CN113506028B - Comprehensive service station resource dynamic combination method and system based on multi-station integration - Google Patents
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Abstract
The invention provides a comprehensive service station resource dynamic combination method and a system based on multi-station integration, wherein the method comprises the steps of dividing response electric quantity of a user into a risky asset and a risky asset; the risky asset provides a response power for the freely responding user; the risk-free asset is a response electric quantity provided by a user who hosts the control authority of the terminal equipment to the comprehensive service station; establishing asset resource response characteristic models of the risk asset and the non-risk asset; establishing a resource dynamic combination model of the comprehensive service station based on asset resource response characteristic models of the risk assets and the non-risk assets; and evaluating the comprehensive service station resource combination mode based on a resource dynamic combination model. Based on this method, a combined system is also proposed. In order to optimize the resource combination of the comprehensive service station, the invention plays the coordination and complementation characteristic of the resources to improve the flexibility of the comprehensive service station, disperses the benefit risk caused by the uncertainty of the response behavior of the user and provides support for the application of the comprehensive service station.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a comprehensive service station resource dynamic combination method and system based on multi-station integration.
Background
Along with the rapid development of economy, the problems of energy and environment are increasingly outstanding, and how to realize clean and efficient utilization of energy becomes the focus of research in recent years, and the comprehensive energy system integrates the production, transmission, conversion and consumption of various energy sources, can fully exert the complementary characteristics and synergistic effect of different energy sources, and is an important means for realizing efficient utilization of energy sources and guaranteeing energy supply safety. In order to strengthen the research of system mechanisms, focus on the key field, take novel smart city and county comprehensive energy as the carrier, develop resource management deeply, accelerate construct the affiliated cooperative work mechanism and capital tie relation, accelerate construct tangible and intangible asset value excavation, system mechanism management operation optimization, development support system of mutual guarantee of hardware and software, comprehensive energy service, multi-station unification, transform the original transformer substation into three-station unification of transformer substation, charging and replacing station and data center station, etc. have become the main business direction.
The comprehensive service station based on multi-station integration plays a role of 'up and down' in the operation process. The method comprises the steps of obtaining the adjustment capability of a comprehensive service station aggregate by integrating the factors of regional distribution, access voltage class and the like of each resource in the comprehensive service station; the comprehensive service station is externally provided with different adjustment requirements for various scenes such as auxiliary services such as power distribution network blocking management, peak shaving and frequency modulation, electric power market transaction and the like, which can participate in, but the adjustment requirements can be divided into two parts including space adjustment requirements and time adjustment requirements in comprehensive view. When the peak regulation and frequency modulation auxiliary service and the energy market are in transaction, the overall regulation requirement of the comprehensive service station is only reflected on a time scale, and the power distribution network blocking management has the regulation requirement of the comprehensive service station on both time and space scales. The interactive resources participating in the comprehensive service station can be flexibly and dynamically combined according to the operation targets of the comprehensive service station. But dispersing the risk of benefit that would be caused by uncertainty in the user response behavior, no effective solution has been proposed in the prior art for the risk of benefit caused by uncertainty.
Disclosure of Invention
In order to solve the technical problems, the invention provides a comprehensive service station resource dynamic combination method and a system based on multi-station integration, which exert the coordination and complementation characteristics of resources to improve the flexibility of the comprehensive service station, disperse the benefit risks caused by uncertainty of response behaviors of users and provide support for the application of the comprehensive service station.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A comprehensive service station resource dynamic combination method based on multi-station integration comprises the following steps:
Dividing the response power of the user into a risky asset and a risky asset; the risk asset provides response electric quantity for a free response user; the free response user is a user who does not host the control authority of the terminal equipment to the comprehensive service station; the risk-free asset is response electric quantity provided by a user who hosts the control authority of the terminal equipment to the comprehensive service station;
Establishing a resource response characteristic model of the risky asset and establishing an asset resource response characteristic model of the risky asset;
establishing a resource dynamic combination model of the comprehensive service station based on the resource response characteristic model of the risky asset and the asset resource response characteristic model of the risky asset;
and evaluating the comprehensive service station resource combination mode based on the resource dynamic combination model.
Further, the resource response characteristic model of the risky asset is established as follows:
s∈SDR,t∈T；
s∈SDR,t∈T；
Wherein s is the user type; s DR is a set of risk asset users; i is the user number; t is the participation response period; t is the scheduling period, ub is the upper limit of the risk asset; lb is the lower limit of the risky asset; The actual response electric quantity of the ith class s user in the t period is obtained; /(I) Is the upper end of the response range; /(I)Is the lower limit of the response range; /(I)The load value before the user responds in the t period; /(I)The load value after the user responds in the t period; /(I)For response deviation;
A response power expected value allocated to the user; /(I) Is a random deviation amount of the user response power.
Further, the method for establishing the resource response characteristic model of the risk-free asset comprises the following steps:
For the purpose of Analyzing to obtain the standard deviation sigma i of the income of each user and the correlation rho ij between the risk assets; a risk-free asset when ρ ij =0.
