CN114614491A - Distributed resource park-containing coordinated operation strategy considering price guide mechanism - Google Patents

Distributed resource park-containing coordinated operation strategy considering price guide mechanism Download PDF

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CN114614491A
CN114614491A CN202210083926.6A CN202210083926A CN114614491A CN 114614491 A CN114614491 A CN 114614491A CN 202210083926 A CN202210083926 A CN 202210083926A CN 114614491 A CN114614491 A CN 114614491A
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park
nth
power
distributed
representing
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徐巍峰
邱海锋
罗曼
翁利国
周建国
周国华
黄媛
刘俊勇
刘友波
许立雄
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State Grid Zhejiang Xiaoshan District Power Supply Co ltd
Zhejiang Zhongxin Electric Power Engineering Construction Co Ltd
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State Grid Zhejiang Xiaoshan District Power Supply Co ltd
Sichuan University
Zhejiang Zhongxin Electric Power Engineering Construction Co Ltd
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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
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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Abstract

The invention discloses a coordinated operation strategy for a park containing distributed resources considering a price guide mechanism, and relates to the field of park energy management and power distribution network optimized operation. And further, based on the constructed distributed resource multi-agent model, a park coordination operation strategy considering the interactive response characteristics of the various distributed resources is provided. And (3) providing an interactive benefit priority strategy, respectively providing interactive benefits of all distributed resource agents, and sequentially responding to all the element agents by the campus agent according to the interactive benefit priority sequence of all the distributed resource agents in each operation time period. And finally, researching the real-time interaction relationship between the power distribution network operator and the park agents, wherein the power distribution network operator conducts optimization-oriented guidance on the coordinated operation strategy of each park through a price mechanism, coordinates the optimized operation of a plurality of independent parks in the power distribution network, and further effectively supports the ordered operation of the high-elasticity power distribution network.

Description

Distributed resource park-containing coordinated operation strategy considering price guide mechanism
Technical Field
The invention relates to the field of park energy management and power distribution network optimized operation, in particular to a coordinated operation strategy of a park containing distributed resources, which takes a price guide mechanism into consideration.
Background
In recent years, the emergence of multi-type integrated parks in power distribution networks has accelerated the transition of power distribution networks from centralized networks to decentralized networks. The park comprises an Energy Storage (ES), a Flexible Load (FL), a distributed power supply (DG), an intelligent electric meter and the like, and flexible resources of the park can be actively managed to perform demand side response (DR). Demand side responses of multiple types of distributed resources inside the campus enable the integrated campus to flexibly achieve supply and demand balance and alleviate local network operation problems, such as load spike problems. Today, china appears to have many types of integrated parks including zero-carbon campuses, commercial buildings, and industrial parks. In the united states, the spontaneous electrical stimulation program (SGIP) in california, which has funded 8890 projects to provide incentives to support the installation of distributed energy resources at the customer site, has installed capacities in excess of 400 MW. In addition, the new york state has implemented a revolutionary energy landscape (REV) strategy to speed up the penetration of micro-grids and the installation of distributed energy sources in end users.
In a traditional power distribution network, power distribution network enterprises have natural monopoly on various internal schedulable resources, and the power distribution network enterprises generally adjust and schedule resources such as a system network structure, a grid-connected reactive compensation device and distributed energy resources in a unified mode when the power distribution network enterprises optimize operation. On the one hand, the inventory capital in the power distribution network is owned by power distribution network enterprises, and the power distribution network enterprises have the capability of realizing economic operation by calling resources in the system in order to obtain higher income on the basis of maintaining safe operation and reliable power supply of the system; on the other hand, because the original related policy does not allow the third party capital to intervene in the power distribution network, the power distribution network enterprises only need to allocate and optimally schedule resources to the system with the self income as the only target on the premise of meeting the power price standard set by the government price department and the power supply standard set by the energy department, and the situation is changed silently in recent years. With the release of the power distribution network to social capital, incremental capital begins to continuously rush in, most of the currently existing various comprehensive parks belong to third-party independent subjects, and each park performs autonomous scheduling by taking the economic benefit maximization of the park as a target. The functions of the power distribution network enterprises begin to be relocated, and the distributed resources in the park can not be directly controlled to carry out demand side response, so that a price incentive mechanism is provided for the park, and the park is encouraged to actively participate in the power distribution network optimization operation process in economic benefits. Under the new background, the power distribution network operator needs to fulfill own functions, and when the operator is responsible for safe supply of electric energy in the system and guarantees the shortage/surplus electric energy, the operator needs to consider pursuits of different market main bodies for own interests, so the power distribution network operator needs to design a price guide mechanism facing various comprehensive parks urgently, guide the optimized configuration relationship among different resources in a marketization mode, and realize the cooperative optimized operation of the power distribution network.
