation auxiliary service

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