Further, the process of establishing the dynamic resource combination model of the comprehensive service station based on the resource response characteristic model of the risky asset and the asset resource response characteristic model of the risky asset further comprises:
Establishing an objective function of the dynamic combination model of the resources;
determining constraint conditions of the objective function;
And converting the resource dynamic combination model into a mixed integer linear programming and carrying out branch power flow linearization constraint on the resource dynamic combination model.
Further, the process of establishing the objective function of the dynamic combination model of the resources is to establish the objective function with the expected benefit maximization as a target:
wherein E (R p) represents the expected revenue for the integrated service station after assembly; representing expected revenue for the integrated service station; /(I) Determining an expected revenue from the energy market after the bidding curve P aim,t for market clearing; C r is retail electricity price; c w is market wholesale electricity price; /(I) Incentive from the grid to provide congestion management service acquisition to the grid; e (C cost) is the expected cost; f 1 provides the incentive cost required by the response electric quantity for incentive users to change the output plan; f 2 is the uncertainty cost generated by the deviation between the actual power of the integrated service station and the target power, and corresponds to the penalty cost from the electric power market in the settlement stage; f 3 is the access cost to communication and computing resources required by maintaining the user to participate in the interaction of the comprehensive service station; /(I)The unit response incentive cost of the ith user of the s-th type resource, namely the internal transaction electricity price; n is a node number and represents the position of a resource access distribution network; lambda is the access cost required by a single resource to participate in regulation; gamma is an uncertainty factor; x i,t is the number of resources; p VPP,t is the actual power of the integrated service station; p aim,t is the target power.
Further, the constraint condition for determining the objective function is:
Wherein, Representing risk and utility constraints of the optimal combination scheme when utility is maximized; Resource response characteristic constraints representing risk assets; /(I) Equality constraints representing aggregate external characteristics of the integrated service station; /(I)Representing application scenario constraints;
The expression of the aggregate external characteristics of the comprehensive service station is as follows:
wherein n represents a participating response interactive resource grid-connected point; s= { S DR,Sshift,Strans,Sre } represents all participating responding user sets; providing a response value for the comprehensive service station at a node n of the distribution network in a t period, namely responding to the change amount of active injection power at the node n in the t period before and after; /(I) Representing the actual load value at the node n in the t period after the response;
After the response characteristics distributed at different geographic positions are aggregated, the time aggregation characteristics of the comprehensive service stations are obtained:
Wherein Δp VPP,t represents a response value provided by the integrated service station in the period t; p VPP,t respectively represents the aggregate load value of the comprehensive service station in the period t after the response;
The comprehensive service station application scene constraint comprises external characteristic constraint and space tide constraint which are suffered by the participation of the comprehensive service station in the electric power market; the external characteristic constraint of the comprehensive service station participating in the electric power market is that
-Er is less than or equal to P VPP,tΔt-Paim,t delta t is less than or equal to er; wherein er is the allowable electric quantity deviation agreed by the comprehensive service station and the electric power market;
the spatial tide constraint is as follows:
-Pmn,N≤Pmn,t≤Pmn,N
Wherein, For the transmission power of the branches m to n at the moment t when not regulated, m and n are respectively the numbers of the connecting nodes at the two ends of the branches; /(I)To adjust the magnitude of the voltage at the front node m; /(I)To adjust the amplitude of the voltage at the back node n; /(I)To adjust the phase of the voltage at the front node m; /(I)To adjust the phase of the voltage at the back node n; g mn is the conductance of branches m to n; b mn is susceptance of branches m to n;
P mn,t is the transmission power of the branch circuit m-n after the comprehensive service station is regulated; p mn,N is the maximum transmission power allowed by the leg; Δp mn,t is the amount of change in the branch transmission power.
Further, the process of converting the resource dynamic combination model into a mixed integer linear program and performing branch power flow linearization constraint on the resource dynamic combination model is as follows:
The sensitivity parameter alpha is introduced to linearize the nonlinear constraint of the branch power flow, and the derivation process of alpha is as follows, because delta P mn,t is determined by delta P n,t and node reactive power injection change delta Q n,t:
Wherein, alpha P,mn,n,t represents the sensitivity of the change of the active injection power of the node n to the change amount of the transmission power of the branch m-n; alpha Q,mn,n,t represents the sensitivity of the change of the reactive injection power of the node n to the change of the transmission power of the branch m-n;
the change ΔP mn of the branch transmission m-n power is expressed as:
Will be described in Is rewritten into a matrix form, as shown in the following formula:
The relation between the change amount of the voltage amplitude and the phase of each node and the change of the injection power of the node is deduced from a power flow balance equation and is represented by a jacobian matrix J PQ,θV, as follows:
The above matrix is transformed, and then:
The relationship between the amount of change Δp mn of the branch transmission m-n power and the amount of change of the injection power of each node is as follows:
At this time, the introduced sensitivity vector represents the influence of the network power flow change on the branch m-n transmission power, as shown in the following formula:
ΔPmn＝αmn[ΔP1,…ΔPN,ΔQ1,…ΔQN]T
Then it is Equivalent to the formula:
ΔPmn＝αmn[ΔP1,…ΔPN,ΔQ1,…ΔQN]T
Due to the delta P n,t accounting for The ratio is smaller, and the power factor angle (phi n) of the node before and after adjustment is not changed/>Rewritable as a form of the formula:
Further, the process of evaluating the comprehensive service station resource combination mode based on the resource dynamic combination model is as follows: and analyzing the resource combination mode by using the calculation example analysis, wherein the analysis specifically comprises application analysis based on the calculation example information, benefit risk analysis based on the calculation example information and comparison analysis based on the calculation example information.