Disclosure of Invention
In view of the technical shortcomings, the invention provides a coordination operation strategy of a resource park with distributed resources, which takes the price guide mechanism into consideration.
In order to realize the purpose of the invention, the technical scheme of the invention is as follows:
a coordination operation strategy of a park containing distributed resources considering price guide mechanism comprises the following steps:
step 1: constructing a park optimization scheduling model containing various distributed resources, and modeling the distributed resources by adopting a multi-agent theory to obtain a plurality of distributed resource agents; meanwhile, based on a second-order cone relaxation theory, establishing a power distribution network operator optimized dispatching model to obtain guiding marginal electricity prices corresponding to various parks;
step 2: determining the interactive benefits of each distributed resource agent based on an interactive benefit priority principle, and performing priority sequencing on the interactive benefits;
and step 3: and each park carries out coordination optimization response according to the guiding marginal electricity price and the priority sequence.
Preferably, in step 1, the campus optimization scheduling model is:
Figure BDA0003482787220000021
Figure BDA0003482787220000022
in the formula:
Figure BDA0003482787220000023
representing the response power of the nth distributed resource agent at the moment t;
Figure BDA0003482787220000024
representing the response electricity price at the t moment of the nth park;
Figure BDA0003482787220000025
representing the power of the transaction with the power distribution network operator at the time t of the nth park;
Figure BDA0003482787220000026
representing the price of the nth campus at time t for transactions with the distribution network operator at the guaranteed base.
Preferably, the campus optimization scheduling model further includes a campus real-time operation constraint, where the campus real-time operation constraint is:
Figure BDA0003482787220000027
Figure BDA0003482787220000028
Figure BDA0003482787220000029
Figure BDA00034827872200000210
in the formula:
Figure BDA00034827872200000211
the output power of the nth distributed power supply agent at the moment t is represented;
Figure BDA00034827872200000212
representing the upper limit of the output power of the nth distributed power supply agent at the moment t;
Figure BDA00034827872200000213
representing the load power of the nth controllable load agent at the moment t;
Figure BDA00034827872200000214
representing the upper and lower limits of the load power of the nth controllable load agent at the moment t;
Figure BDA00034827872200000215
representing the charging and discharging power of the nth energy storage agent at the moment t;
Figure BDA00034827872200000216
representing the upper and lower limits of the charging and discharging power of the nth energy storage agent at the moment t;
Figure BDA00034827872200000217
representing the initial energy of the nth energy-storing agent of the nth park;
Figure BDA00034827872200000218
representing the charging and discharging power efficiency of the nth energy storage agent of the nth park;
Figure BDA00034827872200000219
represents the upper limit of the stored energy of the d energy storage agency of the nth park.
Preferably, in step 1, the power distribution network optimal scheduling model may be modeled as:
Figure BDA0003482787220000031
Figure BDA0003482787220000032
Figure BDA0003482787220000033
Figure BDA0003482787220000034
Figure BDA0003482787220000035
(Vi,t)2-(Vj,t)2＝2(rijPij,t+xijQij,t)-(Iij,t)2[(rij)2+(xij)2]
(Vi,t)2(Iij,t)2＝(Pij,t)2+(Qij,t)2
Figure BDA0003482787220000036
Figure BDA0003482787220000037
Figure BDA0003482787220000038
Figure BDA0003482787220000039
in the formula: pi (i) is a set of branch head nodes with node i as the last node, and phi (i) is a branch with node i as the head nodeSet of end nodes, rki、xkiResistance and reactance, r, of branch kiij、xijResistance and reactance, I, of branch ij, respectivelyki,tAnd Iij,tCurrent, P, of branch ki, branch ij respectivelyki,t、Qki,tActive and reactive power, P, of branch ki respectivelyij,t、Qij,tRespectively the active and reactive power of branch ij,
Figure BDA00034827872200000310
and
Figure BDA00034827872200000311
respectively indicating the power purchased by the distribution grid at campus n-i,
Figure BDA00034827872200000312
and
Figure BDA00034827872200000313
respectively representing the active and reactive power of the original load. Vi,tAnd Vj,tRepresents the node voltage; sijRepresents the apparent power ceiling for branch ij; vmin/VmaxRepresenting the upper and lower limits of the node voltage; i isij,maxRepresenting the upper node current limit.