The invention also provides a comprehensive service station resource dynamic combination system based on multi-station integration, which comprises a classification module, a first establishment module, a second establishment module and an evaluation module;
The classification module is used for classifying the response electric quantity of the user into a risky asset and a risky asset; the risk asset provides response electric quantity for a free response user; the free response user is a user who does not host the control authority of the terminal equipment to the comprehensive service station; the risk-free asset is response electric quantity provided by a user who hosts the control authority of the terminal equipment to the comprehensive service station;
The first establishing module is used for establishing a resource response characteristic model of the risky asset and establishing an asset resource response characteristic model of the risky asset;
the second establishing module is used for establishing a resource dynamic combination model of the comprehensive service station based on the resource response characteristic model of the risky asset and the asset resource response characteristic model of the risky asset;
the evaluation module is used for evaluating the comprehensive service station resource combination mode based on the resource dynamic combination model.
Further, the second building module comprises a model building sub-module, an objective function building sub-module, a constraint condition determining sub-module and a conversion sub-module;
the model building submodule is used for building a dynamic resource combination model based on the resource response characteristics;
the objective function building submodule is used for building an objective function of the dynamic resource combination model;
the constraint condition determination submodule is used for determining constraint conditions of the objective function;
The conversion submodule is used for converting the resource dynamic combination model into a mixed integer linear programming and carrying out branch power flow linearization constraint on the resource dynamic combination model.
The effects provided in the summary of the invention are merely effects of embodiments, not all effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
The invention provides a comprehensive service station resource dynamic combination method and a system based on multi-station integration, wherein the method comprises the steps of dividing response electric quantity of a user into a risky asset and a risky asset; the risky asset provides a response power for the freely responding user; the free response user is a user who does not host the control authority of the terminal equipment to the comprehensive service station; the risk-free asset is a response electric quantity provided by a user who hosts the control authority of the terminal equipment to the comprehensive service station; establishing a resource response characteristic model of the risk asset and establishing an asset resource response characteristic model of the risk-free asset; establishing a dynamic resource combination model of the comprehensive service station based on the resource response characteristic model of the risk asset and the asset resource response characteristic model of the non-risk asset; and evaluating the comprehensive service station resource combination mode based on a resource dynamic combination model. A comprehensive service station resource dynamic combination method based on multi-station integration is also provided. In order to optimize the resource combination of the comprehensive service station, the invention plays the coordination and complementation characteristic of the resources to improve the flexibility of the comprehensive service station, disperses the benefit risk caused by the uncertainty of the response behavior of the user and provides support for the application of the comprehensive service station.
Drawings
Fig. 1 is a flow chart of a comprehensive service station resource dynamic combination method based on multi-station integration in embodiment 1 of the present invention;
Fig. 2 is a topology diagram of a 20kV distribution network disclosed in embodiment 1 of the present invention;
fig. 3 is a diagram of calculation errors of branch transmission power disclosed in embodiment 1 of the present invention;
fig. 4 is a system schematic diagram of a comprehensive service station resource dynamic combination method based on multi-station integration according to embodiment 2 of the present invention.
Detailed Description
In order to clearly illustrate the technical features of the present solution, the present invention will be described in detail below with reference to the following detailed description and the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different structures of the invention. In order to simplify the present disclosure, components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and processes are omitted so as to not unnecessarily obscure the present invention.
Example 1
The embodiment 1 of the invention provides a comprehensive service station resource dynamic combination method based on multi-station integration, and as shown in fig. 1, a flow chart of the comprehensive service station resource dynamic combination method based on multi-station integration in the embodiment 1 of the invention is provided.
In step S100, the response power of the user is divided into a risky asset and a risky asset; the risky asset provides a response power for the freely responding user; the free response user is a user who does not host the control authority of the terminal equipment to the comprehensive service station; the risk-free asset is a response power provided by a user hosting control rights of the terminal device to the integrated service station.
The response electric quantity of the comprehensive service station is the difference between the actual power and the baseline power of the comprehensive service station, and the difference can be regarded as a product when the comprehensive service station trades. The response capability of the comprehensive service station is derived from internal interactive resources, and the user adjusts the electricity utilization plan of the terminal equipment according to the adjustment instruction distributed by the comprehensive service station. In actual response, the method is influenced by the response will of the user, and a part of actual response electric quantity provided by the user has certain uncertainty, so that the combined comprehensive service station has a benefit risk. According to the operation framework of the comprehensive service station, the comprehensive service station and a user can make a contract before the operation week, and the user agrees whether the control authority of the terminal equipment is hosted to the comprehensive service station in the operation period, so that the response electric quantity of the user is divided into two types, namely an asset with risk and an asset without risk.
In step S200, a resource response characteristic model of the risky asset and an asset resource response characteristic model of the non-risky asset are built.
The risk asset is the response electric quantity provided by the free response user, wherein the free response user is the user who does not host the control authority of the terminal equipment to the comprehensive service station in the contract of the comprehensive service station and the user. Most of the risky users are large-scale load users, such as campuses, office buildings and the like, and the risky users are internally provided with various devices with adjustable electricity utilization characteristics and are provided with an energy management system. Such user response actions are similar to incentive-based demand responses (IBDR), with the integrated service station acting as an aggregator and without knowing the specific response patterns of such users, only with attention to the actual response effects of the users. In order to reasonably optimize a user's electricity usage plan, such users need to submit electricity purchasing demands for each period of the scheduling period to the integrated service station prior to the scheduling periodResponse Range/>The integrated service station considers that the user can realize any response power in the response capability.
The resource response characteristics model of the risky asset is built as:
s∈SDR,t∈T；
s∈SDR,t∈T；
Wherein s is the user type; s DR is a set of risk asset users; i is the user number; t is the participation response period; t is the scheduling period, ub is the upper limit of the risk asset; lb is the lower limit of the risky asset; The actual response electric quantity of the ith class s user in the t period is obtained; /(I) Is the upper end of the response range; /(I)Is the lower limit of the response range; /(I)The load value before the user responds in the t period; /(I)The load value after the user responds in the t period; /(I)For response deviation;
Wherein, With uncertainty, causing a risk of revenue for the integrated service station. The response uncertainty essence of a common IBDR user is derived from the lack of information, and the response capability of the common IBDR user is difficult to match with the response requirement of a system; however, in the management mode of the comprehensive service station, after the comprehensive service station comprehensively considers the external requirements and the response capacity of the internal resources, the response electric quantity of each user participating in the response is redistributed, so that the response of the free response user according to the allocation scheme of the comprehensive service station can realize global optimum, and the response deviation only brings economic loss to the comprehensive service station. After considering the uncertainty,/>The rewriting is as follows:
A response power expected value allocated to the user; /(I) Is a random deviation amount of the user response power.
Varying the amount of power based on historical response of users participating in responses of risk assetsAnalysis can be carried out to obtain that the standard deviation sigma i of the income of each user and the correlation rho ij,ρij > 0 between the risk assets indicate that the response errors of the risk asset user i and the risk asset user j have consistency, and always the responses or the under responses occur simultaneously; conversely, ρ ij < 0 indicates that the response errors between users have complementarity.
On the other hand, the risk-free asset is the response electric quantity provided by the user who hosts the control authority of the terminal equipment to the comprehensive service station in the contract of the comprehensive service station and the user. Such a user-provided risk-free asset having a response power of σ=0 in nature, ρ=0 may circumvent the revenue risk caused by the user's willingness to respond. The technical characteristics of the terminal devices hosted to the integrated service station are considered to be different. Considering that the technical characteristics of the terminal devices hosted to the integrated service stations are different, the interactive resources are divided into three types of translatable load (Shiftable Load), translatable load (Transferable Load) and reducible load (Reducible Load).
The translatable load is applicable to industrial production line operation, electrical equipment fixed by current process, and the like. Common load-transferable devices include ice cold storage air conditioners, electric automobile power exchange stations, electric energy storage and the like. The response characteristics of the transferable load users are more flexible because the transferable load users are not constrained by the continuity of the current process, and the battery energy storage device (Energy Storage System, ESS) is taken as a typical transferable load for research, and the influence of the overcharge and the discharge on the service life of the battery is considered, so that the charge state of the battery is required to be always kept within a certain range in the response process:
Wherein, SOC t represents the state of charge of the ESS in the t period; And SOC indicates the upper and lower limits of the state of charge allowed by the ESS user during the scheduling period, the application is set to [0.2,0.9]. Load-reducible users can provide response power to the integrated service station by reducing or interrupting loads, and common load-reducible loads are temperature-controlled loads, lighting loads and distributed power sources on the power source side. The user side can cut down the purchase electricity quantity reduction after load response, and can be regarded as providing negative response electricity quantity; the load-reducible power source side reduces the power generation amount of the user, and is equivalent to the positive response power provided by the user side.
In step S300, a dynamic combination model of resources of the integrated service station is established based on the asset response characteristic model of the risky asset and the asset response characteristic model of the risky asset.
The resource dynamic combination problem of the comprehensive service station is similar to the unit combination problem, but the unit combination problem focuses on the power generation capacity of the unit, the comprehensive service station focuses on the response capacity of the resource, the unsuitable comprehensive service station resource combination mode cannot meet the requirement of an application scene on the product characteristics of the comprehensive service station, and good economy is difficult to obtain. The process of establishing the dynamic resource combination model of the comprehensive service station further comprises the following steps:
establishing an objective function of a dynamic combination model of the resources;
determining constraint conditions of an objective function;
and converting the resource dynamic combination model into a mixed integer linear programming and carrying out branch power flow linearization constraint on the resource dynamic combination model.