Preferably, in step 1, the distributed resources include distributed energy resources, adjustable loads, energy storage, and regular loads.
Preferably, in step 1, the distributed resource broker is composed of a plurality of small-capacity distributed resources of the same type in the campus, and aggregation and unified control of the distributed resource broker are performed.
Preferably, in the step 2, the interaction interests are sorted, and when the multiple parks sequentially respond according to the priority order, the highest value of the interaction gains confirmed by the parks is used as the first response.
Preferably, in the step 3, the guidance marginal electricity price of each park is the marginal electricity price of the power distribution network node of the node where the park is located.
The beneficial effects of the invention are:
the invention firstly adopts a multi-agent theory to construct a multi-agent model of the operation characteristics of various distributed resources such as distributed energy sources, distributed energy storage, controllable load and the like in a park. And further, based on the constructed distributed resource multi-agent model, a park coordination operation strategy considering the interactive response characteristics of the various distributed resources is provided. And (3) providing an interactive benefit priority strategy, respectively providing interactive benefits of all distributed resource agents, and sequentially responding to all the element agents by the campus agent according to the interactive benefit priority sequence of all the distributed resource agents in each operation time period. And finally, researching the real-time interaction relationship between the power distribution network operator and the park agent, wherein the power distribution network operator conducts optimization-oriented guidance on the coordination operation strategy of each park through a price mechanism, coordinates the optimized operation of a plurality of independent parks in the power distribution network, and further effectively supports the ordered operation of the high-elasticity power distribution network.
Drawings
FIG. 1 is a flow chart of a wind farm energy storage power station multi-market participation strategy considering double uncertainties of output prediction and price;
FIG. 2 is a schematic diagram of a park optimization scheduling model in a wind farm energy storage power station multi-market participation strategy considering double uncertainties of output prediction and price;
FIG. 3 is a flow chart of demand side responses based on interaction benefit priority ranking inside a park in a wind farm energy storage power station multi-market participation strategy considering double uncertainty of output prediction and price.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses a more complete description of the embodiments of the present invention. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a coordination operation strategy for a campus including distributed resources, which takes into account a price guide mechanism, includes the following steps:
step 1: constructing a park optimization scheduling model containing various distributed resources, and modeling the distributed resources by adopting a multi-agent theory to obtain a plurality of distributed resource agents; meanwhile, based on a second-order cone relaxation theory, establishing a power distribution network operator optimized dispatching model to obtain guiding marginal electricity prices corresponding to various parks;
step 2: determining the interactive benefits of each distributed resource agent based on an interactive benefit priority principle, and carrying out priority sequencing on the interactive benefits;
and step 3: and each park carries out coordination optimization response according to the guiding marginal electricity price and the priority sequence.
As shown in fig. 2, further, in step 1, the campus optimization scheduling model is:
Figure BDA0003482787220000051
in the formula:
Figure BDA0003482787220000052
representing the response power of the nth distributed resource agent at the moment t;
Figure BDA0003482787220000053
representing the response electricity price at the t moment of the nth park;
Figure BDA0003482787220000054
representing the power of the transaction with the power distribution network operator at the time t of the nth park;
Figure BDA0003482787220000055
indicating the price of the nth campus at time t for trade with the distribution network operator's warranty.