The process of establishing the objective function of the dynamic combination model of the resources is to establish the objective function with the expected benefit maximization as the target:
wherein E (R p) represents the expected revenue for the integrated service station after assembly; representing expected revenue for the integrated service station; /(I) Determining an expected revenue from the energy market after the bidding curve P aim,t for market clearing; C r is retail electricity price; c w is market wholesale electricity price; /(I) Incentive from the grid to provide congestion management service acquisition to the grid; e (C cost) is the expected cost; f 1 provides the incentive cost required by the response electric quantity for incentive users to change the output plan; f 2 is the uncertainty cost generated by the deviation between the actual power of the integrated service station and the target power, and corresponds to the penalty cost from the electric power market in the settlement stage; f 3 is the access cost to communication and computing resources required by maintaining the user to participate in the interaction of the comprehensive service station; /(I)The unit response incentive cost of the ith user of the s-th type resource, namely the internal transaction electricity price; n is a node number and represents the position of a resource access distribution network; lambda is the access cost required by a single resource to participate in regulation; gamma is an uncertainty factor; x i,t is the number of resources; p VPP,t is the actual power of the integrated service station; p aim,t is the target power.
The constraint conditions of the objective function are determined as follows:
Wherein, Representing risk and utility constraints of the optimal combination scheme when utility is maximized; Resource response characteristic constraints representing risk assets; /(I) Equality constraints representing aggregate external characteristics of the integrated service station; /(I)Representing application scenario constraints;
The expression of the aggregate external characteristics of the comprehensive service station is as follows:
wherein n represents a participating response interactive resource grid-connected point; s= { S DR,Sshift,Strans,Sre } represents all participating responding user sets; providing a response value for the comprehensive service station at a node n of the distribution network in a t period, namely responding to the change amount of active injection power at the node n in the t period before and after; /(I) Representing the actual load value at the node n in the t period after the response;
After the response characteristics distributed at different geographic positions are aggregated, the time aggregation characteristics of the comprehensive service stations are obtained:
Wherein Δp VPP,t represents a response value provided by the integrated service station in the period t; p VPP,t respectively represents the aggregate load value of the comprehensive service station in the period t after the response;
The comprehensive service station application scene constraint comprises external characteristic constraint and space tide constraint which are suffered by the participation of the comprehensive service station in the electric power market; the external characteristic constraints suffered by the participation of the comprehensive service station in the electric power market are as follows:
-er is less than or equal to P VPP,tΔt-Paim,t delta t is less than or equal to er; wherein er is the allowable power deviation of the integrated service station from the power market contract.
The power transmission capacity of each branch of the distribution network is limited, and uneven load distribution can cause transmission power of partial branches to be out of limit, so that the operation safety of the distribution network is affected. According to the load prediction result in the dispatching period of the distribution network area provided by the DSO, the comprehensive service station can obtain the voltage amplitude and the phase distribution of each node through load flow calculation, and then according to the following formula, the transmission power prediction value of each branch in any period in the dispatching period can be calculated. The blocking management of the power distribution network requires that the power flow of the power distribution network is not out of limit, if the power flow out of limit condition exists in the branch, the transmission power of the branch needs to be changed, the transmission power of any branch at any moment in the dispatching period is within the allowable safety range, and the space power flow constraint is as follows:
-Pmn,N≤Pmn,t≤Pmn,N
Wherein, For the transmission power of the branches m to n at the moment t when not regulated, m and n are respectively the numbers of the connecting nodes at the two ends of the branches; /(I)To adjust the magnitude of the voltage at the front node m; /(I)To adjust the amplitude of the voltage at the back node n; /(I)To adjust the phase of the voltage at the front node m; /(I)To adjust the phase of the voltage at the back node n; g mn is the conductance of branches m to n; b mn is susceptance of branches m to n;
P mn,t is the transmission power of the branch circuit m-n after the comprehensive service station is regulated; p mn,N is the maximum transmission power allowed by the leg; Δp mn,t is the amount of change in the branch transmission power.
The process of converting the resource dynamic combination model into a mixed integer linear programming and carrying out branch power flow linearization constraint on the resource dynamic combination model comprises the following steps:
The essence of the comprehensive service station participating in DSO blocking management is to regulate the running state of the interactive resource, change the active injection power of the node of the distribution network where the interactive resource is located, thereby changing the tide distribution of the distribution network and relieving the branch blocking. And introducing a sensitivity parameter alpha to linearize nonlinear constraint of the branch power flow. The sensitivity parameter alpha is introduced to linearize the nonlinear constraint of the branch power flow, and the derivation process of alpha is as follows, because delta P mn,t is determined by delta P n,t and node reactive power injection change delta Q n,t:
Wherein, alpha P,mn,n,t represents the sensitivity of the change of the active injection power of the node n to the change amount of the transmission power of the branch m-n; alpha Q,mn,n,t represents the sensitivity of the change of the reactive injection power of the node n to the change of the transmission power of the branch m-n;
When the branch power flow is linearized, the sensitivity alpha is related to the working point,
In the formulaAs can be seen from the calculation formula of the transmission power of the branch m-n, the transmission power of the branch changes along with the distribution of the power flow in the network, the voltage amplitude and the phase of each node change, and g and b are the inherent properties of the branch and do not change along with the power flow of the network, namely/>The amount of change in (2) is determined by the voltage amplitude and phase change at the nodes of the two ends of the branch. The nodes are interconnected by branches, and a change in the injection power of each node causes a change in the transmission power of the branches m-n. The change ΔP mn of the branch transmission m-n power is expressed as:
Will be described in Is rewritten into a matrix form, as shown in the following formula:
/>
The relation between the change amount of the voltage amplitude and the phase of each node and the change of the injection power of the node is deduced from a power flow balance equation and is represented by a jacobian matrix J PQ,θV, as follows:
The above matrix is transformed, and then:
The relationship between the amount of change Δp mn of the branch transmission m-n power and the amount of change of the injection power of each node is as follows:
At this time, the introduced sensitivity vector represents the influence of the network power flow change on the branch m-n transmission power, as shown in the following formula:
Then it is Equivalent to the formula:
ΔPmn＝αmn[ΔP1,…ΔPN,ΔQ1,…ΔQN]T
Due to the delta P n,t accounting for The ratio is smaller, and the power factor angle (phi n) of the node before and after adjustment is not changed/>Rewritable as a form of the formula:
In step S400, the comprehensive service station resource combination mode is evaluated based on the resource dynamic combination model. And analyzing the resource combination mode by using the calculation example analysis, wherein the analysis specifically comprises application analysis based on the calculation example information, benefit risk analysis based on the calculation example information and comparison analysis based on the calculation example information.
Simulation analysis is carried out on the model by adopting a 20kV medium-voltage distribution system of CIGRE, as shown in FIG. 2, which is a 20kV distribution network topological diagram disclosed in embodiment 1 of the invention, switches S1, S2 and S3 are all in a closed state, and the following table is a geographical position distribution table of users in the distribution network:
| user' s | Grid-connected point |
| Risky asset | 1,2,…,14 |
| Translatable load | 2,4,6,8,10 |
| Load transferable | 1,8,3 |
| Load can be reduced | 1,2,…,14 |
According to the branch load rate distribution of the distribution network area, the power overload phenomenon occurs to the branch 1-2.
Obtaining basic information of the risk-free asset according to the contract with the user: the load can be reduced by two types of factory load and distributed Photovoltaic (PV), and the reduction proportion of 10% and 5% is allowed respectively, so that response values of about-400 kW to 250kW can be provided; translatable load with electric vehicle users (EVs); the transferable load ESS allows a maximum charge/discharge power of 400kW, with a total installed 1MW.
The comparative analysis is to obtain the change amount of the injection power of the distribution network node at any time period in the dispatching period according to the model solving result, calculate the distribution network power flow distribution after the response of the comprehensive service station by using the N-R algorithm, obtain the branch load rate information after the response, compare with the load rate obtained by the sensitivity calculation proposed herein, and calculate an error map for the branch transmission power disclosed in the embodiment 1 of the invention, wherein the error is in the range of 0-2.8x10-4, and the influence of the change of the node load on the power flow distribution can be considered to be represented by using the sensitivity parameter.
According to the application scenes of the comprehensive service station, 6 operation scenes are set as shown in table 2, and the asset combination modes and related parameters in different operation scenes are shown in table 3. And (3) transversely comparing, wherein the response characteristics of the comprehensive service station are optimized on a single time and space scale in the scene I and the scene II respectively. The scene III coordinates and optimizes the time and space response characteristics of the comprehensive service station, so that the utilization of resources is more efficient: when the space response requirement contradicts the response requirement on the time scale, the high-sensitivity node response plan is preferentially distributed to meet the space trend requirement, and the resource response plan at the low-sensitivity node is redistributed to meet the aggregation characteristic requirement of the comprehensive service station on time. Compared with the optimization of an independent single scene, the demand of the response electric quantity in the coordination optimization scheme of the scene III is reduced by 219.51kWh, the required interactive users are also reduced, and the total expected cost can be saved by 1386.31 yuan.
Wherein, the table 2 comprehensive service station operation scene table is:
| Operation mode | Day-ahead energy market | Blocking management | Day-ahead energy market + blocking management |
| Optimized combination | Scene I | Scene II | Scene III |
| Big alliance | Scene IV | Scene V | Scene VI |
The asset combination modes in different scenes in the table 3 are as follows:
And when the response capacities are consistent, the response capacity of the spare is larger as the response capacity of the spare is smaller, and the flexibility is stronger. The response capability of the comprehensive service station is determined by the combination mode, and the large alliance is a set formed by all interactive users in limited selectable interactive resource users, and the comprehensive service station alliance after the optimization combination is a subset of the large alliance, so that the response resources are not more than the large alliance. According to the total response electric quantity of six running scenes of scenes I to VI, the response electric quantity required by scenes IV to VI is lower than that required by scenes I to III in the same application scene, the excitation cost is proportional to the response electric quantity, and the excitation cost of scenes IV to VI is also lower. However, the increase of the number and variety of the interactive resources tends to cause the increase of the access cost, and in the scene V, which is an operation scene with the largest standby response capacity under a large alliance, a large number of alliance members do not contribute to the response electric quantity, the utilization rate is low, and the saved excitation cost is difficult to cover the high access cost, so that the total expected cost is increased by 550 yuan compared with the scene II. In the scene III and the scene VI with more requirements on the response electric quantity, the resource utilization rate in the scene VI is high, and the total expected cost is reduced after the incentive cost balances the access cost. Thus, proper combination is a necessary condition for obtaining good economy.