Furthermore, the park optimization scheduling model further comprises park real-time operation constraints, wherein the park real-time operation constraints are as follows:
Figure BDA0003482787220000056
Figure BDA0003482787220000057
Figure BDA0003482787220000058
Figure BDA0003482787220000059
in the formula:
Figure BDA00034827872200000510
the output power of the nth distributed power supply agent at the moment t is represented;
Figure BDA00034827872200000511
representing the upper limit of the output power of the nth distributed power supply agent at the moment t;
Figure BDA00034827872200000512
representing the load power of the nth controllable load agent at the moment t;
Figure BDA00034827872200000513
representing the upper and lower limits of the load power of the nth controllable load agent at the moment t;
Figure BDA00034827872200000514
representing the charging and discharging power of the nth energy storage agent at the moment t;
Figure BDA00034827872200000515
represents the d-th energy storage generation of the nth parkThe upper and lower limits of charge-discharge power at the moment t are managed;
Figure BDA00034827872200000516
representing the initial energy of the nth energy-storing agent of the nth park;
Figure BDA00034827872200000517
representing the charging and discharging power efficiency of the nth energy storage agent of the nth park;
Figure BDA00034827872200000518
represents the upper limit of the stored energy of the d energy storage agency of the nth park.
Each campus contains different types of distributed resources including distributed energy, adjustable load, stored energy, and regular load. The campus may adjust the optimal operating point of the distributed resources within its control area. The distributed resources contained within the campus are modeled as a plurality of distributed resource brokers using a multi-broker theory. A distributed resource broker (DERA) consists of a number of small capacity distributed resources of the same type within a campus. Through aggregation and unified control of distributed resource brokers, small-capacity distributed resources can participate in demand response processes within a campus. And coordinating the demand side response income of the distributed resource agent by the park to realize the maximization of the economic surplus inside the park.
As shown in fig. 3, the campus responds to a plurality of distributed resource brokers within the campus in turn according to a benefit priority principle, based on an interactive benefit priority principle. In each iteration process, the campus selects proper DERA response power according to the interaction profit ranking until balance is achieved or distributed resource response energy is not left. In particular, the interactive benefits of the distributed power agent, the energy storage agent, and the controllable load agent are shown in equations (6) - (8)
Figure BDA0003482787220000061
Figure BDA0003482787220000062
Figure BDA0003482787220000063
In the formula:
Figure BDA0003482787220000064
representing the net power at time t for the nth park,
Figure BDA0003482787220000065
indicating that power was excessive at time t for the nth park,
Figure BDA0003482787220000066
indicating that the power is insufficient at the time t of the nth park;
Figure BDA0003482787220000067
indicating the power distribution network guide electricity price at the t moment of the nth park;
Figure BDA0003482787220000068
representing the power price of the power distribution network at the time t;
Figure BDA0003482787220000069
and the time-of-use electricity price of the power distribution network at the time t is shown.
Whenever unbalanced power occurs inside the campus, the campus broadcasts unbalanced power and guide price (marginal price of electricity at the node given by the distribution network operator) information to each distributed resource broker. Then, each distributed resource agent considers the technical constraint of the distributed resource agent and the corresponding interactive profit to generate a feasible interactive response strategy. Each distributed resource broker then sends the actual response energy and the interaction revenue back to the campus. And finally, the park induces the interactive income of each distributed resource agent and carries out priority sequencing on the interactive income.
The park distributes response quotas to the distributed resource agents according to the interactive income in each bid section, and the qualification of each bid section allows the winner to respond to the PΔThe above process is repeated by the number of powers, the campus and the distributed resource broker until power balance is achieved inside the campus, or all available distributed resource powers are completely responded, and the specific response flow is shown in fig. 2.