Example 2
Based on the multi-station-integration-based comprehensive service station resource dynamic combination method provided by the embodiment 1 of the present invention, the embodiment 2 of the present invention further provides a multi-station-integration-based comprehensive service station resource dynamic combination system, as shown in fig. 4, which is a system schematic diagram of the multi-station-integration-based comprehensive service station resource dynamic combination method of the embodiment 2 of the present invention, where the system includes: the system comprises a classification module, a first establishment module, a second establishment module and an evaluation module;
the classification module is used for classifying the response electric quantity of the user into a risky asset and a risky asset; the risky asset provides a response power for the freely responding user; the free response user is a user who does not host the control authority of the terminal equipment to the comprehensive service station; the risk-free asset is response electric quantity provided by a user who hosts the control authority of the terminal equipment to the comprehensive service station;
the first establishing module is used for establishing a resource response characteristic model of the risky asset and establishing an asset resource response characteristic model of the risky asset;
The second building module is used for building a resource dynamic combination model of the comprehensive service station based on the resource response characteristic model of the risk asset and the asset resource response characteristic model of the risk-free asset;
And the evaluation module is used for evaluating the comprehensive service station resource combination mode based on the resource dynamic combination model.
The second building module comprises a model building sub-module, an objective function building sub-module, a constraint condition determining sub-module and a conversion sub-module;
the model building submodule is used for building a dynamic resource combination model based on the resource response characteristics;
the objective function building submodule is used for building an objective function of the dynamic resource combination model;
the constraint condition determination submodule is used for determining the constraint condition of the objective function;
the conversion submodule is used for converting the resource dynamic combination model into a mixed integer linear programming and carrying out branch power flow linearization constraint on the resource dynamic combination model.
In order to optimize the resource combination of the comprehensive service station, the embodiment 2 of the invention exerts the coordination and complementation characteristics of the resources to improve the flexibility of the comprehensive service station, disperses the benefit risks caused by the uncertainty of the response behaviors of users, and provides support for the application of the comprehensive service station.
While the specific embodiments of the present invention have been described above with reference to the drawings, the scope of the present invention is not limited thereto. Other modifications and variations to the present invention will be apparent to those of skill in the art upon review of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. On the basis of the technical scheme of the invention, various modifications or variations which can be made by the person skilled in the art without the need of creative efforts are still within the protection scope of the invention.
Claims (2)
1. The comprehensive service station resource dynamic combination method based on multi-station integration is characterized by comprising the following steps:
dividing the response power of the user into a risky asset and a risky asset; the risk asset provides response electric quantity for a free response user; the free response user is a user who does not host the control authority of the terminal equipment to the comprehensive service station; the risk-free asset is response electric quantity provided by a user who hosts the control authority of the terminal equipment to the comprehensive service station; the resource response characteristic model of the risky asset is established as follows:
Wherein s is the user type; s DR is a set of risk asset users; i is the user number; t is the participation response period; t is the scheduling period, ub is the upper limit of the risk asset; lb is the lower limit of the risky asset; The actual response electric quantity of the ith class s user in the t period is obtained; /(I) Is the upper end of the response range; /(I)Is the lower limit of the response range; /(I)The load value before the user responds in the t period; /(I)The load value after the user responds in the t period; /(I)For response deviation;
A response power expected value allocated to the user; /(I) A random deviation amount of the user response electric quantity;
the method for establishing the resource response characteristic model of the risk-free asset comprises the following steps:
For the purpose of Analyzing to obtain the standard deviation sigma i of the income of each user and the correlation rho ij between the risk assets; a risk-free asset when ρ ij =0;
Establishing a resource response characteristic model of the risky asset and establishing an asset resource response characteristic model of the risky asset; the process of establishing the dynamic combination model of the resources of the comprehensive service station based on the resource response characteristic model of the risky asset and the asset resource response characteristic model of the risky asset further comprises the following steps:
Establishing an objective function of the dynamic combination model of the resources;
The process of establishing the objective function of the dynamic combination model of the resources is to establish the objective function with the expected benefit maximization as the target:
wherein E (R p) represents the expected revenue for the integrated service station after assembly; representing expected revenue for the integrated service station; /(I) Determining an expected revenue from the energy market after the bidding curve P aim,t for market clearing; C r is retail electricity price; c w is market wholesale electricity price; /(I) Incentive from the grid to provide congestion management service acquisition to the grid; e (C cost) is the expected cost; f 1 provides the incentive cost required by the response electric quantity for incentive users to change the output plan; f 2 is the uncertainty cost generated by the deviation between the actual power of the integrated service station and the target power, and corresponds to the penalty cost from the electric power market in the settlement stage; f 3 is the access cost to communication and computing resources required by maintaining the user to participate in the interaction of the comprehensive service station; /(I)The unit response incentive cost of the ith user of the s-th type resource, namely the internal transaction electricity price; n is a node number and represents the position of a resource access distribution network; lambda is the access cost required by a single resource to participate in regulation; gamma is an uncertainty factor; x i,t is the number of resources; p VPP,t is the aggregate load value of the comprehensive service station in the period t after response; p aim,t is the target power;
determining constraint conditions of the objective function;
The constraint condition for determining the objective function is as follows:
Wherein, Representing risk and utility constraints of the optimal combination scheme when utility is maximized; /(I)Resource response characteristic constraints representing risk assets; /(I)Equality constraints representing aggregate external characteristics of the integrated service station; representing application scenario constraints;
The expression of the aggregate external characteristics of the comprehensive service station is as follows:
wherein n represents a participating response interactive resource grid-connected point; s= { S DR,Sshift,Strans,Sre } represents all participating responding user sets; providing a response value for the comprehensive service station at a node n of the distribution network in a t period, namely responding to the change amount of active injection power at the node n in the t period before and after; /(I) Representing the actual load value at the node n in the t period after the response;
After the response characteristics distributed at different geographic positions are aggregated, the time aggregation characteristics of the comprehensive service stations are obtained:
Wherein Δp VPP,t represents a response value provided by the integrated service station in the period t; p VPP,t respectively represents the aggregate load value of the comprehensive service station in the period t after the response;
The comprehensive service station application scene constraint comprises external characteristic constraint and space tide constraint which are suffered by the participation of the comprehensive service station in the electric power market; the external characteristic constraint of the comprehensive service station participating in the electric power market is that-er is less than or equal to P VPP,tΔt-Paim,t delta t is less than or equal to er; wherein er is the allowable electric quantity deviation agreed by the comprehensive service station and the electric power market;
the spatial tide constraint is as follows:
-Pmn,N≤Pmn,t≤Pmn,N
Wherein, For the transmission power of the branches m to n at the moment t when not regulated, m and n are respectively the numbers of the connecting nodes at the two ends of the branches; /(I)To adjust the magnitude of the voltage at the front node m; /(I)To adjust the amplitude of the voltage at the back node n; /(I)To adjust the phase of the voltage at the front node m; /(I)To adjust the phase of the voltage at the back node n; g mn is the conductance of branches m to n; b mn is susceptance of branches m to n;
P mn,t is the transmission power of the branch circuit m-n after the comprehensive service station is regulated; p mn,N is the maximum transmission power allowed by the leg; Δp mn,t is the amount of change in the branch transmission power;
Converting the resource dynamic combination model into a mixed integer linear programming and carrying out branch power flow linearization constraint on the resource dynamic combination model;
the process of converting the resource dynamic combination model into a mixed integer linear programming and carrying out branch power flow linearization constraint on the resource dynamic combination model comprises the following steps:
The sensitivity parameter alpha is introduced to linearize the nonlinear constraint of the branch power flow, and the derivation process of alpha is as follows, because delta P mn,t is determined by delta P n,t and node reactive power injection change delta Q n,t:
Wherein, alpha P,mn,n,t represents the sensitivity of the change of the active injection power of the node n to the change amount of the transmission power of the branch m-n; alpha Q,mn,n,t represents the sensitivity of the change of the reactive injection power of the node n to the change of the transmission power of the branch m-n;
the change ΔP mn of the branch transmission m-n power is expressed as:
Will be described in Is rewritten into a matrix form, as shown in the following formula:
ΔP mn represents the amount of change in the branch transmission m-n power;
The relation between the change amount of the voltage amplitude and the phase of each node and the change of the injection power of the node is deduced from a power flow balance equation and is represented by a jacobian matrix J PQ,θV, as follows:
The above matrix is transformed, and then:
The relationship between the amount of change Δp mn of the branch transmission m-n power and the amount of change of the injection power of each node is as follows:
At this time, the introduced sensitivity vector represents the influence of the network power flow change on the branch m-n transmission power, as shown in the following formula:
ΔPmn＝αmn[ΔP1,…ΔPN,ΔQ1,…ΔQN]T
Then it is Equivalent to the formula:
ΔPmn＝αmn[ΔP1,…ΔPN,ΔQ1,…ΔQN]T
Due to the delta P n,t accounting for The ratio is smaller, and the power factor angle phi n of the node before and after adjustment is considered to be unchanged/>Rewritable as a form of the formula:
establishing a resource dynamic combination model of the comprehensive service station based on the resource response characteristic model of the risky asset and the asset resource response characteristic model of the risky asset;
Evaluating the comprehensive service station resource combination mode based on the resource dynamic combination model; the process for evaluating the comprehensive service station resource combination mode based on the resource dynamic combination model comprises the following steps: and analyzing the resource combination mode by using the calculation example analysis, wherein the analysis specifically comprises application analysis based on the calculation example information, benefit risk analysis based on the calculation example information and comparison analysis based on the calculation example information.
2. The comprehensive service station resource dynamic combination system based on multi-station integration is used for realizing the comprehensive service station resource dynamic combination method based on multi-station integration as claimed in claim 1, and is characterized by comprising a classification module, a first establishment module, a second establishment module and an evaluation module;
The classification module is used for classifying the response electric quantity of the user into a risky asset and a risky asset; the risk asset provides response electric quantity for a free response user; the free response user is a user who does not host the control authority of the terminal equipment to the comprehensive service station; the risk-free asset is response electric quantity provided by a user who hosts the control authority of the terminal equipment to the comprehensive service station;
The first establishing module is used for establishing a resource response characteristic model of the risky asset and establishing an asset resource response characteristic model of the risky asset;
the second establishing module is used for establishing a resource dynamic combination model of the comprehensive service station based on the resource response characteristic model of the risky asset and the asset resource response characteristic model of the risky asset;
the evaluation module is used for evaluating the comprehensive service station resource combination mode based on the resource dynamic combination model.
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