Furthermore, in step 1, the optimal scheduling model of the power distribution network may be modeled as:
Figure BDA00034827872200000610
Figure BDA00034827872200000611
Figure BDA00034827872200000612
Figure BDA00034827872200000613
Figure BDA00034827872200000614
(Vi,t)2-(Vj,t)2＝2(rijPij,t+xijQij,t)-(Iij,t)2[(rij)2+(xij)2] (14)
(Vi,t)2(Iij,t)2＝(Pij,t)2+(Qij,t)2 (15)
Figure BDA0003482787220000071
Figure BDA0003482787220000072
Figure BDA0003482787220000073
Figure BDA0003482787220000074
in the formula: pi (i) is a set of branch end nodes with node i as the end node, phi (i) is a set of branch end nodes with node i as the end node, rki、xkiResistance and reactance, r, of branch kiij、xijResistance and reactance, I, of branch ij, respectivelyki,tAnd Iij,tCurrent, P, of branch ki, branch ij respectivelyki,t、Qki,tActive and reactive power, P, of branch ki respectivelyij,t、Qij,tRespectively the active and reactive power of branch ij,
Figure BDA0003482787220000075
and
Figure BDA0003482787220000076
respectively indicating the power purchased by the distribution grid at campus n-i,
Figure BDA0003482787220000077
and
Figure BDA0003482787220000078
respectively representing the active and reactive power of the original load. Vi,tAnd Vj,tRepresents the node voltage; sijRepresents the apparent power ceiling of branch ij; vmin/VmaxRepresenting the upper and lower limits of the node voltage; i isij,maxRepresenting the upper node current limit.
Based on the optimal operation model of the power distribution network given by the formulas (9) - (19), the calculation mode of the marginal price of the power distribution network node can be obtained and is shown as a formula (20)
Figure BDA0003482787220000079
Wherein the function A1,……,A5Is obtained based on the calculation of the alternating current optimal power flow, and is Pl,t,Ql,t,Il,tThe non-linear function of (2) is specifically shown in equations (21) to (25). As shown in the formula (20), the calculated marginal price of the power distribution network node quantifies the influence of the constraints in the formulas (6) to (16), and the marginal price of the node can indicate the influence of the loss of the power distribution line, the load flow limit and the node voltage limit.
Figure BDA00034827872200000710
Figure BDA00034827872200000711
Figure BDA00034827872200000712
Figure BDA00034827872200000713
Figure BDA0003482787220000081
Further, in step 1, the distributed resources include distributed energy resources, adjustable loads, energy storage, and regular loads.
Furthermore, in step 1, the distributed resource broker is composed of a plurality of small-capacity distributed resources of the same type in the campus, and aggregation and unified control are performed by the distributed resource broker.
Furthermore, in the step 2, the interaction interests are sorted, and when the multiple parks sequentially respond according to the priority order, the highest value of the interaction benefits confirmed by the parks is used as the first response.
Furthermore, in step 3, the marginal electricity price of each campus is the marginal electricity price of the power distribution network node of the node where the campus is located.
Based on the marginal electricity price of the power distribution network node calculated by the power distribution network operator, the power distribution network guide electricity price issued to each park is the marginal electricity price of the power distribution network node of the node where the park is located, which can be expressed as:
Figure BDA0003482787220000082
and (3) based on the park guidance electricity price given by the formula (26), each park independently carries out the demand side response strategy given by the second part, and determines the response power and the net power under the current guidance electricity price at the current moment so as to participate in the coordination optimization operation process of the power distribution network.

Claims (8)

1. A coordinated operation strategy of a campus containing distributed resources considering a price guide mechanism, comprising the following steps:
step 1: constructing a park optimization scheduling model containing various distributed resources, and modeling the distributed resources by adopting a multi-agent theory to obtain a plurality of distributed resource agents; meanwhile, based on a second-order cone relaxation theory, establishing a power distribution network operator optimized dispatching model to obtain guiding marginal electricity prices corresponding to various parks;
step 2: determining the interactive benefits of each distributed resource agent based on an interactive benefit priority principle, and performing priority sequencing on the interactive benefits;
and step 3: and each park carries out coordination optimization response according to the guiding marginal electricity price and the priority sequence.
2. The coordination operation strategy for the campus with distributed resources considering the price guide mechanism as claimed in claim 1, wherein in step 1, the optimal scheduling model for the campus is:
Figure FDA0003482787210000011
Figure FDA0003482787210000012
in the formula:
Figure FDA0003482787210000013
representing the response power of the nth distributed resource agent at the moment t;
Figure FDA0003482787210000014
representing the response electricity price at the t moment of the nth park;
Figure FDA0003482787210000015
representing the power of the transaction with the distribution network operator at the nth park at the moment t;
Figure FDA0003482787210000016
indicating the price of the nth campus at time t for trade with the distribution network operator's warranty.
3. The coordinated operation strategy of the campus containing distributed resources with the price guide mechanism in consideration of claim 2, wherein the campus optimization scheduling model further comprises a campus real-time operation constraint, and the campus real-time operation constraint is as follows:
Figure FDA0003482787210000017
Figure FDA0003482787210000018
Figure FDA0003482787210000019
Figure FDA00034827872100000110
in the formula:
Figure FDA00034827872100000111
the output power of the nth distributed power supply agent at the moment t is represented;
Figure FDA00034827872100000112
representing the upper limit of the output power of the d distributed power supply agent of the nth park at the moment t;
Figure FDA00034827872100000113
representing the load power of the nth controllable load agent of the nth park at the moment t;
Figure FDA00034827872100000114
representing the upper and lower limits of the load power of the nth controllable load agent at the moment t;
Figure FDA00034827872100000115
representing the charging and discharging power of the nth energy storage agent at the moment t;
Figure FDA00034827872100000116
representing the upper and lower limits of the charging and discharging power of the nth energy storage agent at the moment t;
Figure FDA00034827872100000117
representing the initial energy of the nth energy-storing agent of the nth park;
Figure FDA00034827872100000118
represents the nth parkThe charging and discharging power efficiency of the d-th energy storage agent;
Figure FDA00034827872100000119
representing the upper limit of the stored energy of the d-th energy-storing agent of the nth park.
4. The coordinated operation strategy of the distributed resource park involved in the price guide mechanism in claim 1 is characterized in that in step 1, the optimal scheduling model of the power distribution network can be modeled as:
Figure FDA0003482787210000021
s.t.(λ0):
Figure FDA0003482787210000022
(λi):
Figure FDA0003482787210000023
(μ0):
Figure FDA0003482787210000024
(μi):
Figure FDA0003482787210000025
(Vi,t)2-(Vj,t)2＝2(rijPij,t+xijQij,t)-(Iij,t)2[(rij)2+(xij)2]
(Vi,t)2(Iij,t)2＝(Pij,t)2+(Qij,t)2
(ηl +):
Figure FDA0003482787210000026
(ηl -):
Figure FDA0003482787210000027
Figure FDA0003482787210000028
Figure FDA0003482787210000029
in the formula: pi (i) is a set of branch end nodes with node i as the end node, phi (i) is a set of branch end nodes with node i as the end node, rki、xkiResistance and reactance, r, of branch kiij、xijResistance and reactance, I, of branch ij, respectivelyki,tAnd Iij,tCurrent, P, of branch ki, branch ij respectivelyki,t、Qki,tActive and reactive power, P, of branch ki respectivelyij,t、Qij,tRespectively the active and reactive power of branch ij,
Figure FDA00034827872100000210
and
Figure FDA00034827872100000211
respectively indicating the power purchased by the distribution grid at campus n-i,
Figure FDA00034827872100000212
and
Figure FDA00034827872100000213
respectively representing the active and reactive power of the original load. Vi,tAnd Vj,tRepresents the node voltage; sijRepresents the apparent power ceiling for branch ij; vmin/VmaxRepresenting the upper and lower limits of the node voltage; I.C. Aij,maxRepresenting the upper node current limit.
5. The coordination operation strategy for parks with distributed resources considering price guide mechanism in accordance with claim 1, wherein in step 1, the distributed resources comprise distributed energy resources, adjustable loads, energy storage and regular loads.
6. The coordination operation strategy for the campus containing distributed resources considering the price guide mechanism in claim 1, wherein in step 1, the distributed resource broker is composed of a plurality of small-capacity distributed resources of the same type in the campus, and aggregation and unified control are performed by the distributed resource broker.
7. The strategy of claim 1, wherein in step 2, the interaction interests are sorted, and when the multiple parks respond in sequence according to the priority order, the highest value of the interaction interests confirmed by the parks is used as the first response.
8. The coordinated operation strategy of the parks with distributed resources, which takes account of the price guide mechanism, according to claim 1, wherein in the step 3, the guiding marginal electricity price of each park is the marginal electricity price of the distribution network node where the park is located.
CN202210083926.6A 2022-01-21 2022-01-21 Distributed resource park-containing coordinated operation strategy considering price guide mechanism Pending CN114614491A (en)

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